To Maximize the Value of AI, Invest in Your People
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Authored by Julie Bedard, Jessica Beidelman, Elizabeth Lyle, and Reinhard Messenböck
Julie Bedard
Managing Director & Partner; Member of the BCG Institute, Boston
Jessica Beidelman
Associate Director
Brooklyn
Elizabeth Lyle
Managing Director & Partner, Boston
Reinhard Messenböck
Managing Director & Senior Partner, Leader of the Business Transformation Suite, Berlin
Key Points
Transforming how people work and focusing on high-value areas serves as the bridge between adopting AI and achieving a competitive advantage. To accomplish this, organizations must guide their employees through a learning process—ranging from fundamentals to practical application and deep integration—that fosters lasting behavioral change. Beyond leaders establishing a shared vision and modeling the desired behaviors, this transformation requires actions such as:
Starting from a realistic baseline by taking into account existing AI capabilities.
Designing role-specific learning paths, rather than applying a “one-size-fits-all” approach.
Fostering employee confidence and motivation through empathy and transparency.
Measuring success based on the value generated, rather than solely on the tools adopted.
AI is everywhere, offering revolutionary potential for the world of work. However, the transformation required to realize this potential is progressing slowly. Although most organizations have adopted AI tools, few have achieved significant changes in how their employees work—changes that would unlock the highest-value areas: the parts of the business where AI generates the greatest and fastest returns.
A recent BCG study reveals that while most companies are experimenting with AI, only about 5% are generating value at scale; in fact, nearly 60% report little to no impact to date. Faced with this gap, many organizations choose to ramp up the adoption of more AI tools, rather than developing the human capabilities needed to translate that adoption into real business value.
Traditional training is insufficient given the scale and urgency demanded by the era of generative AI. The most successful companies are building comprehensive, enterprise-wide training systems that integrate AI into how people think, work, and lead. Furthermore, their efforts are focused on the areas of the business where the transformative potential of AI-augmented work is greatest.
Capturing the full value potential of AI requires a qualitative shift in an organization’s capabilities—a blend of technical mastery and human skills, supported by behaviors, mindsets, and systems that enable people to work differently—and more effectively—with AI.
BCG U helps organizations develop the capabilities their workforce needs to thrive in the age of AI.
We have guided executives through complex, AI-related mindset shifts, helped global organizations build AI competencies at scale, and designed hands-on labs and behavioral interventions—integrated into daily workflows—that accelerate the use of generative AI (GenAI) to transform how work gets done, ultimately translating into business value.
Based on these experiences, we have observed that capability building only generates a real impact when organizations go beyond traditional training programs. What works best is a three-stage learning progression that achieves lasting behavioral change:
Foundations. Acquiring new knowledge through key concepts, frameworks, terminology, and “aha!” moments.
Application. Transforming knowing into doing through on-the-job practice, directly linked to actual workflows.
Integration. Turning doing into a habit by embedding new practices into ways of working, role expectations, support structures, and incentives.
Traditional training fails to generate impact because it stops at the first stage.
However, completing all three stages is a complex, resource-intensive process. Therefore, organizations should prioritize capability building in business areas where the potential value of AI—once realized—can deliver the highest return on investment.
In this article, we offer insights on how to build AI capabilities within the workplace, as well as lessons learned from helping our clients navigate the journey from experimenting with AI to achieving tangible results.
Establishing AI capability building within a clear context
To achieve a real impact when building AI capabilities, it is essential to clearly define the context of the initiative by identifying areas with high value potential and redesigning the corresponding workflows. Furthermore, the organization must assess its current AI capabilities to establish a realistic starting point. Most organizations—and even the teams and workflows within them—are at different stages of AI maturity, often spanning more than one stage simultaneously. (See Appendix 1). In practice, determining a starting point means understanding which phase of its AI journey the organization is currently in. The next step is to define where you want to be, whether at the team level or in terms of workflows. Only then is it possible to develop the specific capabilities needed to move up the curve and tap into the sources of value with the greatest potential.
From Adoption to Advantage: Developing AI Capabilities | Appendix 1
We collaborated with a leading European retail bank as it reinvented its credit operations, integrating generative AI (GenAI) into end-to-end workflows to enhance both efficiency and the customer experience. The organization radically streamlined its core credit processes and implemented an “operational AI agent” to automate document validation, plausibility checks, and data transfers.
The results were transformative: the bank achieved a productivity increase of over 50% across its credit workflows, reduced manual processing time by 70%, and—in some cases—shortened approval cycles from days to less than 30 minutes. Employees were able to focus on higher-value tasks, while customers benefited from faster, more transparent interactions.
Several key activities drove this transformation. The bank began by focusing on a high-value segment with the greatest potential for business impact—in this case, credit operations, where automation could reduce costs and improve the customer experience. Next, they redesigned workflows around this opportunity, restructuring credit processes to boost efficiency through AI, identifying friction points, redesigning information handoffs, and codifying new ways of working. Finally, the bank assessed its existing AI capabilities to establish a starting point, defined an ambitious vision for the future, and developed a roadmap to build the capabilities required to capture this value.
Developing role-specific capabilities
AI-driven transformation requires the participation of everyone. However, not all participants are involved to the same degree. To optimize learning, our design philosophy at BCG U is based on a role-based approach that encompasses four AI-related archetypes:
Creators: Executives and senior leaders who define the AI vision for the organization.
Leaders: Managers who create the conditions for AI scalability.
Transformers: Team leads who restructure workflows to incorporate new AI tools.
