Using artificial intelligence as a health and safety analysis tool in your industry

Warehouses are areas where health and safety risks can be high due to the presence of heavy machinery, chemicals, and material handling activities. To improve worker safety and ensure regulatory compliance, using artificial intelligence (AI) to monitor and detect health and safety issues from surveillance footage becomes a solution. These systems have the ability to generate tailored simulations, predictions, and recommendations to create safer and more efficient work environments.

From images from surveillance cameras installed in the warehouse, the AI ​​system can:

  • Analyze images and detect anomalies or dangerous behaviors.
  • Identify if an aisle is obstructed by pallets or boxes, if an employee is not wearing a helmet or protective gloves, or if there is a liquid spill that could cause injuries.
  • Identify if your inventory is stored safely, for example, quickly warn that a stack of pallets is moving and will eventually collapse

Such a system can generate regular reports on detected incidents, allowing managers to identify trends and areas requiring improvement. These reports can also be used for safety audits and to demonstrate compliance with current regulations.

Applications of health and safety monitoring in industries

The use of generative AI for health and safety monitoring varies across industries due to specific risks and unique work environments. Here are some use cases across different industries:

Manufacturing industry: Monitoring the use of protective equipment

AI can be trained to recognize different types of PPE (personal protective equipment), such as helmets, goggles, gloves, safety boots, etc. Video feeds from surveillance cameras can be analyzed to detect the presence or absence of these equipment on employees. Companies can thus reduce the risk of accidents by ensuring that employees are constantly wearing their PPE.

Construction: Fall detection

Fall detection is an application of generative AI in the construction sector, where such accidents are common. This application ensures faster detection of falls, allowing for immediate interventions, reducing the severity of injuries. In addition to detecting falls, AI can analyze surrounding contexts, such as the presence of guardrails or safety nets. Monitoring facilitates compliance of operations with safety standards, thus avoiding sanctions and penalties.

Health: crowd management in waiting rooms

Generative AI can be extremely effective for crowd management, especially in environments like waiting rooms where congestion and panic situations can occur. By detecting and preventing congestion, AI helps reduce the risk of panic and associated accidents. By anticipating crowds, managers can optimize the use of waiting rooms and adjacent spaces. AI data can be used to plan staff attendance and the opening of additional facilities as needed.

Transport and logistics: monitoring of loading docks

Loading dock monitoring to ensure compliance with safety protocols during loading and unloading of goods. Generative AI can identify violations during loading procedures from defined safety protocols and surveillance imagery. Using such a system allows for the rapid detection and treatment of violations, ensuring a safe and compliant work environment. In addition to reducing the risk of accidents on loading docks, this technology reduces the risk of damage to goods.

The benefits

Applied generative AI can transform health and safety management, making processes more responsive, accurate, and based on real-world visual data.

Analyzing images and videos to automatically detect potential hazards, such as missing safety equipment, obstacles in traffic paths, or unsafe behaviors, enables rapid interventions to prevent serious accidents.

AI can compare images captured in the workplace with established safety standards to ensure protocols are being followed.

Real-time monitoring

Surveillance systems powered by generative AI can analyze video feeds from security cameras in real time to detect anomalies or dangerous situations. This enables immediate response to potential incidents.

Incident reconstruction

In the event of an accident, AI can generate detailed reconstructions based on available images and videos. This helps to understand the causes of the incident and take measures to avoid similar situations in the future.

Improved inspections

Drones equipped with cameras and generative AI can inspect areas that are difficult to access or dangerous for humans. Analyzing the captured images can identify structural failures, leaks, or other risks without putting inspectors at risk.

Post-event analysis

After an incident, AI can analyze available images and videos to provide detailed insights into the causes and circumstances of the event, helping to develop future prevention strategies.

Customizing Security Audits

AI can analyze workplace images to customize safety audits based on the specifics of each site. This helps identify specific risks and propose tailored solutions.

Proactive risk management

By combining image analysis with other data (such as incident histories and environmental conditions), AI can generate predictive models to anticipate and proactively manage risks.

Automated documentation and reporting

Generative AI can automate the creation of detailed reports from analyzed images, making it easier to document safety inspections and audits.

It is important to consider the risks and limitations associated with the use of these LLMs. The use of generative AI tools for business is not negligible for safe professional use. Learn more about the practical applications of generative AI in the manufacturing industry-> Read the full article

Do more with less

One of the key benefits of generative AI is its ability to run continuously, giving you 24/7 expertise to analyze your environments, identify areas for improvement, and propose solutions. This technology optimizes your resource management by providing real-time analytics and accurate recommendations, allowing you to prioritize tasks more strategically.

Generative AI also facilitates collaboration and communication within your teams by ensuring that all stakeholders have the information they need to complete their tasks efficiently. By automating the creation of tasks from photos or videos, AI ensures smooth coordination and improves operational efficiency.

By integrating generative AI into your daily processes, you not only optimize the distribution of tasks, but also strengthen decision-making and the overall efficiency of your operations.


Want to explore more use cases for generative AI? This article is part of our comprehensive guide that covers many potential applications for this technology across various industries. To learn more and find use cases for your field, read our guide now.

Read the guide


Moov AI uses generative AI in the creation of this blog.
Here’s how our marketing team is using generative AI to improve this blog. We used a combination of ChatGPT and Gemini to generate examples of use cases across various industries, based on a real-world case developed by Moov AI. Some of the text was generated and corrected with the help of generative AI. The blog’s header image was also created using the following prompt: An abstract l representation of a health and safety analysis in a warehouse, creative security camera, creamy, wavy , bobbly cloud and geometrics forms , 3D highlighting safety protocols, and a color scheme of soft green and orange color in Midjourney. Then, we used Adobe Firefly, integrated via Photoshop beta, to adjust the image to the correct format.


Optimize operations: generate the right task list for your employees from photos

GenAI-Liste-taches

With labor shortages on the rise and fewer and fewer hours available to get the job done, you need to make sure your employees are efficient and productive… and working on the right things in the right order. Efficient task management is essential, and generative artificial intelligence (AI) is one solution for optimizing your day-to-day processes.

Generative AI is a strategic solution for optimizing your day-to-day processes. By automating repetitive tasks, this technology analyzes photos and videos to identify necessary actions and generate corresponding tasks automatically. In this way, it enables your staff to focus on crucial tasks at the right time, while freeing up planning teams to focus on higher value-added activities. This article will explore how generative AI can transform your operations, dramatically increasing your efficiency.

Image recognition for more efficient supermarket tasks

Imagine you’re a grocery store manager. You have to manage a mountain of daily tasks that your employees have to perform. You’re also facing a labor shortage. So prioritizing tasks and assigning them efficiently is crucial to keeping your business running smoothly. But fortunately, you’re using AI: your employees are automatically given the right tasks to perform at the right time

From a simple photo of a refrigerator or counter, generative artificial intelligence can identify all the tasks a clerk should perform, taking into account the time of day. Based on the information in the photo, we automatically generated a list of tasks for the clerks

And not just any tasks. By using generative AI well, and integrating data and processes, the right tasks can be suggested. Imagine a platform residing on a mobile device that, following a quick scan of the counters, suggests tasks for the employee to perform:

  • Replenish missing products.
  • Clean and organize the refrigerator for better product visibility.
  • Highlight promotions or products to be highlighted.
  • Take inventory of stock to anticipate future orders.


Here’s what the application could look like:

Other task management applications


This technological revolution doesn’t just apply to the retail industry; here are a few other possible applications in various industries.

Logistics: space and inventory optimization

In logistics, space is money. Warehouses and transport vehicles must be used to maximum capacity to ensure profitability.

Generative AI can analyze the dimensions and weights of various products, and understand the constraints of storage space or transport vehicles. In addition, generative AI can identify when items are missing.

AI integration can generate tasks from a photo, such as:

  • Identifying missing items: Generative AI can analyze stockroom images to detect missing or out-of-stock items. This visual analysis ensures that inventory is always up to date.
  • Generation of replenishment lists: Based on data on missing items, AI can create replenishment lists, simplifying both inventory management and the reduction of replenishment lead times.
  • Detection of poorly stocked products: AI can spot poorly stored products, preventing inefficient use of space and reducing the risk of product damage.
  • Generating instructions for reorganizing shelves: Based on the detection of poorly stocked products, AI can provide instructions for reorganizing shelves in an optimal way, thus improving space utilization.

Building maintenance and management: anomaly detection

The same technology can be used for building maintenance and safety. Using photos or videos, the system can flag up problems such as water leaks, physical damage such as cracks, and electrical problems.

By identifying problems early on, interventions can be planned before they become critical. This proactive approach prevents minor problems from turning into major ones requiring costly repairs.

By pinpointing anomalies, maintenance teams can be deployed more efficiently. Time-consuming manual inspections are no longer necessary, and teams can concentrate on the areas that need the most attention.

Regular, in-depth inspections powered by AI can significantly improve building safety. By detecting and dealing with problems quickly, the risk of accidents caused by structural damage or electrical problems can be significantly reduced.

Osedea-stm

Société de transport de Montréal (STM)  has implemented such an AI solution to automate maintenance tasks in the Montreal metro. The project was entrusted to Montreal-based Osedea and their dog Spot.

It’s even simpler and more accessible to apply this use case using images from security cameras. Thanks to real-time analysis of surveillance videos, generative AI ensures continuous surveillance, because these robots are very cool and sometimes extremely useful, but on the other hand, they are expensive and difficult to integrate into one’s operations.

Food: Ensuring sanitary standards are met

Food processors must maintain high standards of food hygiene, ensuring cleanliness while maximizing operational efficiency.

AI can transform day-to-day management by making it easier to proactively identify cleaning, replenishment and maintenance needs based on visual analysis. By analyzing photos of facilities and inventory, AI can identify areas in need of cleaning, detect when stocks are low, and flag equipment in need of maintenance. This technology reduces the time spent manually checking stock and equipment, enabling staff to concentrate on food preparation and quality assurance.

Municipality : Management of public and urban spaces

Municipalities face multiple challenges that they must overcome with limited resources.

AI can be used to optimize the management of municipal parks. Instead of making full rounds, AI can precisely target areas requiring intervention. By analyzing images of public spaces, AI is able to identify areas in need of cleaning, detect damaged public equipment and generate detailed maintenance reports for maintenance teams.

This enables maintenance teams to focus their efforts on the areas that need their attention most, saving time and resources. Early detection of damaged equipment by AI also avoids more costly repairs in the long term.

Implementation challenges

Implementing generative AI requires a robust IT infrastructure and integration with existing systems. For a successful implementation:

  • Technology assessment: Assess the complexity of integration and ensure that existing systems are compatible.
  • Training and adoption: Ensure that users are properly trained to maximize the adoption and effectiveness of the solution.
  • Ethical considerations: Comply with data protection regulations and monitor algorithmic biases to ensure ethical use.

It is essential to consider the risks and limitations associated with using these LLMs. Using generative AI tools for business is significant for safe professional use. -> Read full article

Do more with less

One of the main advantages of generative AI is its ability to operate continuously, giving you 24/7 expertise to analyze your environments, identify areas for improvement and propose solutions. This technology optimizes the management of your resources by providing real-time analysis and precise recommendations, enabling you to prioritize tasks more strategically.

Generative AI also facilitates collaboration and communication within your teams, ensuring that all stakeholders have the information they need to complete their tasks efficiently. By automating the creation of tasks from photos or videos, AI ensures smooth coordination and improves operational efficiency.

By integrating generative AI into your daily processes, you not only optimize task allocation, but also enhance decision-making and the overall efficiency of your operations.


Gen AI guide

Want to discover more use cases for generative AI? This article is an excerpt from our comprehensive guide to the many potential applications of this technology in various sectors. To learn more and find use cases for your field, read our guide now. Read our guide


Moov AI uses generative AI to create this blog. 
Here’s how our marketing team is using generative AI to improve this blog. We used a combination of ChatGPT, Copilot and Gemini to generate examples of use cases in various industries, based on a real-life case developed by Moov AI. Parts of the text were generated, corrected and translated with the help of generative AI. The blog header image was also created using the following prompt: A futuristic glassmorphism to-do list with checkboxes and a photo stack, artistic abstract illustration in the the style blobby wavy 3d, blue gradient color in Midjourney. We then used Adobe Firefly, integrated via Photoshop beta, to adjust the image to the correct format.


Use cases for generative AI that will be really useful in 2024

In 2024, artificial intelligence (AI) has become a must-have for companies looking to innovate, thanks in large part to the mass adoption of generative AI, capable of creating original content ranging from text to images to entirely new concepts. We’re not the first to say it: this technology opens up the prospect of an explosion in productivity for companies in every industry.

Some fairly simple use cases for generative AI are now well known and documented on the Internet: chat engines, text and image generation, and so on.

But in concrete terms, how can tools like ChatGPT, Copilot or Gemini help your employees become more productive? How can your company improve its processes?

In short, what are the use cases that will really help you improve productivity, whether you operate a factory, a grocery store, a distribution center or a consumer goods company?

This guide aims to provide you with potential applications of generative AI in your industry. Whether you are responsible for corporate strategy, innovation management, or daily operations, this guide offers examples of how you can integrate generative AI into your initiatives, maximizing its potential to create added value.

Here are some advanced examples of generative AI solutions that are currently being used by companies.


1. Task list generation from photos

Improving task efficiency in supermarkets with image recognition

Imagine you’re a grocery store manager. You have to manage a mountain of daily tasks that your employees have to perform. You’re also facing a labor shortage. So prioritizing tasks and assigning them efficiently is crucial to keeping your business running smoothly. But fortunately, you’re using AI: your employees are automatically given the right tasks to do at the right time.

