Author: IRPA AI Senior Analyst, Kieran Gilmurray
Over 80% of AI projects fail, which is roughly twice the failure rate of traditional IT projects. That is not internet folklore. It is a sobering finding echoed in a 2024 study from the RAND Corporation, which analysed the anti patterns that derail AI initiatives and prevent organisations from Building effective, scalable systems. In 2025, S&P Global reported a sharp reversal in AI momentum: the share of companies that abandoned most of their AI initiatives jumped from 17% to 42% in a single year.
Gartner’s independent forecast aligns with this direction: at least 30% of GenAI projects will be abandoned after proof-of-concept by the end of 2025 – largely due to poor data, weak risk controls, and unclear business value. And MIT’s Project NANDA adds a more stinging coda: only about 5% of generative-AI pilots deliver rapid revenue acceleration; the rest stall with little measurable P&L impact whatsoever.
That is the backdrop for your build-vs-buy decision on agentic AI. You’re not choosing between two tech strategies; you’re selecting a path with explicit consequences for time-to-value, cost, risk, competitive advantage, and most critically, your probability of shipping an AI solution or strategy that has an achievable and measurable business impact.
In today’s market, almost every company is investing in AI, yet just 1% say they have reached AI maturity. This chapter provides a candid view of AI to help you decide what to build, what to buy, and when a hybrid build-and-buy approach is a winning move.
A strategic framework for build vs. buy technology decisions
Traditional software is like following a recipe. If you use the same ingredients and steps, you’ll always get the same result. That’s called deterministic AI.
Agentic AI is probabilistic. Results change with data, context, and feedback. Early architectural choices therefore echo for years through integration, operating cost, and compliance. The wrong early decision amplifies later risks, e.g., fragile integrations, hidden operating costs, and compliance gaps that only surface at scale. This chapter addresses this choice: build in-house or buy with a partner.
This abridged excerpt sets the foundation. The book is now live, and the full chapter goes further. It explains the real DIY failure patterns that derail internal builds, the talent gap and cost realities that most teams underestimate, and the scenarios where building in-house actually makes sense. It also provides a decision framework across urgency, capability, budget, and long-term strategy, plus a practical build vs buy vs hybrid matrix, red flags, partner evaluation questions, and a phased hybrid path that ships value now while building internal capability over time. For the practical build path, click here. The only open question is who moves early enough to benefit.
If you’re interested in reading more from our new book, Click Here
About the Author: Kieran Gilmurray
Kieran is a globally recognized authority on AI, automation, and digital transformation, having authored multiple influential books and hundreds of articles that have earned him prestigious accolades, including being named a Top 50 Global Thought Leader and Influencer on Generative AI in 2024, a Best LinkedIn Influencer for AI and Marketing, Top 50 Global Thought Leaders and Influencers on Manufacturing 2024, Top 14 people to follow in data and one of the World’s Top 200 Business and Technology Innovators.
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Originally posted on 2026-03-06 in the IRPA AI Network — Announcements & Updates