Manage AI as a portfolio of initiatives tied to business objectives—not as a series of model purchases. For each proposed use, specify the outcome, establish how it will be measured, assess the conditions and risks, and fund a bounded implementation. Expand only when evidence shows the initiative is delivering enough value to justify its full lifecycle cost and risk.
Why AI investment is more than a technology choice
An AI initiative depends on more than a model or software license. Data quality, infrastructure, integration, workforce skills, procurement, governance and external partnerships can all determine whether a project works in practice. The OECD identifies these as important enablers in its government-focused guidance on trustworthy AI; for a company, they are useful planning dimensions rather than public-sector requirements. OECD guidance on enablers, guardrails and engagement.
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That broader view also changes how leaders should compare proposals. A promising demonstration is not the same as a sustainable investment: the initiative must fit a real organizational need, work with available systems and people, and remain manageable after deployment.
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What the available adoption figures do—and do not—show
OECD analysis published in 2025 examined 200 AI use cases in government. It found that 57% supported automated, streamlined or tailored processes and services; 45% enhanced decision-making, sense-making or forecasting; and 30% sought to improve accountability or anomaly detection. The OECD also reported that 15% of governments had an AI investments framework in 2023. These figures describe public-sector use and investment frameworks, not company outcomes or expected returns. OECD, Governing with Artificial Intelligence.
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A decision cycle for evaluating AI initiatives
The following cycle is a practical synthesis of OECD investment guidance and NIST risk-management resources, not a prescribed scoring formula. Use it to make proposals comparable and to decide what evidence is needed before committing to broader deployment.
1. Define the problem and intended outcome
Start with a business or operational problem, not a preferred tool. State who is affected, what process or decision should change, and what a successful result would look like. For example, a proposal to assist customer-support teams should identify the targeted task and the intended improvement—such as faster handling or more consistent responses—without assuming that introducing AI will achieve it.
2. Establish a baseline and a way to measure value
Record how the process performs today, using measures relevant to the proposed outcome. Decide how the pilot will distinguish an AI contribution from changes that would have happened anyway: that comparison is the counterfactual. Specify the measurement period, data source and accountable owner before launch. Without impact measurement, an organization may struggle to demonstrate return on investment or prioritize later proposals, a concern reflected in OECD guidance on strategic planning and assessment. OECD investment and impact guidance.
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3. Test feasibility, lifecycle cost and risk
Assess whether the organization can deliver and sustain the use case, not merely build a prototype. Include implementation and integration effort, ongoing operating and maintenance needs, data readiness, infrastructure, skills, procurement and dependencies. Identify operational, financial, legal, security and societal risks in the context where the system will be used; determine who can intervene if performance or circumstances change.
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There is no universal scoring formula or dependable private-sector ROI benchmark established by the guidance cited here. A company should therefore make assumptions visible, compare proposals using consistent criteria, and avoid presenting an unvalidated estimate as a forecast.
4. Fund the enabling capabilities as well as the use case
If data access, infrastructure, training, governance or procurement is a prerequisite, include it in the investment case. These capabilities may support multiple initiatives, but their cost and ownership should still be explicit. The OECD government framework highlights these kinds of enablers and recommends context-appropriate, risk-based guardrails; applying that thinking in a company is an analogy, not a direct obligation. OECD on enablers and proportionate guardrails.
5. Run a bounded implementation and monitor it
Set a defined scope, duration, users and decision checkpoints. Monitor the measures selected for the intended outcome alongside relevant risks and operational performance. Assign responsibility for reviewing results and for handling issues; a pilot without an owner or a decision date can drift into ongoing spend without resolving whether it merits expansion.
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At review, compare observed results with the baseline and the original value proposition, then account for lifecycle cost, feasibility and risk. Scale when evidence and operating conditions support expansion; revise when a remediable gap remains; stop when the expected benefit is not materializing or the risks and costs are not acceptable. Record the decision and its rationale so future investment choices can use what the organization learned.
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Use risk management throughout the AI lifecycle
The National Institute of Standards and Technology (NIST) describes its AI Risk Management Framework as “intended for voluntary use.” It is a voluntary resource for incorporating trustworthiness considerations into the design, development, use and evaluation of AI systems—not a universal mandate. Its companion Playbook, based on AI RMF 1.0, suggests actions under four functions: Govern, Map, Measure and Manage. Organizations can use those functions to structure accountability, understand context, evaluate risks and choose responses. NIST AI Risk Management Framework and NIST AI RMF Playbook.
For enterprise due diligence, the OECD’s Due Diligence Guidance for Responsible AI, published on 19 February 2026, is aimed at enterprises involved in developing and using AI. It connects responsible business conduct with the OECD AI Principles. OECD Due Diligence Guidance for Responsible AI.
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