From a simple photo of a refrigerator or a counter, generative AI can identify all the tasks a clerk should perform, taking into account the time of day. Based on the information from the photo, we automatically generated a list of tasks for the employees.

And not just any tasks. The beauty of generative AI is that it is based on your process guides and employee guidelines to suggest the right tasks to perform. Imagine a platform residing on a mobile device that, after a quick scan of the counters, suggests tasks that the employee should perform:

  1. Replenish missing products.
  2. Clean and organize the refrigerator for better product visibility.
  3. Highlight promotions or products to be featured.
  4. Take inventory of stock to anticipate orders to be placed.

Here’s what the application might look like:

Other task generation applications

This technological revolution doesn’t just apply to the retail industry: here are some other possible applications in several industries.

Optimizing space and inventory : Identify missing items, generate replenishment lists, detect incorrectly stocked products and generate instructions for reorganizing shelves.

Building maintenance and management: Detect building anomalies such as leaks and cracks, enabling preventive planning of repairs and the efficiency of maintenance teams.

Ensuring compliance with sanitary standards: Identify cleaning and maintenance needs, to free up staff for food preparation and quality control.

Managing public and urban spaces: Efficiently manage resources by targeting interventions in public spaces, facilitating maintenance and cleaning.


2. Health and safety analysis tool

Warehouse risk reduction using AI for hazard detection

Warehouses are areas where health and safety risks can be high due to the presence of heavy machinery, chemicals and handling activities. Generative AI offers great solutions for improving worker safety and ensuring regulatory compliance by monitoring and detecting problems.

Using images from surveillance cameras installed in the warehouse, the AI system can :

  • Analyze images or videos and detect anomalies or dangerous behavior.
  • Identify if an aisle is obstructed by pallets or boxes, if an employee is not wearing a hard hat or protective gloves, or if there is a liquid spill that could cause injury.
  • Identify whether your inventory is stored safely, for example, by providing early warning that a stack of pallets is moving and will eventually collapse.

As well as generating alerts, such a system can generate regular reports on detected incidents, enabling managers to identify trends and areas requiring improvement. These reports can also be used for safety audits and to demonstrate compliance with current regulations.

Risk analysis applications in other industries

Monitoring the use of protective equipment: Ensure that employees are wearing the appropriate PPE, such as hard hats, goggles and gloves.

Fall detection : Quickly identify worker falls on construction sites.

Loading dock supervision: Ensure that safety protocols are followed when loading and unloading goods.

Crowd management: Monitor waiting rooms to avoid overcrowding and panic situations.


3. Chatbots

Improved customer service with chatbots that respond to complex requests.

Qualification Québec is a skills recognition portal offering all the resources you need to work in Quebec. The organization has implemented a conversational agent powered by generative AI to enhance the experience and enable its users to converse naturally with the agent in order to find their equivalent occupation according to the National Occupational Classification (NOC).

Using generative AI, the chatbot analyzes the user’s responses and compares this information with the NOC database. It identifies possible equivalent professions and provides a list of potential matches. The chatbot delivers fast, accurate results and enables personalized, context-sensitive interactions, enhancing the user experience. The implementation of this tool enables users to obtain information at any time.

Conversational agent applications in industry

Shopping assistance: Help customers find products based on their preferences and purchase history, and recommend similar or complementary items to improve cross-selling.

Assist your customer support team: Help your agents find the right information in the multitude of documentation you work with. From user and maintenance manuals, through automated answers to the most frequently asked questions, to precise documentation on how they work.

Customer service: Answer questions about products, delivery times, and return policies, and automate the processing of customer requests for fast, efficient responses.

Financial advice: Offer basic advice on money management, investment options and personal financial planning, while maintaining a 360° view of the customer’s specific needs.

Employee onboarding: Provide information on the company, internal policies and benefits, and assist with initial training and integration of new team members.


4. Automated generation of metadata in ERP systems

Optimize product catalogs through metadata automation

You’re a large retailer or chain of stores with a vast inventory of products to display on your website or in a catalog. To ensure that all products are properly listed, a considerable amount of data must be entered manually. This process is not only time-consuming, but also requires large teams dedicated to entering and generating information in various systems. Not to mention new product arrivals that overload your team.

Often, this information includes duplicates or requires conversions between different systems. Sometimes, it’s a matter of analyzing product descriptions to categorize them correctly. This task is essential, whether in ERP (Enterprise Resource Planning) or other types of systems.

To improve the efficiency and accuracy of this process, generative AI can be used to automate the generation of metadata from product texts or images. This not only reduces the workload for teams, but also ensures greater consistency and accuracy of the information listed. Generative AI can analyze product descriptions, generate relevant metadata and automatically integrate it into the appropriate systems, facilitating the management and display of thousands of products.

Applications of metadata generation in industry

Technical documentation: Automatically create and update technical documentation for products and machines.

Medical data annotation: Generate metadata for medical records and X-ray images.

Generate web content: Create web content including SEO-optimized meta tags, such as keywords and descriptions.

Risk assessment: Provide risk metadata to support insurance and investment decision-making.

Automated Generation of Metadata: Solutions for SAP, Oracle, and Microsoft Dynamics Inventories -> Read full article


5. Metadata extraction

Optimization of quotation production from customer emails

The ability to respond quickly to customer requests for quotes is essential to remain competitive and meet consumer expectations. Manually processing each email containing requests is a time-consuming and error-prone task. Generative AI can automate and optimize this process.

AI starts by analyzing incoming e-mails to extract relevant information. This includes the specifications of the products or services requested, the quantities required, the desired lead times, and any other critical details. It identifies key terms, specific conditions and preferences mentioned in the e-mails.

Based on pre-established templates, the tool can automatically generate quotations. These quotations are structured to include all necessary elements such as unit prices, additional charges, payment terms, etc. Based on the extracted data, quotations can be customized to meet the specific needs of each customer.

Other metadata extraction applications

Ticket classification: Use metadata to classify and prioritize support tickets, and assign them to the right technicians.

Contract tracking: Extract metadata such as dates, parties involved, and conditions from e-mails, and generate reminders for important contract deadlines.

Invoice processing: Retreive information from invoices sent by e-mail (amount, date, supplier) and generate accounting entries or payment proposals.

Processing citizen requests: Information extraction from citizens’ e-mail to process public service requests and generate responses or appointment proposals.


6. Automated test generation

Validation of code quality

As a company that develops solutions for our customers, it’s crucial to equip ourselves with the best tools to guarantee the robustness and reliability of our deliverables. To this end, our teams have adopted generative AI to automate test generation. This approach not only increases our operational efficiency, but also reduces the costs associated with testing. What’s more, the use of AI improves test coverage while minimizing human error, and ensures high-quality end products.

Generative AI scans source code and feature specifications to understand feature logic and expectations. It uses natural language processing (NLP) models to extract requirements and convert them into test scenarios. This solution can analyze source code, feature specifications and bug histories to generate unit tests, integration tests and regression tests.

Other applications for test generation

Security and confidentiality of medical records: Create test cases to verify that patient data is protected and that the system complies with confidentiality regulations.

Risk scenarios: Simulate various risk scenarios to test underwriting and claims management algorithms.

Inventory management system performance: Create test cases to ensure that inventory management systems can handle large quantities of data in real time.


7. Text correction

Optimizing teaching by automating text correction

100% of French teachers admit to needing help with text corrections. Collège Sainte-Anne understands that the well-being of teachers is crucial to providing the best possible education for their students. Collège Sainte-Anne approached Moov AI to explore the feasibility of automating correction, with the aim of assisting teachers and benefiting students

Émilia, a tool powered by artificial intelligence, assists teachers in the correction of texts by identifying errors while basing itself on the students’ teaching program in order to monitor their learning.

AI can detect errors in text consistency, sentence syntax, punctuation, vocabulary, grammatical spelling and usage spelling, depending on the student’s level.

The use of artificial intelligence improves teachers’ quality of life by reducing the time spent on correction, freeing up more time for pedagogy with students, thus promoting better feedback and learning.

Automated Generation of Metadata: Solutions for SAP, Oracle, and Microsoft Dynamics Inventories -> Read full article

Other applications

Customer support and documentation: Write and correct technical documentation and responses to customers, improving customer satisfaction and product understanding.

Correction of legal documents: Check legal documents for conformity and detect errors in contracts and other legal texts.

News article correction: Use generative AI to correct articles before publication, improving editorial quality and content consistency.

Correction of ad copy: Correct ad copy before it goes to press, improving content quality and ensuring editorial consistency.

Medical and scientific documentation: Ensuring the accuracy of reports, research articles and other medical and scientific publications.

Beyond text correction

Generative AI has demonstrated its ability to perform text correction, but these examples also show its capacity to automate business processes. For example, it can create meeting summaries, translate texts from one language to another, or create presentations based on collected data.

By automating these processes and entrusting repetitive tasks to generative AI, employees can be freed up to concentrate on higher value-added activities. This also improves the accuracy of operations and consistency in task execution.


The importance of doing things right

Implementing such applications also requires rigorous internal policies to ensure responsible use. It is important for companies that data is processed by trusted technology partners with high ethical standards and data protection protocols to prevent misuse.

To guide companies in the responsible use of generative AI systems, the Canadian government has developed a voluntary code of practice. This code aims to enable developers, disseminators and operators of these systems to avoid harmful effects, build trust and prepare for a smooth transition to compliance with the future Artificial Intelligence and Data Act.

It’s also important for companies to equip themselves with governance structures that monitor AI use, ensure regulatory compliance and foster a culture of transparency. At the same time, it’s essential to train employees in AI best practice, so that they understand the ethical implications and are able to identify and resolve potential issues.

Proactive risk management is also a practice to be adopted, enabling potential incidents to be identified and preventive measures put in place to avoid them. This includes implementing contingency plans, assessing vulnerabilities and preparing for emergency scenarios.

Generative AI in business

Generative AI represents a major opportunity to rethink the way companies deliver value to their customers and increase the productivity of their teams. This technology, which 96% of organizations consider to be a major topic of discussion is gaining in popularity. It’s essential to take the time to identify the functions in your company that could benefit from this breakthrough.

We hope this guide has sparked your curiosity and stimulated your desire to explore the possibilities offered by generative AI in your field. By highlighting specific use cases for different functions and industries, we wanted to show the transformative potential of generative AI for organizations in all sectors.

While the adoption of generative AI raises questions and presents challenges, it is undeniably part of the future. Organizations need to start preparing now to stay competitive and take advantage of this technology. By taking proactive steps to integrate generative AI, you can not only improve your operational efficiency, but also provide better service to your customers.


Moov AI uses generative AI to create this blog. 
Here’s how our marketing team is using generative AI to improve this blog. We used a combination of ChatGPT, Copilot and Gemini to generate examples of use cases in various industries, based on a real-life case developed by Moov AI. Parts of the text were generated, corrected and translated with the help of generative AI. The blog header image was also created using the following prompt: Artistic abstract generative AI guide book, in the style blobby 3D, blue gradient color in Midjourney. We then used Adobe Firefly, integrated via Photoshop beta, to adjust the image to the correct format.


The importance of AI in business

Just a few years ago, artificial intelligence was merely a playground reserved for innovative giants of Silicon Valley. However, the landscape has changed, and today, its evolution and democratization are undeniable: AI is the new industrial revolution. All companies must now include it in their operations to remain competitive over the next 5, 10, or even 15 years.

Businesses are now faced with the necessity not only to review their strategy but also to integrate AI significantly into the core of this strategy. This is where the key to sustained and lasting growth lies.

What is interesting about AI (and this is what we advocate) is adopting an iterative and incremental approach. We recommend starting with simple projects that will quickly generate significant impact, thereby facilitating a smooth integration of AI within the organization.

In this article, we will explore why this is a necessity.

Why AI is a necessity in business?

AI transforms decision-making by providing in-depth analytics.

AI revolutionizes decision-making by enabling conclusions to be drawn from available data. By harnessing this wealth of information, AI offers businesses a clearer understanding of trends, consumer behaviors, and market dynamics.

This informed vision allows decision-makers to better anticipate potential challenges, identify growth opportunities, and make more informed strategic decisions. By reducing uncertainty and complexity, AI helps minimize the risks associated with business decisions while maximizing profitability by directing resources towards the most promising initiatives (if that’s what the company is trying to optimize). In fact, AI enables the optimization of business objectives.

Maximizing operational efficiency through AI.

By leveraging the insights generated by these systems, a company can gain a better understanding of its current situation, enabling it to make more informed decisions while optimizing its operations. This allows for predicting events or quantities based on all available signals that influence demand.

For example, by considering data such as the correlation between sunny weather, ongoing promotions on ice cream cones, and sales history, it’s possible to accurately predict demand for ice cream cones. In this simple scenario, the benefits are manifold :

  • Optimizing inventory management by anticipating the required quantity of ice cream.
  • Efficient scheduling of work hours based on sales forecasts.
  • Achieving business assumptions by maximizing operational efficiency and profitability while reducing workload for the team.

Thus, the judicious use of AI enables a company to better respond to demand, improve its operations, and achieve its business objectives more effectively.

Personalizing the customer experience strengthens brand loyalty.

Personalizing the customer experience is a powerful driver of brand loyalty. According to a recent study by McKinsey, 76% of consumers are more likely to consider purchases from companies that interact in a personalized manner, while 78% state that such content encourages them to repurchase. The integration of AI offers unique opportunities to achieve this.

By leveraging customer data, companies can provide tailored support experiences. By harnessing technology, they can offer more efficient, effective, and personalized services, thereby enhancing customer satisfaction and loyalty.

