How AI’s real power comes from creating new value

Most organizations use AI, but transformative, enterprise-wide impact remains elusive

AI is everywhere, but the real results are still limited. Implementing AI in one department or process is no longer an achievement, it’s a baseline. McKinsey reports that 88% of organizations use AI in at least one function, yet only 6% see significant impact across their entire operation. That figure speaks volumes. It’s not a problem of access or awareness; it’s a problem of strategic depth. Most companies use AI to optimize narrow tasks. Few evaluate how it redefines the business itself.

The real challenge is about redesigning how decisions are made, how customers are served, and how new value is generated. Treating AI as a support function will limit returns. Using it as a business amplifier, integrated into leadership, strategy, and design, transforms outcomes. For executives, the goal should shift from cost-cutting to value creation, from adding AI to operations to building operations around intelligent systems.

Speed alone won’t sustain advantage. The organizations that win will integrate AI into the core of their strategic models and decision frameworks. They will train leadership to think in systems. Efficiency is good; transformation is better. Executives must push AI beyond isolated use cases, that’s where the opportunity now lies.

The current phase of AI adoption mirrors early automotive innovation

Businesses are moving fast with AI but often following old patterns. Many executives are applying advanced technology to outdated ways of working. The technology is ready, but the structural thinking around it hasn’t evolved at the same pace. Most corporate experiments with AI focus on faster execution. According to McKinsey, only 23% of companies using generative AI have redesigned their workflows to fit the new technology.

That’s a critical gap because the biggest opportunities don’t come from doing familiar tasks more efficiently. They come from reframing what the business itself can be. Generative AI can redesign workflows, eliminate entire layers of repetitive process, and expand the boundary of what an organization can deliver. The question for leaders is not “How can we use AI to improve what we do?” but “What would this company look like if we built it around AI from day one?”

Executives should see this as a leadership challenge. Restructuring workflows around AI means rethinking accountability, performance measures, and even talent structures. It means putting adaptability at the center of corporate design. Leaders who move first will capture greater long-term advantage because they’ll not only be efficient, they’ll be relevant in a new era defined by intelligence and speed.

The true opportunity in AI lies in creating new value rather than solely enhancing efficiency

The companies that stand out in the next decade will be those using AI not just to save time or money but to create entirely new value. Efficiency matters; it drives margins and improves execution. But efficiency alone doesn’t build the future. Peter Drucker’s principle remains relevant, effectiveness, or doing the right things, is where real growth begins. AI gives businesses a unique tool to explore what those “right things” are and to turn insight into new products, services, and revenue streams.

Executives need to reframe their approach. Instead of asking where AI can cut costs, they should ask where it can open new market opportunities. AI is an accelerator for discovery and reinvention, capable of turning data into decisions, patterns into strategies, and insights into action. Viewing AI through a creative lens, rather than a purely operational one, positions a company to lead rather than follow.

The nuance is that playing it safe is the biggest risk right now. Competitive edge comes from experimentation and scale. Organizations that focus only on automation risk being outpaced by those using AI to redesign customer experience, redefine products, and build entirely new ecosystems around intelligence-driven value. The goal isn’t to be the most efficient, it’s to be the most adaptive.

A balanced approach between the “factory mindset” and the “laboratory mindset” is essential to harness AI fully

AI delivers both measurable improvement and unpredictable innovation. The “factory” side demands structure, consistency, and clear metrics. It focuses on control, quality, and performance. The “laboratory” side thrives on flexibility, curiosity, and learning through experimentation. Companies need both. One ensures stability; the other drives advancement. Many organizations lean too heavily on the first, chasing safe, predictable returns while missing opportunities to push boundaries.

For executives, managing this balance is about mindset and governance. It requires allocating resources to maintain core performance while funding exploratory projects designed to find new opportunities. Leadership must be comfortable with temporary uncertainty, many experiments will fail, but the insights gained from them often shape the next wave of breakthroughs. The discipline to measure results must coexist with the willingness to explore without guaranteed outcomes.

Long-term success in AI is not achieved by optimizing what already exists but by continuously experimenting with what could exist next. Combining operational discipline with a culture that supports testing and learning allows organizations to remain both efficient in execution and innovative in direction. The leaders who understand how to sustain this duality will set the standard in the coming AI-driven era.

Experimentation with AI catalyzes breakthrough innovations and opens new revenue channels

Experimentation is the real engine behind AI-driven success. When organizations prioritize experimentation, they gain knowledge faster and uncover opportunities others overlook. Pieter Levels, a tech entrepreneur, demonstrates this well through his continuous, small-scale testing of new ventures, an approach that has resulted in several projects generating more than $250,000 in monthly revenue. This is about systematic exploration, rapid iteration, and learning from every outcome.

IKEA followed a similar path, using data and observation to turn a service limitation into a growth opportunity. In 2021, it launched “Billie,” a chatbot designed to handle customer inquiries. The system resolved 47% of 3.2 million requests. The remaining 53% presented questions it couldn’t handle, which many companies might have considered a failure. Instead, IKEA recognized a new path forward. It retrained 8,500 call center staff as remote interior design advisers, creating a completely new service category. This move generated €1.3 billion in new revenue in 2022, all from an experiment that began with customer support automation.

The nuance for executives is simple: experimentation isn’t a byproduct of progress; it’s the driver. Failure during experimentation is part of the value. Each iteration delivers insight, even when results aren’t immediately profitable. The leaders who institutionalize experimentation turn uncertainty into data and data into direction. In fast-moving fields like AI, these organizations advance faster because their learning cycles are shorter.

Executives should view experimentation as a measurable investment. Structured pilots, rapid prototyping, and open feedback loops create a framework where new ideas can evolve into scalable innovations. Those willing to experiment relentlessly will not only capture early value but will define entirely new business models in the process.

Key highlights

• AI adoption without transformation limits impact: Most companies use AI somewhere in their operations, but few see real enterprise-wide results. Leaders should shift from isolated deployments to full business redesigns that embed AI into strategy and structure.
• Technology is ahead of organizational redesign: Few companies have restructured workflows for AI, leaving much of its potential untapped. Executives must rebuild processes around AI capabilities to stay competitive and scalable.
• Creating new value matters more than chasing efficiency: Efficiency improves margins, but true growth comes from using AI to discover entirely new sources of value. Leaders should focus on innovation-driven uses of AI to unlock fresh revenue streams.
• Balance structure with experimentation for sustained growth: Measured, process-driven execution must coexist with agile experimentation. Decision-makers need to maintain stable operations while actively investing in AI-driven innovation to fuel long-term advantage.
• Experimentation drives new business opportunities: Continuous testing leads to major breakthroughs, as shown by IKEA’s chatbot initiative turning a support task into €1.3 billion in new revenue. Executives should view experimentation as a strategic investment that expands value creation and accelerates learning.