Why this matters now

Boards and executives in Canada are feeling the heat to "make progress with AI," but many initial attempts don't lead to tangible benefits. The era of just experimenting is coming to an end. What will succeed in 2025 is actually focusing on value first, quickly demonstrating ROI, building trust, and scaling responsibly.

Attention: When AI pilots stall

Pilots often falter for common reasons: unclear metrics for success, weak integration into essential workflows, and a lack of planning for change management or governance. This leads to a pile of demos that don't make an impact. To change this, shift the focus from "what the model can do" to "what measurable gains the business can achieve".

Interest: The value‑first playbook

Top organizations eliminate the mumbo jumbo and focus on value by following a practical approach.

1) Start with measurable value. Identify business problems instead of model features. Choose use cases that link directly to financial outcomes or risk reduction. Measure savings in time, reductions in cycle time, quality improvements, and additional revenue. Track the total cost of ownership (TCO) to ensure ROI is authentic and not just theoretical.

2) Treat AI as a cultural transformation. Enterprise AI thrives when leaders support the change, invest in AI literacy, and foster a safe space for experimentation. Equip staff with the tools and training they need to utilize AI effectively; celebrate quick wins and share best practices.

3) Augmentation over replacement. Quick wins free individuals from tedious work while keeping human oversight for judgment, context, and accountability. Design processes so that AI speeds up high‑value decisions rather than outright replacing them.

Proof in practice: signals from leaders

• Morningstar exemplifies what measurable value looks like: AI-driven content workflows and automation that save thousands of hours and expedite delivery.

• Financial Times (FT) illustrates reliable adoption: principles for responsible AI, company-wide AI literacy, and a targeted internal assistant trained on their journalism, fitting seamlessly with current workflows and editorial standards.

Desire: Sustainable and trusted adoption

Sustainable adoption combines clear rollout strategies with trust.

Rollout clarity

• Select narrow, high-impact use cases (e.g., summarizing complex documents, automated triage, drafting customer responses) where data availability and explicit quality benchmarks are assured.

• Appoint an executive sponsor and internal champions. Form a cross-functional team (business owner, data/IT, risk, change) with weekly value monitoring.

• Plan a scaling path upfront: if the pilot reaches its goals, what's the next phase? What integrations or licences are needed? Who manages the operational budget?

Trust and integration

• Develop a governance framework that addresses data access, security, safety, and human oversight. Create user-friendly guidelines, not just for audits.

• Embed AI where users already work (email, documents, CRM, task systems). When users have to change context, adoption rates drop.

• Transparent performance: Implement your use cases; present quality and drift metrics to sponsors and users.

Strategic execution Stop asking, “What can AI do?” Rather ask, “What can AI do for us—right now?” Connect each use case to a business goal, a baseline, and a forecast. Treat AI like any other investment with stages and reviews after each implementation.

Action: The rollout playbook

Follow this five‑step playbook to move from pilots to real ROI.

Step 1 — Prioritize a use-case portfolio Create a small portfolio (5–10 candidates). Rate by business value, feasibility, data readiness, and risk. Select 1–3 to launch now and keep the remainder for future quarters.

Step 2 — Define value and guardrails Document for each use case: problem statement; users in scope; data sources; success metrics; TCO (build + operate); risks; human-in-the-loop points; approval workflow. Write a brief model card or assurance note.

Step 3 — Build within the workflow Implement where people already spend their time. For example, introduce summarization into document review, or add an assistive sidebar to CRM. Begin with audit logs and prompt/version tracking from day one.

Step 4 — Prove value fast Conduct a 4–8 week sprint with weekly value updates. Compare against baselines (time/cost/quality). Gather qualitative feedback: what made tasks easier, what posed new challenges, and where the model struggled.

Step 5 — Scale with controls If targets are achieved, extend to nearby teams, add integrations, and establish support. Use a change-management plan: communications, training, champions, and feedback channels. Update your governance playbook with lessons learned.

Practical examples to emulate

• Internal research assistants trained on premium proprietary content to expedite analysis and briefings.

• Automated intake and triage for operations or shared services to minimize queues and speed up cycle times.

• Content and knowledge pipelines that standardize output and eliminate manual formatting.

Operating principles for leaders

• One owner per use case. Prevent diffusion of responsibility.

• Value cadence. Weekly updates of metrics and decisions.

• Human oversight. Clearly assign roles for reviewers and approvers.

• Data minimization. Use the smallest amount of data necessary to achieve the goal.

• Iterate on prompts and UX. Treat prompts as product innovations, not magic solutions.

• Continuous education. Integrate AI literacy into onboarding and leadership training.

What Generation Digital delivers

• AI strategy & webinar: A prioritized portfolio, value models, and business cases.

• Governance & risk: Policies that are practical, assure confidence, and model cards that enhance trust without hindering progress.

• Implementation: Assistants embedded within workflows, integrations, telemetry, and adoption programs.

Ready to move beyond demonstrations? We'll help you craft a pragmatic roadmap, deploy your first high-impact use cases, and scale with certainty.

FAQ

How do we choose our first AI use case? Begin with a specific task that has real financial or risk implications, available data, and involves a human reviewer. Establish a baseline (time/quality/cost) and a 4–8 week target.

What governance do we need? Develop policies for data access, privacy, safety, human involvement, evaluation, and incident reporting. Provide templates (e.g., model cards) and manager training.

How do we demonstrate ROI? Track time savings, cycle-time reduction, error rates, and throughput. Include TCO (build + run) and adoption rates. Report weekly to your sponsor, with comparisons of before and after.

Should we build or buy? Opt for purchases for general capabilities; build where your data/process offers an advantage. A hybrid approach is common: vendor platform plus in-house prompts, guardrails, and integrations.