Let’s take the example of an online retailer increasing its sales through an AI-powered recommendation system. This system, integrated into the website, provides real-time personalized recommendations based on customers’ preferences and purchase histories. This proactive approach not only streamlines the purchasing experience but also strengthens brand loyalty by providing personalized customer service while reducing the workload of the existing customer service team.

By analyzing the data generated by these interactions, companies refine their understanding of consumer behavior, adjust their offerings, and anticipate future needs. This deep market knowledge allows the company to remain agile and responsive, maximizing sales opportunities and minimizing potential losses.

Concrete artificial intelligence projects completed

Since 2018, our teams have generated innovation for our clients by developing applied artificial intelligence solutions. We have assisted dozens of organizations in discovering and accelerating projects. Here are some examples to help you understand what can be achieved with artificial intelligence.

Discover more AI projects

The future of AI in business

Artificial intelligence (AI) is transforming the business world, and its impact will only continue to grow in the years to come. Numerous studies and reports from leading consulting firms confirm this trend.

Here are some key points to remember:

  • McKinsey : By 2030, AI could generate up to an additional $13 trillion in global GDP.
  • Accenture : AI could create up to 26 million jobs by 2025 (yes, you read that correctly).
  • EY : 75% of business leaders say that AI is essential to the growth of their company.
  • Deloitte : AI could help companies reduce their costs by 9% by 2025.
  • BCG : AI could help companies increase their revenues by 10% by 2025.

The future of AI in business is promising, but it is important to remember that AI is not a miracle solution. It is essential to implement a clear AI strategy and have the skills and resources necessary to implement it.

Here are some tips for businesses looking to leverage AI :

  • Start by identifying the most relevant use cases for your company.
  • Develop a clear AI strategy and define your objectives.
  • Invest in the skills needed to implement your AI strategy.
  • Be prepared to experiment by starting with a project that offers high potential value and presents minimal risk.
  • Collaborate with external partners to get the help you need.

In conclusion, AI is a powerful technology that can help businesses improve their performance and stay competitive. By adopting AI strategically, companies can prepare for a prosperous future.

Generative Artificial Intelligence Applied in the Manufacturing Industry

The Context

The current challenges in the manufacturing industry in Quebec are numerous and complex. Disruptions in the supply chain, whether orchestrated by external global events such as the pandemic, trade conflicts, or internal issues like logistical dilemmas, can trigger disruptions, shortages of raw materials, and potentially result in financial losses equivalent to 45% of the average annual profits of a company over the next decade.

Adding to this complexity is a workforce shortage affecting nearly 80% of Canadian manufacturing companies, creating a challenging mix of issues.

In the face of these challenges, innovative solutions need to be implemented.

AI and Generative AI as a Solution?

Artificial intelligence is employed by manufacturing companies for its ability to optimize manufacturing operations, increase worker productivity, reduce costs, and improve customer satisfaction.

Generative AI can also play a dominant role in the manufacturing sector at a lower cost, faster, and without requiring as much data as “traditional” artificial intelligence projects. It is also a style of solution that is typically better embraced by workers impacted by these changes, thanks to a user experience that promotes collaboration and reduces the need for deep AI knowledge.

Indeed, generative artificial intelligence is accessible because it is possible to quickly test a solution with a proof of concept, without the need for pre-existing data or advanced programming skills.

Thanks to its ability to process vast amounts of data and generate intelligent responses, generative AI can transform maintenance workflows, solve problems in real-time, recommend ways to improve production line efficiency, and become an indispensable tool for fostering the design of new products!

According to the Google Cloud Gen AI Benchmarking Study of July 2023, 82% of organizations considering or currently using generative AI believe that it will bring significant changes, even a radical transformation to their industry.


Series on generative artificial intelligence


Generative AI is Not Just a Decision Support Tool

While most people know generative AI as ChatGPT, Gemini, or Midjourney as a “generic” assistant, it is possible to deploy generative AI in a more specific manner to exploit opportunities and solve particular problems.

Naturally, using the input bar, we can dictate our commands (prompts) to get answers. However, with the same technology, it is also conceivable to program it to solve targeted problems.

Generative AI System for General vs. Narrow Purposes

Generative AI System for General Purposes: An example of this type of system is ChatGPT, where the user receives a textual response based on their query. The user can provide information related to any task they want to accomplish and receive a response tailored to the scope of their question.

Generative AI System for Narrow Purposes: In the manufacturing domain, a concrete example of such a system would be a generative model specifically designed to optimize the production planning process. Rather than simply answering general questions, this system would be trained to analyze specific production chain data, such as stock levels, supplier delivery times, and customer demands.

For example, a production manager could use this system by providing information about current orders, current production capacities, and resource constraints. In return, the system could generate proposals for optimized production plans, taking into account deadlines, costs, and available resources.

Thus, this narrowly specialized generative AI model would be capable of providing precise and targeted solutions to improve operational efficiency in the manufacturing context, going beyond the generic functions of general AI systems like ChatGPT, Gemini, or Midjourney.

Practical Applications of Generative AI in the Manufacturing Industry

Here is how generative AI is used to create value in the manufacturing industry.

The revolution of generative AI is that it uses YOUR data, YOUR ways of doing things.

One of the risks of using general-purpose generative AI solutions is receiving a response that is not truthful or personalized to our context. Asking ChatGPT to generate a quote for a manufacturing plant is possible, but each company has its own identity, methods, processes, and brand tone. These are important elements to consider. It would be far-fetched to assume that all manufacturing companies are identical!

What will truly revolutionize your approach with generative AI is considering YOUR own database. By making your company’s data available in a secure environment, it allows your “Enterprise GPT” to understand your reality and the format of historical submissions, thus generating a result faithful to your methods. Moreover, when talking about databases, it can be any data, whether structured or simple web pages containing useful information.

In the manufacturing sector, this approach allows companies to fully leverage their own data, such as historical data, past submissions, tender documents, production plans, and engineering plans. By using these resources specific to each company, generative AI shapes customized solutions in the image of the company itself, often rivaling (and often surpassing) the quality obtained through manual processes. It is a capitalization on accumulated expertise over time, paving the way for rapid and extremely effective innovation.

With a generative AI solution based on your data, you can (among other things):

  • Instantly generate a new quote for a new client, with new parameters, but based on your past submissions.
  • Create new project plans based on your historical plans.
  • Respond to tenders based on your past responses and your way of responding to tenders.
  • etc.

1. Automation of Customer Service

Conversational agents, also known as chatbots, which are increasingly powered by generative AI, offer a natural and seamless interaction with users while adhering to internal governance policies and brand image. Capable of generating relevant and consistent responses to posed questions, chatbots significantly improve user experience and customer service efficiency.

In the manufacturing sector, companies leverage these conversational agents to facilitate product troubleshooting, order spare parts, schedule services, and provide information about products and their operation.

Manufacturers and industrialists can also benefit from internal conversational agents, assisting employees in quickly searching and retrieving information from vast databases, including their own internal database. Employees can interact naturally with these agents to ask complex questions and get relevant answers, facilitating decision-making and access to internal knowledge.

Imagine equipping your colleagues with virtual and instant assistants that provide answers to challenging questions about the operations or maintenance of your machines and equipment, or assist your engineers in drafting documents that must comply with the numerous standards your company must juggle.

2. Research, Synthesis, and Document Production

Generative AI excels in text understanding and can greatly aid in the synthesis, modification, and creation of text through in-depth research of internal documents from various sources.

A mechanic in the manufacturing sector can benefit from the technology to have a summary of maintenance instructions in seconds, saving repair time and ultimately returning to production more quickly.

Moreover, an engineer can use this technology to generate instruction manuals and documentation for factory machines or accompanying finished products.

Here’s what this means for manufacturing companies.

Internal Chatbot for Employees: Deploying an internal chatbot to help employees quickly access internal information and procedures, which can improve operational efficiency.

Manufacturing Data Analysis: Analyzing production data and identifying trends, inefficiencies, or anomalies, thus contributing to the optimization of manufacturing processes.

Support for Technical Experts: Generating summaries of complex engineering reports, patents, and research documents to help engineers and technical experts stay abreast of the latest industry developments.

360-Degree View of Customers in Manufacturing: Centralizing customer data to have a comprehensive view of their needs, interactions, and histories, which can improve product and service personalization.

Feedback Analysis: Summarizing customer or employee reviews and feedback to identify recurring issues, possible improvements, and positive feedback, which can help improve product quality and production.

Customer Segmentation: Using AI to segment customers based on their purchase history, preferences, and needs, which can help personalize offers and marketing campaigns.

3. Generative AI to Accelerate Prototyping

One of the main contributions of generative AI lies in its ability to create. One crucial advantage is its ability to speed up the design process.

Traditionally, prototyping is a laborious and time-consuming process involving many iterations. Generative AI, on the other hand, can propose ideas and quickly generate prototypes, reducing the time needed to move from the design phase to the production phase.

This acceleration of the development cycle allows companies to respond more quickly to market demands and remain competitive in a constantly evolving business environment.

In terms of costs, generative AI offers significant advantages. By automating part of the design process, companies can reduce labor and prototype iteration costs. Moreover, AI’s ability to optimize materials and structures can lead to substantial long-term savings on production costs.

Imagine being able to generate 3D plans more quickly, receive suggestions on the best packaging for a given product, or automatically generate a design that adheres to the customer’s color scheme.

4. Advisor in Supply Chain, Continuous Improvement, and Legal Documents

Generative AI positions itself as a strategic guide within supply chains, broadening the perspective within complex networks and issuing recommendations for the most suitable suppliers based on relevant criteria. These criteria encompass not only detailed specifications of bills of materials but also parameters such as raw material availability, delivery deadlines, and sustainability indicators.

Endowed with a particular skill in natural language analysis, generative AI excels in extracting relevant provisions from legal and contractual documents. This ability to decipher the ins and outs of legal agreements allows generative AI to play an essential role in the continuous improvement of operations, thereby strengthening the overall efficiency of the supply chain.

5. Generative AI in Predictive Maintenance

AI is already well-utilized in predictive maintenance with forecasting. Let’s take an example in the aerospace industry. Pratt & Whitney uses an artificial intelligence model that predicts the maintenance schedule for a given engine. By cross-referencing these activities with P&WC’s clients, a prioritized list of customers to contact is produced for their sales team.

Generative AI, on the other hand, distinguishes itself by its ability to not only suggest potential solutions when identifying a problem but also to proactively develop comprehensive service plans.

In this context, consider a scenario where a problem is detected. Generative AI steps in not only to provide solution suggestions but also to develop a detailed plan guiding maintenance teams through the entire resolution process, all using your data and guidelines.

A key feature of this technology is its increased accessibility to manufacturing engineers. They can interact intuitively with AI, using natural language and common queries. This ease of use makes generative AI not only accessible to the existing workforce but also particularly attractive to new employees, thus opening up new perspectives in the field of predictive maintenance in the manufacturing sector. Here’s a video produced by Google demonstrating how generative AI helps a transport company solve a problem with a defective locomotive.

6. Metadata Generation for ERPs

For new products, AI automatically generates descriptions based on similar products in inventory or brief information provided by the user. The generative AI system can be integrated into SAP, Oracle, or Microsoft Dynamics. This can be achieved through API integrations or custom modules, ensuring that the generated metadata seamlessly integrates into the raw material and stock management system.

This innovation simplifies and streamlines inventory management, allowing teams to focus on higher-value tasks and accelerate the launch of new products.

I present in this article a demonstration of how metadata generation works from text and from an image.

Implementation of Generative AI in the Manufacturing Industry

To successfully integrate generative AI, operations and innovation managers must follow crucial steps, including selecting appropriate partners and tools, developing a robust and functional solution, training staff and managing change, and implementing a long-term strategy to maximize the benefits of generative AI. Here’s a link to an ebook that guides managers in their first AI project.

In summary, generative AI represents an unprecedented opportunity for the Quebec manufacturing industry. In a constantly evolving industrial landscape, the adoption of generative AI represents a bold step towards efficiency, innovation, and competitiveness. By following these emerging trends, the manufacturing industry in Quebec can not only overcome current challenges but also thrive in a technologically advanced future.

Artificial Intelligence in Retail: The Essential List of Use Cases

Anticipating future trends is the key to success for retail, food, and consumer goods (CPG) companies. And when it comes to prediction, artificial intelligence (AI) is emerging as the super fuel for this forecasting machine.

Nothing is forever except change.

Bouddha

Managers dealing with seasonality, sudden changes in demand levels, events, supplier price fluctuations, strikes, and economic upheavals don’t view forecasting tools as a luxury but as a necessity for making informed decisions.

And it’s understandable. The impact of poor decisions made “blindly” or with inaccurate predictions can far outweigh the cost of integrating AI to obtain precise forecasts.

This article explores how retail, consumer goods, and food companies can enhance their decision-making through artificial intelligence. We will highlight the potential benefits through concrete examples of usage.

From Data to Benefits

The artificial intelligence solution serves as the engine of the model, enabling the extraction of insights and, ultimately, facilitating better decision-making based on these insights. Data is the fuel, and AI is built on observed data, making predictions extremely reliable and highly accurate.

Multidimensional Signals

Simultaneously evaluating the impact of multiple variables is an extremely complex, if not impossible, task for humans. AI is capable of detecting variables that have a real impact on predictions and quantifying their influence on your business. Among these variables, referred to as “multidimensional signals,” are meteorological data, production data, transaction data (POS), current and upcoming promotions, sales prices, essentially all data that can impact demand.

Some of these variables are designated as “levers,” such as price and promotions. These are variables over which the company has complete control (unlike the weather, which would be convenient, though) and with which it is possible to conduct simulations to determine optimal pricing or even implement dynamic pricing.

