Your Leaders Support Analytics. So Why Aren't People Using It?
Imagine an organisation that has just invested in a new analytics platform. Senior management has approved the budget, department heads have been briefed, and employees have attended training. The implementation team reports that the rollout is complete, with access granted and dashboards ready for use.
Everything appears to be in place. Yet several months later, managers are still requesting Excel reports, employees continue maintaining their own spreadsheets, and some are exporting data from the new system just to analyse it elsewhere.
The technology has been introduced, but it hasn't really become part of everyday work.
Management naturally starts asking questions. Are employees resistant to change? Do they need more training? Perhaps the organisation hasn't developed a strong enough data culture.
These are reasonable possibilities, but I wonder whether we're overlooking something more fundamental.
What if employees aren't rejecting analytics? What if the new platform simply isn't helping them do their work any better?
It's an important distinction because it changes where we should look for answers.
When leadership support isn't enough
We're often told that successful digital transformation starts at the top. Leaders must communicate a clear vision, commit resources and demonstrate their support. Without that commitment, many initiatives would struggle to get started, let alone survive the challenges of implementation.
But does strong leadership support necessarily mean employees will embrace the technology? A study published in October 2026 offers an interesting perspective. Researchers Benedict Langer and Alex Feld surveyed 368 employees from German small and medium-sized enterprises to understand what influenced their intention to adopt cloud-based analytics.
Their research model explained approximately 78% of the variation in employees' adoption intentions. Among the factors examined, perceived usefulness was the strongest direct predictor of intention to adopt. Put simply, employees were more inclined to use analytics when they believed it would help them perform their work better.
Another important finding concerned compatibility: how well the technology fitted existing workflows, tools and organisational practices. Compatibility was the strongest contextual predictor of whether employees considered the system useful and easy to use. The visible advantages offered by the technology also mattered.
Perhaps the most thought-provoking finding was that leadership support did not have a statistically significant direct relationship with employees' perceptions of usefulness or ease of use.
Does that mean leadership support is unimportant? Certainly not. The study did not establish that leadership has no influence on adoption. Leaders may still shape adoption indirectly through the systems they select, the resources they provide and the obstacles they help remove.
What the findings suggest is that leadership can create the conditions for adoption, but it cannot manufacture usefulness. Employees make that judgement through their everyday experience of the technology.
Are employees resisting change, or protecting their workflow?
Consider a fictional company that introduces a dashboard for its regional managers. The objective is sensible: provide a consistent view of performance, reduce manual reporting and help managers make better decisions.
Senior management supports the initiative, and everyone attends the required training. The dashboard looks impressive, with performance indicators, interactive charts and filters that allow users to explore the data. Yet the managers keep returning to Excel.
At first, this seems disappointing. After all, the organisation has invested considerable effort in providing what should be a better reporting tool. But when someone takes the time to understand how the managers prepare for their monthly meetings, a different picture emerges.
The dashboard refreshes only once a week, while managers need more current figures. Some regional definitions don't match those used in operational discussions, so figures must be reconciled manually. The dashboard provides a useful overview, but detailed investigations still require Excel. Managers also need to add explanations and annotations before presenting their findings, something the new system doesn't easily support.
As a result, preparing the management meeting now involves using both the dashboard and the old spreadsheets. Instead of eliminating work, the new system has introduced another step.
Are these managers resisting analytics, or are they simply trying to get their work done?
I think this is where organisations need to become more curious about employee behaviour. Workarounds are often interpreted as evidence that people are unwilling to change. Sometimes that may be true. But they can also reveal what the official system is missing.
When employees repeatedly export data, perhaps they need greater flexibility. When they maintain separate reports, perhaps the new system doesn't answer an important question. And when they continue relying on colleagues for information, perhaps the data isn't available in the form or at the time they need it. Rather than immediately treating these behaviours as problems to eliminate, we could treat them as clues about how the work is actually performed.
In our dashboard example, adoption might improve if the organisation aligned its reporting definitions, provided more timely information, enabled detailed investigation and incorporated the dashboard into the management reporting cycle. Just as importantly, it could retire some of the old reports so employees aren't expected to maintain two parallel processes.
Notice that none of these improvements necessarily requires a more sophisticated dashboard. What they require is a better understanding of the work.
Does the new tool earn its place in the workflow?
One practical way to approach adoption is to examine the proposed system from the employee's perspective. Rather than beginning with the features available, we could ask four questions.
Does it fit the work? Can employees use the analytics platform while carrying out their normal responsibilities, or must they repeatedly switch between systems, reconcile information and duplicate effort?
Is the advantage visible? Can users explain what becomes easier or better because the platform exists? Perhaps they can identify problems sooner, reduce reporting errors or investigate an issue without waiting for someone else to prepare the information. If the benefits are clear only to senior management, employees may struggle to see why they should change their routines.
