When implementing any kind of technology, the difference between offering employees training and supporting them in adoption is all too frequently misunderstood.
But the difference is a significant one. Training is usually a time-limited, structured educational initiative to help people build technical competence and learn how a system works. Adoption, on the other hand, is a continuous, long-term process by which employees use technology to change how they work in order to deliver more value.
Nowhere is this distinction more vital than in the emerging world of agentic AI. To date, many organizations have been experimenting with the technology to automate discrete tasks, particularly in areas such as software engineering, customer support, and operations.
Over the next two years though, Gartner expects deployment to jump from the current 17 percent of businesses to more like 60 percent. Their key aims in going down this route include automating complex multistep workflows, boosting operational efficiency, and maximizing productivity.
But despite the perceived value of agentic AI here, many organizations are currently struggling to create such value in real terms. This includes generating a return on investment.
Identifying the value gap
In fact, according to WalkMe's 'The State of Digital Adoption 2026' report, a huge 40 percent of all expenditure on digital transformation underperforms expectations, mainly due to user adoption challenges. As the study, which is based on input from 3,750 respondents worldwide, says: "The technology does what it's supposed to do. The adoption execution around it doesn't."
As such, it found the average employee loses a full working day each week (7.9 hours) to "friction". For example, they spend 1.34 hours per week re-entering the same information across multiple applications. Another hour disappears on finding workarounds when tools are unable to communicate effectively.
A further 3.69 hours vanish due to a lack of guidance on how to use such tools. This includes 1.03 hours being spent trying to understand unclear instructions. A final 1.88 hours each week are spent on reworking AI prompts and waiting for faulty tools to be fixed.
Ofir Hatsor is WalkMe's Senior Vice President of Sales for Europe, the Middle East and Africa. He believes that a good chunk of this friction is due to the complex and highly regulated environment in which enterprises function. This means many of them struggle to change how they operate day-to-day - even though it is only in doing so that they can create true value for the business.
"There's currently a very big gap between perceived value and existing value, or the actual value that will follow," he points out. "I think over time this gap will be mitigated, but it's not going to be tomorrow - more like the next three, five, or seven years, and in some companies, it'll never close."
The difficulties in affecting change
As to why affecting meaningful operational change is often so difficult to achieve, especially when moving to an agentic AI environment, Hatsor points to several reasons.
The first is a "visibility gap".
As WalkMe's study indicates, most executives believe their organizations use an average of 35 applications when the actual number is 661 - a visibility gap of 1,789 percent. For AI-powered tools in particular, leaders estimate teams use 21 when the real count is 80.
The point here, as the report says, is that: "You can't optimize workflows you can't see, govern AI tools you don't know exist, or measure productivity when your instrumentation covers a fraction of where work actually happens."
A key part of the problem is shadow AI. For instance, 45 percent of workers admitted to using unapproved AI tools in the previous 30 days as authorized systems failed to fit current workflows. Unfortunately, 36 percent of them also used confidential company data in the process.
A big challenge, Hatsor explains, is the ease with which employees can get hold of AI development tools today.
"So, they create their own little vibe-coding applications and share them between themselves," he says. "But if I'm the chief information officer or chief security officer, my visibility into that landscape is limited at best."
This is made even harder when employees aren’t honest with their employers or themselves about AI use. WalkMe’s most recent AI Pulse Survey showed 32.5 percent of workers have passed AI work off as their own, and 28.3 percent have pretended to know AI in a meeting. An improvement over 2025, but still some way to go.
Unsurprisingly, this lack of official approval or control is creating big risks for companies, not least in terms of data privacy and governance. The upshot is that many organizations are slowing their rate of agentic AI adoption until they are in a position to see, mitigate and reduce such risks.
Traversing murky waters
A second challenge many organizations face, meanwhile, is the actual cost of implementing agentic AI versus its real-world benefits. A key issue here is the underlying assumptions that many proposed financial gains are based on. These include boosting productivity and cutting headcount when the real benefits may actually be found in enhancing work quality and augmenting human activity.
A third consideration is AI tokens. The problem here is that many organizations do not know either the number of tokens they are consuming or what they cost, which "can become very expensive".
"So, there are many murky waters, which make it very difficult to get a real hard return on investment at this point because of all the moving parts and uncertainties," Hatsor says.
The situation is made even more tricky by the sheer amount and speed of change that AI is generating. This is especially true among employees who usually have less access to new tools than their leaders and tend to be slower technology adopters anyway. Many are also fearful about a future that feels uncertain.
Unsurprisingly then, according to the WalkMe report, there is a huge chasm between leaders who believe employees have been given access to adequate tooling (88 percent) compared with employees who take the same view (21 percent). This situation is inevitably hitting adoption.
As Hatsor points out: "We're not giving our workforce the tools, or the confidence to use the tools, but there's also a lack of understanding of the difference between training and adoption. We can train someone to use a tool, but unless we help them adopt it and make it their own, they won't use it effectively."
The power of Digital Adoption Platforms
This is where systems, such as WalkMe's Digital Adoption Platform (DAP), come in. DAP offers in-app guidance - in a similar fashion to GPS systems - to navigate employees through a specific workflow. It offers information in a range of formats based on individual preferences, such as walk-through demonstrations, long-form text, or video.
To discover how to create a quote in a system of record, for example, they simply open WalkMe’s action bar and view a video or PDF of how to complete the task.
The system can even automate certain tasks. So, if for instance a salesperson is required to write a report confirming a particular deal, they could use WalkMe’s action bar to check the suitability of what they had written for compliance purposes and even suggest better wording, if appropriate.
The key benefits of such an approach, Hatsor says, are that: "People feel supported. They also feel guided because when you're sitting in front of the screen, one of the worst things is not knowing what to do next, so we help with that."
Having such support and guidance also leads to greater trust in the agentic AI system being deployed too. For instance, the WalkMe study indicates that workers who receive in-flow contextual support are 1.9 to 3.7 times more likely to feel confidence in such systems compared to those not receiving it. A huge 79 percent also say it helps them complete their work more easily.
Linking business strategy and people
What this demonstrates then, believes Hatsor, is just how important it is that "every agentic AI process starts with the people it's supposed to serve."
But another vital consideration is ensuring a sound governance structure is in place. This is imperative as clear guardrails ensure everything, and everyone, operates safely and smoothly.
This again is where WalkMe's DAP comes in.
Therefore, Hatsor says: "The platform is completely secure in the sense we don't track users, we track usage, so we can see someone is interacting with a specific system, but we don't know who it is. This is important in supporting enterprise customers in terms of governance, regulation, and security."
Ultimately though, WalkMe's DAP makes it possible to deploy agentic AI in a way that supports organizations' overall business strategy. It does so by clearly linking this strategy to how the technology is adopted and used by the workforce - an approach that is crucial in enabling business success.
"Ignoring the human factor is a grave mistake in my view and will have an impact on the business if organizations do it. But WalkMe is one of the few pieces of technology ever developed with the sole aim of helping humans interact with enterprise IT systems - and that's the big benefit," Hatsor concludes.
Sponsored by WalkMe
