AI’s EBITDA Promise – And, why most companies fail to realize it.

A recent Bain & Company study (Sep ’25) cited 20–25% EBITDA gains from AI adoption, with agentic AI visions starting to take shape. While promising, these numbers largely reflect outcomes from tech-forward companies that have an advantage in mature foundational (data) and processing (talent) capabilities.

For most non-tech-forward enterprises, the challenge lies in translating the belief into reality – defining the right AI strategy, setting realistic ROI expectations, aligning investments to business outcomes, and procuring services that align with their overarching goals. In my view, this is where it becomes imperative to work with AI practitioners, who can collaboratively help “translate” the AI promise into an organization’s specific context. These experienced practitioners help outline AI roadmap for strategic advantage, define the right KPIs for ROI measurement, prioritize a step approach to a pragmatic AI investments philosophy, and can be a great "interlocuters" between the technology providers and the functional teams.

The same Bain study also highlights familiar barriers in data silos, IP and security risks, vendor lock-in, and missing standards. I would argue the reality is often worse – many organizations struggle with a fragmented understanding of the benefits AI can truly deliver (and how to measure it), difficulty prioritizing use cases with credible ROI, limited budget flexibility for experimentation, and executive hesitation to place big bets.

This is where AI practitioners can act as “human in the loop,” who helps “shape decisions” like; defining target use cases, outlining budget-talent requirements, creating flexible architectural views, and avoiding common pitfalls, while navigating fragmented data, modeling efficacy, application relevance, and the absence of industry-defining standards.

AI transformation isn’t a technology problem – it’s a business translation problem.