AI Makes It Easier Than Ever to Produce More Work. But Are We Creating More Value?

By Leah Clelland Jochim | Convergence Technology Solutions & L&T Ventures

AI is dramatically increasing how much work we can produce.

But that raises a harder question:

Are we producing more value — or just more output?

Studies suggest AI coding assistants can increase developer productivity by 20–55%, depending on the task. In controlled environments, generative AI has increased code output by 50% or more.

More broadly, economists estimate that AI adoption could double the long-term growth rate of labor productivity in the United States.

However the numbers ultimately settle, one thing is clear:

We are entering an era where it is dramatically easier to produce more output.

More code. More documents. More prototypes. More experiments. More artifacts of work.

And yet one question remains:

So what?

Because activity is not the same as impact.

The Output vs. Outcomes Problem

In many organizations, productivity is still measured by how much work gets completed.

Agile frameworks like Scrum track velocity, stories delivered, and sprint commitments. These signals can reflect effort and consistency. They can indicate that a team is executing well.

But none of it matters unless it translates into meaningful outcomes.

Teams that deliver 100 features are often perceived as outperforming teams that deliver 20.

Yet if those features go unused, fail to solve a real problem, or do not move the business forward, the count itself is irrelevant.

Activity alone does not create value.

As AI accelerates our ability to generate output, this distinction becomes even more important.

Measuring What Actually Matters

Which raises a critical leadership question:

Are you clear on how you measure value? And are you confident it is the right measure?

John Doerr's Measure What Matters helped popularize Objectives and Key Results (OKRs) as a framework for keeping organizations focused on outcomes rather than activity.

At their best, OKRs create clarity in four important ways:

Focus and commit to priorities Teams identify what truly matters rather than trying to pursue everything at once.

Align and connect teams around shared outcomes Work across the organization begins to move in the same direction.

Track progress with accountability Leaders and teams can see whether efforts are actually moving the needle.

Stretch toward meaningful achievements Ambitious goals encourage innovation and better thinking.

Larry Page, Alphabet CEO and Google cofounder, described OKRs as:

"A simple process that helps drive varied organizations forward… giving visibility and a productive way to push back."

When priorities and outcomes are visible, teams gain the ability to challenge work that creates activity without creating value.

Technology Amplifies Effort — It Doesn't Replace Clarity

Much of the current conversation around AI focuses on capability.

What tools can generate. How quickly we can build. How many artifacts can be produced in a fraction of the time.

These capabilities are powerful.

But focusing solely on output risks missing the point.

Technology amplifies effort. It does not replace clarity.

I am a strong advocate for getting started and learning through doing. Real-world experimentation matters. You rarely understand a problem completely until you begin working through it.

At the same time, experience reinforces the importance of grounding experimentation in fundamentals.

Before building, ask yourself:

• What outcomes actually matter?
• What are the possible paths to achieving them?
• How will we know if we have succeeded?

As Alan Lakein famously said:

"Failing to plan is planning to fail."

Planning does not mean rigid documents or inflexible roadmaps. It means pausing long enough to gain clarity about the outcome you are pursuing and why it matters.

Five Questions That Bring Clarity

One practical way to refine a problem is to ensure you can clearly answer five critical questions:

1. Who is this for?
2. What is it for?
3. Why does it matter?
4. What makes it meaningfully different or better than what already exists?
5. What will success actually accomplish?

These questions sound simple. In practice, they often require deep thinking and honest discussion.

But once those answers come into focus, the path forward becomes much clearer.

Ideas can be evaluated more thoughtfully. Strategies begin to emerge. Priorities start to sort themselves out.

A roadmap built on this clarity connects execution directly to outcomes.

Execution Still Matters

This is where modern tools — including AI — truly shine.

They allow teams to move faster, test ideas earlier, and deliver quality outcomes with greater efficiency.

Used well, they accelerate progress toward meaningful goals.

But they are still tools.

The responsibility for direction, judgment, and prioritization remains human.

Dwight D. Eisenhower captured this balance well:

"Plans are worthless, but planning is everything."

Planning forces clarity. It surfaces assumptions. It aligns teams around purpose. It prepares organizations to adapt as reality unfolds.

The Leadership Challenge in the Age of AI

In the coming years, the gap between output and outcomes may become one of the most important leadership challenges.

AI will make it increasingly easy to generate work — more features, more analysis, more prototypes, more artifacts.

But organizations do not succeed because they produce more work. They succeed because they produce meaningful outcomes.

The leaders who create the most impact will not simply adopt powerful tools. They will maintain clarity on three things:

1. The outcomes that actually matter
2. The strategies most likely to achieve them
3. The metrics that reveal real progress

AI can accelerate execution. But judgment, prioritization, and clarity of purpose remain fundamentally human responsibilities.

Guardrails for Meaningful Progress

Metrics that matter act as guardrails.

They help ensure that efforts remain aligned with the outcomes that truly matter. Reporting at a thoughtful and realistic cadence allows leaders and teams to see whether they are on track or whether adjustments are needed.

Sometimes the right decision is to continue and scale. Sometimes it is to refine. And sometimes the most responsible decision is to stop altogether and redirect the learning toward a better opportunity.

That too is progress.

Final Thought

In an era where technology makes it easier than ever to produce more work, the real differentiator will not be how much we create.

It will be how clearly we understand the outcomes we are pursuing — and how thoughtfully we align our efforts to achieve them.

Real accomplishment has never been about activity alone. It has always been about purpose, judgment, and the discipline to focus on what truly matters.

I'd genuinely love to hear what you're seeing — especially if you're navigating this in a high-stakes environment where outcomes matter more than ever.

Leah Clelland Jochim is Co-Founder & Partner at Convergence Technology Solutions & L&T Ventures, an AI and technology transformation advisory firm. She advises C-suite executives on AI strategy, operating model design, and outcome-driven product leadership. Connect with her at [email protected].