Beyond Doing More with Less? Rethinking Work in the Age of AI
It’s budget season.
Which means familiar conversations are happening in conference rooms everywhere:
Where can we cut?
What can we postpone?
Do we really need to replace that position?
How do we improve margins?
How do we get more productivity from the resources we already have?
For the past several years, the answer has often been some version of:
Do more with less.
But AI is changing that conversation.
The question is no longer simply:
How do we do more with less?
It is becoming:
What could we accomplish differently if AI changes what our people are capable of doing?
That is a much bigger question.
And it belongs in the budget conversation.
AI Is Not Just Another Technology Investment
It would be easy to approach AI the way organizations have approached other technology investments.
Buy the tools.
Train people how to use them.
Look for efficiencies.
Reduce costs.
Some of that will happen.
But the bigger opportunity is not simply doing today’s work faster.
It’s reconsidering the work itself.
Research from the Federal Reserve Bank of Atlanta based on nearly 750 executives found that more than half of companies surveyed had already invested in AI. Productivity gains were positive and expected to strengthen during 2026, but the researchers also found something important: the gains were uneven, and AI was beginning to shift the composition of jobs rather than simply eliminate work.
Similarly, S&P Global found that organizations are prioritizing process efficiency and employee productivity in their AI investments far more frequently than headcount reduction. Yet only 46% of AI initiatives launched during the previous year were considered on track to achieve positive ROI within 12 months.
That gap should get every executive team’s attention.
Buying AI is relatively easy. Changing how your organization works because of AI is much harder.
And that is largely a leadership challenge.
“Doing More With Less” Needs a New Definition
Too often, doing more with less has meant asking fewer people to carry more work.
That isn’t transformation.
That’s workload redistribution.
AI gives organizations the opportunity to define “less” differently:
- Less low-value work.
- Less administrative friction.
- Less duplication.
- Less time searching for information.
- Less unnecessary complexity.
- Less work that exists simply because “we’ve always done it this way.”
And then define “more” differently too:
- More time with customers.
- More innovation.
- More thoughtful decision-making.
- More problem solving.
- More coaching and development.
- More attention to strategic priorities.
- More value created by every person in the organization.
That’s a fundamentally different mindset.
It is also consistent with something we’ve argued before: doing more with less isn’t a strategy. The better question is how organizations create more value through smarter choices.
AI makes that question much more interesting.
Don’t Start With Headcount. Start With Work.
Here’s where budgeting conversations can go sideways.
If leadership assumes that AI equals efficiency and efficiency equals fewer people, the organization may race toward the wrong conclusion.
Instead, look at the work.
Ask:
What work should disappear?
What activities exist because of outdated processes, manual handoffs, reporting requirements, or organizational habits that AI could substantially reduce?
What work should become dramatically easier?
Where could AI accelerate research, analysis, documentation, communication, knowledge sharing, or decision support?
What work becomes more valuable?
If AI handles more routine activity, what becomes more important for humans? Judgment? Customer relationships? Creativity? Coaching? Complex problem solving? Decision-making?
What new work becomes possible?
Could your team analyze information it couldn’t reasonably process previously? Serve customers differently? Develop offerings faster? Personalize experiences at greater scale?
Those questions produce a very different workforce strategy than simply asking:
How many people can we eliminate?
This Is Where Talent Management Has to Change
If work changes, talent strategy has to change with it.
The traditional model has largely been:
Job → skills → person.
Increasingly, organizations may need to think:
Business challenge → work required → human + AI capabilities → talent.
That means talent leaders should be sitting alongside finance, strategy, operations, and technology during planning discussions.
Not after decisions have been made.
During them.
Harvard Business Impact’s 2026 Global Leadership Study found that 50% of organizations were prioritizing the adoption or expansion of AI-based talent management and internal mobility.
That’s significant.
Because AI doesn’t just change how work gets done. It creates the opportunity to rethink who does it, which capabilities matter, where talent should move, and what people need to learn next.
An effective talent strategy should therefore help the business answer:
- Which roles are likely to change most?
- Which capabilities become more valuable?
- Which skills will become less differentiating?
- Where can existing employees be redeployed rather than replaced?
- Which people can grow into entirely new responsibilities?
- Where should development dollars be concentrated?
- Which positions remain critical to succession and business continuity?
Talent management shifts from managing jobs to building organizational capability.
Leadership Development Has to Change Too
Then comes the leadership question.
If AI changes work, managers can’t simply supervise the new version of the old organization.
They have to lead the transition to the new one.
And that requires different muscles.
Leaders will need to become much better at:
Challenging how work gets done
Instead of asking, “How do we get this all done?” leaders need to ask, “Why are we doing all of this in the first place?”
Leading through ambiguity
Many organizations don’t yet know exactly how AI will affect every role. Leaders need to provide clarity without pretending they have certainty.
During disruption, leaders need to separate what is known from what is being assumed and help people identify the next useful move.
Exercising judgment
AI can surface options, summarize information, generate alternatives, and accelerate analysis.
It doesn’t eliminate the need for judgment.
It raises its value.
In fact, 53% of respondents in Harvard Business Impact’s study expected leaders to make greater use of AI in strategic decision-making during 2026.
That means leaders need to understand when to trust AI, when to question it, and when human context matters more.
Developing people for what’s next
Managers will increasingly have to help employees answer an uncomfortable but necessary question:
If AI takes some of what I do today, what should I become better at tomorrow?
That conversation could become one of the defining leadership responsibilities of the next several years.
Don’t Cut the Very Capability You Need to Make AI Work
This creates an interesting budgeting paradox.
At exactly the moment organizations are looking for efficiencies, they may be tempted to reduce investment in leadership development and talent development.
But those capabilities may be precisely what determines whether the AI investment pays off.
Leadership development doesn’t exist off to the side of business performance. Leadership capability affects decision speed, retention, execution, productivity, adoption of change, and whether strategy actually gets implemented.
The goal isn’t leadership development for its own sake. It is building the clarity, alignment, trust, accountability, and action required for people to produce results.
That distinction matters enormously in an AI environment.
Because companies don’t need more leadership programs.
They need leaders capable of redesigning work, developing talent, making better decisions, creating clarity during uncertainty, and helping people adapt faster.
That’s an investment in business performance.
The Better Budget Conversation
So perhaps the budgeting conversation needs another update.
Instead of:
Where can we spend less?
Ask:
Where can we create more value?
Instead of:
How much headcount do we need?
Ask:
What combination of people, skills, leadership and AI will produce the outcomes we need?
Instead of:
Which development programs can we afford?
Ask:
Which leadership capabilities are essential to executing our strategy?
And instead of:
How can our people do more?
Ask:
How can we eliminate low-value work so our people can do more of what matters?
That’s a much more useful version of “doing more with less.”
And arguably a much bigger opportunity.
AI may be one of the most powerful productivity tools organizations have ever been given.
But technology alone won’t determine whether an organization captures that value.
People still have to rethink the work.
Leaders still have to make the choices.
Teams still have to change how they operate.
And organizations still have to turn all of that potential into performance.
The winners won’t simply be the companies that spend the most on AI.
They’ll be the ones that develop the leadership and talent needed to turn AI into momentum.
About Coaching Right Now
At Coaching Right Now, we help organizations solve business challenges through their people. That means developing the leadership capability, talent, alignment, and momentum necessary to navigate change and strengthen execution for stronger business performance.
