Technology does not transform a business. People do, by changing how they work.
A couple of weeks ago, we brought together 17 operational leaders in Amsterdam to talk about where decision-making is heading. One theme kept coming back: the technology is often the easy part. The harder part is earning enough trust for people to change a familiar process and act on information that may challenge years of experience.
That trust has to be earned through evidence, transparency and involvement.
But transformation also needs commitment. If the old spreadsheet or workaround remains the easier option, people will keep using it.
This month’s stories reinforce the same point. MIT Sloan looks at the hidden work behind AI adoption, while Gartner argues that supply-chain planning will remain substantially human-led.
The technology matters. Changing how people make and act on decisions is what turns it into transformation.
Phil, CEO
McKinsey finds AI and digital tools are now commonplace across logistics. The companies seeing stronger returns make operational trade-offs clearer, move analysis closer to the decision and connect those decisions more directly to execution. (12 mins)
Gartner expects supply-chain planning to remain substantially human-led even as AI investment grows. Its advice is to start with the decision: define what should be supported, augmented or automated, then measure whether decision quality actually improves. (4 mins)
DHL’s latest Logistics Trend Radar sees AI, robotics and automation taking on more operational work, while people remain central to logistics. The emerging model is one of human expertise working alongside increasingly capable physical and digital systems. (5 mins)
BCG’s 2026 Applied AI Index suggests enterprise AI is beginning to create broader value. The strongest performers focus on high-value workflows, operating-model change and governance rather than treating AI as a collection of individual tools. (12 mins)
MIT Sloan’s research suggests the hidden work of AI adoption can be as important as the technology itself. Organisations that support employees to test, refine and share applications are more likely to turn experimentation into solutions that persist and scale. (5 mins)
McKinsey argues that one of AI’s biggest opportunities sits between existing processes: the hand-offs where teams reconcile data, constraints and approvals. Redesigning those interfaces could reduce delay while keeping people focused on the exceptions and trade-offs that require judgement. (9 mins)
The IMF argues Europe is well placed to benefit from AI, but productivity gains will come with labour, energy and distributional pressures. Skills, workforce mobility and infrastructure will help determine how widely those gains are shared. (12 mins)
About Predyktable
Predyktable helps warehouse and logistics teams make better labour decisions before execution begins.
Our Labour Decision Intelligence platform turns demand, workload, productivity and operational constraints into clearer labour plans; helping teams decide what labour to commit, where, and when - and be clear on what trade-offs are involved.
The result is earlier, more deliberate decisions, better visibility of cost and service risk, and less reliance on late corrective action such as overtime, agency and firefighting.
We work with retailers, grocers and logistics operators where labour is a major cost, operations are complex and/or volatile, and better labour decisions can materially improve control, service and performance.
Until next month,
Team Predyktable