The Tool Desk
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1. Set the business ambition before choosing technology
Translate the organization’s strategy into outcomes AI might help improve: service quality, cycle time, decision support, cost, resilience or employee capacity. Anchor each ambition in an existing organizational goal and, where possible, a baseline. “Use AI to transform operations” is not yet a strategy; “reduce avoidable delays in this process while maintaining its service standard” gives leaders a result they can assess.
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Microsoft’s AI strategy guidance recommends starting with business problems and use-case identification, with each use case connected to business value. Microsoft AI strategy guidance
Turn ambition into a decision brief
Write down the outcome, who benefits, which workflow is in scope, how the organization measures the workflow today, and what change would count as meaningful. This brief gives business and technology leaders a common basis for deciding whether AI is relevant at all—and for comparing AI with process redesign or other non-AI options.
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2. Find and prioritize use cases
Ask business owners and frontline staff where work is repetitive, slow, information-heavy or error-prone. For each candidate, record the user, process, current performance, desired result, data dependencies, required workflow change and consequences if the system is wrong. That detail keeps a proposal grounded in the work it is supposed to improve.
Compare candidates as a portfolio, rather than selecting the most visible demo or treating every proposal as equally ready. The following questions combine business value, feasibility and readiness with lifecycle risk, cost and strategic fit; they are a practical comparison framework, not a universal scoring formula.
| Dimension | Question to ask | What to establish |
|---|---|---|
| Business value | Which business outcome could improve, and how would the organization observe that improvement? | A named outcome, baseline and accountable business owner. |
| Feasibility | Can the proposed capability work with the available process, systems and constraints? | Technical dependencies, integration needs and unresolved assumptions. |
| Data and workflow readiness | Are relevant data accessible and fit for use, and can the workflow accommodate the change? | Data access and quality needs, affected users, and process changes. |
| Risk and consequence of error | Who could be affected by an incorrect, unreliable or misused result? | Potential harms, required human review, and controls proportionate to impact. |
| Time to value | How soon can the organization test whether the intended outcome is plausible? | Evaluation milestones and the evidence needed before further investment. |
| Cost and operating burden | What will it take to deploy, integrate, monitor and maintain the use case? | Lifecycle costs, support needs and ongoing ownership. |
| Reusability and strategic fit | Does the work support a strategic priority or create capability useful elsewhere? | Alignment with enterprise goals and any reusable data, process or platform work. |
Gartner’s CIO guidance likewise frames prioritization around value, feasibility and readiness, and recommends balancing risk, return and time to value across a portfolio. It is commercial guidance, not proof that a particular initiative will produce a return. Gartner CIO guidance
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A useful planning heuristic is to combine lower-risk opportunities that help the organization learn with a smaller number of strategically important investments, within its risk tolerance and delivery capacity. This is a portfolio-design choice, not a universal ratio. Avoid counting pilots as progress unless they test a decision the organization can act on, such as whether the workflow, data, controls and outcome are viable.
3. Establish governance and accountability
Governance should make decisions and ownership explicit at each stage. For every use case, identify who sponsors it, owns the business outcome and data, approves risk, validates performance, responds to incidents, and decides whether to expand, change or stop it. Where one person cannot sensibly hold all those responsibilities, assign them separately; the important point is that none is left implicit.
NIST AI RMF 1.0 organizes risk work into four functions: Govern, Map, Measure and Manage. The framework is voluntary and use-case agnostic; its Playbook suggests actions but is not a checklist every organization must follow. Apply the structure in a way that fits the application and the organization, and check NIST’s framework page for the latest version before adopting it. NIST AI Risk Management Framework; NIST AI RMF Playbook; NIST AI RMF crosswalks
Apply controls to the use case, not just the technology label
Risk depends on the application, the data involved, the people affected and the consequences of failure. A generative AI tool used to draft low-impact internal text does not necessarily call for the same controls as one whose output can influence consequential decisions. For generative AI risk work, NIST AI 600-1—the cross-sector Generative AI Profile, published July 26, 2024—describes risks that are novel to or exacerbated by generative AI and suggested actions aligned with the AI RMF. NIST Generative AI Profile (AI 600-1)
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchFor each application, decide what human review is needed, what output can be used for, how issues are escalated, and what evidence is required before deployment or expansion. Applicable laws, regulations, privacy obligations, procurement rules and contracts vary by sector and jurisdiction; have the relevant legal and compliance owners determine which requirements apply to the organization.
