The way we work isn’t working. UK employees are losing nearly two full working days each week to low- or no-value tasks – from form-filling to chasing approvals – with nearly 45% of administrative work considered inefficient, according to PwC’s UK Workforce Hopes and Fears Survey 2024.
AI promised to improve this situation by taking on the tasks that stop people from doing engaging and impactful work. But as many organisations are discovering, it’s not quite that simple.
Layering AI onto flawed processes and workflows rarely results in long-term productivity gains, let alone more time for high-level work. In fact, it can actually make life more complicated for teams.
That’s not to say AI isn’t a potent tool for knowledge workers: research by Miro found that 76 per cent believe AI could benefit their role.
However, leaders need to rethink their approach to implementing the technology to ensure their teams – and the organisation – truly reap the benefits.
Today, for instance, 54% of workers struggle to know when to use AI, while 35% describe their AI skills as “nonexistent”.
They are also getting mixed signals from their organisations, which adds to the confusion around the technology.
For example, 39% report that their company often abandons AI efforts, while 46% agree that there is more talk than action.
“The complexity of bringing AI to organisations is often due to thinking it is just a [technology] implementation,” says Tomás Dostal Freire, CIO and head of business transformation at Miro, a collaboration platform with AI features designed to streamline workflows and accelerate innovation.
“You need to take a step back and really think through how AI can transform how you operate.”
Amplifying human skills
The hype around AI is part of the problem. All those articles and LinkedIn posts promising mind-blowing productivity gains with one simple tool have arguably created unrealistic expectations about what can be achieved.
But the thing leaders really need to think about is: “What does it mean to the organisation – not in terms of full automation and replacing people, but rather as an augmenting force for employees?” asks Dostal Freire.
This demands a different kind of leadership approach – one that asks not how much AI can automate, but where it can amplify people’s ability to collaborate, innovate and solve complex problems.
Smarter implementation of AI focuses on where teams tend to lose momentum, for example – perhaps due to information silos, excessive time spent searching for resources, or an inability to ideate at speed – and how the technology can address these friction points.
You need to take a step back and really think through how AI can transform how you operate
A human-centric, rather than tool-led, strategy focuses on how AI can help people connect and contribute more effectively, strengthen feedback loops, and innovate at speed.
Organisations that successfully leverage AI to amplify human potential tend to focus first on the desired outcomes, then, in the following order, the people, processes and technology needed to achieve them. Crucially, they place a strong emphasis on employee education.
Indeed, employees should be encouraged to upskill not only for the employer’s benefit, but for their own long-term career value. For as Dostal Freire says, “AI literacy is the new digital literacy.”
Inspiring employees
Despite this fact, initiatives that support and develop AI literacy are clearly lacking today. Notably, formal training is the number one thing the 8,000 global employees recently surveyed by Miro said would help them feel more confident about adopting AI.
This training needs to reflect the nuances of AI usage. “When you think of a traditional technology implementation, you usually end up training people in how to use the tool, so it’s a tool-centric approach,” says Dostal Freire.
“We need to shift towards a people-centric and workflow-centric approach. So it’s no longer only about ‘how to’ use AI, but also a lot more about ‘when to’ and ‘why to’ use AI.”
To successfully implement human-centric AI, organisations should also focus on three interconnected principles: educate, inspire and empower.
The education element, as mentioned, moves beyond tool training to help people understand context and application.
Inspiration, meanwhile, involves showing relevant examples of how it’s successfully amplifying human ability.
“Show the art of the possible. Even if you think you know what AI can do, you still need to share what is best-in-class from peers…and it should not be AI at large, but for a team or department – so finance, marketing, etc.” Dostal Freire explains.
Finally, empowerment means creating safe environments for employees to use the technology to its full potential. “Once they know what AI is, and they’re pumped about what it can do…you need to give them platforms to play around with it and discover it for themselves.”
This requires security teams to become enablers rather than blockers, adapting governance at the speed of AI innovation—a challenge that can be met through close collaboration between the CIO, CISO, and other members of the security leadership team.
Measuring success
The way leaders gauge the success of AI implementations also requires a rethink. Measuring how many people are using a platform, for example, is much less relevant a metric than whether the business outcomes that were the driver for adoption have been achieved.
The concept of Return on Employee (ROE) can also complement ROI when evaluating AI’s impact.
Rather than simply focusing on the financial return, it reveals the broader value of an implementation by examining how it impacts employees, factoring in things like job satisfaction, improvement in collaboration and the quality of work produced.
“It’s harder to quantify…but if you have more engaged employees and faster decision-making, ultimately you do see the results in better outcomes,” says Dostal Freire.
Almost two-thirds of workers agree that AI can improve wellbeing and job satisfaction, for example, which, in turn, can bolster productivity and innovation. More than a third also believe it can enhance creativity, while 29% believe it can lead to better communication.
As AI advances toward more sophisticated applications, including autonomous agents, the human amplification model will become even more critical for success.
Dostal Freire says that empathy should inform AI deployment decisions and is a powerful way to assess which processes should be automated and which should remain primarily human-led.
High complexity, high empathy activities would involve a human taking the lead, for example, perhaps with AI as a copilot.
“However, where there’s low human touch advantage and high complexity, or low human touch advantage and high repetition, that’s where you could rethink and automate,” says Dostal Freire.
In the end, the best path forward isn’t about choosing between human capability and artificial intelligence. Instead, it’s about creating conditions where AI amplifies what makes humans most valuable: the ability to collaborate, innovate and solve complex problems together.
For more information, please visit: www.miro.com
The way we work isn’t working. UK employees are losing nearly two full working days each week to low- or no-value tasks – from form-filling to chasing approvals – with nearly 45% of administrative work considered inefficient, according to PwC’s UK Workforce Hopes and Fears Survey 2024.
AI promised to improve this situation by taking on the tasks that stop people from doing engaging and impactful work. But as many organisations are discovering, it’s not quite that simple.
Layering AI onto flawed processes and workflows rarely results in long-term productivity gains, let alone more time for high-level work. In fact, it can actually make life more complicated for teams.
