How AI Creates New Value, Not Just Efficiency

A striking paradox defines the current state of artificial intelligence in business. While 88% of organizations now use AI in at least one function, according to McKinsey, a mere 6% report significant enterprise-wide impact. This gap is not a sign that AI adoption is failing. Rather, it reveals a fundamental misstep in how most companies deploy the technology.

Consider the early automobile. The first cars were essentially horse-drawn carriages with an engine bolted on. They used the same frame, the same seating arrangement, and traveled on the same roads. It took years before designers reimagined the chassis entirely. The technology arrived, but the thinking lagged behind. Only then could the car be truly reinvented.

History is repeating itself with AI. Companies are using the new tool to optimize existing tasks without rethinking the underlying business model. The same McKinsey study shows that only 23% of organizations using generative AI have redesigned their workflows to accommodate it. The rest are building very fast carriages, unaware that they are missing the opportunity to build something entirely new.

The real breakthrough from AI will not come from doing old work faster. It will come from discovering new ways to create value and generate revenue that did not exist before.

The Four Stages of AI Value

Peter Drucker drew a clear line between efficiency and effectiveness. Efficiency is “doing things right.” Effectiveness is “doing the right things.” Efficiency saves money by speeding up work and cutting costs for an existing pie. Effectiveness makes money by growing the entire pie. Both are essential, but they demand different organizational muscles.

The first two stages of AI value resemble factory work. They focus on scalability, predictability, and high performance. These are cost-driven, measurable, and easy to justify in a budget meeting.

The third and fourth stages are more like laboratory work. They are built for experimentation, agility, and flexibility. Here, companies test unproven journeys where the outcome is uncertain.

The factory mindset almost always wins in internal budgeting because efficiency gains are visible and quantifiable. The laboratory mindset struggles to get funding because its value is invisible until an experiment succeeds.

The Power of Experimentation

Tech entrepreneur Pieter Levels demonstrates the laboratory mindset in action. He believed the only way to know if a company would work was to ship it and see what happened. After many experiments, several of his projects now generate more than $250,000 per month combined.

IKEA offers a more corporate example. In 2021, the company deployed a chatbot named “Billie” to handle customer service. Billie resolved 47% of all customer inquiries, or 3.2 million interactions. Costs dropped immediately. That is a classic stage one outcome.

But Billie could not answer the remaining 53% of questions. Most companies would see this as a failure. IKEA saw it as an opportunity. Instead of firing the call center workers, the company reskilled 8,500 employees as remote interior design advisers. This created an entirely new sales channel.

The result? IKEA generated €1.3 billion in new revenue in 2022 from a channel that did not exist before the experiment.

Marketing vs. the Four Horsemen

Advertising executive Rory Sutherland diagnoses the problem in his essay “The 4 Corporate Enemies of Innovation.” Most large organizations are obsessed with cost-cutting and regulatory paranoia. Innovation takes a back seat.

Sutherland identifies finance, compliance, procurement, and HR as the “four horsemen of the bureaucratic apocalypse.” These departments are disproportionately punished when something goes wrong. As a result, they are disincentivized from trying anything new.

The mandate for experimentation should come from marketing, specifically marketing operations. Marketing ops sits at the intersection of data, technology, customer signals, and commercial outcomes. It can run experiments quickly and inexpensively.

In the IKEA example, the solution surfaced in a customer interaction log through experimentation, not in a boardroom. The people best equipped to read that log and act on it were in marketing.

How to Create AI Value for Your Company

If your organization has recently adopted AI, you are likely in stage one or two. You are using a factory mindset, focusing on efficiency, and pleasing shareholders with cost savings. This efficiency wave is a necessary precondition for going further.

Stage three and four AI value cannot be planned in advance. It must be discovered through deliberate, fast, and inexpensive experimentation. A rigid AI roadmap is not the answer. What you need is the muscle to experiment at volume, follow the right signals, and build something new when the opportunity appears.