AI-Powered Forecasting: The Backbone of the Solution

AI-driven forecasting supports all the previously mentioned data and automatically integrates it into the AI model in an automated process. This AI model can identify complex patterns within the data, thereby discerning true trends and generating crucial insights for decision-making.

In the retail, food, and consumer goods sectors, forecasting goes far beyond simple predictions. It represents an essential tool for anticipating future trends, optimizing operations, and maintaining competitiveness.

Here is an example of the precision achievable in your forecasts through AI

In this example, the precision of AI allows us to forecast future demand for screws at a retailer, within a 90% confidence interval.

The Benefits

By deriving insights, we gain a better understanding of the situation, enabling us to make more informed decisions and optimize our operations. For a simple example, with information like the correlation between a sunny day’s weather, the ongoing promotion on hot dog buns, and historical sales data, we can accurately predict the right quantity of hot dogs. In this simple use case, we achieve:

  • Optimizing inventory management
  • Optimizing schedules by knowing the quantity of items expected to be sold

This, in turn, allows us to meet your assumptions, maximize operational efficiency, profitability, all while reducing your team’s workload.

The Complete List of AI Use Cases in Retail

In this section, we will explore various concrete use cases of forecasting with artificial intelligence in detail, demonstrating how this innovative technology can fundamentally transform how companies approach demand, production, inventory management, and more.

Using real-world examples and inspiring case studies, we will discover how AI is redefining the standards for strategic and operational planning.

Without further ado, here is the list of all use cases. Use this article as a reference, using the links below, which will take you to a description of each use case.

1. Automatic Metadata Generation with AI

To begin, this is a use case of generative artificial intelligence that revolutionizes metadata management by automating the creation of product labels and descriptions. Integrated into existing systems, it ensures consistency in line with the company’s standards. Leveraging advanced natural language processing and image analysis, it generates precise and relevant metadata.

In practice, the current metadata structure of the company is used as an example to provide to the generative AI model. With descriptions of future products, the model generates new metadata following the same logic.

This simplifies inventory management and enhances the user experience on e-commerce platforms. I delve into this use case in-depth in a video segment within this article. In my opinion, it brings significant value through cost savings while improving task efficiency.

2. Demand Forecasting

Un système d’IA bien conçu pour aider les gérants en magasin à commander la bonne quantité de marchandises qui seront vendues dans les prochains jours ou semaines. C’est ce que nous avons fait pour les épiceries Métro et leur demande en produits périssables.

A well-designed AI system to assist store managers in ordering the right quantity of goods that will be sold in the upcoming days or weeks. This is precisely what we’ve accomplished for Metro grocery stores and their demand for perishable products.

3. Stock Optimization

By accurately forecasting demand, companies can avoid stockouts (and reduce excess inventory). This helps improve customer satisfaction, minimize holding costs, and effectively manage the supply chain.

  • Demand-Driven Stock Planning: Accurately predict future demand to optimize stock levels by stocking the right quantity of products at the right time, thus reducing the risk of stockouts or overstock.
  • Dynamic Replenishment: Adjust stock levels dynamically to minimize stockouts and avoid overstock.
  • Seasonal Demand Planning: Adapt supply, production, and distribution plans to ensure adequate stock availability without excessive holding costs.
  • Just-in-Time Inventory Based on Demand Forecasting.

4. Demand-Driven Production Planning

Manufacturers and suppliers rely on demand forecasts to plan their production schedules and allocate resources efficiently.

By understanding future demand trends, they can adjust production capacity, optimize raw material procurement, and streamline manufacturing processes to meet the anticipated needs of customers.

  • Demand-Centric Production: Adjust production capacity and align manufacturing processes to meet anticipated customer demand.
  • Raw Material Procurement: Forecast demand to adjust procurement schedules, ensure timely availability of materials, and avoid stockouts or excess inventory.
  • Production Planning for Seasonal Variations: Provide insights into expected demand during specific seasons to adjust production schedules, allocate resources appropriately, and optimize stock levels to meet seasonal demand fluctuations.

5. Price Optimization

The fifth application stands as a key element for maximizing revenue while ensuring customer satisfaction and competitiveness in the market. The use of AI in forecasting allows for precise price optimization.

  • Dynamic Pricing: Dynamically adjust prices based on demand fluctuations, competitor behavior, or other market conditions to optimize real-time pricing, maximize revenue, and respond more effectively to market dynamics.
  • Price Elasticity Analysis: Understand price elasticity, which measures the sensitivity of customer demand to price changes, to determine optimal price points that balance revenue maximization and maintaining customer demand. This enables setting prices that achieve the highest possible value without sacrificing sales volume.
  • Personalized Pricing: Segment customers based on their preferences, purchasing behavior, or other relevant factors to generate tailored pricing recommendations for each customer segment. Companies can offer personalized prices or discounts that match individual customer needs and enhance customer loyalty.
  • Promotional Pricing Optimization: Generate insights into the most effective promotional pricing strategies to determine the right discount levels, duration, and timing of promotions to maximize sales and profitability.
  • Competitive Pricing Analysis: Understand competitors’ pricing strategies to adjust your own pricing strategies to remain competitive while preserving profitability.

6. Supply Chain Management

AI-powered forecasting enables accurate demand forecasting, efficient inventory management, streamlined production, and effective logistical planning, leading to a more agile and resilient supply chain.

Improving supply chain planning reduces stockouts and overstocks and enhances overall operational efficiency.

  • Transport Demand Forecasting: To optimize logistical planning, allocate the appropriate transport resources, and enhance delivery efficiency. Accurate forecasts enable better coordination with carriers and reduce transportation costs.
  • Production Capacity Planning: To optimize production planning, ensure optimal resource utilization, and avoid capacity constraints. Precise capacity forecasting improves production scheduling and minimizes disruptions.
  • Lead Time Forecasting: To forecast lead times for raw materials or finished products, optimizing inventory levels, production scheduling, and managing customer expectations. Accurate lead time forecasting enables better inventory replenishment and reduces stockouts or excess inventory.
  • Shipment Delay Forecasting: To predict potential shipment delays proactively manage potential disruptions, adjust production or inventory plans, and communicate timely updates to customers. Accurate shipment delay forecasting improves customer satisfaction and supply chain reliability.
  • Demand Variability Analysis: To understand the extent of demand fluctuations, plan safety stock levels, and adjust inventory strategies to mitigate the impact of demand uncertainties. Precise demand variability analysis minimizes storage costs and stockouts.
  • Inventory Optimization: To optimize stock levels in storage facilities based on product demand and inventory turnover. Accurate storage level forecasting allows for efficient inventory management, reduces inventory costs, and minimizes financial commitments related to stock.
  • Supplier Performance Forecasting: To evaluate supplier reliability, identify bottlenecks or risks, and optimize supplier selection for improved supply chain efficiency.

7. Promotion Optimization

AI models can evaluate the effectiveness of different types of promotions, schedules, and discount levels. By understanding the impact of promotions on demand, businesses can optimize their promotional strategies, allocate resources efficiently, and ensure sufficient stock levels to meet increased demand during promotional periods.

  • Demand Shaping: Predict the potential impact of different promotional scenarios on customer demand. By simulating various promotional strategies, price discounts, or product bundling options, AI models can help retailers and consumer goods companies shape customer demand and optimize promotional plans. This improves stock planning, production scheduling, and resource allocation.
  • Channel-Specific Promotions: AI forecasts can analyze customer preferences, buying behavior, and channel-specific data to optimize promotional strategies for different sales channels. Tailored promotions suited to the unique characteristics of each channel can be recommended. This helps retailers and consumer goods companies offer personalized experiences and boost sales through the most effective channels.
  • Competitive Analysis: AI forecasts can analyze competitor promotional data, market trends, and pricing information to provide insights into competitive promotional strategies. By monitoring and evaluating competitor promotional activities, AI models can help retailers and consumer goods companies stay competitive, adjust prices and promotional tactics, and effectively gain market share.
  • Seasonal and Event Promotions: AI forecasts can analyze historical sales patterns, customer behavior, and external events to optimize seasonal or event-based promotions. By understanding the impact of specific seasons, holidays, or cultural events on customer demand, AI models can recommend optimal promotional strategies and schedules. This allows retailers and consumer goods companies to maximize sales opportunities during peak periods.
  • Assortment Planning/Market Basket Analysis: This is a strategic process in the retail and consumer goods sectors that involves determining the optimal range and mix of products to offer to customers. The goal is to create a well-designed product assortment that matches customer preferences, maximizes sales potential, and enhances overall customer satisfaction. Discovering relationships between products frequently purchased together allows for cross-selling and upselling opportunities. This optimizes product placement, assortment planning, and targeted marketing campaigns to maximize revenue.

8. Sales Forecasting

Businesses can make informed decisions about sales targets, resource allocation, and marketing strategies based on accurate forecasts powered by artificial intelligence.

Ultimately, this results in precise demand forecasting, better sales planning, improved inventory management, and more. This, in turn, provides a competitive advantage.

  • Customer Segmentation: Segmentation of customers to forecast sales and inventory, create marketing campaigns, and optimize operations based on the most performing segments.
  • Category-Specific Demand Forecasting: Provides insights into demand patterns and trends for different product categories to generate accurate forecasts for specific product categories. This enables retailers and consumer goods companies to optimize stock levels, adjust production plans, and plan marketing strategies based on anticipated demand for each category.


9. Workforce Management

AI enables the optimization of workforce planning, efficient scheduling, and skills-based allocation, ultimately leading to improved productivity, reduced labor costs, and an enhanced overall experience for employees and customers.

  • Demand-Based Scheduling: Aligning staffing levels with anticipated demand to ensure adequate coverage during peak hours and minimize labor costs during slower periods. This maximizes productivity and reduces labor inefficiency. It involves matching staff skills and expertise to specific tasks or professional requirements.
  • Staff Allocation: Understanding demand fluctuations to efficiently allocate the workforce to areas requiring more support, optimizing labor resources and improving operational efficiency.
  • Seasonal Workforce Planning: Forecasting future workforce needs. This allows for proactive hiring, training, and planning for temporary or seasonal workers, ensuring adequate staffing levels during peak periods.
  • Training and Skill Development: By providing predictive tools that aid decision-making, Metro has been able to train new employees more quickly to fulfill orders.

Where to Begin?

In an environment with vast possibilities for utilizing artificial intelligence, it’s essential to exercise discretion when choosing use cases to explore.

Before adopting an AI solution, it’s crucial to reflect on the business objectives you’ve set. An AI solution should align with your business goals.

Next, I prioritize Proof of Concept (PoC) to reduce complexity and quickly obtain high value. A PoC is a practical demonstration that validates the technical feasibility and potential value of a solution before committing to a fully integrated solution in your systems. If you want to learn more about the best method for starting an AI project, we have a helpful eBook for you.

In Conclusion

Store managers must juggle a variety of dimensions, products, and data sources. Predicting the demand for each item and converting it into the quantity to order to achieve an optimal balance between increased sales and reduced losses is an extremely challenging task.

Handling this considerable amount of data surpasses the capabilities of the human brain. This project faithfully embodies our vision of “HumanAI”: a tool powered by AI that enhances individuals’ capacity to make better decisions.

Artificial intelligence is revolutionizing how businesses in the retail, food, and consumer goods sectors approach forecasting. With its advanced data processing and machine learning capabilities, AI offers the ability to predict accurately, plan efficiently, and proactively innovate.

It’s no longer merely an option but a necessity for businesses aspiring to thrive in a competitive and ever-evolving commercial environment.

Q&A Section:

How does AI differ from traditional forecasting methods?
AI differs from traditional forecasting methods by leveraging machine learning and analyzing extensive datasets to identify patterns and make precise predictions. This eliminates human biases and reduces errors commonly associated with manual analysis.

What are the advantages of AI forecasting in the retail industry?
Some advantages of AI forecasting in the retail industry include increased accuracy, improved efficiency, real-time insights, and demand optimization. These benefits help retailers optimize their operations, reduce costs, and deliver superior customer experiences.

What challenges are businesses likely to face when implementing AI forecasting?
Businesses may encounter challenges related to data quality and accessibility, model interpretability, and ongoing monitoring and adaptation. To overcome these challenges, having adequate infrastructure, interpretability techniques, and regular updates is necessary to ensure the accuracy and effectiveness of AI forecasting models.

Automated Generation of Metadata: Solutions for SAP, Oracle, and Microsoft Dynamics Inventories

Image pour l'article Génération de métadonnées

Imagine a scenario where the descriptions and categories of your inventory products are generated automatically. This prospect is now within reach thanks to generative artificial intelligence.

Like any innovation project, the aim is to achieve increased productivity, reduced costs, better resource management, all with the ultimate goal of gaining a competitive advantage. And this is precisely what the integration of AI into the ERP enables.

The use case presented in this article, namely metadata generation, is unquestionably an opportunity for businesses in the retail sector that utilize ERPs like SAP, Oracle, and Microsoft Dynamics.

Benefits of Integrating Generative AI with SAP / Oracle / Microsoft Dynamics

Improved Search and Recommendations
Thanks to precisely generated metadata, search and recommendation functionalities within the ERP can be greatly enhanced. For instance, AI-driven search could yield more pertinent results for users seeking specific products or components

Data Enrichment
Beyond basic metadata, generative AI can also contribute to enriching product data. For example, it can suggest potentially complementary products or add-ons based on metadata from other similar products.

Scalability
For companies with vast and constantly evolving inventories, manually updating or creating metadata for each product can be a challenging task. Generative AI can be scaled up to handle thousands of products, ensuring consistent metadata generation and updates.

