Build vs Buy vs Boost — the 2026 Decision Framework

S&P Global's 2025 AI adoption study found that 42 percent of companies had scrapped the majority of their AI initiatives by year end. The failures clustered around a predictable pattern: organisations that chose to build foundation models or deep fine-tuned systems from scratch, underestimating both the capital intensity and the time-to-value gap. The organisations that survived and scaled had largely converged on a different approach, one that is worth naming precisely: Boost.

Build means training or significantly fine-tuning your own model. This is the path of hyperscalers and a handful of well-capitalised vertical players. The compute and talent requirements are real, the time horizon is measured in years, and the returns are defensible only if you have proprietary data at a scale that genuinely cannot be replicated by a foundation model provider. For most enterprises, this is the wrong answer to a question they should not be asking.

Buy means purchasing an AI product off the shelf — a copilot, a summarisation tool, a classification API — and deploying it with minimal adaptation. This works for commodity use cases where your data and process are not differentiating. It fails when the use case requires the model to understand your domain, your data schema, your risk tolerance, or your regulatory constraints. Off-the-shelf models are optimised for the median, not for your organisation.

Boost is the discipline of knowing what to outsource and what to own. You outsource reasoning capability to a frontier model. You own the retrieval layer that surfaces your proprietary data. You own the evaluation pipeline that tells you whether outputs meet your quality bar. You own the permissioning logic that determines what the system is allowed to do in your operational environment. The companies that avoided the 42 percent scrap rate understood that the model is not the product. The harness, the data, and the evals are the product.