1️⃣ Middle managers need capacity HBR’s interviews in two consulting firms found AI adoption piling oversight, coaching and quality control onto managers, while the work systems around them barely changed.
💡 Why it matters If the middle layer absorbs every AI experiment, adoption stops scaling and the leadership pipeline gets thinner.
☕ Coffee talk Who is protecting manager capacity while everyone else celebrates the AI productivity slide?
2️⃣ Transformation starts below the tech MIT SMR’s Pfizer case says the company broke a long-running manufacturing digitization problem by fixing site trust, data flow and repeatable deployment, not by chasing the flashiest AI layer.
💡 Why it matters The boring foundation is where transformation either compounds or dies: workflows, operators, standards and usable data.
☕ Coffee talk How many AI roadmaps are really just paper-on-glass with a better demo?
3️⃣ Friction needs a playbook INSEAD’s change case argues that adaptive transformations cannot wait for a full roadmap: leaders set direction, then let teams own decisions through explicit roles, charters and feedback loops.
💡 Why it matters Decentralization only works when leaders separate accountability for the process from ownership of the work.
☕ Coffee talk Is the team actually empowered, or just being asked to guess where the guardrails are?