Most companies feel stuck in generative AI experimentation. Real change comes from business redesign, not tool deployment.
Despite the potential of AI, most companies remain stuck in pilot mode. Fewer than 20 percent of enterprises have scaled their generative AI efforts in a meaningful way, according to Bain & Company’s quarterly survey.
We believe that’s because most organizations are treating generative AI as a tool rather than a business transformation.
Unlike past tech waves, generative AI doesn’t create value through adoption alone. ROI comes from rethinking how work gets done and how the business competes. And that takes something bigger: business redesign, with AI at the core.
For companies already deep in experimentation, the challenge isn’t getting started. It’s starting over. The ones breaking through are making four strategic moves to turn AI from a tool into a transformation engine.
1. Lead from the top
Grassroots innovation sparks ideas, but it rarely scales. True AI transformation starts in the C-suite. The companies pulling ahead have leaders who set clear ambitions, align AI with strategy, use the tools themselves, and make AI a visible priority.
Take Shopify. CEO Tobi Lütke made AI usage the default, requiring employees to justify why AI can’t perform a task before seeking additional resources. Other companies are linking AI adoption to incentives and launching companywide upskilling programs. In these firms, sponsorship isn’t performative. It’s active, intentional, and tied to outcomes.
2. Make fewer, bolder bets
The best AI strategies are targeted. Instead of running hundreds of isolated pilots, leaders focus on four or five domains that matter most and concentrate their efforts there.
Across industries, a handful of domains are emerging as places where competitive advantage will be won or lost. In tech, it’s software development. In healthcare, it is drug discovery, regulatory management, and patient engagement. In consumer products and retail, it’s personalization and content creation, as well as dynamic pricing and demand forecasting.
These aren’t standalone use cases but interconnected systems of work. In B2B sales, for instance, go-to-market can span dozens of micro-tasks. To unlock real value, companies must rewire the entire sales journey, not just one part of it.
The companies that win don’t guess. They do the challenging work up front: defining the right domains, setting a clear value thesis, and building the engines to measure, manage, and scale over time.
3. Redesign from the ground up
Transformation doesn’t come from layering AI on top of broken workflows. It comes from zero-based redesign, or rebuilding processes with AI at the center.
One major bank did just that. It stood up teams focused on improving engagement at key moments in the customer life cycle. Rather than pushing sales messages according to campaign calendars, these teams use intelligent “triggers” to deliver personalized, timely outreach, such as notifying customers who have been withdrawing cash from ATMs with fees of nearby fee-free ATMs. The teams use a purpose-built AI tool to mine insights, test ideas, and continuously improve. As a result, campaign turnaround shrank from months to a single day.
4. Build an operating model for transformation
Even with strong leadership, strategic prioritization, and detailed process mapping, transformation stalls without an operating model built for change. The companies making real progress have established a transformation team to facilitate ongoing transparency and adaptability. This small team supports business-owned solution teams that design, test, iterate, and scale changes across the organization.
Emerging leaders have two speeds – running the business and changing the business. Business functions play roles in both, with a focus on six critical areas.
- End-to-end process. Companies can cut across organizational silos to reimagine how key sources of value meet strategic and financial goals.
- Solution team mobilization and drumbeat. High-performing organizations equip their dedicated solution teams to test and scale. That includes clear steps to eliminate bottlenecks and accelerate funding.
- Data infrastructure and governance. Leaders focus on the data efforts and investments that add the most value. They build systems to manage unstructured and synthetic data, with strong governance that ensures quality, reusability, and business alignment.
- Scaling. Change doesn’t happen through pilots. Leading companies will scale proven solutions across the enterprise, whether by region, customer segment, or factory, quickly and effectively.
- Adoption. Sustained change depends on adoption. Companies can install feedback loops like weekly adoption reporting to support solution teams in scaling and visibility.
- Business and technology partnership health. Organizations can’t afford misalignment between tech and business. Leaders increase visibility of enabling platforms, reuse opportunities, and appropriate governance.
Exercising the muscles to evolve continuously, by running and changing the business, will be critical as companies integrate AI agents, manage new data types, and prepare for a hybrid workforce of humans, agents, and potentially robots.
From pilot to performance
The question is no longer how to use AI. It’s how to compete when everyone is using it. Those pulling ahead aren’t experimenting more. They are transforming boldly and intentionally.
For those still stuck, the path forward is clear. It requires more than tech. It demands strategic choices, combined with the operational rigor to follow through.
As Senior Partner at Bain & Company, Mohan Jayaraman is responsible for AI consulting practice across APAC for the Financial Services Sector and manages AI, ML, Data related client work across SEA and India across all sectors. He is a senior business executive with 27+ years of experience in data sciences, AI / ML / Gen AI, and digital capabilities in financial services and technology sectors across multiple APAC markets including India, Southeast Asia, Japan, South Korea, China and Australia, and has significant experience in building and scaling Gen AI / AI applications across industries.
Sarah Elk is a Partner at Bain & Company, where she leads the AI Solutions Practice in the Americas and leads Global Growth across Bain’s technology and AI solution offerings. She is the former global leader of the People and Organization practice. She is also a senior leader in the Energy and Natural Resources practice and serves as a member of Bain’s global governance committee for compensation and promotion.
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