4. Build the capabilities the portfolio needs
Assess enabling work against the prioritized use cases rather than treating capability-building as a separate technology shopping exercise. The main areas to examine are:
- Data: quality, access, permissions, lineage and whether the data can support the intended use.
- Security and privacy: protection for organizational and personal information, access controls, and handling of inputs and outputs.
- Architecture and integration: how the system will fit with existing applications, workflows and identity or access arrangements.
- Evaluation and monitoring: how performance, reliability and safety will be checked before launch and during operation.
- People and operating model: needed skills, user training, support, maintenance and ownership after deployment.
- Procurement: supplier evaluation, contractual needs, data handling and ongoing dependency management.
Make build-versus-buy a use-case decision
Compare building and buying in light of the capability the organization already has, required control, integration, total cost, risk and ability to maintain the system. Neither route is automatically preferable across an enterprise. A supplier may accelerate delivery but create dependencies; internal development may offer more control but requires skills and ongoing support. Include those trade-offs in the use-case decision rather than leaving them until procurement or implementation.
Canada’s federal AI strategy offers a public-sector example of enabling priorities, including central AI capacity, policy and governance, talent and training, and engagement and value. It also discusses use-case identification, data readiness, risk assessment, build-or-buy decisions, governance and procurement. These are useful areas to consider, not a required corporate blueprint. Canada’s AI strategy priority areas
5. Sequence delivery and measure outcomes
For each funded use case, agree on the baseline, target, accountable business owner, delivery phases, evaluation criteria and conditions for expansion or pause before scaling. A roadmap should connect each investment to a decision point: what evidence will justify the next phase, and what result or risk would trigger a change in direction?
Measure the business result and the system’s behavior
Use two complementary kinds of measures:
- Business measures show whether the initiative matters to the organization—for example, the chosen service, cost, cycle-time or capacity outcome compared with its baseline.
- Model and operational measures help explain how the system behaves, including reliability, safety, service performance and relevant incidents.
Choose measures appropriate to the use case rather than relying on a generic “AI productivity” score. Gartner advises connecting AI performance to financial and operational outcomes and tracking value through deployment; that is commercial guidance, not a guarantee that a project will achieve ROI. Gartner CIO guidance
At each checkpoint, compare observed results with the initial case. If the expected outcome is not emerging, investigate whether the cause is the model, data, process change, adoption, or an assumption in the business case. Then decide whether to correct, narrow, expand or stop the work, and update portfolio priorities accordingly.
6. Review the strategy as a living portfolio
Set a recurring review cadence that matches the organization’s pace and the risks of its systems. Use it to review use cases, performance, incidents, costs, data readiness, policy changes and supplier dependencies. Also check whether the business outcome remains important and whether the organization still has the capacity to operate the system responsibly.
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Canada’s federal strategy describes frequent review of its strategy and implementation plan, reporting through a quarterly tracker, and renewal in 2027. That is an example of one public-sector approach, not a schedule binding on private companies. Canada’s AI strategy priority areas
A CIO’s first strategy brief
Before presenting the strategy for funding, make sure it gives executives and delivery teams a shared, decision-ready view of the portfolio:
- The business outcomes the organization is pursuing and their baselines.
- The prioritized use cases, their owners, dependencies and reasons for sequencing.
- The risks, controls and review responsibilities for each application.
- The data, people, architecture, procurement and operating capabilities required.
- The measures and decision points that will determine whether each initiative proceeds, changes or stops.
This brief should make it possible to see not only what the organization intends to build, but also why it matters, what must be in place, who is accountable and what evidence will guide the next decision.
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