The article argues that many AI transformation efforts falter because companies neglect three intertwined pillars: strategic alignment, value governance, and organizational discipline. Simply deploying AI tools or running pilots is not enough — businesses need a clear vision of how AI links to their long-term strategy and value creation. Without that, AI risks becoming a siloed technology exercise rather than a core business driver.
A central point is that governance matters deeply. AI governance shouldn’t just be about risk control or compliance; it must also involve processes to measure, capture and track the actual business value AI delivers. This means defining KPIs, linking them to financial outcomes, and establishing cross-functional oversight (involving product, finance, legal, HR) to ensure that value doesn’t leak or go unrealized.
The author also calls out the common mistake of focusing too much on technology (“We need more models”) while under-investing in process redesign and change management. Real AI transformation often requires rethinking workflows, training people, rearchitecting data pipelines, and building governance structures that embed AI safely into daily operations — not just layering it on top.