Operation of Generative AI and Integration into SAP / Oracle / Microsoft Dynamics

Metadata generation from text

Metadata generation from an image

1. Metadata Generation

Automated Generation of Descriptions
For new products, AI automatically generates descriptions based on similar products in the inventory or on brief information provided by the user.


Categorization and Labeling
Generative AI can suggest or generate categories or labels for products based on their descriptions, images, or other attributes.

Localization
If you operate in multiple regions, AI can be trained to generate product metadata in several languages, facilitating the localization of inventory items.

2. Quality Control and Refinement

Feedback Loop
To continually enhance accuracy, a feedback mechanism is implemented where incorrect or inadequate metadata generated by AI is corrected by humans. These corrected data points serve as additional training data, refining the AI’s results over time.

Validation Process
Before newly generated metadata is accepted, a validation step is conducted to ensure accuracy and relevance.

3. Integration with the ERP

The generative AI system can be integrated with SAP, Oracle, or Microsoft Dynamics. This can be achieved through API integrations or custom modules, ensuring that the generated metadata seamlessly integrates into the inventory management system.


Series on generative artificial intelligence

This article is part of a series we have produced to help businesses better understand generative AI and its possibilities.


Metadata Generation with Artificial Intelligence [Video featuring Olivier Blais] (French)

In this video, I delve into how generative AI provides businesses working with solutions like SAP, Oracle, and Microsoft Dynamics the opportunity to streamline the automatic generation of metadata for their inventory products. Applying this technology to this specific context undoubtedly stands as one of the most promising prospects in the realm of retail. I wish you an enjoyable viewing experience.

[This article is a verbatim translation of the video segment by Olivier Blais, generated by generative artificial intelligence tools and corrected by a human.]

Introduction to Generative AI for Metadata Generation

Hello everyone. This week, we’re going to discuss a use case of generative artificial intelligence that particularly interests me. Why? Because it’s a case that will truly save time and help us address a rather tedious issue, which is creating metadata.

I’m not sure if you’re aware, but for a store to display thousands of products on a website, for example, or to ensure that all products, all items are properly cataloged, it takes a lot of manual effort, a lot of time, and requires large teams to enter information into systems. Often, this information is duplicated, and sometimes it involves taking information from one system and converting it to be used in another system. Sometimes, it’s about analyzing a product description to be able to categorize it. And this is a requirement, something we always need, whether it’s in ERP-style systems or all sorts of other systems, and it’s a lot of work that’s needed.

Here, from the beginning, for years, we’ve been saying that we’ll try to do things as best as we can. So, we end up creating processes that are a little more efficient. We save minutes here and there by simplifying the metadata generation process.

Opportunity in Retail

However, with generative AI, we bring solutions that will change how we generate our information. Here, all we need is to have a product description, for example, and to know what the different fields are, what the requirements are, a few constraints to be able to generate metadata very precisely.

We’ll not only be able to save time, but also increase speed. We might see a reduction of fifty to seventy-five percent in the time it takes to generate metadata, but we’ll also gain in accuracy. We’ll be able to generate information of much higher quality.

How We Generate Metadata

How does this work? It’s based on the metadata we already have in our systems. So here, we don’t even need to leave our systems. We take information we already have and draw inspiration from it. We take these examples and provide them to the generative AI.

So, we take a product description, we take examples of metadata, and essentially say to replicate this structure. And there you have it.

Practical Application of AI for Metadata Generation

In fact, here’s an example. Let’s take a hardware store or a grocery store, for instance. Consider a grocery store; it has a lot of inventory items.

You go to the grocery store, there are tens of thousands of items. And every month, every week, there are even new items. So, what that means is that every week, you potentially have a team of a hundred people whose task is to enter information about a new type of tomato or new cans of soup in order to properly catalog and sell them. This is tedious, time-consuming, and doesn’t add much value.

What we’re talking about here is essentially taking information provided by suppliers, entering it into a generative AI solution, and getting back the necessary metadata to enter into an ERP solution like SAP. Once the metadata is in the SAP solution, the job is done, and you can start selling the item and put it on the shelves.

Feel free to share if you have other ideas for use cases; I’d be happy to discuss them.

Conclusion and Perspectives

In conclusion, the integration of generative artificial intelligence into business practices paves the way for significant and transformative advancements.

The approach to metadata generation relies on information already present in the company’s systems, thus avoiding unnecessary efforts and redundancies. By simply providing a product description and examples of metadata, companies can swiftly and accurately obtain the data needed to fuel their ERP systems. This innovation simplifies and streamlines inventory management, allowing teams to focus on higher-value tasks and accelerating the introduction of new products to the market.

The use case in the retail sector is just the beginning of a broader exploration of the possibilities offered by generative artificial intelligence.

The Essential Use Cases of Generative Artificial Intelligence

Featured image_Les cas d’usage essentiels de l’intelligence artificielle générative

Generative artificial intelligence: why is everyone talking about it?

With the growing popularity of generative artificial intelligence technologies, such as PaLM 2 and ChatGPT, more and more companies are seeking ways to integrate AI into their daily operations. According to a McKinsey report, generative AI will have a significant impact on the economy by increasing the economic value of AI by 15 to 40%, representing an estimated annual value of $2.6 to $4.4 trillion. That’s huge!

Generative AI has the potential to revolutionize several sectors, and currently, generative AI solutions are already being deployed to simplify tasks and optimize processes. Google and Microsoft now offer tools specifically designed to facilitate the integration of this technology in enterprises. By the way, if you haven’t already read it, we recommend using generative AI tools in workplaces by opting for a solution tailored for businesses.

In short, generative AI offers the potential to automate, improve, and accelerate various tasks. In this article, our goal is to explore how this technology can enhance work and demonstrate how companies can benefit from it.

In all the examples below, we advise, as with all AI systems we develop, involving humans in the process. For us, generative AI improves the efficiency of your employees, but it is essential to keep humans in the loop.

Without further ado, here are a few examples.

 

Use of Generative AI

1. Content Generation

Generative AI opens up impressive new perspectives in the realm of dynamic content creation. A well-known use case, generative AI can be employed to automatically generate text. This technology can be applied in various contexts, here are the most relevant ones:

  • Generating Metadata for Products.
    Generative AI is revolutionizing how businesses manage product metadata in their inventory by automating tagging, description creation, and categorization. Through advanced natural language processing and image analysis, generative AI extracts essential attributes of products, generating accurate and relevant metadata. These operations optimize stock update processes and also enhance search functionality and user experience on e-commerce platforms. The scalability and efficiency of generative AI make it an invaluable tool for companies seeking to optimize their product information management.
  • External Conversational Agent.
    Conversational agents or chatbots powered by generative AI can interact with users in a natural and seamless manner while adhering to internal governance policies and your brand image. They can generate relevant and coherent responses based on posed questions, thereby improving user experience and customer service efficiency.
  • Document Generation.
    Generative AI can produce comprehensive documents such as reports, blog articles, summaries, etc., based on provided input information. This can be particularly useful for generating extensive content, for example, legal reports and case analyses for lawyers.

2. Text Summarization

Generative AI’s ability to distill the essence of a text and summarize it concisely finds diverse applications:

  • Customer Flow Analysis.
    By analyzing customer comments, reviews, and reactions, generative AI can generate summaries that provide valuable insights into customer trends and preferences, thus helping businesses make informed decisions.
  • Research Assistance for Experts.
    In technical or specialized fields, generative AI can assist experts by generating summaries of complex research or condensing technical documents into understandable key points. For instance, in the banking sector, generative AI can play a crucial role in supporting experts in understanding and interpreting complex research related to finance, economics, and markets. A concrete example would be analyzing and synthesizing detailed financial reports and academic research papers.
  • Item Segmentation into Categories.
    Generative AI can aid in segmenting large amounts of text into relevant categories, which is useful for organization and subsequent data analysis. In marketing, companies often collect vast amounts of data from various sources, including social media, surveys, and market analyses. Generative AI can be used to segment this data into relevant categories. For instance, a fashion company can use AI to classify customer comments based on style trends, color preferences, or reactions to different collections. To facilitate inventory management, businesses can segment items, stores, or customers using structured data. Generative AI rapidly identifies dominant customer opinions and behaviors, enabling better decision-making while maintaining effective stock management.


Series on generative artificial intelligence

This article is part of a series we have produced to help businesses better understand generative AI and its possibilities.


3. Generating Multiple Content Types

Generative AI is transforming the creation of computer code and software solutions:

  • Code Generation (Text-to-Code Conversion).
    By understanding natural language instructions, generative AI can convert functional specifications into source code, thereby accelerating the development process.
  • Image Personalization.
    Generative AI can create customized images based on textual descriptions, offering new possibilities for visual customization. In product design, design teams can quickly and automatically explore different visual variations of a product based on textual specifications. This can expedite the prototyping process and allow for the exploration of visual concepts before materializing them.
  • Recommendation Engine.
    Generative AI excels in creating tailor-made code recommendations and software architectures. This results in enhanced development team efficiency, enabling rapid detection of code anomalies and instant receipt of improvement suggestions.

4. Semantic Research

Generative AI is an asset in complex data research and analysis:

  • Internal Conversational Agent.
    Organizations can benefit from internal conversational agents that assist employees in quickly searching for and retrieving information from vast databases, including their internal database. Employees can interact with the agent naturally to ask complex questions and receive relevant answers, facilitating decision-making and access to internal knowledge.
  • Insight Generation.
    Generative AI can help identify trends and hidden insights in large and diverse datasets, offering a fresh perspective on research. This can be useful for analyzing unstructured data, identifying trends, creating customer segments, or predicting future trends. This capability allows businesses to rapidly extract impactful information from documents and transform them into actionable knowledge.
  • Customer 360° View.
    Generative AI can be used to aggregate and unify heterogeneous data into a comprehensive view of each customer. Using advanced machine learning and natural language processing techniques, AI can identify relationships between different data points and create enriched customer profiles. This enables sales, marketing, and customer service teams to have a deep understanding of each customer’s preferences, behaviors, and needs.

Essential Use Cases of Generative Artificial Intelligence

[Cheat sheet]

Essential use cases for generative artificial intelligence

Download our Generative AI use case checklist. Simply fill in the form and you’ll receive your copy by e-mail.


Initiate by selecting a low-complexity, high-value use case for your organization

When embarking on a generative AI project, it’s often advisable to begin with a Proof of Concept (PoC) that offers low complexity and rapid high value. A PoC is a practical demonstration that validates the technical feasibility and potential value of a solution before committing to a fully integrated system.

Let’s consider the concrete example of a Proof of Concept for a generative AI-powered virtual assistant. Such a system enables customer support agents to easily access internal knowledge sources, ask questions, and receive relevant real-time answers. Swiftly showcasing the power of such a solution on your data and within your corporate context can not only enhance employee productivity but also generate enthusiasm by highlighting the benefits of generative AI within the organization.

Furthermore, through an internal virtual assistant PoC, a company can test the effectiveness of generative AI before applying it to customer-facing applications. This helps comprehend limitations and necessary improvements while minimizing the risks associated with implementing new technology.

“With great power comes great responsibility.” 

-Uncle Ben

At Moov AI, we believe in the immense potential of generative artificial intelligence and advocate for a more responsible use of AI through the leadership of Olivier Blais in LIAD, ISO standards on AI, and with the Quebec Innovation Council. Just like with any AI project, we aim to reduce risk levels. You can watch Oliver’s conference on generative AI, where the risks associated with this type of project are addressed along with how to mitigate them. It’s crucial to maintain cautious optimism. While the technology is impressive, it needs to be explored with security at the forefront.

In conclusion 

In an environment where the possibilities for using generative AI are vast, it’s essential to be discerning when choosing which use cases to explore. Before adopting a generative AI solution, as with all AI projects, it’s vital to think about the business objectives you’ve set yourself. An AI solution must meet your business objectives.

Now that we’ve outlined the various use cases for generative AI, you need to ask yourself what the next steps are. The first thing we’d advise you to do is to consider the questions proposed by McKinsey.

  • To what extent can technology help or disrupt our industry and/or our company’s value chain?
  • What are our policies and positions? For example, do we wait cautiously to see how the technology develops, invest in pilot projects, or seek to develop a new business? Should the position vary according to areas of the business?
  • Given the limitations of the models, what are our criteria for selecting which use cases to target?
  • How do we go about creating an effective ecosystem of partners, communities and platforms?
  • What legal and community standards must these models comply with so that we can maintain the trust of our stakeholders?

If you want to know more about the best way to start an AI (or generative AI) project, we’ve got a good eBook for you.

Getting started with a proof of concept can be a beneficial approach, offering quick value while enabling your organization to become familiar with generative AI and develop internal traction in the face of innovation. By taking these preliminary steps, you’ll be better prepared to maximize the benefits of this emerging technology, while addressing the specific needs of your business.

Why you should use ChatGPT in a business context

The challenges of integrating innovation like generative artificial intelligence solutions in your company

OpenAI made a big impact in the field of artificial intelligence by unveiling ChatGPT, sparking a frenzy of adoption among millions of people. For the first time, we witnessed a true democratization of artificial intelligence. This innovation opened the eyes of everyday individuals and the business world to new possibilities. Generative AI enables everyone to explore the capabilities of this advanced technology almost instantly.

Following OpenAI’s success, other companies such as Google (Gemini), Anthropic (Claude), and Meta (LLaMa) also released their large language models (LLMs) to compete with OpenAI (ChatGPT, GPT-4). These solutions are all powerful generative AI tools that can generate precise and rich responses from prompts.

However, the rapid success of these LLMs raises several risks, including societal and reputational concerns. Questions have also been raised about their integration in a professional context. This brings us to a well-known concept in business: innovation management. It’s not just about using new technology for the sake of it, but rather implementing it with a specific goal to achieve a competitive advantage. Like any other AI solution, considering the adoption of generative AI must begin with the business objectives you have set.

