AI’s agriculture moment: From experimentation to implementation

Farm Credit Canada’s newly released report, AI in Canadian Agriculture: Present Challenges and Future Prospects, reinforces what many across the industry are already seeing firsthand: Artificial intelligence has the potential to transform Canadian agriculture, but adoption is not happening quickly enough. FCC notes that AI can improve productivity, resilience and competitiveness throughout the agricultural value chain, yet adoption remains uneven and lags many global competitors.

Perhaps the most important takeaway from the report is that the challenge is not technology.

Canadian agriculture already has access to AI tools and solutions that are popping up from every direction. The obstacles identified by FCC are broader ecosystem issues: Talent shortages, infrastructure gaps, capital constraints and uncertainty around governance and regulation.

In many respects, these barriers all point to the same underlying issue: Organizational readiness.

The businesses that are successfully adopting AI are not necessarily those with the largest technology budgets or the most sophisticated tools. They are the organizations that have a clear strategy, leadership buy-in, appropriate governance and a willingness to rethink how work gets done, reflecting a broader shift occurring across industries.

The conversation has evolved rapidly over the past year. Businesses are no longer asking whether AI matters. They are asking how to implement it responsibly while protecting their data, intellectual property, customer relationships and competitive advantage.

At the same time, many organizations remain focused on relatively modest use cases. Automating routine tasks and experimenting with generative AI tools can generate immediate value, but the larger opportunity lies in fundamentally rethinking operations, decision-making and business processes.

Few sectors are better positioned to benefit than agriculture. From precision agriculture and predictive analytics to supply chain optimization and decision support, AI has the potential to help address some of the industry’s most pressing challenges, including labour shortages, rising costs, climate volatility and increasing global competition.

The Prairies have an opportunity to lead

Western Canada is home to an evolving ecosystem of producers, researchers, ag-tech companies and innovation organizations working to advance AI adoption. Initiatives such as the AIVA Network are helping connect stakeholders across the agricultural value chain and accelerate the commercialization and adoption of new technologies. FCC identifies this type of collaboration as essential if Canada hopes to close the adoption gap and realize the full value of AI across the sector.

This point deserves emphasis. No single organization can solve the adoption challenge alone. Investment, talent development, infrastructure, governance and commercialization all need to advance together.

For individual businesses, however, waiting for every barrier to be solved is not a strategy.

Organizations can begin preparing now by establishing the foundations that make AI adoption possible. That includes identifying high-value use cases, investing in workforce education, evaluating data readiness, developing governance structures and creating clear policies for the responsible use of AI.

In our experience, organizations that make progress in these areas are significantly better positioned to evaluate opportunities, engage with technology vendors, attract investment and scale successful AI initiatives throughout their operations.

Turning ambition into action

This is where trusted advisers can play an important role. At MLT Aikins, much of the discussion with clients today is about how to move from experimentation to implementation when it comes to AI tools.

That work often begins with helping organizations establish practical AI governance frameworks and internal policies that provide employees with clear direction on when and how AI can be used. From there, the conversation frequently expands to issues such as data governance, privacy compliance, intellectual property ownership, cybersecurity, procurement strategies and vendor contracting.

Increasingly, organizations are also looking for guidance when evaluating AI-enabled products and services. Understanding how data is collected and used, who owns AI-generated outputs, how liability is allocated, what happens if an AI system produces inaccurate results and whether contractual protections are sufficient can be just as important as selecting the technology itself.

We also see organizations grappling with more strategic questions. Which use cases should be prioritized? How should AI initiatives be governed? What training should employees receive? How can organizations encourage innovation while maintaining appropriate safeguards? Answering these questions early can help reduce uncertainty and build the organizational confidence needed to move projects forward.

Through our AI and emerging technology practice and our involvement in AI education and readiness initiatives across Western Canada, including our collaboration with the Manitoba Chamber of Commerce AI Pathways Program, we continue to help organizations navigate these challenges in real time.

To support organizations at every stage of their AI journey, MLT Aikins has also developed a range of practical resources, including our Technology, Intellectual Property & Privacy Legal Risk Management Checklist, AI, Cybersecurity, IP and the Law e-book and other AI-focused tools and educational materials. These resources are designed to help organizations assess their readiness, identify risks and take practical steps toward responsible adoption.

Key takeaways

FCC’s report identifies talent shortages, infrastructure gaps, capital constraints and governance uncertainty as barriers to AI adoption.

While no single organization can solve those challenges alone, businesses do not need to wait for perfect conditions before acting. The organizations that build workforce capability, establish governance frameworks, develop trusted partnerships and create a clear roadmap for adoption today will be best positioned to capture the productivity, resilience and growth opportunities AI promises tomorrow.

Canada has the agricultural expertise, research capacity and innovation ecosystem to lead. It’s up to us to build the foundations necessary to turn that potential into results.

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