Mr. Chair and members of the committee, thank you for giving me the opportunity to say a few words about industrial AI.
Next Generation Manufacturing Canada, or NGen, is the industry-led not-for-profit organization that spearheads Canada's global innovation cluster for advanced manufacturing. As such, we're deeply involved in projects that integrate AI and manufacturing processes and equipment. We're dedicated to building world-leading advanced manufacturing capabilities in Canada for the benefit of Canadians. We do that by bridging the gap between research and technology on one hand and the needs of manufacturers on the other—the adoption by the customer. We do that by providing non-dilutive funding, project management and IP commercialization support for collaborative ventures among researchers, technology providers and manufacturers.
The projects we fund integrate technologies to develop, scale and accelerate the adoption of new manufacturing processes in Canada while keeping the benefits of the IP in Canada. That is exactly what's needed for the adoption of industrial AI. Our funding comes from both public and private sources. Since our inception in 2017, it has come primarily from Innovation, Science and Economic Development Canada.
The focus we have on transformation, collaboration and commercialization has really paid off. To date we've invested in 281 projects, with close to $1.2 billion in overall investment. Of 652 industry partners, 90% are SMEs. Every dollar of our funding has been matched by more than $1.70 from industry. So far, our projects have leveraged $4.2 billion in follow-on investments. They've generated over $8.2 billion in revenue. They've created 57 new companies, 4,500 new jobs, over 1,600 new IP assets and an estimated $1.2 billion in tax revenue that flows back to the federal government. That's approximately $5.70 for every dollar we've invested in completed projects.
We've invested in 153 projects that implement AI in manufacturing processes. These aren't projects that develop new large language models, but they're examples of industrial AI, where partnerships are important in building the technology stacks required for AI adoption. Industrial AI differs from purely digital LLMs, because it involves the integration of AI models with physical systems like sensors, vision systems, robotics and equipment, or the use of equipment like smart robotics and vision and automation systems in which AI is already embedded. It requires secure and segregated networks and edge computing solutions for real-time communications and control, as well as integration with operating software and systems architectures. Its skills requirements are different, requiring a deep understanding of industrial processes as well as IT software and data analytics expertise. So too are the tolerances in which it must work to ensure reliable, safe and compliant operations.
I have provided the committee with a table that contrasts industrial and digital AI. This is in annex A of the document that was provided to the committee.
The adoption of industrial AI is complex, although it's not really all that expensive when compared with capital expenditures in manufacturing. In fact, when consulting companies report that the majority of industrial AI implementations fail, it's not because of the technology. It's because adopters don't have the data quality, digital and technology infrastructures, skills or often the business plan and management systems required for the successful adoption and productive use of AI.
Success depends on partnerships among AI providers and manufacturers. It's not simply a transactional vendor relationship. It depends on integration with production technologies and operating systems. It depends on AI readiness. Do companies have a plan about how this will lead to improvements in critical operating processes? Do they have the management and operational skills, data systems, technology infrastructure, cybersecurity and AI risk mitigation practices required to deploy AI in their operations? Money alone isn't going to guarantee success.
The criteria we use in assessing the readiness of manufacturers to adopt industrial AI applications are outlined in annex B of the document provided to the committee.
We know that Canadian manufacturers need to boost their productivity performance. We also know how important it is for manufacturers to do so in order to continue to drive the Canadian economy and provide the production capacity to supply our needs for homebuilding, infrastructure, health care, energy, environmental sustainability and defence. Industrial AI applications offer them the best opportunity to rapidly improve productivity and build that capacity.
Real use cases from Canadian industrial AI solution providers show that 30% improvements in throughput, quality control, equipment, operating efficiency, energy efficiency and delivery times can be rapidly achieved—if not significantly more. I've provided the committee some of the results of the 122 use cases that have been curated by Canada's AI4M, a manufacturing consortium that we support.
Industrial AI implementations like these do not replace jobs. In a sector beleaguered by labour and skill shortages and facing existential challenges, they enhance and protect jobs.
As the government refreshes Canada's AI strategy, it's going to be crucial to focus on how to accelerate the adoption of industrial AI. There's an important role for government to play in underwriting the risks, especially for SMEs, in a sector that's so vital to Canada's economy.
As we've shown, industry partnerships that integrate technologies, help manufacturers prepare for successful implementation and develop the workforce skills required to use AI-enabled tools and technologies effectively will be instrumental in achieving the step-change improvements in industrial and economic productivity upon which all Canadians will depend.
Thank you.