Concern 890 · Working draft · editorial review in progress
Make-vs-buy decisions
A build-or-buy decision can create AI risk when an organization chooses a development approach without understanding the expertise, maintenance and control it requires.
Understanding the concern
Buying a complete service, adapting an existing model and building a system internally are different commitments. A purchased tool may be quick to start but hard to customize. Internal development may offer control while requiring skills and ongoing support. The cheapest initial option may not have the lowest total cost.
UK guidance recommends beginning with user needs and available capabilities. Think of deciding whether to buy bread, bake from a mix or run a bakery: each option changes the work you must take responsibility for. Teams need to consider data access, testing, updates and an exit plan, as well as the first demonstration. Often the sensible choice combines purchased components with internal expertise.
An example
An organization builds a custom AI tool but has no budget or staff to maintain it after the original developer leaves.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
The guidance offers decision principles, not current product comparisons or a claim that building or buying is always better.
UK Government · Procurement guidance
Assessing if artificial intelligence is the right solution ↗ (opens in a new tab)Discusses user needs, skills and building versus buying. 2019 guidance supplies decision principles, not current vendor prices or a universal recommendation.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
- ISO/IEC 42001 (opens in a new tab)
standard
A question to discuss
Which responsibilities come with this option, and can the organization sustain them over time?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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