Productivity momentum and planning
However, there is excitement now about how much faster work can get done with strategic workforce planning.
An analysis that once took an afternoon can sometimes be completed in an hour. A first draft appears in minutes. Meetings can be summarized automatically. Information that once required searching through folders, emails, reports, and systems can increasingly be assembled almost instantly.
That's real progress.
However, with strategic workforce planning, I've started wondering whether we're asking enough about what happens after the time is saved.
If a report that once required four hours now takes forty minutes, we can measure the improvement easily. This trend demonstrates how efficiency gains align with strategic workforce planning goals and the need to optimize how work is allocated across teams.
Moreover, we saved three hours and twenty minutes, and those gains hint at larger cumulative effects across the organization. Put enough of those improvements together and the productivity story can become very impressive for stakeholders. This pattern could be repeated across departments over time.
However, strategic workforce planning questions whether anyone needs the report?
What if the faster analysis doesn't lead to a better decision? What if the employee uses the recovered three hours to produce even more work that someone else now has to review? What if speed increases output but also increases corrections, meetings, approvals, and information coming at everyone else?
Suddenly the productivity calculation becomes more complicated.
Maybe the question organizations should be asking isn't simply, “How much faster are we?”
Maybe it's, “What became better because we got faster?”
Speed Is Easy to See
One reason organizations naturally gravitate toward speed is that it's relatively easy to measure.
We can compare how long something took before and after a new technology was introduced. We can count transactions processed, calls handled, reports generated, cases completed, documents created, or customers served.
Executives like numbers they can see, and understandably so.
The problem begins when speed becomes the outcome rather than a means to an outcome.
Think about a customer service function. Suppose technology allows representatives to handle significantly more customer interactions each day. On paper, productivity has improved.
But what happened to the customers?
Were their problems resolved more quickly? Did repeat calls decline? Did customer satisfaction improve? Were employees able to solve more complicated problems? Did the organization reduce cost without sacrificing service?
Or did everyone simply handle more interactions?
Those are very different definitions of productivity.
The same distinction exists throughout an organization. Producing more reports isn't necessarily valuable. Processing more emails isn't necessarily valuable. Creating more presentations isn't necessarily valuable. Attending shorter meetings isn't necessarily valuable.
The business benefit depends on what those activities actually accomplish.
We May Be Measuring the Wrong End of the Work
Most organizations have spent years measuring work from the activity side.
How many?
How fast?
How much?
How often?
Those measures have their place, particularly in work where volume and throughput directly influence business results. But technology is making it increasingly important to measure the other end of the work.
What happened because the work was performed?
A finance team might produce forecasts faster, but the real value is whether leaders can make better decisions sooner. A recruiting team might screen candidates faster, but the business value is filling critical roles with people who can perform. A marketing team might produce dramatically more content, but the value isn't the amount of content. It's whether the content creates meaningful business results.
This sounds obvious when we say it out loud.
Yet organizations can become so focused on improving the production of work that they forget to question the value of what is being produced.
AI simply makes that problem easier to see because it can dramatically accelerate production.
Faster Bad Work Is Still Bad Work
There is something almost irresistible about automating an inefficient process.
We find a workflow that requires six manual steps and discover technology can complete four of them automatically. Everyone celebrates the efficiency improvement.
But there is a question worth asking before the celebration begins.
Why does the process have six steps?
Perhaps all six are necessary. Maybe there are legitimate regulatory, financial, safety, or quality reasons behind them.
Or perhaps the process accumulated over time. One step was added after a mistake. Another exists because an executive who left four years ago wanted a particular report. A third compensates for two systems that don't communicate with each other.
Technology can make that process faster.
It doesn't necessarily make the process better.
This is where productivity improvement and work redesign need to meet. Before automating the work, organizations should understand why the work exists, what outcome it produces, and whether the process still makes sense.
Otherwise, we risk becoming extraordinarily efficient at doing things we shouldn't be doing.
More Output Creates Work for Someone Else
There is another side of productivity that doesn't receive enough attention.
One person's output often becomes another person's input.
If employees can suddenly create twice as many reports, proposals, analyses, presentations, emails, recommendations, and project documents, someone may have to read them.
That person doesn't automatically receive twice as much capacity.
This creates an interesting organizational problem. Technology can dramatically increase the speed at which information is created without increasing the speed at which humans can thoughtfully absorb, evaluate, and make decisions about it.
You can probably already see this happening.
The problem isn't that people don't have enough information. In many organizations, they have more information than they can reasonably process.
If AI dramatically increases the amount of material flowing through an organization, productivity could improve at the point of creation while declining somewhere downstream.
The employee creating the analysis saved three hours.
The five people reviewing the additional analyses may not feel quite as productive.
That's why productivity needs to be considered across the workflow, not simply at the individual task.
Rework Changes the Math
Quality creates another complication.
Imagine an employee uses AI to create something in twenty minutes that previously required two hours. That's an impressive improvement.
Then the employee spends thirty minutes correcting it.
A manager spends another twenty reviewing questionable information.
Someone discovers an error after the work has been distributed.
Now several people spend additional time fixing the problem.
Was the work still faster?
Possibly.
Was it more productive?
Maybe.
Was it better?
That's the question.
