Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsStart with the outcome a person needs, not with a model or vendor. AI is worth considering only if it can improve that outcome over the existing process or a simpler alternative—and a small, measured trial can test whether it does.
1. Define the need before choosing a tool
Write down who needs what, what a successful result looks like, and where the current process falls short. This keeps the decision tied to the user rather than to the availability or novelty of AI. GOV.UK describes AI as “just another tool to help deliver services” and advises starting service design with user needs: Assessing if artificial intelligence is the right solution.
Make the outcome observable. Depending on the task, that might mean fewer errors, faster completion, more consistent decisions, or making a service accessible to people who cannot use the current process. State the required quality and any constraints before comparing approaches.
2. Specify the task and AI’s proposed role
Describe the work as activities rather than as a broad label such as “automate support.” Be specific about whether AI would classify incoming requests, summarize documents, generate a draft, or assist a person with another defined activity. Also identify which parts remain human-led and what the AI output is meant to enable.
#1 Best Overall
NIST’s 2024 human-centered AI Use Taxonomy identifies 16 activities independent of AI technique or domain. It is intended to help describe tasks in terms of human goals and outcomes, rather than assuming that a particular technology is the task: NIST IR 8367r1.
3. Screen for practical fit
AI is a plausible candidate—not a guaranteed fit—when the work is repetitive and large-scale, suitable data is available, and the output can support a real action or result. If the task is occasional, depends on information that is unavailable, or produces an answer no one can use, AI may add complexity without solving the underlying problem. GOV.UK’s suitability guidance emphasizes these questions rather than prescribing a universal threshold.
Check the data, not just its volume
Assess whether the data is accurate, complete, unique, timely, valid, sufficient, relevant, representative, and consistent for the intended use. Establish that it can be used safely and ethically, including whether collection and use are appropriate for the people affected. A large dataset is not necessarily a suitable one.
Check whether outputs can lead to action
Identify who will use the output, what decision or action follows, and what happens when the output is wrong, missing, or uncertain. An output that cannot be integrated into a useful workflow is not evidence of value by itself.
4. Compare AI with the actual alternatives
Keep the intended outcome fixed and compare AI against the current process and simpler options, such as a clearer procedure, a form change, search, rules-based automation, or additional human capacity. These comparison axes are a practical synthesis of the cited guidance, not a formally validated scoring system.
| Question | What to establish |
|---|---|
| Effectiveness | Does the approach meet the user need at the required quality? |
| Scale and repetition | Is there enough repeated work to address a meaningful bottleneck? |
| Data fitness | Are the required data accurate, sufficient, representative, current, and relevant? |
| Risk and oversight | What harms or foreseeable misuse are possible, and what human review is needed? |
| Feasibility | Can the organization integrate, operate, maintain, and govern the approach? |
| Evidence and reversibility | Can a bounded trial test the case, and can the organization change course? |
For a candidate AI system, assess risk in its specific context: who will use or be affected by it, the goal, data sources, human involvement, deployment setting, system competence, and plausible misuse. OECD guidance recommends escalating cases with higher-risk indicators and revisiting findings when material circumstances change: OECD Due Diligence Guidance for Responsible AI.
5. Test the hypothesis on a small scale
Before committing to a deployment, write a testable hypothesis: for example, “For this defined task, the proposed approach will meet the required quality while reducing completion time, without increasing unacceptable errors or review burden.” Set the comparison method and success criteria in advance, then run a small proof of concept on a bounded use case.
Measure what matters for the task: output quality, error types, time or cost, human review effort, and adverse impacts. Include the cost of preparing data and integrating the trial where relevant. Do not treat a plausible demonstration as proof that the system will work in production or for different users and conditions.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Decide whether it can be delivered and maintained
If the trial supports the case, compare building, buying, reusing, or combining solutions. The right path depends on how distinctive the need is, the maturity of available products, integration requirements, internal skills, and the ability to operate and maintain the system. Include ongoing review and governance in the decision, not only initial implementation.
Assign responsibility for failures across the parts of the system: data, model design, software, and deployment. Preserve the ability to revise or stop the approach if evidence, user needs, or operating conditions change. OECD’s 2025 report on governing with AI discusses monitoring after deployment and audits that can examine technical behavior, compliance, or wider social effects: Governing with Artificial Intelligence.
7. Revisit the decision when conditions change
The answer is not permanent. Changes in the task, users, data, system capability, or deployment context can alter whether AI is suitable and what risks need attention. NIST’s voluntary AI Risk Management Framework is intended to incorporate trustworthiness into AI design, development, use, and evaluation; NIST says AI RMF 1.0 is being revised, so check its current status before relying on it: NIST AI Risk Management Framework.
The Tool Desk
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