AI has become a fixture in corporate strategy. Companies are investing in new tools, employees are experimenting with AI assistants, and executives are feeling the pressure to demonstrate progress with AI.
But there’s a question every CEO should be asking: Is any of this making the business perform better?
According to McKinsey’s 2026 research, nearly nine in ten organizations now use AI regularly in at least one business function. Around 80% report improvements in individual productivity, yet only 37% say AI has positively affected operating profits.
It’s a striking gap. Companies have become good at getting people to use AI, but translating that activity into financial and operational gains is proving much harder.
Thousands of AI licenses, successful pilots, and enthusiastic employees might look encouraging in a board presentation. But those numbers mean little if the investments aren’t improving customer experiences, reducing costs, accelerating delivery, or driving revenue.
The problem with AI for AI’s sake
Experimentation is essential when adopting new technology. Businesses need opportunities to test ideas, understand capabilities, and identify potential applications. The trouble starts when experimentation becomes an end in itself.
Too often, organizations begin with the technology rather than the problem they’re trying to solve. A new AI model launches, a competitor announces an ambitious initiative, or a vendor demonstrates an impressive capability. Suddenly, there’s pressure to find somewhere to deploy it.
The result can be a growing number of pilots that generate plenty of excitement but little measurable value.
Consider Air Canada, which was held liable after its customer service chatbot provided incorrect information about the airline’s bereavement policy. Meanwhile, US-based enterprise software company Retool reported that an experimental AI out-of-office responder unexpectedly began generating approximately $10,000 a day in processing costs.
These are very different examples, but both illustrate what can go wrong when AI is introduced without sufficient oversight. The technology must work reliably, deliver a useful outcome, and justify the cost of running it. Executives should expect nothing less.
Start with what’s broken, not what’s new
To avoid investing in AI for its own sake, businesses need to start with the problem, not the technology.
Perhaps customer service teams are struggling with lengthy resolution times. Maybe software releases are being delayed by repetitive manual work, or employees are spending hours moving information between disconnected systems.
These are the kinds of issues where AI has the potential to make a measurable difference.
Before committing resources, leadership teams should establish a clear picture of how the existing process performs, including how much time it takes, what it costs, where errors occur, and how it affects customers or employees.
With that baseline established, it’s much easier to define what a successful outcome should look like.
For a customer service operation, that might mean reducing resolution times without compromising accuracy or satisfaction. For an engineering team, it could mean accelerating software releases while maintaining quality. In cybersecurity, it might involve detecting and responding to threats more quickly.
The next step is to test whether AI can deliver those improvements, taking into account the costs, risks, and operational complexity involved.
If the results justify further investment, the business can consider scaling. If they don’t, leaders should be prepared to redesign the approach or walk away.
Not every promising experiment deserves a permanent place in the business.
When faster work doesn’t mean better business
One of AI’s most compelling promises is its ability to help employees work faster, and there’s genuine value in that. AI can summarize complex information, analyze documents, assist with coding, and take repetitive tasks off employees’ hands. But those individual time savings don’t always translate into better business performance.
Imagine an employee who previously spent an hour preparing a report can now complete it in ten minutes. That’s an impressive efficiency gain. But if the report then sits in a manager’s inbox for two days awaiting approval, the overall process hasn’t improved much. The employee is faster, but the business isn’t necessarily delivering results any sooner.
Yet many organizations continue to judge AI success by how widely tools are being used. They track how many employees have activated a tool, how frequently it’s being used, or how many tasks it completes.
Those metrics can help assess adoption, but they tell executives very little about whether the investment is paying off.
A customer spending 20 minutes interacting with an AI chatbot might appear highly engaged. But if that customer is repeatedly explaining the same issue before eventually calling a human representative, the technology may be creating frustration rather than resolving it.
A more useful approach is to measure what has actually changed, from the time it takes to complete a process and the number of errors made, to improvements in customer service and the impact on costs or revenue.
Fix the workflow, not just the task
Measuring improvements is only part of the challenge. To realize the full benefits of AI, businesses need to look at the entire workflow, rather than focusing on individual tasks.
That means understanding how work moves through the organization: where a process begins, how information passes between teams, which decisions require human judgment, and where delays or opportunities for automation exist.
This often reveals that the biggest barriers to performance aren’t necessarily technological. Fragmented systems, inconsistent data, unclear responsibilities, and outdated approval processes can all prevent organizations from realizing the benefits of AI.
A tool might perform exceptionally well in a controlled pilot, using clean data and a small group of enthusiastic employees. But scaling it across a large organization introduces an entirely different set of challenges.
In practice, that same tool has to work with incomplete information, meet security requirements, handle unexpected scenarios, and support thousands of users.
Some of the risks are also harder to spot. A system drawing on inconsistent company data might produce an answer that sounds authoritative but is factually incorrect. A model that performs reliably during testing may behave differently as the underlying technology changes.
That’s why it’s important to treat AI like production software from day one. It needs reliable data, appropriate governance, continuous monitoring, and clear accountability. Businesses must be able to spot problems early and intervene when something goes wrong.
The questions every CEO should be asking
For CEOs, the question is no longer simply whether to invest in AI, but where it can make the greatest difference.
That calls for a closer look at what’s happening beyond the adoption figures. Are productivity improvements translating into measurable gains? Can successful pilots deliver the same results at scale?
Leaders also need to be comfortable calling time on initiatives that aren’t delivering. AI has enormous potential, but that doesn’t mean every application is worth pursuing.
AI investment is only going to increase, but the focus should be on delivering meaningful returns rather than deploying AI for its own sake. AI doesn’t need to be everywhere in an organization; it just needs to make a difference where it’s used.
Written by Divya Sampath.
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