The challenges lie in innovating with generative AI, deploying it at scale, integrating it into the current company system, and managing the associated risks. When a solution arrives on the market so abruptly, it is essential to understand it thoroughly before adopting it too quickly and potentially having to backtrack.

Thus, companies quickly face the limitations and potential risks of generative AI. Let’s be clear: using the free version to automate business processes is a bad idea. Despite its performance and opportunities, this tool is not a B2B solution. This raises the question: how can we extend these capabilities to our professional activities more appropriately while mitigating the risks?

Thankfully, there are solutions specifically designed for enterprise use and offering business capabilities. B2B tools provided by Google, Microsoft, and AWS cater to the specific needs of businesses, allowing them to fully leverage the benefits of generative AI while ensuring optimal security and efficiency.

Risks and limitations of generative AI

Before delving into the topic, let us explain why no company should use the public version of ChatGPT (or other similar tools) to blindly automate critical processes within their business.

Data security

Data security is a major concern when using public platforms like Gemini and ChatGPT. It is crucial to adopt preventive measures now to avoid sharing sensitive information through these tools.

By default, the data input into these tools creates a security breach as it goes to a third-party server. This information is transmitted to the servers of the company that created these solutions. All information provided via prompts to ChatGPT, for example, can be used by OpenAI.

Now, this is not for the purpose of “stealing information to dominate the world.” Instead, it’s to deepen the understanding of use cases and improve the technology. However, it is essential to recognize the potential risk this poses to the security of our company’s information. There are already examples of misusing ChatGPT, like the former Samsung employee who used it to optimize his code, inadvertently sharing sensitive company information with OpenAI’s servers. This represents an internal information breach.

Therefore, it is highly recommended to exercise caution and not share sensitive information that could compromise data confidentiality and security when using OpenAI’s APIs.

ChatGPT is trained until 2021

It’s also important to note that ChatGPT was trained until September 2021, which means its knowledge and capabilities may not be up-to-date with the latest information. This applies to all other LLMs trained on past data as well. For example, if you ask a generative AI solution about the latest financial statements of Shopify, you might get outdated information. This highlights the importance of understanding the temporal limitations of generative AI solutions and not considering their responses as up-to-date information in all respects. If you want recent information, it’s essential to include the most recent data in your query and base the response on that information.

Hallucinations

Chatbot responses can be useful, humorous, or, in some cases, outright invented. Grand language models can sometimes hallucinate. Hallucination here refers to false information in the generated text that may seem plausible but is actually incorrect. With generative AI text solutions, the responses are delivered with such confidence that they can easily mislead. If you want to write a poem about haystacks using ChatGPT for entertainment purposes, the impact of a potential hallucination is minimal. However, in a professional environment, where you consult information to make critical decisions, data accuracy is paramount.

When using generative AI solutions’ query prompts without providing sufficient context, you receive the most statistically plausible response, but the LLM may misinterpret it due to a lack of context surrounding the request. This puts you at risk of receiving a response that seems appropriate but might contain false information. It is our responsibility to validate the generated information to ensure accuracy before publishing it, to avoid unintentionally creating “fake news.”

In short, we cannot fully trust what the machine regurgitates, and that is problematic in a business context.


Series on generative artificial intelligence

This article is part of a series we have produced to help businesses better understand generative AI and its possibilities.


Generative AI solutions adapted for enterprises

As mentioned earlier, ChatGPT is more of a B2C tool and comes with risks concerning the security of business information.

Thankfully, there are now tools specifically designed for enterprises provided by Google, Microsoft and AWS. These tools are recommended for professional use.

For example, Google Cloud’s suite now includes several tools such as Generative AI App Builder, Duet AI for Google Workspace , and generative AI support on Vertex. With these, you have the robust and secure structure of Google for your business projects.

Security is reinforced as the content remains within a secure and private shell, ensuring that the information transmitted to the model will not be stored publicly. You can also apply your data governance plan to comply with your internal security processes, which is much more reassuring.

Creating your own generative AI solution in-house

An interesting practice with professional generative AI versions is the possibility for a company to integrate its own technical documents and create an in-house solution by defining specific limits and parameters. This approach allows for developing a personalized virtual assistant accessible to all members of the company, offering easy access to internal knowledge bases. This initiative encourages collaboration and simplifies the dissemination of information within the organization, thereby enhancing overall efficiency and productivity. By customizing generative AI to meet the company’s needs, you can maximize the benefits of this technology while adhering to the organization’s specific policies and requirements. Both Google and Microsoft allow you to create a company-specific interface and offer a secure generative AI solution that considers the organization’s specific parameters.

This practical example provides an opportunity to embark on a generative AI project that offers great value while presenting low risks.

Validating before using

How do you estimate if generative AI performs well in solving your problem? It’s simple; just measure the success rate over 50-100 similar queries. This will help estimate the potential success when using the solution in a normal context and give you more confidence.

It’s super powerful to be able to create query templates or a process once certain tasks have been validated by users. This way, you can generate a platform that can act as a more generalist personal assistant while automating or optimizing specific tasks. Project teams are encouraged to stay alert for tasks that bring the most value and try to generate these templates or processes to make them accessible to all users.

An overview of a generative AI solution

Let’s consider a company that wants to automate the automatic writing of submissions to address new business opportunities.

Task to automate: Automatic writing of submissions based on customer data from your CRM, historical submission data from the past, and various knowledge bases such as internal documentation, customer exchanges, or other data.

Query interface: A software interface integrated with your existing tools (Teams, Slack, Salesforce, etc.) that allows your users to write queries in natural language that activate the generative AI solution. For example: “Write a submission for our new client Olivier from Moov AI company for a proof of concept to automate submissions for a particular project using generative AI.”

API (input and output): API stands for “Application Programming Interface.” An API defines the methods and data formats that developers can use to access the functionalities of software, platforms, or third-party services. These two APIs facilitate information exchange and integration between your systems and the generative AI solution.

Data: The generative AI solution will have been previously personalized with your data. Think of generative AI tools as empty shells into which you can integrate your data and leverage the same response power as open solutions. The response of the generative AI solution will be based on your data.

This data can include your CRM data, customer email exchanges, knowledge base, documentation, past submissions, project reports, actual project costs, etc. Anything relevant and written can be integrated into these solutions.

Professional cloud platform: These solutions no longer need an introduction. Unlike solutions offered to the general public like ChatGPT, cloud solutions offer increased data security, the possibility to apply your data governance plan, and monitoring capabilities for your environments, data, and models. Your data and the various commands you issue will remain in your secure Cloud environment, safe from external eyes.

Generative AI solution and task executed: Your query will be processed by the generative AI solution using your data and your company as context. The result will be, for example, the drafting of a new submission tailored to your service offering and the specific needs of the client you just targeted. This draft submission will be complete and ready for review by a human colleague who will send it to the client. All of this in just a few minutes.

Good tools for good work

The launch of ChatGPT by OpenAI has opened exciting new perspectives in the field of artificial intelligence. The democratization of this advanced technology has allowed millions of users to explore its capabilities almost instantly. However, it is essential to consider the risks and limitations associated with using these LLMs. Using generative AI tools for business is significant for safe professional use.

Ultimately, by understanding the risks and using these technologies judiciously, we can leverage their full potential while ensuring the security and protection of sensitive data. Artificial intelligence continues to evolve, and it is important to adopt a balanced and thoughtful approach in its use to make the most of it.

How to leverage generative artificial intelligence solutions in business without drifting

Exploiter sans dérive Gen AI_Featured image

With the advent of ChatGPT and other text and image generation tools, generative artificial intelligence (AI) solutions offer truly revolutionary prospects for your business. Generative AI solutions will have more impact on our businesses and ways of working than the arrival of the Internet.

In this presentation, Olivier shows you how to leverage these technologies right now to go beyond simple question-and-answer scenarios and achieve your development goals while accelerating your capabilities.

Through concrete examples and in-depth analysis, Olivier explores the various applications of generative AI, as well as the limitations and challenges associated with these tools, which, it must be reiterated, are still in their infancy.

Olivier also addresses the ethical considerations related to the use of generative AI tools and proposes ways to ensure the quality of solutions delivered using generative AI.


Series on generative artificial intelligence

This article is part of a series we have produced to help businesses better understand generative AI and its possibilities.


Conference on demand (French)

In-Depth Verbatim Conference Translation from French


[This article is a verbatim transcription translated from French of the conference presented by Olivier Blais :

‘How to Harness Generative Artificial Intelligence Solutions in Business Without Drift’.

It is worth noting that this transcription and translation were done using artificial intelligence tools. A job that would have taken 3 to 5 hours was completed in just a few minutes.]

Introduction

Olivier – Speaker

Thank you very much for your time. It’s greatly appreciated. I know we’re all busy here on a Thursday morning. We’re anxious to get back to the office, but at the same time, I think generative AI has something special. I think it taps into the imagination. I can understand why you’re here. I’m also super excited to talk about this topic. I want to know, has everyone arrived? The absentees are missing out, so let’s get started. Before we begin the presentation, I had a little survey for you. By a show of hands, I was wondering who in the room has certain concerns about generative AI? I see hands going up very quickly. Perfect. Thank you. Also, I had a question about the capabilities of generative AI. Who is excited about generative AI? Who wants to use it? Excellent. We’ll send a sales representative to talk to you shortly. No, that’s a joke. It’s a joke, but not really. I’m glad to have seen both worried and excited hands. In fact, for me, there’s still a duality between the two.

The duality of generative AI

[01min 24s]
I am confident but cautious about the technology. That’s what we’re going to talk about today. That’s why we’re discussing generative AI, but without going too far. How to effectively use these technologies to generate benefits without excessive risk. What I’m going to do is… Excuse me, first, I’m going to introduce myself. My name is Olivier Blais, co-founder of Moov AI. I am in charge of innovation for the company and a speaker. I speak from time to time; I like listening to myself speak. What we’re going to do is talk about generative AI, of course. We’ll start from the beginning. We’ll introduce the topic, but we’ll go further. Why? Because we are all AI developers at the moment. It’s special, but with generative AI, it’s a paradigm shift. I’m no longer just speaking to a couple of mathematicians who studied artificial intelligence. I’m speaking to Mr. and Mrs. Everyone because now everyone has the opportunity to use these technologies to generate results. So, everyone needs to be aware, everyone needs to understand how to effectively exploit the technology.

The Hype Cycle of Generative AI

[02min 38s]
But don’t worry, I’ll try to keep it fairly soft. We won’t dive into mathematics, I promise you. And I’ll also talk about responsible AI, which is key. Making sure that when we do things, we do them correctly. So, we’ll delve into a slightly more theoretical period, but first, I’m curious to know more about where you are in the hype cycle. It’s extremely hyped right now, ChatGPT, it’s all you hear about. I don’t even go on LinkedIn anymore because that’s all it is now. GPT this, GPT that. But in fact, it’s really a curve. When I saw this curve for the first time, it stuck with me. And everyone here, whether you realize it or not, you’re at one of these stages of the curve. I won’t ask everyone where they are, that would be really complex, but I find it interesting to know what the upcoming stages are in our journey. For me, initially, it was around January, I would say. At Moov AI, we started using GPT-2, GPT-3 since 2019.

[03min 56s]
We found ourselves making a documentary, for example, with Matthieu Dugal where we generated a conversational agent. It’s been a while, but since we heard about ChatGPT, that’s when it really awakened people and created hype. At first, we think it’s magic. You input something like “give me a poem about haystacks,” and it generates a poem about haystacks, and it’s incredible. It really feels like magic, but at some point, when you start using it for something useful, that’s when you fall into small “rabbit holes,” that’s when you encounter irregularities. For example, you might question it about a person, a public figure, and then it gets everything wrong. You might use it for calculations, and it can make mistakes in calculations. You scratch your head and think, “Okay, what’s happening?” And increasingly, you find yourself identifying weaknesses in these models. Ultimately, it’s not necessarily magic.

Graphique sur le cycle d'adoption de l'IA générative

Risks and Limitations of Generative AI

[05min]
It’s a lot on the surface. However, there are some issues to consider with ChatGPT. Firstly, it’s not updated daily. The last update was in January 2021. This means that if you ask questions about current news or events, it may not be aware of them. So, we can identify this as a weakness. Additionally, when using ChatGPT, your data is transmitted to OpenAI, creating a potential security vulnerability. This reveals the weaknesses in these otherwise cool tools. While there are ways to mitigate these risks, it’s essential to be aware of and understand how to properly leverage the technology, using best practices and learning from examples.

Advantages and Best Practices of Generative AI

[06min 12s]
Despite these challenges, it’s fascinating to see what can be achieved with generative AI. While we acknowledge the problems, we strive to overcome them and mitigate the identified risks. Many people are currently using generative AI in functional, deployed applications. It works. And now, we can move forward and apply the same principles to real-world use cases. Speaking of applications, let’s take a step back. Our focus is not just on ChatGPT; it’s one solution among many. For instance, Google has its own solution. I refer to this as B2C (Business-to-Consumer) – something that is accessible to everyone, a democratized technology meant for widespread utilization.

Generative AI Tools for Businesses

[07min 34s]
It’s exciting because generative AI enables various text analysis and offers numerous use cases that benefit everyone. There are also tools available specifically for businesses (B2B). It’s important to understand the distinction between the two. We don’t recommend using consumer-oriented tools for enterprise purposes. Instead, we suggest utilizing tools tailored for businesses. For example, solutions like GPT4, Palm, ChatGPT, and BART are just a few examples among hundreds. We refer to these solutions as Large Language Models (LLM). Some of you may have heard of LLM before, and it’s good to see a few hands raised, indicating familiarity. LLMs are language models trained on scraped internet data from various sources, making them highly proficient in understanding and generating text.

