Implementing AI: From experimentation to real transformation

Over the past few years, generative AI has surged onto the business agenda, promising to fundamentally reshape the way we work and compete. Yet, the reality is less revolutionary than the headlines suggest: most companies remain stuck in experimentation, running pilots and isolated use cases that rarely scale. Jordi Ciuró, partner at Bain & Company, shares three lessons from successful pioneers.

In fact, fewer than 20% of organizations have managed to achieve meaningful impact at scale through their generative AI initiatives.

This stagnation is not the result of underinvestment in technology, but rather of a flawed mindset. As Bain & Company’s ‘Unsticking Your AI Transformation’ report points out, too many companies treat AI purely as a technology deployment rather than as a business transformation. The distinction is crucial: the former produces flashy but shallow trials, while the latter redefines how firms compete.

The leaders pulling ahead recognize that AI is not just another tool, it is a catalyst for deep reinvention. These pioneers share three defining characteristics.

What pioneers are doing right

First, transformation starts at the top. C-suite leaders must embed AI into the company’s strategy and set clear expectations for its adoption. Consider Shopify: CEO Tobi Lütke established the use of AI as a minimum expectation for all employees, sending a clear message of strategic commitment.

Second, they concentrate on a few high-value domains. Instead of scattering resources across dozens of disconnected initiatives, successful companies double down on the areas that matter most to their industry. In technology, that may be the software development lifecycle; in healthcare, drug discovery and regulatory management; in retail, personalization, content creation, and demand forecasting.

These are not isolated use cases, but full systems of work where AI can deliver substantial impact.

Third, they redesign processes from the ground up. Adding AI to outdated workflows only patches inefficiencies. By contrast, companies that rebuild their processes with AI at the core see transformative results. Take the case of a tech company that reimagined its marketing and content workflows. By embedding AI-native processes end-to-end, it reduced campaign design and deployment cycles from 60+ days to less than one, while simultaneously cutting FTE effort by more than 80%.

The result: leaner operations, fewer handoffs, and dramatically faster speed-to-market with consistently higher-quality output.

The difference between marginal gains and exponential improvements lies in the willingness to rethink how work gets done. To sustain that shift, organizations need an operating model that supports long-term transformation. That includes transformation teams that provide ongoing support, clear metrics for scaling solutions, strong data governance, and the discipline to balance “running the business” with “transforming the business.”

Ultimately, the true measure of AI transformation is not the number of pilots launched but the ability to reimagine how value is created in the domains that will shape future competitiveness. It is not about piling on more use cases, it is about redesigning entire processes, embedding AI in corporate strategy, and scaling results with discipline.

Companies that act decisively now – those willing to go beyond experimentation and commit to genuine reinvention – will not only turn AI’s promise into tangible impact but also set the rules of the game in markets where speed and depth of transformation will define success.