By Dr. Ollie Kaiser

July 13, 2026

The Carney government’s ambition to double Canada’s grid capacity by 2050 is welcome. But a national electricity strategy is only as credible as the regulatory institutions that deliver it.

Major clean electricity projects must navigate complex planning, permitting, consultation, and review processes, making regulatory capacity a central test of electrification.

Accelerating permitting decisions for new clean electricity infrastructure is not typically where artificial intelligence (AI) comes up in the energy-transition conversation.

It is more often discussed as a tool for grid optimization or for improving building efficiency. Little attention has been paid to whether it can support the intensive public-sector work required to build that infrastructure in the first place.

Yet, given the speed and scale of electrification required to meet Canada’s climate and competitiveness objectives, it is worth asking:

Can AI help accelerate the administrative and analytical work behind permitting decisions—without weakening the safeguards that make those decisions legitimate?

Traditional efforts to speed up project decisions often default to “cutting red tape” and streamlining reviews. Canadian policymakers have learned that this approach is fundamentally flawed.

Compressing community engagement or weakening safeguards for environmental review, Indigenous consultation, public participation, and evidence-based decision-making can trigger legal challenges, erode public trust and invite community backlash.

Streamlined processes may seem faster at first, but decisions that rest on thinner review are more vulnerable to challenge, ultimately risking the very delays they were meant to avoid.

Improving the agility of permitting processes in Canada requires moving past this zero-sum framing of delay-versus-deregulation.

We must be smarter about how to move faster.

A serious permitting reform agenda, therefore, means building more agile permitting institutions: public agencies that can plan earlier, coordinate across systems, engage affected communities before conflicts harden and prioritize regulatory attention on projects that are high-risk, complex, or of considerable public value.

Critically, making regulatory processes more agile depends on better information flows. That is where AI may be useful: not as a decision-maker, but as a carefully bounded information-processing tool.

To start, AI should never bypass regulatory judgment, automate decisions, or reduce value-based choices to technical exercises. In permitting, AI’s value could come from helping public agencies organize, verify and translate complex data into decision-useful information.

Human oversight and privacy protections are essential, especially where project reviews involve Indigenous rights, consultation obligations, Indigenous knowledge, or sensitive community information.

Public agencies should start with lower-risk, advisory pilots:

For regulators: AI can synthesize decades of past project impact assessment records, identify recurring information gaps, and support earlier risk assessment. That matters because the sooner regulators understand a project’s likely risks, the sooner they can determine what level of review, evidence, and engagement it requires. Used strategically, and where monitoring and compliance data are available, AI tools can compare similar projects, examine whether past mitigation measures or permit conditions worked as intended, and help distinguish familiar, lower-risk projects from complex projects that require deeper scrutiny. This would strengthen the evidence base for responsible prioritization and help public agencies direct limited capacity where it matters most.

For proponents: AI can improve the quality of applications before they are filed, reducing avoidable delays without lowering evidentiary standards. Many permitting delays begin with incomplete submissions, internal inconsistencies, missing baseline data, or unclear evidence linking project risks to proposed mitigation measures. AI tools could help proponents identify these problems earlier, clarify which requirements apply, and flag where an application is likely to generate follow-up questions. That would reduce avoidable back-and-forth once a project enters review, freeing public officials to focus on the work that cannot be automated: consultation, accountability, expert judgment and the weighing of public-interest trade-offs.

Responsible implementation of these AI applications depends on the ability of public agencies to govern AI throughout its lifecycle. In Canada, where public trust in AI is low, the use of AI in project permitting should be explored strategically and selectively.

Chief among these safeguards is explainability. Public-sector AI must never operate as a “black box.” Human assessors should be able to trace AI outputs back to source material, understand the assumptions built into the tool, and test whether it reproduces biases in the underlying data.

Human oversight and privacy protections are essential, especially where project reviews involve Indigenous rights, consultation obligations, Indigenous knowledge, or sensitive community information.

Equipping public agencies with the resources to safely and responsibly test AI integration into permitting processes requires sustained investment in regulatory capacity.

That means building institutional expertise, data infrastructure, evaluation systems, and legal authorities to scrutinize AI throughout its lifecycle. Agencies will need specialized, trained assessors with the technical literacy to review AI impact assessments and challenge AI outputs.

It also requires clean, reliable data baselines to ensure these tools do not rely on historically flawed or biased inputs. Public agencies must also have clear legal authority to suspend, revise, or reject automated tools when they fail to meet public-interest standards.

The same holds true for other agile permitting strategies. Public agencies need the authority, independence, resources and information systems to coordinate across jurisdictions, engage affected communities, evaluate long-term environmental outcomes and adapt over time.

This is the uncomfortable truth in the permitting debate: moving faster while maintaining public-interest protections is not a low-cost exercise. It is a state capacity-building project.

In the end, the test is not how quickly Canada can issue permitting decisions. It is whether Canada’s institutions are strong enough to build clean infrastructure at speed while preserving the trust, accountability and judgment that make those decisions durable.

Dr. Ollie Kaiser is Director, Policy and Research at the Smart Prosperity Institute at the University of Ottawa. They hold a PhD in Environmental and Urban Change from York University.