Fundamental Models and Language Models

[08min 55s]
This is great because it allows us to do everything we currently do with the technologies we use in our daily lives. However, it fundamentally stems from an approach called foundational models. This approach has been around for over ten years. It’s the ability to model the world. It may sound poetic, “I’m modeling the world,” but that’s essentially what it is. It means that there are human functions that we don’t need to recreate every time, such as image detection. We could create our own model to identify cats from dogs, another model to recognize numbers on a check, and yet another model to identify intruders on a security camera. We could develop them from scratch, but it would require millions of images. Instead, the concept of foundational models emerged, and we thought, “Wait, why don’t we invest more time upfront?” We can develop a model that excels at identifying elements in an image.

The Paradigm Shift with Generative AI

Afterward, we can leverage this capability for tasks related to image detection. It started with image detection, but we quickly realized the benefits in language as well. That’s why we now have generative AI tools that allow us to generate poems, summaries, and perform many other tasks. How were these tools created? They were developed by gathering vast amounts of publicly available internet data. Personally, I don’t have the capacity to do this, but major web players, including the GAFAM companies, can seize this opportunity because they have the storage capacity and advanced capabilities that surpass other organizations. As a result, they have developed highly effective models that understand the world but lack depth. Has anyone here used GPT-3, for example? OGs, I like it—people who were there before ChatGPT. With GPT-3, if you asked it a question, it would essentially provide you with what Wikipedia would say. It wasn’t very interesting from a user experience perspective. This is what sets the new solutions apart. As a second step, there has been an emphasis on dialogue adaptation. It’s fun how we are now getting responses. Organizations have started prioritizing the user experience. This speaks volumes about being more attuned to what users want and how they want to leverage the technology. It makes the experience more enjoyable and motivates people to use and explore it further. Additionally, it has significantly improved the quality because humans prefer to be responded to in a human-like manner. This has increased our adoption of the technology. And now, here we are. Let me explain the difference a bit because it changes many paradigms compared to regular AI solutions. With a regular AI solution, we typically have teams that gather data and train models. Often, each problem has its own model.

Fonctionnement de l'IA générative

[13min 04s]
And after that, once they’ve trained it, then it remains to be deployed so that it can be reused whenever there are new updates or new things that come up. Let me give you an example. Back then, because it’s different now, there was a team at Google, for instance, that focused on sentiment analysis. They would analyze tweets or texts and determine whether the sentiment expressed was positive or negative. So, you had a team solely dedicated to that. They would gather information from the web, identify the associated sentiment, train their model, and then deploy it. From then on, every time a new comment came in, they could identify whether it was positive or not. This had to be done for sentiment analysis, and the same process applied to every new thing you wanted to model. But that’s not the case anymore. The paradigm has changed because there are so many possibilities with language models that you no longer need…

The Possibilities of Generative AI

[14min 20s]
Firstly, it’s already deployed. So, for the average person, for businesses, you no longer need to go through the initial training phase. And that changes the game because it significantly reduces the scope of your project. It’s no longer a project that costs millions to produce because you’ve cut a huge portion of your development costs. Moreover, you don’t need to gather extremely precise data to address the specific problems your model was trained on. Here, you input a question or a prompt, and if your prompt is well-crafted, the result is what you expected to receive. These results now have limitless possibilities. You can have text, predictions, code, tables, images—there’s so much you can get. The benefits we can achieve with these new technologies are incredible. However, I appreciate the duality in the comment made by Google’s CEO on 60 Minutes a few weeks ago: “The urgency to deploy it in a beneficial way is harmful if deployed wrongly.”

[15min 44s]
That’s the real duality. There are so many positives that can come from it, but it can be catastrophic if done incorrectly. It’s the CEO of Google who said that, but honestly, it’s the responsibility of each individual to use these technologies appropriately. If we use them correctly, we can minimize the risks because there are risks. Here, firstly, one risk is that it’s used by everyone. I understand that we might have around 60 people here, but there are hundreds of millions of people who have used ChatGPT, for example. So, it’s now being used by almost everyone, and it’s crucial for each person to be aware of their usage because we could end up causing a disaster if we exploit it in the wrong way. I’ll skip that part. Here, I’ll give examples of things we can do because it’s always helpful to be able to… Sorry, I’ll go back here. These are different use cases, don’t worry, I won’t go through all of them, but there are plenty of use cases. Many of them are related to text analysis.

Nombre d'utilisateurs de ChatGPT en comparaison à TikTok, Instagram, Google translate et Netflix

[17min 00s]
We’re able to analyze a lot of texts, perform classification, and identify elements within a text. Additionally, we can also make predictions, even very conventional ones. So, we can reproduce certain machine learning models using LLM tools. By the way, everything we do with text, we can do with code as well. There might be people who… We’re used to writing text in French or English, but for those who are used to writing in Python, for example, or in C++ or C#, it’s even more efficient because it’s explicit. When you write a function, it’s explicit. Your language is implicit, all the sentence structures, what the words mean. So, let’s consider that the sky is the limit in terms of capabilities. As I mentioned earlier, I think we can agree that question/answer tasks are extremely good by default. So, if we want to build a chatbot, let’s ensure that the chatbots we want to develop have the ability to generate responses. We’ve reached that stage now. What we develop should have the ability to generate responses.

[18min 21s]
What it allows is the ability to address a much wider range of questions. It also reduces development time. You don’t have to think about each individual scenario separately. You give precise instructions to your chatbot, and most of the time, it will provide appropriate responses. You can also control it, which is a possibility. Another element that I really like is the fact that we can trust these models. It’s not just about saying, “Let’s ask questions and see what happens.” We can trust these solutions in certain cases. For example, here we could correct dictations with solutions. For instance, I posed a question here. I tried a little dictation of my own: “Manon bought three cats at the grocery store.” What we can see is the correction that is made. Truly, it was able to go much further. It has been demonstrated that for text correction, it’s incredible. It works really well, and we can trust tools like this for sensitive matters such as dictation correction for our children.

Différents cas d'usage d'IA générative

[19min 47s]
By the way, something quite amusing is that if I ask the Generative AI platform to correct the sentence “Manon buys three cats at the grocery store,” besides correcting the mistakes, they will say that it’s not really at the grocery store where you buy a cat. That’s interesting to know. Otherwise, I don’t really encounter the cat. It’s not necessarily the cat that you want to keep for years. That’s another example. Earlier, I mentioned it when talking about sentiment analysis. But these are things we can do. All the existing APIs will be rapidly replaced. Here, I asked the same question: can you identify the sentiment in the following sentences? You provide the list of sentences, and they return the correct sentiment. That’s exactly the tool that will be used in the upcoming APIs. Once again, we can trust the tools as long as they are used appropriately and optimized for the task you want to accomplish. I even pushed the envelope a bit because earlier, I talked a lot about the risk. Yes, there is a risk, but it’s important to understand the risk properly.

Exemple de correction d'une dictée par ChatGPT

[21min 05s]
I thought, “Wouldn’t it be fun to use Generative AI tools to perform risk analysis of an AI solution?” I thought, “It would be amusing to ask ChatGPT to analyze the risk of ChatGPT itself. Let’s see if I can put it to the test.” I actually received some very interesting responses. First of all, it did a great job. I used a risk framework called NIST, which is highly recognized. I asked the question, “Here are the risks, can you assess the impact, the probability of their occurrence, and even provide justifications?” The task was really well done, and I am extremely satisfied. Here, I’ll give you three examples. The first example is about use cases. Is the use case a risky one? What’s interesting is that it’s not always straightforward, I think we know that, but it’s not due to hallucination or error; it’s a matter of perspective. So, the response I received was, “No, ChatGPT is designed for all use cases.

[22min 19s]
Since we don’t specifically target any use case, we have nothing to blame ourselves for.” I’m exaggerating, but I think it’s interesting because it gives us the impression that we try to avoid as a society. It’s like saying, “You know what? We’ll give the correct answer to everything, and then we can take a step back, and we’re not responsible for what happens.” Our responsibility is to prevent that from happening. It’s like saying, “No, no, look, for each use case, for each scenario we use, we’ll make sure it’s done properly.” So, we’ll be required… There’s an additional level. We can’t just rely on technologies to control each of these cases. It needs to be done in a subsequent step. Another aspect was about the methods for manufacturing, for creating the solution. And here, it made me think a lot. So, OpenAI creates its own model with two or three data scientists. I wonder who has the best capabilities for solution creation.

[23min 27s]
I think I’ll put a lot of my money on these platforms because they have highly qualified people, they have large teams. So, it made me think, and I agree with them. It’s true that the risk is lower. I think the risk is higher if Mr. or Mrs. Average Joe tries to create their own model because they may not have the best methodologies, they may not have the best expertise, and so on. So, there is also a significant benefit that can be gained with these solutions. Lastly, the last point I wanted to discuss is a risk in terms of legal security. And don’t worry, I’ll come back to it later. But something interesting, I got it here. What it told me is that ChatGPT uses third-party data, and that entails risks related to copyright and intellectual property. But all of this is intriguing. Firstly, transparency, being able to understand the model a bit more, but also that generative AI solutions are capable of performing tasks as critical as model risk assessment.

Un tableau de risk assessment of ChatGPT effectué par ChatGPT

[24min 42s]
The guy who is involved with ISO standards is very happy with this type of exercise. Now, I’ll take an even bigger step back. I don’t know if you all agree to use generative AI. I think yes, I saw many hands raised, but some may not be. But I have some bad news for those who are less enthusiastic about using generative AI. You don’t have a choice. Unfortunately or fortunately. Why is that? It’s because technology organizations… Here’s an example from Google, but Microsoft has a very similar roadmap as well. They didn’t just create generative AI tools for everyone to have fun writing poems or creating summaries. They also used them to improve the services they offer to their clients. For example, here are three different levels of generative AI offerings by Google. The clearest one is to say, “I’ll help data scientists and people developing AI solutions to develop generative AI solutions.” I think that’s a given.

[26min 06s]
Everyone is aware that this was coming. But Google takes it a step further and says, “Wait a minute, you don’t always have to have data scientists. There aren’t many people developing AI solutions, but there are many more developers in the world. So, how can we assist developers in their development?” And that’s where the capabilities we’re starting to understand now when using existing solutions come into play. There’s the possibility of using the solutions as they are. For example, Google talks about helping with conversation, helping with search. These are very clear use cases that developers can deploy in existing solutions. They can develop their own applications, select the features they need, and adapt and adjust these features to enhance the end-user experience. And we can go even further than that. So, it’s not just for the average person; there are also business users who have started using tools like Dialogflow.

[27min 22s]
Google has several tools, and these tools will also have generative AI capabilities. What this means is that generative AI is here to stay. We just need to be able to use it effectively. And I have some more news for you: development continues to accelerate. I understand that some people may be happy to know that there are some lingering questions. Apparently, GPT-5 is not being developed, but that doesn’t change the fact that development is ongoing and intensifying. I can provide examples. We have LLaMa, and I’m not talking about the animal, even though there’s a picture of a very technological llama. But LLaMa is a tool that allows you to create your own models, your own internal ChatGPT, for example. So, that continues. We can see the frenzy. Everyone wants a LLaMa. I’m exaggerating. Personally, I would suggest not using that and instead using the right technologies. That’s my take on it. We have much better performance with the existing solutions that have already been tested by millions of people.

[28min 51s]
But I understand that it’s something interesting. It allows us to develop everything on our own laptop. Sure, the geek in me finds it exciting, but the business person finds it a bit overkill for what we’re creating. There are also companies that have their own capabilities in development. For example, Coveo. Coveo is very clear that they have already developed some generative capabilities. Coveo, which is one of Quebec’s gems. And there are other companies like Databricks, a major player in the ecosystem, that is developing Dolly, I believe. So, it will intensify. There will be more and more competition in the market. And there’s also a trend, I’ll just briefly mention it, called “auto GPT,” which is the ability to train GPT, a generative solution, with another generative solution, creating a loop. It’s scary, I agree. Again, it’s important to control it, but for now, it’s a trend that is more prevalent in development, automating certain workflows.

Diapositive représentant différentes innovations dans le domaine de l'intelligence artificielle générative

[30min 09s]
Really, it continues. It’s important to stay informed. It’s important to understand what is happening to ensure we use the best technologies to meet our needs. And to avoid risks, I’m going to talk about three different challenges we have currently. In terms of hallucinations, I’ve been talking about hallucination for a while. What is an hallucination? I’m not talking about hallucination in a desert. An hallucination is an error. Let me give you an example. Everyone has a brother-in-law who says things, he’s so convincing, but sometimes he doesn’t know what he’s talking about. I think everyone has had that brother-in-law or sister-in-law. That’s an hallucination. It’s an error. In the past, the OGs will remember that in a traditional model, you have errors, so sometimes you make incorrect predictions. In this case, it’s a wrong prediction, but it’s so convincing because it’s well-written. An hallucination is a bit more problematic because people who don’t necessarily have the ability to judge the output accurately could be deceived. So, here, you need to be careful every time you produce an output, every time you make a prompt, you need to look at what the result is.

[31min 43s]
Deepfakes, fake news, there are plenty of them, and there will be more and more. So, the ability to ask, “Write me a text about a certain topic,” without fact-checking and posting it on Facebook, is a problem. Why? Hallucination. I think we’re making the connection a bit. It’s much easier to write beautiful texts with false information than it used to be because before, you did it yourself or had it done by people in other countries. But now, it’s much easier. So, we need to ensure that every time we develop things, especially when it’s automated, we avoid the spread of fake news and deepfakes. And finally, in terms of privacy, I think everyone, if people are not aware of it now, I think we will be more and more aware of it. Let’s focus on it because it’s one of the problems we will increasingly see. There are horror stories right now, people copying, pasting trade secrets, putting them into tools. Ultimately, the information is distributed to big companies.

