Building AI that delivers measurable business value<\/p>\n
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AI conversations have moved past the point of curiosity.<\/p>\n
Boards and leadership teams are no longer asking what AI might eventually do.<\/p>\n
They are asking where it is actually working, what measurable value it is creating – and why so many promising experiments still fail to become durable operating advantages.<\/p>\n
Across industries, companies have invested heavily in AI pilots, proofs of concept and impressive demos.<\/p>\n
Yet many remain stuck in what I think of as pilot purgatory: the place where a tool works in a controlled environment but never survives contact with the complexity, exceptions and accountability required in production.<\/p>\n
In my experience, AI initiatives rarely fail because the underlying technology is not powerful enough. They fail because of how the technology is applied. A model can be impressive in a sandbox and still be irrelevant to the business if it is not embedded into a real workflow, connected to the right data, governed appropriately and measured against outcomes that matter.<\/p>\n
That is why access to AI is no longer a differentiator. Anyone can buy access to models or integrate a third-party tool. The real advantage lies in the things that can\u2019t be bought off the shelf: proprietary data, deep domain expertise, and the discipline to continuously improve AI once it is operating at scale.<\/p>\n
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For us, those principles come together in our Lean AI approach, rooted in a Lean operating model that drives continuous improvement through testing, learning, and acting. Instead of chasing technology for technology\u2019s sake, our Lean AI approach helps us move AI beyond experimentation and into production, where it can improve service, boost productivity and create real business value.<\/p>\n
This is where many organizations get stuck. They treat AI as a portfolio of experiments instead of an operating capability. Organizations that successfully operationalize AI tend to do the opposite.<\/p>\n
They prioritize AI opportunities based on business value and points of operational friction, identifying manual, repetitive and high-volume work. Then, they build and deploy agents where automation can improve speed, accuracy, scalability or service quality across the entire customer workflow.<\/p>\n
There is no hobby AI in this model. Every deployment needs a clear business case, a workflow owner, measurement, feedback loops, and a plan to scale. That discipline is especially important with agentic AI, because agents operate with more autonomy than traditional software.<\/p>\n
Progress is not always linear. Systems improve, encounter new edge cases, retrench and improve again. Human oversight isn\u2019t a temporary bridge either; it is part of the architecture.<\/p>\n