You’re Not Behind On AI—You’re Behind On Execution

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AI theater may be the most expensive production in business today. The sets are elaborate, built on enterprise licenses, custom agents, innovation labs and pilot programs.

The audience—boards, investors and employees—can see activity, but what's missing is the measurable business value to justify the performance, with BCG reporting that 60% of companies have seen minimal revenue from AI despite substantial investment.

AI theater is what happens when organizations mistake AI activity for business transformation.

The Pattern I Keep Seeing

Walk into almost any enterprise right now, and you will find AI theater playing in the form of an AI budget sitting in a spreadsheet, a task force meeting regularly or a pilot running in one corner of the business that no one quite knows how to scale.

Many companies have AI budgets but no AI strategy. The bigger problem, however, is that most companies are trying to fix the wrong issue. It's easy to buy tools and software, but actually changing how things get done is hard.

We faced this dilemma at Basware. And we realized that it's not about asking “What can AI do?” but “Where can AI deliver measurable value?” And we overhauled our strategy to stop treating AI as a science project and started treating it as an operational capability.

Here are three fundamental lessons we learned:

1. Don't try to transform everything at once.

Trying to boil the ocean with AI rarely works. Organizations that spread AI across dozens of disconnected initiatives often end up with plenty of activity but little measurable impact. We found it far more effective to focus on one workflow, prove its value and build from there.

The best place to start isn't necessarily the most exciting use case, but the one where success can be measured quickly. To find it, ask the following three questions:

• Is the process high volume and repeated frequently?

• Can we define clear business outcomes, such as reducing costs, shortening cycle times or improving accuracy?

• Can improvements be measured in months rather than years?

Particularly strong starting points are structured, transaction-heavy and already measured against operational and financial KPIs.

One thing I learned is that AI creates the most value when it becomes part of the way work gets done, not when it sits alongside existing processes as an optional tool. Teams need to embed AI into production workflows, establish it as the default way of operating in a focused area and demonstrate measurable improvements before expanding further.

2. Measure business KPIs, not AI KPIs.

AI doesn't need its own scorecard. It should improve the scorecard the business already uses. The number of licenses deployed is not a metric, and neither are the number of pilots completed, workshops run or task force meetings attended. Those are measures of activity, not value.

To measure value, start by defining the business outcome you want to improve and establish a baseline before AI is introduced. For us, the real focus needed to be how much faster we were able to close, how many support tickets we could resolve without escalation, how much more product shipped and how much customer satisfaction has improved.

The key is to isolate the outcome you're trying to influence and measure it consistently over time. AI should improve a business KPI that already matters to the organization, rather than creating a new set of AI-specific metrics.

3. Tie AI to problems people already have.

Google spent years and billions building Google Glass. The technology worked, but the use case didn't. No one could answer the simple question: What problem does this actually solve for real people in real situations? Glass became the most famous expensive pilot that never reached production.

Many enterprise AI programs may be heading the same way.

It was important for us not to create new AI projects, but embed AI into existing pain points. Finance teams were drowning in manual invoice work, support teams were handling the same queries repeatedly, and product teams experienced development bottlenecks.

By putting AI into those places, adoption followed, not because it was mandated, but because it addressed these issues head-on and made people’s lives and jobs easier.

Why This Is The Right Moment

Most companies are still in test mode. Building tools. Running pilots. Forming committees.

Meanwhile, their competitors are actually capturing value. The flipside of the 60% who are not seeing financial impact is that 40% of companies are.

The difference? They combine AI budget with buy-in.

Many companies have the investments approved and the tools in place, but the execution plans are missing, leaving them with no clear path to achieving value. Often, success comes from being the ones who executed first rather than the ones who experimented longest.

The Honest Part

We are not special. We just stopped treating AI as a moonshot and started treating it as an operational capability.

AI won't transform your business by itself. But if you embed it into the right workflows, measure real outcomes and move from pilots to production, you'll be the CFO who can actually answer the ROI question.

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