KPMG Canada Names First Head of AI Research to Bridge Lab and Practice

KPMG Canada named Dr. Andrew Forde its first Head of AI Research, a newly created national role that puts a Partner with a NASA research background and a University of Toronto engineering PhD at the intersection of academic AI and billable client delivery. The appointment, announced March 24, is less a symbolic gesture than a structural change with real implications for how audit, tax, and advisory teams build, document, and defend AI-enabled work.

The mandate is explicit: take ideas out of the lab and turn them into frameworks, tools, and approaches that organizations can actually use. For project teams that have been running AI pilots with minimal validation scaffolding, that language signals a shift. Expect tighter model documentation requirements, reproducible testing pipelines, and a named escalation path for research questions that previously had no formal home inside the firm.

Stephanie Terrill, who leads KPMG Canada's digital practice, framed the appointment as a direct response to client demand for "evidence, not hype," a phrase that carries weight in a regulatory environment where audit committees and financial regulators are increasingly scrutinizing the provenance and governance of AI-assisted outputs.

For staff on the manager-to-partner track, the clearest near-term impact is the rising premium on research translation skills: the ability to scope scientifically defensible pilots, produce reproducible deliverables, and convert academic findings into commercial propositions with measurable return on investment. Those who can move fluently between a research paper and a client proposal are positioning themselves well for the roles this new structure will create.

The governance dimension matters especially for audit and assurance professionals. AI-enabled audit tools are already under scrutiny from securities regulators in Canada and the United States, and Dr. Forde's remit includes embedding scientific rigor into model selection, tuning, and testing records. In practice, that means more structured documentation during project stages that were previously light on record-keeping, particularly during pilots and initial rollouts. It is extra work in the short term; it is also a credential-building opportunity for staff willing to develop published validations and reproducible pipelines.

The broader context explains why KPMG Canada made this move now. Canada's AI research ecosystem, anchored by the Vector Institute in Toronto and deep academic programs in Montreal, produces world-class foundational work. The persistent gap has been commercialization. Dr. Forde's background, spanning academic research, government programs including NASA, and applied industry roles, maps almost precisely onto that gap. His University of Toronto PhD is not incidental; it signals an intent to maintain active university relationships and potentially co-author industry-academic outputs that can raise both individual staff profiles and firm-wide research credibility.

The practical path for the next six to 12 months is becoming clearer. Building familiarity with model documentation frameworks, pursuing secondment or cross-functional opportunities as the new research-to-pilot pipeline gets stood up, and treating AI governance work on current engagements as a portfolio asset are all moves that compound over time. The firm has now put a name and a PhD on the standard it expects teams to meet. Getting ahead of that standard before it becomes a requirement is the kind of positioning that tends to be visible when promotion cycles come around.