At a glance
- Manufacturers have embraced AI solutions, but many are still experimenting and have yet to realize meaningful value.
- Current levels of AI maturity may not be enough to remain competitive and financially sustainable through 2030.
- The next evolution of AI includes autonomous agents that have the potential to reduce bottlenecks, address labour challenges, and improve operational productivity.
- A strategic approach to leadership, governance, people, and execution can help manufacturers embed AI into core operations to support long-term growth.
Artificial intelligence (AI) in manufacturing isn’t a new concept. Companies in Canada have long used physical automation and robotics within their operations. They’ve also used machine learning, particularly in areas like predictive maintenance and demand forecasting.
In recent years, we’ve seen manufacturers embrace AI solutions to enhance human productivity. Some have implemented an AI-powered parts desk and customer service chatbots so staff can focus on higher-value customer interactions. Others have automated quoting services to provide a quicker, more streamlined experience. Many have implemented AI in functional areas, such as in finance to improve the timeliness of invoicing and month-end close processes.
But these activities are only the beginning when it comes to AI in the manufacturing industry. Conversations are already moving beyond generative AI and employee enablement, to AI agents that can act autonomously, reason through and complete tasks across operational workflows, and help to reduce bottlenecks, ease talent shortages, and improve productivity.
Canadian companies aren’t ready for the next evolution of AI in manufacturing—yet.
In our AI Vision Report: Past the pilot to the agentic future of work, we share insights into how agentic AI is changing how businesses operate with lessons from our client work and from our own AI transformation. This report complements our AI Vision 2030, which is grounded in our belief that we’re moving towards a human-led and AI-embedded future.
What might an AI leader in manufacturing look like in 2030?
- Connected manufacturing operations—All critical systems and solutions are designed to work together, from the enterprise resource planning (ERP) systems and manufacturing execution systems (MES) to Internet of Things (IoT) devices to support the trusted use of AI.
- Human-led, AI-embedded workforce—Human managers oversee hybrid teams that include both people and virtual AI agents.
- End-to-end operational visibility—Leaders have real-time visibility across their end-to-end operations, putting them at the top end of the intelligence graph in terms of their decision-making capabilities.
- AI agents integral to production—Manufacturing operations have evolved from humans physically producing all outputs to AI agents producing outputs within appropriate human-defined guardrails.
The challenge in manufacturing is that few companies are ready for the shift to embedded AI. A 2026 BDO Canada survey conducted by Angus Reid showed that when it comes to digital transformation and manufacturers:
Where many manufacturers are today isn’t going to be enough to keep them financially sustainable in 2030. With supply chain costs rising and labour availability in question, they will need to use all the tools at their fingertips, including agentic AI, to power growth.
Building the operating model for agentic AI: Four pillars for success
We’ve worked with manufacturers across all stages of AI maturity, including those wanting to upskill their leaders so they can better manage AI opportunities; those looking to understand the ROI of their fragmented AI activities and determine what to scale; and those looking to make AI an integral part of their business strategy, bringing value to virtually every part of their organization. What we’ve found through our work is that there are four foundational pillars that can help manufacturers focus their AI efforts so they are better positioned to achieve their business objectives, realize practical business value, and measure the impact and ROI of their AI activities.
It’s easy to get caught up in the excitement around AI opportunities, but if you don’t understand what you’re doing and why, you won’t be able to measure your effectiveness. Developing an AI strategy, whether to guide your approach from the beginning or to bring together initiatives you already have underway, is a key starting point for success.
Your strategy should include a clear definition of your strategic objectives and the existing pain points in your manufacturing operations (e.g., high levels of downtime, increasing amount of scrap, slow month-end close processes, lack of visibility into the end-to-end manufacturing lifecycle). This can help you align your AI approach to solving real business problems, while giving you a foundation for measuring the ROI and other impacts of your activities. It can also help you identify a roadmap for change, including quick wins to build support for your activities.
Trust in your data is essential when it comes to embedding AI across your operations to enhance productivity and drive real-time, intelligent decision making—and that’s not something that every manufacturer has in full. Some companies are using legacy systems or highly manual processes. Others are using a large number of disconnected systems. Still others may have updated their ERP but not fully connected it with their Manufacturing Execution System, IOT devices, or other relevant sources of data.
Before you move forward with scaling AI or the use of AI agents, you’ll need to strengthen your data governance and controls so you can be confident the data being used across your systems is accurate, consistent, secure, and regulatory compliant.
You can build leading-edge AI tools and create AI agents to help boost your people’s productivity, but if you don’t provide the right training and change management supports so your people feel confident using the technology, your results will likely fall well short of your expectations. We’ve seen it happen.
Change is hard, particularly for manufacturers whose employees may have been doing their jobs the same way for decades or who may not have used technology in their day-to-day work previously. Making sure your people are supported throughout your AI journey can help you set your organization up for success. This starts by making sure your leadership team is on board and providing clear, consistent communications. It also includes taking time to address your people’s AI anxieties and providing training and upskilling so they can work effectively with AI tools and AI agents.
The AI space is changing fast. It can be difficult to take the leap, particularly when you’re already managing issues like increasing input costs, shrinking margins, and supply chain uncertainties. This is where bringing on a partner to help you navigate your AI journey can make a major difference. The right partner can help you understand how AI is transforming manufacturing business models, where AI can help you address specific business problems, where it can’t, and how best to embed AI into your organization, helping elevate your operations and create more value.
Empowered by AI—let’s work together to build your tomorrow
Whether you’re looking for help enhancing one aspect of your AI approach, such as AI governance or change management and upskilling, or want assistance with embedding AI responsibly into your business model so you can empower your people and create measurable value, we can help.
We’ve worked with a diverse range of manufacturing companies to build practical AI approaches that support intelligent manufacturing and help drive measurable outcomes—from optimizing production scheduling to enabling better insights into equipment effectiveness and production line performance. We’ll bring the lessons we’ve learned from this work, and from our own ongoing experience with AI transformation, to help you navigate the evolving world of AI and forge your own path forward.