AI Beyond Buzzwords: Canadian SMBs Reap Real Gains

“Don’t do AI for the sake of AI” – Danielle Gifford, PwC AI MD

At Inventures 2025, Gifford urged Canadian small‑and‑medium businesses (SMBs) to move past AI experimentation and focus on practical application. Metrics matter: understand what you’re measuring and why.

From Deployment to Optimization

• Deployment stage: many SMBs have already launched AI tools.
• Next challenge: how to fine‑tune these tools, gauge impact, and safeguard data.

Why AI Is a Business Priority Now

AI adoption extends beyond large enterprises. SMBs record tangible productivity returns, yet face privacy, integration, and workforce readiness hurdles.

Institutional Lens on Canada’s Innovation Landscape

Gifford’s insights help Canadian firms navigate AI from a business‑first perspective. AI’s transformation from buzzword to strategic priority continues nationwide.

Watch the full interview below

Adoption is up, but strategic use is lagging

AI Adoption in Canadian SMBs

Survey Results

• 80 % of Canadian SMBs have started exploring AI tools.
• 40 % have already embedded AI into their business strategies.
• A March 2024 Microsoft survey shows 78 % are actively seeking AI.
• Sixty‑five percent encourage employees to use AI.

Return on Investment

“We’re seeing a significant outturn in terms of ROI,” Gifford notes. Productivity gains and cost reductions average 127 % across actual AI applications.

Uneven Distribution of Wins

Despite overall gains, the benefits are not spread evenly. The next phase involves understanding which sectors capture the most ROI and which still lag behind.

Revisiting AI Adoption in Modern Enterprises

Fragmented Approaches Persist

Despite the rapid expansion of AI capabilities, many organizations still employ a piecemeal strategy, lacking unified guidelines or quantifiable goals.

Typical Use Cases

• Finance Automation – Streamlining expense processing and bank reconciliations.
• Operational Efficiencies – Accelerating supply chain and inventory tracking.
• Human Resources – Automating candidate screening and onboarding.
• Marketing Campaigns – Personalizing content and predicting customer churn.

Automation Gains Outpace Governance

Repetitive tasks generate immediate productivity boosts, yet questions around ethical frameworks, governance policies, and long‑term scalability are often postponed.

Direct AI Embedding in Platforms

As AI modules become integral to existing CRM, payroll, and ERP systems, leaders should audit the native functionalities before opting for external solutions.

Why Existing Tech Stacks Matter

“Understanding your current tech stack and enabling built‑in features when feasible offers far more value than building or purchasing a new tool from scratch,” emphasizes the PwC AI managing director.

Optimisation means new thinking and cross-functional teams

Optimizing AI Deployment Beyond the Technology

Once an organization integrates an AI platform, the real challenge shifts to fine‑tuning its human and policy frameworks. Expertise in strategy, governance, and employee experience becomes the linchpin.

Addressing the Human‑Policy Gap

“If you’re simply granting ad‑hoc access to ChatGPT, Grok, Claude, or Loveable, are employees feeding it sensitive personal or client data?” Gifford questions. “Are they exposing your company’s financials?”

In heavily regulated markets—finance, energy, healthcare—clients often embed clauses that mandate vendor‑data residency within Canada or even specific provinces. The debate over data locality, storage methodology, and liability for external‑model training is no longer abstract; it’s a negotiation centerpiece.

Reshaping Leadership in an AI‑Driven Workforce

• Seven out of ten employees now rely on AI. Corporate stances on “do’s” and “don’ts” regarding AI usage are not optional.
• Clear guidance on when and where AI tools should be applied empowers employees and mitigates uncertainty.

Establishing AI Councils and Task Forces

Gifford observes that many organizations form AI councils—cross‑functional committees that bring together HR, legal, risk, finance, and business unit leaders.

“It’s not meant to be rigid governance,” Gifford says. “But it’s a committee that includes representation from HR, finance, legal, risk, and business unit leaders to understand diverse use cases.”

Key Inclusion: HR’s Role

The HR perspective is often omitted from AI discussions, yet employees are the most affected.

• Fear of job loss, confusion about tool engagement, and questions about upskilling are pervasive.
• “Your people are some of your biggest assets,” Gifford emphasizes. “There’s so much noise. People are scared: Will this take my job? Do I need to compete with it?”

Strategic Takeaways

• Define data residency and training policies in alignment with regulatory requirements.
• Develop a company‑wide policy on AI usage, highlighting approved tools and prohibited scenarios.
• Form an AI council that ensures HR, finance, legal, risk, and business unit leaders articulate real‑world use cases.
• Invest in upskilling programs to address employee concerns and foster a culture of collaboration.

AI Leadership at PwC

Danielle Gifford’s Vision

Danielle Gifford serves as Managing Director of Artificial Intelligence at PwC, steering the firm’s innovation agenda toward transformative solutions.

Expert Insights

• Strategic Direction: Gifford champions an AI strategy that aligns with global business objectives.
• Technology Integration: She ensures seamless adoption of emerging tools across PwC’s service lines.
• Thought Leadership: Gifford frequently contributes to industry discussions, influencing AI policy and best practices.

Digital Journal Spotlight

Photo by Jennifer Friesen, captured for Digital Journal, highlights the dynamic interplay between AI expertise and visual storytelling.

Keeping the momentum 

Ensuring AI delivers Real Value Across the Enterprise

Gifford urges companies to treat AI subscriptions the same way they treat any business investment.

Applying Rigorous Scrutiny to AI Licenses

• Cost considerations matter. “If you’re paying $40, $50, $60 a seat for Microsoft Copilot, the budget adds up quickly,” she reminds.
• Benchmarks ensure value. “Before you deploy AI, understand the application and set benchmarks so you know you’re getting good return.”

Keeping AI at the Core of Corporate Agendas

• Visibility matters. Put AI on committee agendas, highlight wins, and treat deployment as an ongoing evolution—not a one‑off project.
• Regular reevaluation keeps AI fresh. Revisit systems, tools, and capabilities on a schedule and avoid letting AI fade into the background.
• Top‑of‑mind culture drives adoption. “Keep it on agendas, keep it top of mind, and ensure people feel heard.”

A leadership lens on responsible adoption

Gifford’s Guidance: Practical, Strategic, and Integrated AI Adoption

Pragmatic focus is at the core of Gifford’s recommendations. Canadian small and medium‑billion enterprises (SMBs) are no longer idle observers. They are actively responding, adjusting, and refining their operations. Yet as artificial intelligence pervades core processes, leadership must sharpen long‑term strategy, enforce cross‑functional governance, and articulate clear return on investment.

AI: From Innovation Thread to National Business Fabric

• Policy alignment – Government frameworks now embed AI as essential to competitive advantage.
• Procurement integration – Public and private sectors procure AI tools not as isolated modules but as functional assets.
• Partnership dynamics – Collaboration between academia, industry, and startups shapes the next wave of innovation.

Tech Stack Evolution

Gifford observes that technology stacks are not static; they evolve.

• Tool synergy – Ensuring tools collaborate seamlessly with internal workflows is crucial.
• Noise management – Abundant technology releases can create confusion; strategic selection mitigates this.

Core Message from Gifford

“Make sure your tools are working for you,” Gifford asserts. “Not just creating more noise.”

Next Steps for Canadian SMBs

• Strategic roadmap development – Define AI adoption milestones aligned with business objectives.
• Cross‑functional governance structures – Establish roles and responsibilities for AI stewardship.
• ROI measurement frameworks – Implement metrics that capture both tangible and intangible gains.

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