FOMO, fraud, & fair play | A closer look at Gartner's 2025 Workplace Predictions Report

A closer look at Gartner's 2025 Workplace Predictions Report
A closer look at Gartner's 2025 Workplace Predictions Report

Gartner's most recent Workplace Predictions Report outlined nine ways ways in which the world of work will be shaped over the next 12 months.

We spoke to Senior Director Analyst Emily Rose McRae, to find out more about the research behind the headlines...

First, let’s talk about AI destroying productivity. Isn’t it supposed to improve it?

Obviously, interest in generative AI is significant right now, but we are seeing the bloom come off the rose in terms of actually seeing productivity results. The promise was that it’s going to allow us to be so much more efficient and effective, but in practice, it’s not that simple.

The companies that are struggling the most are those that have decided to become AI-first companies. Instead of asking, 'Should AI do it?' they ask, 'Can AI do it?' and run after it. Without pausing to think about the upstream and downstream implications of their changes, they often don’t see the results they’re hoping for. For example, if a tool automates responses to proposals and wins bids, do they have a plan for staffing and supporting the projects now on their plate?

Is there an element of companies jumping on the AI bandwagon?

Of course. Many companies are nervous about missing out. But to see a substantive effect, the question CHROs should ask is: 'For this investment, how do we need to change roles and workflows to get the most out of it?' If roles and workflows don’t change, it’s unlikely the investment will have a high impact.

Where roles and workflows do start to change, and organizations rethink how work is done, we might see productivity impacts. But many people expect AI to do incredible things without enough human in the loop. They miss unintended consequences, lack sufficient review and editing, and fail to account for real-world resources and ease of implementation.

Is this a resource problem? Do certain roles need to be in place to implement it properly?

It could be a staffing issue, but that’s not the only reason it’s an issue.

“Employees embrace bots over bosses in pursuit of fairness” feels counterintuitive. What’s behind that?

Managers are struggling. Many receive only a few hours of training on performance reviews and feedback, leaving employees feeling their work isn’t observed or recognized. This subjectivity makes algorithms seem like a better source for performance feedback.

It’s not just about ongoing feedback. Employees also think AI might be fairer for compensation decisions - 58% believe humans are more biased than AI in these decisions.

I do think it speaks more to managers’ performance issues than to AI’s amazing abilities. But people are more open to AI tools now, even with regulatory and employer brand restrictions on using AI exclusively for decisions like hiring and firing. Whether these tools can truly help is still an open question.

For example, early in the pandemic, productivity monitoring tools measured activity (such as mouse movement) rather than outcomes. Measuring outcomes instead of actions highlights gaps in what these tools can do versus what organizations need.

Is this the year of trial and error?

If last year was the year of FOMO, this year is about shifting expectations around generative AI. In Gartner’s Hype Cycle, we’ve moved from the peak of inflated expectations to the trough of disillusionment. The tools just aren’t capable of everything we imagined they would do.

Coming hot on the heels of generative AI is agentic AI, which can interact with programs for us. Generative AI produces average human results (we’re talking C+ or B- level), but agentic AI can perform tasks for you, like booking flights or pulling analytics to help write performance evaluations. It bridges gaps without expensive programming.

Generative AI cannot do data analysis, but agentic AI can use analytical tools and take results to another level. I think next year will see another peak of inflated expectations around agentic AI.

Can you define what is meant by “fraud vs. fair play” with AI?

The rules won’t be decided this year, but we’re figuring out the philosophy. If performance management measures outcomes, do we care how people achieve them? For instance, if a skilled person completes a task manually while another uses AI, should they be rewarded equally?

Critical thinking might be eroded over time by using generative AI. Recent studies suggest it’s more like a muscle that needs regular use. Organizations must consider the long-term implications of AI use on expertise and value.

Featured Resource

AI in Hiring: Trends, Insights and Predictions

AI in Hiring: Trends, Insights and Predictions

As AI revolutionizes the recruitment life cycle at warp speed, HR leaders must stay informed about AI’s advantages and its current shortcomings.

How can we adopt these tools to stay competitive and efficient while retaining the human touch that remains critical to optimizing candidate experience, making informed decisions, and, ultimately, building strong teams and cultures?

That is our industry’s biggest challenge as we navigate this new terrain. We hope these insights, tips, and predictions will help drive innovation and excellence in your hiring practice.

Show more
Show less

The most obvious short term impact of this is around candidates. In hiring, many candidates use generative AI for cover letters or screening questions, but hiring managers often recognize it. This raises questions about generative AI judgment in hiring. Should we expect candidates to use AI for basic answers? And does that mean screening questions need to be more complex to gauge unique value?

Are certain sectors better prepared?

No. Success depends more on resisting FOMO and thinking through second and third-order effects. For example, a law firm using AI for legal discovery might eliminate junior paralegal tasks, leaving no pipeline for developing experienced paralegals. Solutions could include involving juniors in other tasks or paying more to poach talent, but organizations must consider these down the line impacts.

What next for DEI?

Diversity is an outcome that tracks how well inclusion efforts are working. If workforce diversity declines, there may be an inclusion problem to address. For HR leaders, diversity has always been a way to measure progress, but business leaders often see it as a metric to hit before moving on. The real goal is engaging a diverse workforce for better performance.

Companies that focus on diversity have a little bit over 20% higher engagement and employee performance than those that don't. Diversity of thought leads to greater innovation. It's useful.

If it's diversity and inclusion that we're focusing on, the shift is even bigger. Instead of it being the 20% increase in engagement and 21% boosted performance, it becomes a 49% workforce wide boost in engagement and 42% boosted performance.

You can double the impact of having a diverse workforce by focusing on inclusion. That seems like a winning scenario, especially because in 2025 a lot of organizations have CEOs that are pressuring them on growth and efficiency. A lot of times it's growth without increasing head count. So, how are you going to increase performance? A more inclusive workforce, turns out to be one of the ways to do that.

Be the first to comment.

Sign up for a FREE myGrapevine account to have your say.

You are currently previewing this article.

This is the last preview available to you for the next 30 days.

To access more news, features, columns and opinions every day, create a free myGrapevine account.