Organizations need to be careful about celebrating the first twenty minutes while ignoring everything that happens afterward.
Rework has always been one of the quieter consumers of organizational capacity. AI doesn't create that problem, but it can amplify it if speed encourages people to move work forward before it has been sufficiently reviewed.
The faster work becomes, the more important judgment becomes.
Saved Time Has to Go Somewhere
Let's assume the productivity gain is genuine.
The employee really did save three hours. The work is accurate. The quality is good. No additional rework was created.
Now we reach what may be the most interesting question of all:
What happens to the three hours?
Organizations don't typically maintain a bank account for recovered workforce capacity. The three hours don't appear neatly on a balance sheet waiting for executives to allocate them.
They disappear back into the workday.
The employee answers more emails. Attends another meeting. Takes on another assignment. Handles additional volume. Perhaps catches up on work that had been piling up.
All of those things may be useful.
But if leadership expects technology investment to produce a particular business result, hoping saved time finds its way there isn't much of a strategy.
Recovered capacity needs direction.
Perhaps the organization wants employees to serve more customers. Maybe it wants faster product development. Perhaps employees should spend more time building relationships, solving complicated problems, developing new capabilities, or working on strategic initiatives that previously couldn't get enough attention.
There isn't one correct answer.
But there should probably be an answer.
Productivity Can Become an Expectation Treadmill
There's also a human dynamic worth watching.
Organizations have a remarkable ability to absorb productivity gains almost immediately.
If employees can complete ten units of work instead of eight, ten quickly becomes normal. Then someone wonders whether twelve might be possible.
That's not necessarily unreasonable. Productivity improvements are supposed to allow organizations to accomplish more with available resources.
The problem occurs when every efficiency gain automatically becomes more volume.
Employees get faster, but they never experience additional capacity because expectations expand at exactly the same pace.
Over time, the organization can find itself running faster without being entirely sure where it is going.
This is particularly important with AI because the potential productivity gains can be substantial in certain types of work. If leaders don't make conscious choices about where that capacity should go, “more” can become the default answer.
More output. More projects. More communication. More analysis. More activity.
Eventually someone should ask whether more is actually what the business needs.
Better Decisions May Matter More Than Faster Work
Some of the most valuable productivity improvements may not involve producing more of anything.
They may involve making a better decision.
Suppose technology allows an executive team to identify an emerging customer problem sooner. Perhaps a workforce analysis reveals a capability gap before it delays a strategic initiative. Maybe a manager can see operational patterns that previously remained buried in data.
The output itself may be small.
The value can be enormous.
This is where traditional productivity measures sometimes struggle. They tend to reward visible activity, while some of the highest-value work in an organization involves judgment, insight, prevention, and decision-making.
Preventing a bad decision doesn't create another unit of output.
Neither does recognizing a risk early.
Neither does deciding not to pursue a project that would have consumed millions of dollars and thousands of workforce hours.
Yet those may be among the most productive things leaders do all year.
Sometimes the Best Productivity Gain Is Less Work
There is another possibility organizations shouldn't overlook.
Perhaps the goal isn't to perform the work faster.
Perhaps the goal is to stop doing it.
Organizations accumulate work over time. Reports, approvals, meetings, processes, controls, handoffs, and administrative tasks become part of normal operations. People get accustomed to them.
Then technology arrives and the first instinct is to automate everything.
But if a monthly report takes four hours and nobody uses it, reducing production time to fifteen minutes isn't the biggest opportunity.
The biggest opportunity is getting four hours back permanently.
This requires a different mindset about productivity.
Instead of asking, “How can technology help us do this faster?” leaders might first ask, “If we were designing the work today, would we do this at all?”
Sometimes the answer will be yes.
Sometimes the answer could save far more than technology ever will.
This Isn't Really an AI Question
AI is making this conversation urgent, but the underlying issue is much older than AI.
Organizations have always struggled with the difference between activity and value.
We've celebrated full calendars, overflowing inboxes, long hours, rapid responses, high transaction volumes, and employees who appear constantly busy.
Technology simply gives us the opportunity to become busier at extraordinary speed.
That could be transformational.
Or it could create a more technologically sophisticated version of the same organizational clutter.
The difference will depend on whether leaders connect productivity to outcomes.
What are we trying to improve? What becomes possible because this work takes less time? Where should recovered capacity go? What work should disappear? What decisions should improve? What customer or financial result should change?
Those are business questions.
The technology comes afterward.
Maybe Faster Isn't the Finish Line
There is nothing wrong with celebrating efficiency.
If employees can eliminate hours of repetitive work, that's progress. If customers receive answers sooner, that's progress. If technology allows an organization to increase capacity without increasing cost at the same rate, that's progress too.
But speed is an incomplete measure of success.
A company can produce more and still make poor decisions. It can automate processes nobody needs. It can generate enormous quantities of information that nobody has time to absorb. It can save employee hours without ever deciding how those hours should create additional value.
It can become considerably faster without becoming considerably better.
So as organizations measure the productivity gains coming from AI, automation, process improvement, and new ways of working, perhaps the executive conversation needs one more question.
Don't stop at:
How much time did we save?
Ask:
What did that saved time allow the business to do better?
If the organization can't answer that question, the productivity story isn't finished yet.
