For over two decades, I’ve been involved in digital transformation projects and dynamic agile backlogs. Large and small, public and private. Some were beyond complex, and some were relatively straightforward. 

Regardless of size or complexity, one of the most challenging aspects has always been the human element, specifically the aspect of change management that directly applies to employees and new ways of working. 

Now, as organisations embrace AI-powered transformation, I see the same aspirational statements and the same commitment to adopting new ways of working, and so forth. And while I recognise that AI is remarkable, a game-changing phenomenon, and everything is happening at a speed that is hard to keep up with, there is still a common denominator. 

And that is, we are still dealing with and talking about people. 

I’ve noticed the emphasis is placed everywhere else but on the employees and the customer, which is why I believe these transformations often take longer or are more challenging than they need to be.

Which is why this caught my eye when reading this article about a new Ibec report (italics are mine).

"IBEC’s 2024 HR Update Survey found that 70% of respondents recognise AI’s potential to boost productivity, reflecting its transformative impact. Yet, while AI is a powerful tool, it would be naïve to underestimate the continued importance of talent, business operations, and organisational cultures that depend on human interaction and creativity. Realising the full benefits of AI will only be possible if people remain at the centre of it.” - Anne O’Leary, President of IBEC and head of Meta Ireland

AI is not just an incredible tool and is very much worthy of the hype around efficiencies and other benefits. It is an enabler of efficiencies, insights, and innovations that were previously out of reach.

And don’t just take that from me.

As noted in the MIT report, Misunderstood: Shadow AI economy booms. At the same time, headlines cry failure, researchers found that some of the most dramatic cost savings we documented came from back-office automation. Companies saved $2-10 million annually in customer service and document processing by eliminating business process outsourcing contracts, and cut external creative costs by 30%.

These gains came “without material workforce reduction,” the study notes. “Tools accelerated work, but did not change team structures or budgets. Instead, ROI emerged from reduced external spend, eliminating BPO contracts, cutting agency fees, and replacing expensive consultants with AI-powered internal capabilities.”

But I believe that if organisations do not understand that realising and optimising these benefits will only be possible if they also recognise all of the other factors involved, and that it’s more nuanced than simply using this tool means you can do that task in less time. 

#1 Utilise the saved time strategically

We know that AI enables organisations to automate routine tasks, streamline operations and reduce manual workloads.

But efficiency isn’t about minutes saved, it’s about how those minutes are reinvested.

Having all of this extra time and money, in some cases, is only of value if it is used wisely. 

But is it up to each employee to determine what is best for their time or saved costs? 

What is the priority, how will you measure its success, and how does it impact the overall objectives of that person’s role and place in the business?

Saving time is only of value if the ultimate outcome is an improvement in, for example, customer service. 

It is up to management to decide where to focus the freed-up time, and setting aside some of it for brainstorming and innovation can lead to improvements that were previously impossible.

Just imagine what might be proposed by your employees who now have time to think and make suggestions!

#2 Integrate workflows 

Currently, many of us operate on multiple channels and tools. Whatever the platforms of choice are at your job, my guess is that there are quite a few. And so adding another tool or tools, although designed to make things easier, may just become yet another cog in a sea of cogs.

Therefore, before implementing a new AI platform or solution, ensure that it is not seen as one more thing to check, one more alert interrupting your day or confusion.

What we tell clients is that any AI solution has to be designed and implemented as part of an organisation’s ecosystem, not simply an add-on. If it adds to people’s workloads, it is harder to achieve wide-scale adoption and employee buy-in.  A good way to introduce a new tool is by starting with a small-scale proof of concept (PoC). This can help identify exactly where a tool will best integrate smoothly with your existing processes and determine whether it will create friction or unnecessary additional work.  

#3 Adopt a new culture 

I read this recently in a report by McKinsey, and thought, yep, that’s what I’ve been saying for years.

Efficiency is not just about technology; it’s about mindset and process change. For many people, it will open entirely new ways to work, whether that involves adopting a more receptive environment to experimentation and scaling faster than ever before.

And as the report suggests, it is up to management to acknowledge this beforehand and implement the necessary guidelines or ways of working to facilitate these changes. 

We always suggest to clients that they look for and promote their early champions. These early adopters can be more effective at generating interest than even the best internal messaging. Running targeted PoCs where everyone can provide feedback also works and is a great way to help employees feel some sense of ownership or empowerment, which can reduce the fears or uncertainties associated with the unfamiliar. 

#4  Recognise the risks 

Time and again, I see data privacy and security concerns as the main challenges when it comes to AI implementation. 

To me, it’s twofold- there’s the problem of data overload, and then there’s also the issue of ethics and decision fatigue. We’re all still coming to grips with the opportunities AI presents, and we’ve yet to fully understand the implications and how it relates to the law and society as a whole. 

Regulations like the EU AI Act are still relatively new to many business leaders, let alone employees, and the risks and lack of clarity around governance are daunting. And adding further confusion are recent challenges to this legislation and proposals to loosen some of the restrictions. Watch this space. And in the meantime, if you are unfamiliar with the Act, my colleague Fergal Lawler offers some essential reading in his latest blog, AI Governance in 2025:What you need to know.

However, the bottom line is that leadership must step up and establish clear AI policies and guidelines, and do so in a way that balances speed with trust and empathy.

#5 Outline your AI-future 

Fail to plan and you plan for failure. Or something along those lines. I think the biggest challenge for companies today is determining where to start. After all, every day we are bombarded with new applications, new technologies, and it’s all overwhelming.

Which is why a roadmap is essential.

One that:

• Identifies high-impact areas
• Advocated a phased adoption
• Measures results and iterates

And it can be hard to develop this roadmap internally, which is why agencies like All human are key. We have the knowledge, experience and the relevant skills in this arena to confidently guide an organisation’s AI-powered transformation.