The Productivity Dip Nobody Talks About

Part 3 of 4: Why performance often gets worse before it gets better

In the first two parts of this series, I looked at why businesses struggle to get change right, and why winning genuine support has to start well before go-live.

At this point, many leaders start to feel confident. The business case is agreed, the system is selected, employees have been engaged, training has been planned, and the project has been delivered on time.

Then go-live arrives, and something nobody quite planned for happens: productivity falls.

Tasks take longer than before. Questions increase. Support requests multiply.

Employees who seemed confident only weeks earlier suddenly seem less sure of themselves, and the benefits that justified the investment feel further away than expected.

At this point many organisations start to worry the project has failed. In reality, what they're experiencing is one of the most normal and predictable phases of change.

The Dip Nobody Plans For

Technology projects are usually approved on the promise of improvement i.e. greater efficiency, better collaboration, improved service, lower cost. What leaders often fail to anticipate is that getting to those benefits typically involves a temporary decline in performance first.

Think about learning any new skill; driving a car, a new language, unfamiliar software. There's usually a period where you're worse at the underlying task than before you started, simply because things that were automatic now require conscious thought.

The same applies in organisations. When people move to a new CRM, adopt new Microsoft 365 workflows, or start using AI in daily work, they're not just learning a tool. They're changing habits built up over years, and that transition creates disruption almost by definition.

The organisations that feel the most frustration are usually the ones that expected an immediate improvement. The ones that manage change well simply expect the dip and plan around it.

Why Good People Suddenly Struggle

A common mistake is reading the dip as a competence problem, assuming people weren't paying attention in training, or aren't really embracing the change.

Usually the explanation is much simpler: people are operating outside their comfort zone. Under the old system, they knew exactly where information lived and what steps a task required, often without thinking about it. A new system forces them to think about it again. Is this the right menu, has this step changed, am I doing it correctly.

Even capable employees can look slower or less confident during this period. The issue isn't ability. It's familiarity, and it usually resolves with time, practice and support.

I saw this clearly during an accounting automation project, where processing actually slowed down for the first few weeks after go-live. Staff were manually re-checking every automated output against the old spreadsheet-based process, essentially running two systems in parallel out of caution. Leadership initially read this as the automation not working. It was actually the opposite. The controls were working exactly as intended, and what needed to change was confidence in the new process, not the process itself.

A reminder that "100% automation" was never really the goal; a trustworthy, well-governed process was.

The J Curve of Change

I refer to this natural process as the J-curve of change, originally conceived by David Viney. The idea is straightforward: performance dips as people learn new ways of working, then confidence grows, the process becomes familiar, and performance recovers. Eventually moving past where it started, which is where the actual return on the investment shows up. The shape resembles the letter J: down before up.

Understanding this matters because it resets expectations. Leaders who know the J-curve is coming stop treating the dip as evidence of failure and start reading it as evidence that change is actually happening. The goal isn't to avoid the dip entirely — that's rarely realistic. The goal is to keep it shallow and short.

The role of the change manager or my typical role as project assurance, is to minimise the dip and time spent 'below the line', whilst maximising the benefit of the change.

Supporting People Through the Transition

This is where many organisations underestimate the effort required after go-live. Project teams often spend months preparing for implementation, then assume the hard part is over once the system is live. In practice, the weeks immediately after go-live are often where success or failure is actually decided.

People need visible, ongoing support. Training that continues past launch day, questions treated as normal rather than a problem, and managers who expect a temporary dip and say so openly rather than let people assume they're the only one struggling.

The organisations that navigate this well tend to do a few things consistently: keep experienced support genuinely available, encourage people to share what isn't working, fix issues quickly, and above all, show patience.

The lesson here is to delay the post go-live celebrations of success, until after the productivity dip is behind you and there is clear evidence of the value of change. This is the time to celebrate success and then the project team can move onto their next endeavour.

Why AI's Dip Is Harder to Spot

With a new system, the dip announces itself; support tickets rise, error rates spike, someone complains the new process is slower. With AI tools, the dip is less obvious, and that's the part worth watching for.

People don't usually raise a ticket to say an AI assistant isn't helping. They just quietly stop using it and go back to the old way, often without telling anyone, because there's no visible failure to report. It's just a tool that didn't quite fit into how they actually work that day.

From the outside, everything looks fine. Underneath, adoption has already stalled, and it can sit that way for months before anyone notices the tool isn't delivering what it was bought for.

That's a different problem to solve than a support-ticket spike, and it needs a different kind of attention. Not "are people complaining," but "are people actually still using it."

The Value of Patience

One of the biggest advantages a business can have during this period is patience — not passive patience that tolerates poor performance indefinitely, but informed patience that recognises meaningful change takes time. Every project team wants quick proof the effort was worthwhile. Sustainable improvement rarely arrives that fast. It builds gradually, as people gain confidence and find better ways of working within the new system.

Final Thoughts

Most organisations invest heavily in choosing the right technology and planning the implementation. Far fewer prepare for what happens in the weeks after go-live, even though that's often where the real value, or the real cost, gets decided.

A temporary fall in productivity isn't usually a sign something has gone wrong. It's more often a sign people are learning and adapting.

The risk worth worrying about isn't the dip itself, it's pulling the plug on a sound decision halfway through it, on the assumption it's failed, and paying for the disruption twice.

Free Resource

Many change initiatives succeed or fail because of a handful of decisions made early in the journey. If you're planning a significant business or technology change, download my free guide, The 3 Disciplines That Make or Break Change.

It's a practical framework for defining value, creating focus, and establishing accountability before you invest time and money in the wrong solution.

Get your free copy here →