Forecasting has always been about more than numbers. It is the disciplined art of looking ahead, balancing uncertainty with insight, shaping decisions at every level, and helping organisations respond with confidence rather than react out of pressure. In a world of volatility, shifting customer behaviours and increasing operational complexity, forecasting is no longer simply a technical function. It is a strategic capability, a cultural anchor, and a critical enabler of organisational agility. Yet, many organisations still view forecasting through a narrow lens: a model, a spreadsheet, a monthly cycle. This mindset limits its potential. To unlock the real value, forecasting needs a reset, and in many cases, a redefinition, so that it matches the scale of influence it was always meant to have.

Capacity planning has always looked cleaner on paper than it behaves in real life. We like to imagine tidy relationships between inputs and outputs, a straight line between demand, resource, and performance, even though queues react sharply, people behave unpredictably, and averages only tell part of the story. What makes this even harder is that much of the organisation still interprets planning through these linear assumptions, shaping expectations, decisions and pressure in ways that simply don’t match how operations function.

AI adoption in customer operations is not a technology problem. It is a capability, governance and mindset challenge.

Our latest shrinkage benchmarking survey shows average sickness absence at around 8%. At first glance, this appears higher than last year’s 7.6%, suggesting an upward trend. That conclusion would be misleading. A closer look shows the apparent increase is driven by sampling rather than worsening performance. As with last year, there is wide variation across organisations, with sickness rates ranging from around 3.8% to just over 20%. That spread matters far more than the headline average.

As organisations move into a new era of work, we are being pushed to reset long-held assumptions about what capability looks like. Much like customer operations, our own skills and habits carry legacy thinking that no longer fits the world we now work in. AI is accelerating this shift. It challenges the skills we grew up with, changes how expertise is built and forces us to redefine what great looks like. This article explores that reset. It looks at the concerns people have today, the skills we may need to let go of, the new skills that will matter most and how we can use AI intelligently rather than passively. If we want to stay ahead, we need to rethink not just the tools we use, but the foundations of how we learn, decide and create value.

But to shape the future we first need to learn from the past...