Survey of global learning professionals reveals a widening disconnect between AI goals and execution, underscoring the need for learning to lead enterprise AI adoption
Absorb Software, the leading global learning technology company, today released its AI in Learning Report, revealing a growing disconnect between ambition and organizational readiness as AI adoption continues to accelerate across the enterprise. Based on a survey of more than 1,700 learning professionals, the research shows that while enabling personalization at scale is the top goal for AI in L&D (25%), more than a quarter of organizations have yet to adopt AI in learning at all, and many lack the strategic foundations needed to move beyond experimentation.
The findings highlight a field in transition. Learning teams increasingly view AI as a path to more personalized, scalable experiences, yet adoption remains uneven and narrowly applied. Resistance from stakeholders, limited influence in AI decision-making, and weak alignment with business outcomes continue to slow progress. As AI becomes part of everyday work, the gap between ambition and readiness is placing new pressure on L&D teams to help organizations turn AI investment into real results.
The report explores how L&D teams are navigating AI today, including their goals for personalization and efficiency, the barriers slowing progress, limited representation in enterprise AI strategy discussions, and widespread uncertainty around ethics and governance. Taken together, the findings point to a shift in how organizations will achieve AI success, with greater emphasis on building the capabilities, confidence, and leadership needed to support performance over time.
Key Findings: The State of AI in Learning
- Personalization is the top ambition, but it’s pointed in the wrong direction: One quarter of L&D teams cite personalization at scale as their primary AI goal. However, the most common use cases today are content creation (30%) and research support (21%), which improve efficiency but fall short of delivering adaptive, outcome-driven learning experiences. Fewer than 4% of respondents say improving overall business performance is their main AI objective, revealing a gap not just between ambition and execution, but between what L&D is optimizing for and what the business actually needs.
- AI adoption is becoming increasingly divided: While 46% of organizations have started using AI in learning recently and another 27% have used it for years, 27% have not tried it at all, and of those non-users, 36% say they likely never will. Rather than steady, universal adoption, organizations are splitting into a majority moving ahead and a persistent group of long-term laggards, increasing the risk of widening capability and productivity gaps.
- L&D faces resistance while lacking strategic influence: Despite being central to workforce development, L&D teams are included in organizational AI strategy discussions only 22% of the time. At the same time, resistance from stakeholders is the most cited barrier to AI adoption in learning, named by 37% of respondents, creating a cycle where learning teams are expected to enable AI without the authority to shape its direction.
- A readiness gap is widening between ambition and execution: While 73% of L&D professionals use AI for basic tasks like drafting and ideation, only 28% feel confident integrating it into real learning workflows without quality degradation. This gap shows that surface-level adoption is outpacing the organizational readiness needed to deliver real performance outcomes.
- Ethics and organizational readiness remain weak points: Only 15% of learning professionals feel prepared to manage the ethical implications of AI in learning, despite AI’s growing influence on skills development and performance evaluation. An additional 12% cite a lack of internal AI expertise as their primary challenge, reinforcing that many barriers to progress sit at the enterprise level, not within L&D teams themselves.
Learning as the Engine of Intentional AI Adoption
These findings show that many organizations are approaching AI adoption tactically rather than strategically. While experimentation is increasing, long-term value remains constrained by governance gaps, misaligned use cases, and limited organizational readiness. Absorb is built to close that gap, giving organizations the intelligent learning infrastructure they need to move from scattered experimentation to sustained capability and measurable business outcomes.
That gap is exactly what Absorb built Absorb Aura to close. Launched in May 2026, Aura is the agentic AI engine behind Absorb’s learning system, coordinating specialized AI agents that connect every learning moment to a measurable business outcome. Rather than adding another point tool, Aura gives organizations the infrastructure to move from scattered experimentation to sustained capability.
“AI has already helped HR and L&D move faster, but speed was never the goal. The real value is helping people build new capabilities that keep the business ahead of change, and that only happens when learning evolves to guide, coach, and adapt to how people actually work and learn.” Cheryl Yuran, Chief Human Resources Officer, Absorb Software.
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