Is the effort reasonable? Every new system requires some adjustment, whether learning a different interface, changing established habits or adopting new processes. Employees may be willing to make that investment, but the expected improvement needs to justify it.
Does actual behaviour confirm adoption? Have people returned to the platform after training? Are they using it to support real tasks and decisions? Most importantly, has it replaced any of the manual work it was supposed to eliminate?
Together, these questions form what I would call an adoption-fit audit. It isn't a formal model from the research paper, but a practical way of translating its findings into questions managers can use.
The final question deserves particular attention because the research measured employees' intention to adopt cloud analytics, not whether they actually used it consistently over time. Someone might genuinely intend to use a new dashboard after training, only to discover that the demands of everyday work make the old process more practical. That's why training completion, positive feedback and even stated enthusiasm should not be mistaken for genuine adoption.
But should technology always fit the way we work today?
There's a complication worth considering. If we insist that every new system must fit perfectly into existing workflows, we may end up preserving the very inefficiencies the technology was intended to address.
Some existing processes deserve to be changed. A department may have spent years maintaining separate spreadsheets when a shared reporting platform could provide more consistent information. Managers may be accustomed to receiving reports in a particular format even though a different approach would help them make better decisions.
So the objective cannot simply be to accommodate every existing preference. The more useful question is which parts of the workflow should be preserved and which should be redesigned.
Perhaps the new analytics platform requires managers to change how they prepare for meetings. That may be entirely reasonable if it eliminates duplicate reporting, improves the consistency of information and enables more useful discussions. But employees need to understand why the change is worthwhile, and they need sufficient support while making the transition.
Compatibility shouldn't mean avoiding disruption. It should mean making disruption purposeful.
This is where leadership becomes especially important. Leaders can help determine which processes should change, resolve conflicting requirements between departments and make decisions about retiring outdated reports. They can provide resources for improving data quality, support pilot implementations and give employees room to experiment without expecting everything to work perfectly from the beginning.
They can also ensure that training addresses the work employees need to perform, rather than simply demonstrating the features available in the software. Leadership support becomes meaningful when it is translated into practical changes that make adoption possible.
Before rolling out to everyone, what should we check?
When an analytics initiative isn't gaining traction, the natural response may be to organise more training, communicate the benefits again or remind employees that the platform is now the official reporting tool.
Sometimes those actions are necessary. But before expanding the rollout, I'd suggest spending time with a small group of actual users and observing how they work.
Start with the decision or task the platform is supposed to improve. Understand where the new tool enters that process, what information employees need and what additional effort the change introduces. Ask which old report or manual step will disappear, and what advantage users should notice within the first few weeks.
Then look at what people actually do. If they keep returning to Excel, find out why. If they use the dashboard only when management asks them to, understand what prevents it from becoming useful in their regular work. If they access the platform but continue making decisions from separate reports, perhaps the real adoption problem hasn't been solved.
It's tempting to judge an implementation by whether the technology works as specified. But that is only one part of success. The more demanding test is whether the technology has become a useful part of how people work and make decisions.
The research gives us a reason to look beyond executive sponsorship and pay closer attention to usefulness, workflow compatibility and visible operational benefits. Of course, the study involved employees of German SMEs and specifically examined cloud analytics. It was also a one-time survey, so it cannot establish cause and effect or tell us whether respondents eventually became regular users.
Nevertheless, the findings offer a useful perspective for managers considering why a technically successful implementation may still struggle to gain acceptance.
Installation is not adoption
I don't think the lesson here is that leadership matters less. If anything, it suggests that leaders need to think more carefully about how their support is expressed.
Approving a budget, launching a platform and encouraging employees to use it are important steps. But the harder work often comes afterwards, when the organisation must reconcile different ways of working, improve data reliability, remove unnecessary processes and demonstrate that the new system is worth using.
Perhaps we should stop asking only whether our leaders support the analytics initiative and start asking whether the initiative supports the people expected to use it.
An analytics platform is installed when the technology works. It is adopted when people choose to use it as part of their work. Leadership can authorise the first. Its greater contribution may be in creating the conditions for the second.
Mini-appendix: Understanding the research
Perceived usefulness: Whether employees believe a technology will improve their work performance.
Perceived ease of use: Whether employees believe using the technology will require relatively little effort.
Compatibility: How well the technology aligns with existing workflows, tools and organisational practices.
Relative advantage: The benefits employees or organisations expect from a new technology compared with existing alternatives.
Adoption intention versus actual adoption: An intention to use a system does not establish that someone will use it regularly over time.
About the study: Langer and Feld surveyed 368 employees from German SMEs, using responses collected in 2024. Their statistical model explained 78% of the variation in adoption intention. Because the research was cross-sectional, focused on cloud analytics and involved a sample with some sectoral imbalances, the results should not be treated as universal or causal.
Reference
Langer, B., & Feld, A. (2026). Examining determinants of cloud analytics adoption in SMEs. Information Systems and e-Business Management. Published 13 October 2026.
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