Les principaux risques de l'IA générative : les hallucinations, les deep fake et l'atteinte à la vie privée

[32min 52s]
And there, you have just created significant security vulnerabilities. But that’s why at Moov AI, our stance is somewhat similar to what I mentioned earlier – it’s cautious optimism. In fact, I’ll use the quote from Uncle Ben, for those who remember Spiderman, “With great power comes great responsibility.” That’s why we have decided to embark on this journey. We want to assist our clients because if we don’t, people will do it themselves, and they might do it poorly, promoting fake news and causing more problems than benefits. That’s why it’s important to provide guidance and support. That’s why we are actively involved, for example, in advocating for Canada’s Data and Artificial Intelligence Act, Bill C-27. We are helping to accelerate these efforts. We also have a prominent role in ISO standards to regulate and oversee the development and use of artificial intelligence. Our goal is not only to develop useful things but also to ensure proper control over them.

Présentation des différents efforts pour sécuriser le développement de l'intelligence artificielle.

[34min 13s]
This can be leveraged in three different ways. Firstly, in the field of education. For instance, we have Delphine here, who oversees the Moov AI Academy. We will ensure that we assist individuals in achieving their objectives. That’s for certain. We will also contribute to the development of high-quality solutions. We have already begun doing so by employing machine learning methodologies to demonstrate the effectiveness of our solutions. If we can do it for traditional solutions and prove their worth before deploying them, we can do the same for generative AI solutions. Lastly, we aim to fully comprehend the risks associated with the solutions we undertake. Our objective is not to create more problems but to capitalize on opportunities. Now, let’s briefly discuss risks because it is crucial to address them. I brought you here for that purpose. Just kidding! However, it is essential to have a good understanding of risks. Here, I will discuss four main risks: functional risks. When you build a feature, ultimately, a model is a feature.

Exploiter l'IA générative de façon responsable en éduquant, en répondant à des problèmes précis et en comprenant les risques associés aux solutions.

[35min 33s]
Contrary to what ChatGPT was saying, we are not merely creating a platform that provides answers to everything. Our goal is to develop features that meet your specific needs. How can we do this effectively? From a societal standpoint, how can we ensure that we create a solution that is fair and ethical? The key is to ask the right questions. We also need to consider information security and legal aspects. Now, let’s go through the different risks. When it comes to best practices for functional risk, it is important to define the task you want to accomplish clearly. We have examined various tasks extensively. Therefore, it is crucial to break down the problem in the way we want to approach it. Just because we have a powerful tool at our disposal and can input any prompt doesn’t mean it will provide optimal responses for all scenarios. We shouldn’t overlook scoping and settle for just having a search bar where we can do anything. Ideally, we should ensure that we achieve good performance relative to our specific goals. That’s truly the foundation, and I highly recommend everyone to follow this approach.

Les principaux risques de l'IA générative à atténuer : fonctionnel, sociétal, sécurité de l'information et juridique.

Best Practices for Functional Risks

[36min 53s]
Next, what we want to do is optimize our approaches and prompts. I will show you how to do that shortly. And finally, you need to perform validation. It’s a machine learning tool, an artificial intelligence tool. You want to validate it with multiple data points, prompts, and scenarios, just like we do in traditional artificial intelligence. Just because it works once or twice doesn’t mean we can assume it always works. So, one of my recommendations is to use conventional approaches, approaches that have been proven for validation, and validate what we develop. “Okay, yes, it works.” And this is quantifiable. “Okay. What I wanted to develop works well 90% of the time.” So, you’re able to quantify the percentage of correct answers you obtain. This is highly valuable because it allows you to determine whether you’re shooting yourself in the foot or not by continuing the development. Now, let me give you an example regarding prompt optimization. What I mentioned earlier, and it’s really… I think everyone understands that it’s quite simplistic, is that writing a prompt like “Write me a poem about haystacks” isn’t what you’re going to transform into a process or a product.

Diapositive sur les bonnes pratiques pour atténuer les risques fonctionnels.

[38min 29s]
“It’s not about that. It won’t work well. I like to use the expression ‘future-proof.’ It’s not something that will allow you to deploy a solution that will work in the long term. Instead, what you’ll want to do is… Yes, let me give an example. I apologize. I’ll give an example. It’s like if I ask a question to a financial chatbot that I develop, such as ‘Identify three interesting facts in Shopify’s latest income statement.’ Is that a legitimate question? No, excuse me, ChatGPT is only trained until 2021. Okay, but it doesn’t know that. And if you ask it a question about, for example, the financial statements of 2019, it will give you random answers. The numbers won’t be accurate. Why? Because it’s really far back in the tool’s memory. Instead, what you want to do, and the best way to avoid shooting yourself in the foot, is to provide relevant information to the model. If instead, you manually find the financial statements and copy-paste them to test it.”

Graphique représentant le fonctionnement des prompts dans un modèle d'intelligence artificielle générative.

[39min 42s]
I suggest doing it by the way. You will be pleasantly surprised, but do it programmatically within a solution. And then, you say, “Can you identify three interesting facts in Shopify’s income statements based on this document?” It’s full of numbers, difficult to read even for yourself, but Generative AI tools are capable of interpreting the information. And here, I’ve tested it. Will it give you a response? Yes, it will. And by the way, the numbers have been validated. I didn’t have them validated by Raymond Chabot of Grant Thornton. I’m pushing it a bit, but not that much. But it provides real facts, the right information, and that’s future-proof. So, you’ve just created, yes, admittedly, a little extra complexity, but it’s worth it. Now, if I come back to my proposal, it would be to add information. Firstly, start with a knowledge base. Your knowledge base could be an FAQ, documents related to your company. For example, I want to know about my service offerings.

Un exemple de prompt pour ressortir des faits intéressants dans le dernier état des résultats de Shopify.
Exemple de réponse à un prompt demandant des faits intéressants dans le dernier état des résultats de Shopify.

[40min 59s]
In my projects, I have post-mortems, I put them in a database, and after that, I can ask a generative AI tool questions, and it responds to me. You create a knowledge base, and then you go on to create, essentially, a recommendation tool, so you create a simple search tool that gives you recommendations. What information would you like to add to your prompt? Then you optimize it, and there you have a tool that works well, hallucinates less, and is ready for the future. It’s this type of tool that I propose to use because otherwise, we shoot ourselves in the foot. In terms of societal risks, we need to ask the right questions. I mean, quickly, we need to ask the right questions. If you don’t ask the right questions, please don’t automate anything. Automation, when you don’t know what you’re doing, is the enemy. We don’t automate anything before knowing how to ask the right questions. But again, we need to ask the right questions. We don’t know what we don’t know, right?

Graphique démontrant comment optimiser les prompts dans un modèle d'intelligence artificielle générative.

Societal Risks and Asking the Right Questions

[42min 10s]
There are tools for that. I can share them with you. What I’m going to do is, later on, I’ll share a list of tools with you. One of the tools I like is called the reflexivity grid. I didn’t know it was a word, but apparently, it is. It’s about the ethical issues of AI systems, and it was developed by OBVIA, which is a Quebec organization. And here, for example, we have the ten commandments, the ten subjects, ten categories of risk. It’s really interesting because this grid provides very specific questions. For example… But these are questions that make you think. I’ll give you some examples. Can your system harm the user’s psychological well-being? In some cases, yes. About a month ago, there was a suicide that happened due to abusive use of a chatbot. I know it’s a “cherry-picked” example, but it just shows that there can be a connection between psychological well-being and the use of technology. We just need to be able to understand whether or not our system can affect society.

Bonnes pratiques pour mitiger les risques sociétaux : ne pas automatiser à outrance, se questionner quant aux enjeux éthiques potentiels et atténuer les risques avant même le développement.
Grille de réflexivité sur les enjeux éthiques des systèmes IA (OBVIA)

[43min 31s]
In terms of privacy, of course, we discussed it earlier. In terms of caution, what’s the worst that can happen? Do we have mechanisms to prevent the worst from happening? The worst that can happen is information sharing. If it’s just internal information sharing, but then you always have someone who will validate and correct it, it’s not a big risk. But if you end up creating something that is automatic and it sends false information or makes financial decisions, we recommend having mechanisms to mitigate these risks. In terms of responsibilities, who is ultimately responsible for the solution? We can’t just say, “I rolled something out, I pressed ‘run’.” It runs, it’s supposed to perform a task, but there’s no one in the organization who is in charge. The answer is not “Google is in charge because it’s their solution.” No, no, no. You are in charge of what you develop. So, in other words, if we think we’re going to use ChatGPT to automate a department, I have some news for you.

Information Security and API Usage

[44min 44s]
There will be someone in charge of this new virtual department, and all the bad decisions that are made will be attributed to that person. Are we ready as an organization to do that? That’s another good question. I strongly suggest asking yourselves the right questions and trying to answer them as best as possible. That doesn’t mean you will cancel your projects, but you might structure them differently. In terms of information security, what I’m going to propose, as seen in bold, is that first and foremost, starting today, avoid putting sensitive information, whether in Bard or in ChatGPT. These are two solutions where information is passed to the creator. It’s not because of reasons like “We want to steal the information to dominate the world.” It’s to learn more about the usage patterns that are being captured in order to gain insights into the technology’s use. But we should avoid it because it poses a risk to the security of our company’s information. And personally, do not enter your social insurance numbers. There are many things we want to avoid doing.

[46min 05s]
I wonder if everyone knows my name based on my social insurance number. That’s not a good prompt. Also, the other element, that’s for B2C tools that are free. We know there is nothing free in this world. That’s an example. But for now, what I propose is to avoid using OpenAI’s APIs. Currently, OpenAI uses the information for retraining, so the current tools, for cases where information security is more critical, for example, when creating a chatbot where it’s really the end user who communicates. In that case, you cannot control the information that is disclosed. In this case, I would probably use other alternatives, such as using Google, using Microsoft directly. Professional services guarantee that the information will not leave our own environment. That’s much more reassuring. And then, here, I have a small proposal for companies that I’m starting to hear, which is a proposal that people are adopting, is to create their own generative AI platform. If we start seeing it more and more, here, the use case is very simple. If I work at Pratt & Whitney Canada and I start using ChatGPT, copy and paste, I want to validate spare parts, what are the instructions for this material, the spare parts needed for this particular repair?

Bonnes pratiques pour mitiger les risques de sécurité de l'information : éviter de fournir des données sensible à ChatGPT ou Bard, éviter d'utiliser les API actuels de OpenAI, utiliser les solutions professionnelles (Microsoft, Google) et créer sa propre plateforme d'IA Générative.

Legal Risks and Copyright Considerations

[47min 51s]
It’s a good use case, but if you test it, it means you’ve just provided your engine’s technical specifications to OpenAI. You probably don’t want to do that. Instead, what can you do? You can use professional versions. Firstly, you could use professional versions, whether it’s Google or Microsoft, both offer these capabilities, and you create an interface, and that’s it. So, you’ve just created an interface, you can call it whatever you want. Pratgpt, you can have fun with it, add nice colors. You’ve just reduced a risk in using the technology. You’re not preventing it. The worst thing would be to prevent the use of generative AI because it’s impossible to prevent its use. Instead, you control the security around it, and you can even go further. Earlier, I was talking about a knowledge base. With my Pratgpt, I can give it access to all my technical documentation. That way, I can ask questions, and it provides accurate answers. You can create this interface and truly benefit your organization. That’s an example that, as such, solves many problems.

Créer son propre IA Générative via les versions professionnelles.

[49min 12s]
I’m a solution-oriented person, which is why I like proposing this solution because it’s so elegant. Ultimately, we’ll end up with legal risks. We talk about it a lot: copyright, plagiarism. Yes, there can be those issues. So, firstly, what I propose is to assess the risk of that happening. There are certain risks like sentiment analysis, where there are none. You’re asking if something is positive or negative based on information. So, there’s no risk there. In some cases, the risk will be zero or negligible, but in some cases, the risk will be very high. As a journalist, if I want help in creating my article, there’s a higher risk of plagiarism, intellectual property issues. If I want to automate code development, I might end up using portions that have a commercial license, which I shouldn’t be able to use. And you don’t know it because the output is so elegant that sometimes you can be caught off guard. In these extreme cases, what I propose is that, precisely in situations where there is a significant risk in terms of copyright, I would suggest avoiding the use of ChatGPT because GPT is one of the solutions currently available that hasn’t clarified that they only use publicly available information.

Bonnes pratiques pour mitiger les risques juridiques : Évaluer le risque associé au plagiat et au vol de propriété intellectuelle, préconiser certaines solutions selon de corpus d'entraînement utilisé et intégrer des outils d'évaluation des extrants

Alternative Solutions for Copyright Risks

[50min 55s]
So their position is currently unclear. In these cases, it’s not very common, but still, when it happens, there’s GPT 3, there’s GPT 4, there’s PALM, there are several other tools that can be used to address this issue. Additionally, you can approach the problem differently. Instead, you generate something and then have it validated. For example, from a journalistic perspective, there are tools that can check for plagiarism. So, you can add and refine your tool to minimize risks. I hope I haven’t put anyone to sleep talking about risks, but it’s important to me. So thank you for staying. I haven’t seen anyone yawning. I’ll give myself a pat on the back. In conclusion, I’ve said it before, and I’ll say it again. It’s important to get on board now, to embrace the technology. There are so many benefits that can be reaped.

[52min 08s]
With that, thank you very much for listening.