Global corporate investment in AI more than doubled in 2025, according to Stanford University. The Capgemini Research Institute has found that 80% of organizations have increased their generative AI investment since 2023.
Yet, a 2025 MIT report found that nearly 95% of businesses report no return at all on in-house AI investments.
Spending this much, this fast, on transformation is one sign that we are in a new era in workplace tech—an era unlike any we’ve ever seen. Still, these massive investments aren’t carrying their weight in terms of producing business outcomes and results. And surprisingly, it’s not the fault of the technology.
The AI ROI Gap Is Widening and It’s a People Problem
The instinct is to blame the lack of ROI on technology, or even implementation, like a vendor that oversold or integrations that don’t work as expected. Those things do happen. But when the issues are this widespread, the evidence points to something more systemic. If a technology is only delivering for one in seven companies, while everyone else keeps spending more without the same results, it’s a larger phenomenon.
Here’s what’s really happening: Most companies are treating technology deployment as the finish line. They configure the system, run a training session, and declare the rollout a success. But whether or not people can (and do) use the new tools to do their jobs differently is often an afterthought. This is the source of the missing outcomes. The real bottleneck is workforce capability.
Most “AI strategies” are really technology procurement plans with a training event bolted on. The systems mostly work, but the workforce hasn’t been brought along at the pace of change. One training session during a rollout doesn’t equate to workforce readiness when it comes to a skill gap as big as AI. Ultimately, businesses need to find ways to upskill workforces faster—and continually.
Even when the system performs exactly as designed, adoption can falter. That’s enough to jeopardize the success of any technology, which is determined by what the workforce does with it. The gap between technology access and human capability has become the defining barrier to ROI for AI investments and the corresponding business transformation.
A Skills-First Operating Model Changes the Equation
If the big business blocker is a skill gap, embracing a skills-first operating model means that every move in the AI transformation initiative can be based on workforce capability data. Without that visibility, workforce development is guesswork and doesn’t stand a chance of fixing the AI ROI problem.
What is a skills-first operating model?
A skills-first operating model is a system and foundation built around the skills an employee has, what skills they are growing, and where they will develop skills next. It often starts with knowing what capabilities exist (and at what level of proficiency) across the organization at any given moment. It is not centered around static job roles like the traditional model.
This model demands answers that role-based models can’t provide. Which teams are ready for emerging workplace change? Which roles are at risk of rapid change? Which skills and capabilities are growing, and which aren’t?
How does skill visibility translate to AI return on investment?
You can’t build AI capabilities you don’t even know are missing. You can’t properly grow proficiency if you don’t know where your starting point is. Knowing there is a capability gap between the AI tech and the people using it is one thing, but understanding what the gap is and how to fix it is the solution to reach your goal fast.
Not to mention, the more you know, the more personalized learning can become. Skill data is the basis for tailoring learning to the individual. Without the data, content and experiences have to be generic. Once the capability context is there, AI roleplays, coaching, and other dynamic learning experiences become possible and effective.
Example: A Skills-First Operating Model with Degreed + SAP Integration
If you leverage Degreed as your learning system, it creates a unified skills hub across HR and learning ecosystems, giving leaders a clear, actionable picture of workforce capability.
Through integration with platforms like SAP and leading content providers, that unified layer turns skill data into insights that leaders can act on. The strength and interoperability of this partnership is key. In this respect, Degreed is an SAP Endorsed App, which means it is premium-certified and meets SAP’s highest standards for security, testing, and benchmarked performance.
This level of interconnectivity can also solve some of the biggest challenges, like scalability with consistency. For example, one of the hardest challenges in global skills strategy is consistency. A “Level 3” proficiency in London can mean something entirely different than a “Level 3” in Singapore. The skill language is not universal, making the data messy and inaccurate. In this situation, Degreed unifies skill signals from across technology providers and aligns them to a common system of record like SAP’s talent intelligence hub. Suddenly, the data is clear and actionable.
That then supports enterprise-wide talent planning and international workforce mobility, while preserving local flexibility for functional skills.
When SAP SuccessFactors and Degreed operate together, they establish a cohesive talent ecosystem where skill insights directly inform workforce development. SAP SuccessFactors serves as the system of record, identifying roles, workforce needs, and critical skill gaps. Degreed becomes the system of action, delivering customized, adaptive learning experiences.
Together, they jumpstart transformation.
Workforce Readiness Is the Missing Piece of AI Investment
The companies seeing real returns on their AI investment have made a different bet: that building a skilled, capable workforce is as important to their strategy as investing in the right technology at the right time.
That’s where a skills-first operating model and a connected tech stack come into play. When you integrate your system of record in SAP SuccessFactors with a system of action in Degreed, it allows capability data to flow through a cohesive tech stack. That in turn provides a comprehensive view of workforce readiness, which is an essential element of AI and business transformation strategy. In this way, it’s easier to identify the skill gaps creating barriers to AI adoption and ROI, close those gaps faster, and keep development aligned even as priorities continue to shift.
Transformation will only succeed when workforce capability can keep pace with changing technology. The AI era rewards the organizations that are building the workforce to activate AI as a measurable business advantage.
Download the full whitepaper → AI Won’t Transform Your Business. Your People Will. Co-created by Degreed and SAP.
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