Workplace AI adoption can be hindered by unexpected human factors, including employee shame. In this post, an AI expert recounts real-world examples of overcoming these barriers to unlock a virtuous cycle of human-centered innovation.

How Shame Impedes Automation

Consider the following scenario, which may sound familiar:

It’s Monday morning and Jada’s fingers move mechanically across the keyboard, filling in the empty cells. She’s already spent an hour copying and pasting into this mind-numbing spreadsheet. She knows she’s making mistakes. Small errors used to haunt her, but now just feel inevitable. A value pasted into the wrong cell here, an incorrect label applied there. This isn’t what she signed up for. Last year’s promotion was supposed to lead somewhere meaningful, not to mindless tasks. But she doesn’t care anymore. No one even reads these. She sighs and hits ‘save’ on another report for the Workplace Efficiency Taskforce. The irony stings.

If the best applications of AI promise to free us up to focus on more creative, strategic tasks, why aren’t dishes and email solved yet? Rather than enthusiasm, the first wave of enterprise “workplace AIs” has instead met user resistance. Though poor user adoption is usually down to bad UI/UX and overly aggressive rollouts, VCs often attribute failure to things out of their control, such as an aging workforce afraid of being automated out of a job.

But as someone who has been working for two decades now, I’m skeptical about that explanation. It’s hard to imagine masses of workers intentionally sabotaging AI adoption in the interest of protecting the sacred ground of filling out timesheets and skimming emails. Probably they’re just skeptical, too. As an employee, it’s hard not to be a bit skeptical about enterprise software. How many timesheet software tools have you had to learn? How many times have we seen flashy new features released while bugs and inefficient processes go unresolved?

At Rotational, we take the perspective that AI adoption often starts with building (possibly, rebuilding) trust. In my experience, feelings of shame, like those described in Jada’s story above, can prevent employees from identifying use cases that would be ideal for automation, even if doing so might improve employee engagement. In this post, I’ll share three AI use cases based on real projects I’ve worked on, along with some tips on how automation (and a little empathy) could help.

Use Case #1: Data Entry

Emma works as a financial analyst at a mid-sized company. Every Friday, she spends hours manually entering financial data from receipts, invoices, and bank statements into the company’s accounting software. She knows the process is repetitive, time-consuming, and prone to errors. But despite the frustration, she keeps doing it. If she admits she’s doing repetitive data entry, she might be seen as inefficient or lacking in analytical skills. After all, her job title should be about interpreting data, not copying and pasting figures.

Emma’s boss might perceive her reluctance to adopt automation as a sign of resistance to change or a lack of adaptability. The Boss might assume that Emma fearful of being replaced by machines, or stubborn. But this perception leads to negative outcomes for not only for Emma, but also for the Boss and the company, since they create a barrier to effective communication and collaboration around AI implementation.

Instead, I’d coach the Boss to take a different tack, calling for a 1-1 meeting with Emma to say something like:

Emma, I believe that automating your data entry tasks would significantly benefit both you and the team. By implementing tools like Optical Character Recognition (OCR) or AI-powered financial software, we could automate the process of populating accounting records from scanned documents and online receipts. This would free you up from repetitive and error-prone manual work, allowing you to focus on more strategic and impactful tasks, such as financial forecasting and analysis. I envision you playing a crucial role in this new workflow, acting as a supervisor to the automated tools while you leverage your expertise to analyze the data and guide our financial strategy.

By giving voice to Emma’s struggles the Boss fosters trust and encourages Emma to embrace the change, leading to a smoother AI implementation and ultimately, better outcomes for everyone.

Use Case #2: Customer Support Ticket Sorting

Chris works as a customer support specialist for an e-commerce company. Each day, he sifts through dozens of customer inquiries, sorting them into categories and assigning them to the appropriate teams. The process is monotonous and repetitive, often requiring Chris to review each ticket for issues that could easily be identified by software. Chris feels that if he admits that the task of sorting tickets is not only mundane but also inefficient, he might be perceived as unskilled in problem-solving, which is the essence of his job. He’s worried that asking for automation would make him seem less capable of handling the workload.

Evan, Director of AI at this organization, might perceive this reluctance to adopt automation as a sign of Chris’s resistance to change, or his lack of technical skills. Or he might assume that Chris is just another “AI hater.” But that won’t help either of them.

Instead, Evan could take a more empathetic approach, something like:

Chris, it sounds like the current process of sorting customer support tickets can be really time-consuming and repetitive. I’ve been exploring some AI-powered solutions that I think could streamline this process. For instance, we could train a model to analyze the content of each inquiry, write a first draft of the support ticket, suggest the correct department, and even flag urgent issues. This would free you up to focus on more complex and challenging tasks, like providing personalized support to customers. We all know that the special human touch you add is what drives retention.

By acknowledging Chris’s potential concerns, Evan fosters trust and helps Chris understand his necessity as the human-in-the-loop.

Use Case #3: Inventory Management

Sarah is the operations manager at a small retail chain. Each week, she manually updates the inventory system by checking stock levels, comparing them with sales data, and ordering products. This task takes up a significant portion of her week, and she frequently has to deal with discrepancies and human error when inputting data. Yet Sarah is hesitant to acknowledge how inefficient and outdated this manual process is. She feels that as an operations manager, it’s her responsibility to ensure the system runs smoothly — if she were to ask for automation, she might appear as if she’s not capable of keeping track of inventory using standard best practices.

Sarah’s CEO might assume that Sarah is comfortable with the status quo, but this limits not only Sarah’s professional development opportunities but also the growth of the company.

Instead, the CEO could take a more empathetic approach, something like:

Sarah, managing inventory is taking up all your time and I want you to be able to focus on higher-level responsibilities, like process optimization and vendor negotiations. I’ve been looking into AI-powered inventory management solutions to help us automate stock level updates and reorder items when they reach a minimum threshold. I see this as an opportunity for you to contribute more strategically to the growth of our business. What do you think?

By framing the automation as an opportunity for professional growth, the CEO encourages Sarah to embrace change, both for Sarah and for the organization.

The Empathetic AI Leader

For employees like Jada, Emma, Chris, and Sarah, the shame of admitting they need help can make such use cases invisible at first. To establish trust, I resist the urge to dive in immediately to “AI all the things” and instead start by taking some time to learn and understand how the team is currently getting the work done. Once I have an idea for a project, I share it with employees to get their feedback. After some refinement, I present a plan to the leadership team.

The leaders who are most successful with AI understand that being an empathetic ear to the folks in the trenches is the best way. They understand that total automation (i.e. replacing workers with AI) almost never works. They understand that transparency reduces fear and builds confidence. With a culture of trust comes a workplace where employees feel safe enough to identify the tasks ripe for automation. With trust comes a workforce that is motivated to help make better tools and processes. By removing the stigma around automation, we not only improve business outcomes but also empower employees to do the work they were hired to do.

Here’s a tip… Get the ball rolling! At your next All Hands Meeting, share an example of a repetitive, boring task you have had to do (either recently or in the past) and talk about how it made you feel.

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