Right now, organizations are heading into the new year with a freshly inked resolve to upskill their workforces and unlock the full value of their AI investments.
For many, it will be an uphill battle. Despite setting ambitious AI targets, BCG research shows most companies are struggling to reap the benefits.
- Just 5% of companies are achieving AI value at scale.
- 60% are hardly achieving any value at all.
- 35% are somewhere in between.
The So What
The answer to why many companies are lagging on AI is how they approach upskilling.
At present, many organizations are focusing on the launch of AI solutions instead of ensuring people can meaningfully use them in their daily work.
“Workforces are facing a skills gap where they’re not using AI tools effectively in ways that both integrate into and transform their workflows,” says Allison Bailey, BCG senior partner and global co-lead of the firm’s capability-building unit, BCG U.
“Companies launch lots of AI pilots but can’t turn them into repeatable, scalable value. Why? Because there’s too much emphasis on the tech and not enough on skills development for employees.”
AI is set to comprehensively transform the global economy over the coming years. AI-first businesses are already achieving substantial increases in top-line growth and faster product development cycles compared to competitors.
Investing in capability-building is key to scaling this potential. BCG analysis shows that while 10% of AI value creation comes from algorithms, and 20% from technology infrastructure, a striking 70% comes from people, processes, and change management.
AI tools for employees such as chatbots or copilots within existing processes are a good start, but true benefit is harnessed by developing strategies that embed AI in end-to-end workflows.
“We talk about the Golden Triangle of value,” says Bailey. “Increased productivity, better quality decision making, and a workforce that’s more engaged because it’s been freed from mundane tasks.
Companies that don’t go full throttle on AI are likely missing opportunities in each of those three buckets.”
AI now dominates boardroom conversations globally, with business leaders considering it the most central issue for the next three to five years across all industries. But only by acting quickly will they grasp the full potential.
“The AI skills gap doesn’t just sit in the workforce,” Bailey observes. “It sits in the C-suite. If leaders don’t deeply understand AI, they can’t set the right vision or move the organization at the speed this transformation demands.”
Stay ahead with BCG insights on people strategy
Now What
Building capability and achieving AI value at scale requires companies to act across three critical areas:
Anchor on value creation. Focus on where AI can make the biggest difference. Companies that succeed concentrate their efforts on core business processes rather than experimenting at the edges or taking a scatter-gun approach. They set ambitious adoption targets across their workforce, accepting far fewer non-users than their competitors, and establish clear financial metrics to demonstrate results.
Move beyond workshops and e-learning. Effective AI adoption requires more than foundational training. While workshops lay important groundwork, the real transformation happens when employees apply new skills to their actual work. Organizations should create structured opportunities for staff to practice using AI in the core of their work, with coaching support to build their confidence. A one-size-fits-all approach is best avoided. Creating bespoke, persona-based learning journeys has been shown to deliver employee AI adoption at a level 20 times higher than a broad-based approach.
Technology itself can accelerate the upskilling process; AI-powered tutoring tools provide personalized learning adapted to individual starting points and knowledge gaps, enabling workers to rehearse in realistic scenarios with immediate feedback.
Provide time and incentives. The two most common barriers to AI adoption are lack of time and misaligned incentives. Employees need protected time to learn new tools and experiment with different approaches in low-risk environments. Performance metrics, promotion criteria, and reward systems can encourage AI experimentation and workflow changes. C-suites can encourage their workforce by leading by example. When senior executives visibly use AI tools, discuss their own learning journeys, and acknowledge the challenges of acquiring new skills, they create psychological safety for their teams to do the same. Upskilling starts at the top, and that requires a leadership which truly understands the full potential of AI.
