In BCG’s recent Build for the Future x AI 2025 Global Study, which asks C-level executives to gauge companies’ AI maturity, only about 5% of organizations have managed to reap substantial financial gains from AI (defined as increases to revenue or cash flow, along with significant process and workflow improvements). Crucially, that segment also shows stronger financial performance overall and three-year total shareholder returns that are roughly four times higher, on average, than AI laggards.
What separates these “future-built” companies? They realize that value doesn’t just come from the technology but from how they empower their people to capitalize on it. In BCG’s case work involving hundreds of companies, about 10% of value from AI comes from the algorithms themselves and another 20% comes from the technology required to implement them. The remaining 70% comes from rethinking the people component. Our analysis shows that future-built companies plan to upskill more than 50% of employees on AI—compared with 20% for laggards—and they are putting the organizational resources in place to support those goals. Not only that, they are four times more likely to have structured AI-learning programs and to carve out protected time for employees to learn. (See “Most AI Value Comes from People.”)
Most AI Value Comes from People
Secure strategic alignment from the top. First, companies need strong leadership and governance over the technology, ensuring that AI efforts are in service of enterprise priorities, not walled off as a separate “AI transformation.” Leaders should align on a small number of central priorities—typically three to four—rather than spreading their efforts across dozens or hundreds of use cases. Dedicated governance and transformation capabilities, built around clear success metrics and KPIs, ensure that the change program happens on time and on budget, leading to real, measurable value.
Spur adoption and behavioral changes. To get employees to embrace AI and change their daily working behaviors, companies should build a holistic change plan, starting with an inspiring narrative about how the technology will help the company improve its performance and better meet its objectives. Strong governance and guardrails around AI can build trust across the workforce. Companies should invest in upskilling measures to increase fluency to foster at-scale adoption. And, critically, employees should understand the objective to move beyond initial experimentation and create real change.
Assess the impact on the workforce. Leaders should determine how the workforce will be impacted by AI—at the level of positions, employees, and skills—and identify any gaps. To close those gaps, companies can launch structured upskilling and reskilling initiatives, along with programs aimed at recruiting and retaining leaders and employees who already have target capabilities.
Develop an AI-enhanced operating model. Last, companies need to define an operating model that combines human employees and AI, including roles, governance, and organizational design. That entails redesigning workflows and new ways of working to capitalize on AI.
Get Leaders and Managers Involved
Successful strategy starts with alignment from the top. Executive engagement is one of the strongest predictors of AI maturity. Companies that treat AI as a CEO-level priority—rather than just a tech initiative—scale faster and generate more value.
Employees in future-built companies see AI not as something mandated from corporate headquarters but as something their direct managers are using every day.
Leaders need to set the overall vision and communicate consistently the “why” to the workforce with a narrative designed to inspire, so that people at all levels understand the rationale for implementing AI, how it will improve company performance, and what their role is in that change.
Vision then needs to be translated into action. Among the future-built companies in our research, 88% of managers who role model AI use and actively incorporate it into decision making and daily operations—versus 25% at AI laggards. (See the exhibit.) Employees in these organizations see AI not as something mandated from corporate headquarters but as something their direct managers are using every day.
Anticipate the Skills Impacts of AI and Future Talent Needs
The transformative effect AI will have on the workforce is well documented. Routine tasks within many roles will become automated, changing the nature of many entry-level positions. This shift will require companies to create innovative career paths and apprenticeship models for new graduates, who will be increasingly needed to manage AI-enabled systems. Overall, workplaces will evolve, creating new opportunities through effective human-AI partnerships.
Fundamental change such as this requires careful forethought and planning, not least because technology moves quickly while human behavior change takes time. In our data, future-built companies are five times more likely to do strategic workforce planning than laggards. They anticipate talent requirements and reshape job architectures and organizational structures with AI at the core. This planning phase is crucial for understanding where to focus investment to ensure that any upskilling of existing full-time employees is successful.
Fundamental change such as this requires careful forethought and planning, not least because technology moves quickly while human behavior change takes time.
Adopt a Holistic Upskilling Approach
Foundational skills such as prompt writing and AI literacy are an important investment for firms looking to upskill their full-time employees. Beyond that, it is crucial to develop the core skills, such as contextual judgment, framing problems, and interpreting results, that enable workers to realize the full value of new AI tools across entire workflows and at a systems level.
Giving workers these critical AI skills at scale happens when three elements are in place:
- The learning is embedded into daily work. Rather than once-a-year workshops and scenario-based exercises, employees learn best when new skills are integrated into the flow of work—when they use real tools to accomplish real tasks, and they receive real feedback. This reinforces both AI-related competencies and core “human” skills such as problem solving, communication, collaboration, and creativity.
- The design is grounded in behavioral science. People need to see leaders modeling change, understand the why, and experience wins early.
- Robust tracking is in place. Measuring the effectiveness of upskilling initiatives ensures that programs remain aligned with strategic priorities. Metrics should assess not only the acquisition of AI-related skills but also progress in developing the new core competencies that are vital to future roles. Regular assessment allows companies to demonstrate tangible returns on investment and adapt training initiatives as organizational needs evolve.
When you get all the elements right, upskilling becomes more than a learning initiative. It becomes part of how the company evolves. And by prioritizing the workforce, companies can give their people the skills and confidence to embrace the technology and change how they work—and how the company ultimately creates value.
