Most organizations assign process improvement to the people who know the process best. It seems logical. In practice, it often produces the least imaginative solutions.
Too often, I see companies invest heavily in building or buying the right tool — and then completely underinvest in making people actually use it. The result is predictable: six weeks after launch, usage has collapsed. The tool becomes a sunk cost on the P&L, and the team moves on to the next initiative. I’ve watched this cycle repeat itself too many times to call it an exception. It is the rule — and the data confirms it: according to McKinsey, 70% of software implementations fail due to poor user adoption, not poor technology.
A sales force that manages only its sell-in is not managing commercial performance.It is managing its own revenue number — and nothing else.Not the financial health of its dealer network. Not real end-market demand. Not its own 12-month sustainability. Sell-out is the truth signal. Everything else is an accounting artifact.
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A month ago, I deployed 15 AI agents across a mid-size industrial operation, presented an AI operating model to 100 CIOs at the IDC AI & Data Summit, and ran daily executive decisions with AI as my operational co-pilot. I hired zero developers to do it. The dominant belief in 2026 — that AI transformation requires a technical team — is not just wrong. It’s the single most expensive misconception a leader can hold.
Most digital transformation projects don’t fail in production. They fail in the meeting where someone decides not to listen. I’ve seen it repeatedly across B2B industrial markets: tools designed in isolation, validated by org chart, deployed into a vacuum. The result is always the same — abandoned software, wasted budget, and a field that has learned to distrust IT. The pattern has a cost. And it’s rarely measured.