Author: IRPA AI Senior Analyst, Kieran Gilmurray
Why you need a personalized AI roadmap
Impact compounds when it is steered like a system or program, not sprinkled like glitter. AI adoption across businesses is rising, yet consistent value still clusters among a small group of high performing companies. They run multiple scaled use cases, invest with intent, and treat risk management as a core product requirement.
At this point, the decision between building, buying, or taking a hybrid approach, as outlined in Chapter 11, has already been made. You have shipped a pilot and are capturing baseline KPIs. The next step is not to add more system features but to scale outcomes with discipline.
Industry data shows movement without scale is a common business problem. Adoption is now widespread, with around 72 percent of businesses using some form of advanced analytics or intelligent systems. Yet only a small subset of companies, roughly 46 out of 876 respondents, report a substantial impact on EBIT from these investments.
McKinsey’s 2024 research found that while organizations are embracing AI more broadly, value is increasingly concentrated on operating models and governance as AI technology advances.
A comparative analysis indicates that meaningful business results emerge when companies redesign work processes and transform end to end workflows using these tools. Less successful organisations rarely attempt to scale. Instead, they deploy capabilities in isolated units or focus on automating narrow tasks.
Overall, AI service-operations studies reveal a significant execution gap between successful and less successful adopters of AI. The harsh fact business leaders face today is that there are relatively few firms running multiple scaled use cases across their core business operations.
The message from these industry reports is clear: scaling advanced capabilities requires a deliberate operating model. A stack of polished tools on its own will not suffice. This chapter outlines that operating model. It shows how to run the program in three parts: early-phase setup, a small number of continuously executing core feedback and delivery loops, and domain-level decision gates.
This abridged excerpt sets the foundation. The book is now live, and the full chapter goes further. It explains how to run the Agentic Readiness Assessment, how to build an operating model that sustains scale, how to prioritise and choose an anchor domain, and how to pick the right pathway and quarter roadmap based on readiness. It also covers the continuous loops that keep performance and governance tight, and the gates that control expansion by domain so scale stays reliable, safe, and cost disciplined. The only open question is who moves early enough to benefit.
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 in the IRPA AI Network — Announcements & Updates