Pursuing its mission to democratize AI, Moov AI mentioned a predictive maintenance project at Pratt & Whitney and an initiative in the education sector aimed at adapting training courses to the strengths and weaknesses of each participant, based on a prediction of their performance within the program.
Service provider IVADO Labs, which will celebrate its 5th anniversary in 2023, mentioned a few examples of innovative projects, among which is a solution for the healthcare sector built to optimize surgery planning while minimizing overtime for employees, and an initiative in the transportation industry, so the Port de Montréal can more effectively predict the arrival of ships and optimize the position of its containers and the intermodal connectivity with trucks and trains.
IVADO Labs—which brings together more than 10 university researchers in AI and 30 data scientists, including several experts in time series analysis and operational research—is also increasingly working on projects that combine needs for forecasting and decision-making. Marie-Claude Côté, VP Data Science at IVADO Labs: “In the retail sector, for example, we try to forecast demand based on a very large number of variables, in order to guide decision-making for each store’s purchasing department. This forecast will never be 100% accurate, but the goal is to make the best decision based on the information that is available to us. »
As an alumni of the Creative Destruction Labs and NextAI programs, Airudi presented several examples of projects in its field of expertise: AI for human resources. The company has enabled agricultural businesses to plan the allocation of their workforce based on the data collected on the state of the crops. Sébastien Pelletier, Project Manager at Airudi: “We are offering an AI solution that plugs into our customers’ tools, which allows them to be operational within 12 to 18 months.” Airudi has also recently announced the deployment of the Galileo project, a major initiative designed in partnership with the Association of Maritime Employers (AEM) and with the support of Scale AI, with the goal to accurately forecast the ships’ time of arrival in order to optimize workforce planning.
These concrete use cases prove that AI is out there and gaining momentum, for the benefit of all players involved.
AI Adoption: Challenges and Opportunities
Marie-Claude Côté of IVADO Labs: “Many businesses think that implementing AI is either an insurmountable mountain or that it is pure magic. The truth is it is neither, but it’s also a bit of both. It’s not a smooth ride, but the gains that can be made along the way are tremendous. To implement AI, a company must first clean up its data, and even just by doing so, the benefits can be significant. Following this, the provider and the customer will explore the possibilities offered by this data, and uncover opportunities that did not exist before. From the beginning of the project to the adoption by the end user, each step brings its own set of challenges and opportunities. This therefore requires all parties to remain open-minded and agile throughout the AI project.”
Sébastien Pelletier of Airudi: “An underestimated element is the fact that a company is sometimes in competition with the other players involved in its supply chain. An important challenge for AI projects is to have access to data from the entire supply chain in order to set up efficient models. It is not easy to make sure all actors collaborate so that useful data is shared across the supply chain. In the case of our project at the Port of Montreal, the AEM has done an incredible job in giving us access to the data required so we can allow each stakeholder to make the best possible decisions. Because at the end of the day, we have to keep in mind that the implementation of AI will be beneficial to each and every part of the supply chain, as they will be able to optimize their efficiency, and therefore their profits. We have everything to gain from working hand in hand.”
Olivier Blais, co-founder and VP decision science at Moov AI: “Before going forward with a specific AI project, one must identify a given problem and break down the solution into concrete initiatives. For example, if our goal is the automation of our supply chain, this can take many forms: do we want to optimize inventory? Do we want to implement predictive maintenance? Optimize workforce planning?… We encourage businesses to approach a goal such as the automation of their supply chain by focusing on simpler projects for which quality data already exists. By choosing this approach, we are able to implement concrete solutions quickly, to demonstrate that they have real impacts, and to create a data-driven practice and mindset. Then, we can work step by step and ultimately achieve our initial objective of automating the supply chain. AI is not so complex to implement–if you think about it, AI is closer to advanced statistics, so it should not be seen as something inaccessible or unattainable.”
Download our white papers for more real-world examples:
- How AI can fuel growth for retailers: Download our white paper
- Why AI is a game changer for the transportation industry
- Why AI is a key building block of Industry 4.0
