Why Generative AI Transformations Get Stuck—and How Leaders Can Break Through

Unsticking Your AI Transformation

Treating GenAI as a tool isn’t working. It’s time to redesign work, rethink operating models, and lead from the top.

The launch of ChatGPT in 2022 marked the beginning of a major shift in how work gets done. Yet, most companies remain stuck in the early stages of generative AI (GenAI) experimentation rather than achieving real transformation. Despite the clear potential of AI to reshape industries, fewer than 20% of enterprises have scaled their GenAI efforts in a meaningful way.

The main problem is that many organizations treat GenAI as just another technology to deploy, rather than a catalyst for business transformation. Unlike past technology waves, GenAI creates value by reimagining how work is performed and how companies compete. This requires redesigning business processes with AI at the center.

Instead, many fall into the “micro-productivity trap” — an abundance of isolated pilots that yield small efficiency gains but fail to scale. Tools get rolled out, demos impress, but real outcomes don’t follow. This isn’t just a missed opportunity; it’s a strategic risk as AI continues to evolve and the stakes rise across all sectors.

For companies already experimenting, the challenge is to reset and focus on outcomes rather than scattered activities. The leaders who break free from this trap commit to four key moves that drive real impact.

Top-down leadership

Strong leadership commitment is the starting point for successful AI transformation. Grassroots AI efforts spark innovation but rarely create enterprise-wide impact without clear direction from the C-suite. Leadership must set bold ambitions grounded in business strategy and make AI adoption a priority backed by ownership and accountability.

For example, Shopify’s CEO mandated AI integration into daily work, setting AI usage as a baseline expectation. Some companies embed AI objectives into performance reviews and bonuses, while others run large upskilling initiatives to build AI fluency across teams.

In the most successful organizations, executives actively drive the agenda, use AI themselves, and make adoption visible and outcome-focused. Leadership sponsorship is hands-on, not just symbolic.

Fewer, bigger bets

AI offers countless possibilities, but spreading efforts thin across dozens of pilots rarely leads to meaningful impact. The most effective companies focus on four to five high-value domains—clusters of interrelated use cases—and concentrate transformation efforts there.

These domains vary by industry but represent where competitive advantage will be won or lost. In technology, for example, it’s the software development lifecycle; in healthcare, areas like drug discovery and patient engagement; in retail, personalization and demand forecasting. These are not isolated tasks but entire systems of work that need coordinated redesign.

Take software development: it includes over 40 discrete use cases. Productivity gains come from transforming design, testing, code review, and planning—not just deploying copilots. Likewise, in B2B sales, real impact requires reworking the entire sales lifecycle rather than focusing on one isolated task.

Leaders do the hard work upfront: defining the right domains, setting top-down value targets, and building mechanisms to measure and scale transformation over time.

Process redesign from the ground up

Automation alone won’t deliver transformation. True GenAI impact demands rethinking work itself. This means zero-based process design—mapping current workflows and reimagining them with AI embedded from the start.

It’s not about adding AI tools on top of broken processes but rebuilding workflows with AI at the center. In many cases, process redesign—not the technology—creates the bulk of value.

A major bank’s example shows this clearly. After building a digital foundation with a comprehensive customer view, it redesigned customer engagement around AI-native workflows. Instead of pushing campaigns, teams trigger personalized interactions based on customer behavior, such as notifying customers about fee-free ATMs.

This approach reduced campaign turnaround from up to 100 days to just one day and cut staff requirements dramatically. The secret was a dedicated AI-powered tool that enables teams to quickly test, measure, and optimize customer engagement strategies. This kind of deep process reengineering separates marginal improvements from step-change performance.

An operating model that delivers transformation

Transformation is hard work. It requires strategic prioritization, future-focused goals, detailed process mapping, smart technology deployment, behavior change, and governance. While tools matter, technology and data foundations must support the transformation effort effectively.

Successful companies establish a transformation team to ensure transparency and adaptability. This team supports business-owned solution teams responsible for designing, testing, and scaling changes aligned with leadership goals. The model enables repeatable, coordinated, and sustained value creation.

Emerging leaders operate at two speeds: running the business and changing it. Business functions engage in both, focusing on six critical areas:

• End-to-end process: Reimagine work across silos to meet strategic and financial goals.
• Solution team mobilization and drumbeat: Ensure teams can test and scale solutions with clear processes to remove obstacles and allocate funds.
• Data infrastructure and governance: Target data investments on high-value areas and build capabilities for managing unstructured and synthetic data with strong governance.
• Scaling: Commit to scaling solutions quickly across relevant units like territories or customer segments.
• Adoption: Maintain feedback loops such as weekly adoption reports to support scaling and visibility.
• Business and technology partnership health: Increase transparency of platforms, opportunities for reuse, and appropriate governance across the organization.

This ongoing transformation approach must become a core feature of modern enterprises. As AI advances, companies will constantly balance running and transforming their businesses. Challenges like agentic workflows, managing AI-generated data, and leading hybrid human-AI teams require a permanent transformation capability.

From experimentation to enterprise transformation

AI is not just another technology update; it changes how value is created and work is performed. Most companies face the question: How do we compete in a world where everyone uses AI?

The winners won’t be those with the most pilots or biggest budgets, but those making strategic choices with operational discipline. One company reported doubling EBIT margins compared to competitors after a year of redesigning work with AI at the core.

Getting unstuck is critical. Companies that act decisively to integrate GenAI into their operations will reap real results. Others risk falling behind as AI becomes a baseline expectation.

To explore practical AI skills and training that can support your transformation journey, visit Complete AI Training’s latest courses.