Despite aggressive investment in AI tools, platforms, and experimentation, most enterprises are not yet seeing strong returns.
In our 2026 survey of 500+ US and UK enterprise leaders, conducted with YouGov:
- 21% report significant positive ROI from AI
- 42% report moderate ROI
- 17% report no positive ROI
The question isn’t whether organizations are investing in AI, it’s why the ROI of AI varies so dramatically across enterprises. The answer points to workforce capability.
The gap in AI ROI reflects deeper differences in enterprise readiness. Our 2026 overview breaks down how AI literacy, data literacy, and upskilling maturity shape performance outcomes across organizations.
The AI ROI divide
The most striking finding in the data is this: Among organizations with a mature, organization-wide data or AI literacy upskilling program, reports of significant positive AI ROI nearly double.
- Significant AI ROI jumps from 21% overall to 42%
- Reports of no positive ROI drop from 17% to 11%
In other words, enterprises that pair AI investment with structured workforce capability building are nearly twice as likely to see strong returns.
AI tools alone do not create ROI. Workforce capability does.
Why AI tools don’t automatically generate ROI
AI ROI is not simply a function of tool sophistication. It depends on whether employees can:
- Identify appropriate use cases
- Apply AI tools to real workflows
- Evaluate outputs critically
- Translate insights into decisions
- Measure business impact
Without foundational data and AI literacy in the workplace, AI tools can increase speed, but not necessarily correctness or clarity. And in some cases, they amplify risk. Leaders identify key risks associated with inadequate AI skills:
- 32% cite inaccurate decision-making
- 25% cite slow decision-making
- 27% cite inability to keep pace with competitors
- 16% cite security incidents
Bottom line: When workforce capability lags behind tool adoption, ROI suffers.
The capability multiplier effect
When data and AI literacy is strong, the benefits compound. Leaders associate AI skills with:
- Faster decision-making (48%)
- Stronger innovation (46%)
- More accurate decision-making (41%)
In terms of productivity expectations, the most common expected efficiency uplift from AI adoption falls in the 10–20% range, though many anticipate gains above 20%. These returns depend on employees knowing how to use AI effectively—not just having access to it.
In other words, AI is a multiplier, but it multiplies capability. If capability is low, returns remain low.
Why AI training often fails to improve ROI
Most organizations are offering at least some AI training:
- 77% report providing some form of AI training
- 68% say employees have access to AI learning resources
Yet only 35% report having a mature, workforce-wide upskilling program.
Leaders that do provide some training (particularly that’s online and self-paced) cite common challenges, including:
- 24% say there are not enough hands-on projects or labs
- 23% say learning paths are not role-tailored
- 26% struggle to measure ROI from training
If training is passive, fragmented, or disconnected from real workflows, it won’t translate into measurable AI ROI. Structured capability building is the differentiator.
The full breakdown of ROI and training findings is available in the 2026 State of Data & AI Literacy Report.
What high-ROI organizations do differently
Organizations that report strong AI ROI share several characteristics:
- They invest in enterprise-wide data and AI literacy, not just in training for technical teams
- They embed learning into real workflows
- They reinforce skills over time
- They measure capability progression
- They align AI initiatives with business use cases
For those leading the pack, AI ROI is not accidental; it’s designed through building the required capabilities.
Proof in practice
Consider Bayer’s enterprise Data Academy, which structured foundational AI and data literacy across roles, from general digital fluency to advanced practitioners. More than 90% of learners reported developing innovative ideas or improved processes after completing training.
Similarly, Rolls-Royce implemented role-specific upskilling programs that significantly accelerated data handling processes, in some cases increasing speed by 100x.
In both cases, AI ROI followed structured capability building—not the other way around.
From AI adoption to AI return
The enterprise conversation is shifting. Early AI strategy focused on experimentation and deployment; now the focus is shifting toward return on AI investment.
The 2026 data suggests a clear pattern: Organizations that treat workforce capability as core infrastructure, not an afterthought, are significantly more likely to see meaningful AI ROI.
For enterprise leaders evaluating AI investments, the critical question is no longer “Which AI tools should we adopt?” It’s “Is our workforce ready to use them effectively?”
How DataCamp supports AI ROI
DataCamp for Business is designed to build the kind of structured, role-relevant data and AI literacy that drives measurable returns.
Through hands-on learning, AI-powered personalization, skill assessments, and workforce-wide benchmarking, organizations can move from fragmented AI experimentation to sustained capability building.
If you’re evaluating how to improve the ROI of AI investments across your organization, reach out so we can show you how DataCamp for Business supports enterprise AI upskilling. If you want to see how structured, applied learning supports AI ROI, explore some of our most popular data and AI literacy courses:
