When Should Businesses Build, Buy, or Wait on AI Capabilities?

AI for Leaders: Strategy to Action Programme

Explore Programme

For many organisations, the AI conversation is no longer about whether to invest. It is about where AI can create a real advantage and where it may simply add cost and complexity. Adoption is moving quickly, but value creation is proving harder. McKinsey’s 2025 research found that 71% of respondents said their organisations regularly used generative AI in at least one function, while only 21% had fundamentally redesigned some workflows around it. For leaders deciding when to invest in AI, the focus must therefore go beyond choosing the right technology.

Build when the capability can become difficult to replicate

Building an AI capability internally makes sense when it draws on something unique to the organisation, such as proprietary data, specialised processes or deep institutional knowledge. For example, years of business data could help create AI tools tailored to better decision-making. But building comes with ongoing demands around data, integration, governance, cybersecurity, talent and performance. That is why the build vs buy AI decision is not simply about whether a company can build something. It is about whether owning the capability will create enough strategic value to justify the cost and complexity.

Buy when differentiation lies elsewhere

Buying can make more sense when a capability matters to the business but does not set it apart. Tools for summarising documents, generating routine content, coding support or searching internal knowledge are becoming easier to access. Building them in-house may take time and resources without adding much competitive value. But buying is not a shortcut to results. Businesses still need to think about integration, data security, vendor dependence, governance and changes to existing workflows. McKinsey’s research also highlights workflow redesign as a key factor in turning generative AI investment into measurable business impact.

Wait when uncertainty has more value than speed

Waiting is often seen as falling behind. But with AI, that is not necessarily true.

The technology is changing so quickly that making a large investment too early can leave businesses tied to systems, vendors or processes that may soon need to be replaced. When there is still uncertainty, taking a step back can be sensible.

Waiting does not mean doing nothing. Businesses can run small pilots, test use cases and learn along the way. This can also reveal whether the real challenge is the technology, or something more basic, such as poor data, unclear ownership or inefficient processes.

An AI investment decision framework should begin with value

A practical AI investment decision framework can help leaders ask four simple questions:

Strategic differentiation: Will owning the capability create a meaningful advantage?
Capability readiness: Do we have the data, talent, governance and processes to support it?
Cost of commitment: How difficult will it be to change course if the market shifts?
Cost of waiting: What could we lose by delaying the decision?

For leaders, deciding whether to build, buy or wait requires more than understanding the technology. It calls for the ability to assess strategic value, evaluate organisational readiness and weigh long-term implications against immediate business needs. ISB’s AI for Leaders: Strategy to Action Programme help leaders develop the strategic judgement, digital understanding and leadership capabilities needed to evaluate these trade-offs and make informed AI investment decisions as technologies and business priorities evolve.

FAQs

What does build vs buy AI mean?

It means deciding whether to develop an AI capability in-house or use an existing solution. The decision depends on cost, control, business needs and the advantage it can create.

When should a business build its own AI capability?

Building makes sense when the business has unique data, processes or expertise that an external tool cannot easily match.

When should businesses wait before investing in AI?

Waiting can be sensible when the use case is still unclear, technology is evolving quickly or the business is not ready.

What should an AI investment decision framework consider?

Look at business value, costs, readiness, flexibility and the risks of waiting.