Frontline Contributors: Individuals who use AI tools in their daily work.
Our experience has shown that when fluency and confidence are developed—tailored to the specific demands of each role—momentum is inevitable. (See Figure 2.)
From Adoption to Advantage: Developing AI Capabilities | Figure 2
For example, a global fast-moving consumer goods (FMCG) company recognized early on that generative AI (GenAI) would transform competition in areas such as marketing, supply chain, R&D, and product innovation. Despite some initial experiments with various tools, progress was uneven, and the impact remained fragmented. Leadership made a bold decision: to bet that rapidly developing AI capabilities across the entire organization would create a competitive advantage.
Together, we designed a bespoke “AI Accelerator,” which includes learning paths for key roles based on specific archetypes:
Strategic profile: Senior executives who must motivate, inspire, and serve as role models in the use of AI.
Their learning path focuses on building a shared narrative and practicing how to lead teams through periods of change. These leaders participate in live virtual sessions, case study analyses, and immersive labs where they apply generative AI to real-world business challenges.
Implementation profile: Managers and operational staff who need to integrate AI into their daily work. Their learning path emphasizes hands-on simulations and use cases directly linked to workflows in marketing, supply chain, and R&D. This enables employees to move from learning the fundamentals of AI to incorporating new habits into their everyday tasks.
To ensure the program feels authentic, we developed customized lessons that highlight transformation stories from within the company itself—showcasing early successes, sharing lessons learned, and spotlighting colleagues who are already reimagining how AI creates value.
The “AI Accelerator” has been rolled out to several thousand employees (with that number continuing to grow).
As a result, the company is seeing not only increased use of AI tools but also measurable improvements in productivity and confidence.
Employees experience a clear boost: through the program, they develop a common language around AI and its benefits,
learn how to integrate these tools into their daily tasks, and grasp the business rationale behind their implementation. We have observed a consistent shift in behavior, which is driving greater engagement with AI-driven transformation across business units throughout the global organization.
As another example, we recently collaborated with a global biopharmaceutical company to put this approach into practice. We segmented over 100,000 employees into four AI archetypes, each with a tailored learning path. Leaders received support in defining a vision, along with practical guides to help them serve as role models. Those working closer to the front lines participated in hands-on simulations and workshops based on real-world use cases. As a result, the company increased the adoption of AI tools from approximately 20% to nearly 90%.
These experiences demonstrate that carefully designed learning paths can transform curiosity into engagement, engagement into sustained behavioral change, and that change into concrete business benefits.
Leaders must share—and embody—a unified vision.
The most effective AI-driven transformations begin when leaders are prepared to spearhead the process, modeling the behaviors they wish to see throughout the organization. However, different groups within the same organization sometimes seem to speak different “languages.” For leadership by example to be effective, all managers must be aligned regarding the transformation—from the processes involved to the objectives. This requires far more than a basic understanding; it entails explicitly defining the desired behaviors, demonstrating them visibly, and reinforcing them through clear accountability structures.
In the words of Kaye Foster, an executive coach at BCG and former Chief Human Resources Officer (CHRO) at Johnson & Johnson: “We often assume that if leaders know what to do, they will do it. But that is not the case.
More often than not, leaders revert to behaviors that are reinforced—not those they learned during a training session.”
We worked with a client’s executive team to strengthen their mastery of AI, enabling them to lead their organization’s transformation in a coherent and compelling way. The executives found it challenging to address technical issues, while the technology leaders struggled to frame solutions in terms of business value. Consequently, conversations between the two groups often focused on isolated, point solutions rather than the fundamental investments required to transform the company.
We brought the leaders together for an immersive learning experience, championed by the CEO. The summit combined cutting-edge training on AI technology with business strategy, leadership, and execution. The gathering provided the executive team with the space to step back, challenge deeply held assumptions, and engage in honest, cross-functional dialogue. The leaders examined real-world use cases and grappled with the reality of transforming a large, established organization—complete with legacy systems—under intense competitive pressure. The discussions centered on business transformation and its impact.
The impact was immediate: both business and technology leaders gained greater confidence and a common language to guide decision-making.
The executives developed technical fluency, while the technology leaders gained a deeper perspective on business objectives. Together, they converged on a fundamental premise: there is no such thing as a standalone “AI strategy”—there is only a business strategy empowered by AI. This shared vision transformed fragmented conversations into a strategic, collaborative problem-solving process, focused on scaling value creation. By setting the tone from the top, these leaders modeled the behaviors they expected to see throughout the organization: curiosity, collaboration, and continuous learning. This visible commitment to change translated alignment into concrete action.
Trust and motivation are key to AI-driven transformation.
Implementing AI can trigger emotional reactions. Some individuals are deeply skeptical of this technology, particularly due to concerns about how it might affect their job security. Others express reservations regarding the quality of AI-generated outputs or how to use these new tools responsibly. For AI-based transformation programs to succeed, organizations must address these concerns head-on.
If that trust is not built first, no AI initiative will succeed.
According to Brad Strock, an executive coach at BCG and former Chief Information Officer (CIO) of PayPal: “The real challenge with AI is not the technology itself, but getting people to trust it. If that trust is not built first, no AI initiative will succeed.”
Recently, we collaborated with BCG X to help a global technology company design and pilot a training program aimed at accelerating the adoption of generative AI (GenAI) among its software engineering teams. Initially, developers approached the pilot project with skepticism and uncertainty regarding how AI might affect their roles; this reluctance resulted in low participation in any AI-related initiatives. To build trust and alleviate concerns, we first trained managers on how to navigate generative AI
