The screen glowed, a sterile white against the muted grays of the cubicle farm. “Okay, now remember,” Brenda’s voice chirped, echoing just a little too brightly in the quiet office, “after every call, you absolutely have to fill out these 14 fields. Yes, 14. Even if only a 4 of them seem relevant to you, the rest are critical for the manager’s daily sync report. Otherwise, it’s like the call never even happened for us, right?” She offered a reassuring, albeit slightly strained, smile. The new hire, a flicker of bewildered exhaustion already in their eyes, nodded slowly, fingers hovering over a keyboard that looked suspiciously like a relic from 2004. This wasn’t onboarding; it was initiation into a new, unpaid, part-time job as a data entry specialist.

Success Rate

This is where we often get it wrong.

We pour millions into enterprise software, convinced it will streamline operations, unlock insights, and drive efficiency. When adoption stalls, when employees resist, the immediate, often unchallenged, diagnosis is a ‘training problem.’ If only we had better videos, clearer manuals, more patient Brenda’s! But what if the problem isn’t a lack of training, but an abundance of user-hostility? What if the software isn’t designed for human productivity at all, but primarily for data extraction, turning every employee into a reluctant human API? I’ve seen it play out 44 times in my career, maybe more, and I’ve even been guilty of pushing the training narrative myself once or twice, thinking if only people understood the value, they’d comply. I was wrong, and I was definitely not happy about being wrong after arguing for it for 24 minutes straight.

Consider Emma P., a safety compliance auditor. Her job is inherently about detail and documentation. Every broken guard rail, every near-miss reported, every safety training session conducted, every piece of personal protective equipment checked, requires meticulous record-keeping. When her department rolled out a new ‘all-encompassing’ compliance suite, the pitch promised a unified view, predictive analytics, and effortless reporting. What Emma got was a system that, after she’d done her on-site inspection, demanded she re-enter findings into 4 distinct modules, each with its own set of 24 required fields. A simple incident report, which used to take 4 minutes with the old system and a quick email, now consumed 14 minutes, sometimes 24, just to navigate dropdown menus and text boxes that reset randomly or threw obscure error codes ending in 4.

Her actual audit work? That remained the same. Walking the factory floor, observing procedures, interviewing staff – that was where her expertise truly delivered value. But the new software, far from supporting this, felt like an anchor. It wasn’t about capturing data for her to use; it was about her feeding the system for someone else’s dashboard. Her colleagues, initially optimistic, quickly developed intricate shadow workarounds. They’d jot notes on paper forms (the old, reliable, 2004 versions), compile them over a 4-hour period, and then dump it all into the system in one dreaded batch entry session, fueled by lukewarm coffee and a growing sense of resentment. The system, theoretically designed to improve safety, was making Emma spend less time on the floor identifying risks and more time staring at a screen, clicking through fields that offered no practical value to her daily mission.

Minutes (Old System)

This dynamic erodes trust.

When employees perceive a tool as a burden rather than a benefit, they disengage. They see the promise of efficiency evaporate into an hour-long chore of data transcription. It subtly, but powerfully, redefines what a ‘good employee’ means. It stops being about the quality of the audit, the ingenuity of the solution, or the positive impact on safety metrics, and starts being about how diligently one fills out those 14, often irrelevant, fields. The real problem isn’t that Emma needs more training on how to use the software. The real problem is that the software is a bad deal for Emma, extracting her time and effort without giving proportional value back.

This isn’t to say all software is inherently bad or that data isn’t important.

Of course, it is. But the disconnect often lies in the design philosophy. Too many systems are engineered from the top-down, focused on what leadership needs to see – the aggregate reports, the compliance checkboxes – rather than what the actual user needs to do their job effectively. When the primary purpose becomes a glorified digital filing cabinet for other departments, rather than an empowering tool for the person at the coalface, adoption is doomed. It’s a fundamental misunderstanding of human-computer interaction, a prioritisation of surveillance over enablement. It’s a systemic design flaw, not a human learning deficit. We often miss this crucial point, distracted by the shiny veneer of ‘innovation.’

Success Rate

What if the software worked for you?

What if the software worked for you instead of the other way around? The aspiration, the true promise of technological advancement, should be to liberate human potential, not to shackle it to endless data entry. When systems can seamlessly integrate data from disparate sources, when machines can communicate with each other, gathering information without human intervention, it frees people like Emma to focus on the truly complex, value-added tasks only humans can perform: critical thinking, problem-solving, empathy, and strategic decision-making. Imagine Emma spending an extra 24 minutes on a factory floor, observing a new process, or mentoring a junior auditor, instead of wrestling with a clunky interface. That’s where the real transformation lies, and that’s the ethos behind solutions that understand the human element, rather than treating it as another input node. It’s about designing for collaboration, not just extraction, allowing the system to do the heavy lifting of data collection so that human effort is directed towards higher-value insights and actions. Companies that prioritize such an approach often integrate their operations with robust, intuitive systems designed for real human workflows, alleviating the data burden significantly, allowing teams to focus on core tasks without becoming involuntary data technicians. For instance, platforms like OneBusiness ERP focus on unifying business processes and automating data flow, aiming to drastically cut down on the manual ‘feeding the system’ burden, freeing employees to concentrate on their actual roles and strategic initiatives, rather than becoming glorified data-entry clerks. This isn’t just about efficiency; it’s about reclaiming human dignity in the workplace.

We chase the dream of real-time insights, but forget the very human cost of acquiring that data. Every field filled manually is a moment taken away from generating revenue, solving a customer problem, or simply thinking creatively. Every frustrating user experience is a chipped piece of employee morale. We spend $474 on elaborate dashboards, then wonder why the data feeding them is incomplete or inaccurate. It’s because the people who hold that data feel like they’re performing busywork, not contributing to something meaningful for them. A system that demands human beings act as mindless data conduits fundamentally misunderstands the value proposition of human capital. It’s not a solution; it’s a tax on time and spirit, an additional, unlisted, part-time job that no one applied for, but everyone is expected to perform. And eventually, something has to give.