Holden Bale, Global Chief Strategy Officer at Merkle, told Shoptalk Europe that 88% of enterprises have deployed at least one AI application since the end of last year, but only 6% can draw a straight line to EBITDA value (RetailNews.ai). This finding comes from Merkle's research across 100 enterprises with revenues over a billion dollars. Bale attributed the 82-point gap to organisations mistaking performance for execution—visible activity that photographs well but fails to measurably improve employee work or business outcomes.
Bale identified five patterns that separate real AI value from activity: starting where an organisation's data advantage is genuinely strongest; tying every initiative to a clearly defined, time-bound outcome with a confidence interval of ROI; redesigning the actual work itself rather than layering new technology onto unchanged processes; engineering trust deliberately through visibility into how AI systems reach conclusions; and matching the use case to the user's altitude, recognising that the right AI application looks completely different depending on seniority and role (RetailNews.ai). For commerce practitioners, this framework shifts focus from pilot projects to proof of concrete business impact—whether an employee's job actually improved and whether measurable ROI targets are met within defined timeframes.
Bale closed with specific predictions: by early next year, he expects 100% of employees at sophisticated retail organisations to have access to role-ready conversational insight tools tailored to their specific job, and a 50 to 70% reduction in the time it takes to move from insight to published content (RetailNews.ai). The underlying message is that the gap between AI adoption and measurable return will close only for organisations disciplined enough to apply all five patterns simultaneously, not merely the ones that happen to be convenient.