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Choose technology only after you can name the problem it must solve, who experiences it, and what measurable improvement would count as success. Start with the work or user experience—not a product category or vendor—and compare process changes, existing tools, configuration, buying, building, or a combination only after you understand the need and constraints.
How do you know whether technology solves your problem?
Write down who is affected, what task or outcome is at issue, what happens today, and why the current approach falls short. Separate what you have observed from what you are assuming. Include the operating context and constraints, such as security, accessibility, interoperability, data, policy, budget, and the capacity to run and maintain a solution.
This keeps the problem statement from quietly turning into a specification for a product you already favor. The UK Digital, Data and Technology Playbook puts the principle plainly: “When specifying products and services, it is essential that we focus on the user and the problem we’re trying to solve, rather than a particular solution.” The Digital, Data and Technology Playbook and Digital.gov’s guide to digital acquisitions both emphasize user needs and problem definition.
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Define success before comparing features
Describe the outcome in observable terms and decide how you will assess progress before weighing products. Choose measures that fit the service and its users; government guidance does not prescribe one universal metric for every technology decision. Include quality and service outcomes as well as cost, and set milestones when the solution or the problem is still uncertain.
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Outcome-focused requirements help a team compare different ways of meeting the same need instead of rewarding whichever option has the longest feature list. New Zealand Government Procurement’s planning guidance connects outcome-focused requirements and whole-of-life value with procurement planning, while the UK Playbook recommends measurable outcomes monitored through milestones and iterations.
Should you build or buy technology?
There is no universally best answer. Keep multiple approaches open until you understand user needs, the market, organizational capabilities, lifecycle obligations, and constraints. Depending on the problem, the answer might be a process change, an existing tool, configuration, a commercial product, a new build, or a combination.
Assess whether your organization has the skills and architecture to implement and operate each option. Consider accessibility and interoperability alongside purchase or development costs, maintenance, management, operation, and eventual exit. The UK government’s purchasing strategy guidance discusses build-and-buy choices, market understanding, organizational capability, lifecycle, and costs; Australian Government guidance likewise emphasizes fit with business needs and operating context.
Compare options against the need, not the sales pitch
For each viable approach, assess how well it could improve the defined outcome and what evidence supports that expectation. Weight the criteria to match the problem rather than applying a generic scorecard.
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- Outcome: Does it address the user or operating problem you defined?
- Usability and accessibility: Can intended users complete representative tasks, including users with relevant accessibility needs?
- Implementation and capability: Can your organization integrate, configure, deliver, and operate it with the skills and architecture available?
- Whole-life value: What are the implementation, purchase or build, maintenance, management, operating, and exit costs, and what quality or service value accompanies them?
- Constraints and resilience: Does it meet security, data, interoperability, policy, and architecture requirements? Can it be maintained, adapted, or exited without unacceptable dependence?
- Evidence and uncertainty: What has been demonstrated, what remains an assumption, and what is the cost of being wrong?
Cost alone is not a verdict: a lower initial price may not mean better value across the solution’s life. The New Zealand procurement guidance and UK Playbook both frame value in terms broader than initial purchase cost.
Test fit with realistic users and tasks
A polished demonstration shows what a product can look like under prepared conditions; it does not establish that it works for your users’ actual tasks. Where access is available, let representative users try the option in a sandbox, trial, or other realistic setting. Observe task performance, usability, and accessibility, and record where the solution succeeds or creates friction.
Digital.gov’s acquisition guidance recommends hands-on usability evaluation. Scale testing to the project’s size, complexity, and uncertainty. A pilot or iterative release is useful when it can answer a material question; decide in advance what evidence would lead you to continue, adjust, or stop. A pilot is a learning step, not an automatic commitment to full rollout.
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Technology decisions do not end at launch. Make the handover, documentation, ongoing maintenance, management, and operating capacity part of the evaluation. Ask how the solution can be adapted as needs change, what happens if a supplier or service is no longer suitable, and what work would be needed to move away.
The life-cycle view matters both for value and for feasibility: a solution that meets the immediate need but cannot be supported or exited on acceptable terms may not be fit for the organization’s context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply additional scrutiny when AI is proposed
Ask why AI is relevant to the defined problem and whether a non-AI approach could meet the need more appropriately. Before committing, examine data quality and access, known limitations, governance, risk, oversight, explainability, information assurance, and supplier dependence. Match the checks to the use case and its risk rather than treating “AI” as a justification by itself.
The UK government’s AI procurement guidance calls for a clear connection between AI and the problem, consideration of alternatives, and attention to data, limitations, governance, and lock-in across the lifecycle.
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These recommendations draw on public-sector guidance from the United States, United Kingdom, Australia, and New Zealand. They are decision principles, not evidence that a particular technology or supplier will deliver value in every organization. Apply the laws, procurement rules, and internal policies that govern your own organization.
Australian Government digital leadership guidance asks, “What is fit-for-purpose? Just because you can do it, doesn’t mean you should do it.” The page presents this as a practitioner perspective without naming its speaker. Its underlying test is practical: does the proposed approach fit the business need, context, and longer-term operating reality?
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