The next competitive advantage in artificial intelligence (AI) will belong to companies willing to redesign how they operate, not simply those buying the newest tools.
Business leaders are pouring money into AI while retaining management structures, hiring practices, and workflows built for another era. That mismatch has consequences for every employee, customer, and investor. Technology can accelerate individual work, but only organizational change can turn that speed into business performance.
McKinsey’s 2026 State of AI research highlights the gap. Eighty percent of respondents reported that AI had improved their individual productivity, yet only 37 percent said it had contributed positively to their organization's earnings before interest and taxes (EBIT), a key financial metric.
Just six percent met McKinsey's definition of AI high performers.
These findings raise a question for business leaders: if employees can accomplish more, why isn't that progress translating into broader financial results?
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In my experience of building and transforming engineering organizations, the answer often starts with how a company makes decisions, assigns responsibility, and gets work across the finish line.
AI exposes these weaknesses because it increases the pace of execution without automatically improving the system around it. Consider what happens when a task that once took a month is suddenly expected in three days because AI is available.
The expectation may sound reasonable to a leader watching technology advance, but faster production does not guarantee sound decisions, reliable systems, or useful results.
AI can generate predictions and outputs. People still need to define the problem, evaluate the answers, and take responsibility for the consequences.
Yet many businesses respond to competitive pressure by demanding more from employees while leaving their operating models intact.
A Costly Mismatch
Teams face shorter deadlines, managers coordinate growing volumes of work, and executives introduce another tool without reconsidering the process it is supposed to improve.
This creates a costly mismatch. Employees may become more efficient at individual tasks, while approval processes remain slow, departments struggle to align their priorities, and responsibility becomes harder to coordinate across management layers.
The company produces more activity without necessarily creating more value.
The stakes extend beyond corporate balance sheets. Customers experience delays, employees absorb mounting pressure, and investors pay for technology that fails to deliver its intended return.
In an economy where competitors can move quickly, organizational friction becomes a strategic liability.
The answer is to redesign work before buying another solution. Start by identifying the constraint holding the business back. Is it a lack of technical expertise, slow approvals, fragmented teams, or repetitive work that consumes skilled employees’ time?
Each problem requires a different response, and another AI subscription will rarely solve all of them.
Then build an operating model around capabilities rather than headcount. Let AI handle suitable routine tasks, give people clear ownership of decisions, and measure results against business objectives. Keep human oversight where decisions carry financial, legal, security, or reputational consequences.
Structure and Accountability Matter
Leadership must change, too. Growing companies often assume they need a permanent executive whenever complexity increases. Sometimes they do. Other times, they need experienced leadership to navigate a specific transformation, restructure an engineering function, or establish an AI strategy.
Interim and fractional executives can provide that expertise while the company determines what its long-term organization should look like.
I have seen how much structure and accountability matter when teams must work across products, countries, and competing priorities.
The lesson is that adding people and technology without addressing the underlying operating model can increase complexity faster than capability.
The upside of getting this right is significant. Smaller teams can deliver more, employees can devote greater attention to creative and consequential work, and businesses can direct resources toward growth instead of unnecessary coordination.
Companies can become faster and more adaptable without treating every new challenge as a reason to expand the payroll.
An Expensive Cycle
The alternative is an expensive cycle: buy more technology, demand more output, preserve the same bottlenecks, and watch competitors turn organizational flexibility into a commercial advantage.
CEOs, founders, and private-equity leaders should act on that challenge today. Map the processes that slow decisions, question management layers that add little value, identify work AI can handle responsibly, and establish clear accountability for measurable outcomes.
Before approving the next technology investment or executive search, ask what problem the business actually needs to solve.
The companies that thrive will combine capable people, well-governed AI, and leadership that can turn strategy into execution. The goal is to build businesses that can turn speed into lasting value.
The AI era rewards leaders who redesign the business, not just the tools it uses.
Daniel Kirichanski is the founder of Prime Path Global, which advises companies on technology strategy, fractional engineering leadership and AI adoption. All views expressed in this article are his own.