Q&A: Nick Wailes on how AI is transforming the workplace

It is hard to underestimate how disruptive and transformative AI might be for the workplace in the future. We are already seeing some of these changes and they will only accelerate over the next 3 to 5 years. At a very basic level, AI is and will continue to transform who is doing work in your organisation. 

As an individual, you will be working with AI companions that help you do your work, whether that be drafting emails, reviewing and summarising data and information or in crafting new approaches to problems. To get the most out of these companions, you will have to understand their strengths and weaknesses (what AI is good at and what it isn’t) and ensure that you are engaging with it and refining it to ensure that it is producing quality outcomes. 

At a team level, more of the work will be undertaken by AI agents that are able to act autonomously. While this has the potential to dramatically improve performance and increase productivity, you are going to need to be able to monitor and improve what they are doing and also understand how that complements and interacts with the work of others in the team. 

The workplace is becoming a much more complex set of interactions between humans and technology and careful attention will need to be given to how these interactions are structured. As part of this, individuals and organisations will need to better understand, foster and develop the uniquely human capabilities that AI can’t reproduce and ensure that these are not designed out of work. As AI becomes more ubiquitous, it will be these human capabilities that are the source of competitive advantage.

AI means that leaders need to become more ambidextrous: comfortable working with technology but at the same time focused on creating working environments that allow humans to develop and flourish.

I think there is a huge misconception that all that you need to do is give people access to AI and they will start using it. The evidence doesn’t support this. Most organisations that implement AI find that adoption rates are low and that it is hard to identify a clear ROI. 

There is a key role for leaders in reducing barriers to adoption. They need to ensure that the organisation addresses issues like privacy and ethics, to make the use of AI safe and that their people have the tools and training they need to incorporate AI into their roles. 

They need to create a culture where individuals have the time and the permission to experiment with AI and they need to celebrate those who are exploring the possibilities of AI. And they also need to reassure people that they are valued and that the AI is meant to be a tool not a competitor.

In an AI world, employees need two sets of skills. First, they need to become experts at making the most effective use of AI they can in their roles. This means really investing time and effort in exploring what these tools can do and actively experimenting with them in their day-to-day roles. 

Second, they really need to continue to develop the uniquely human capabilities that will set them apart. These include the ability to use their judgement to make sense of complexity and to collaborate effectively with others to identify and implement solutions. 

These in turn require the development of skills such as critical thinking, judgement, empathy, storytelling, sensemaking and creativity. Keeping your skills up to date has never been more important and if you are not getting development opportunities in your current role you really should be thinking about changing roles or whether there are other ways that you can be building these skills.

In the context of rapid change, the key elements of a successful AI strategy are likely to be

a. Doing something: Sitting back and waiting for the technology to stop developing isn’t going to be successful. You need to get going with something and start learning from it. The skills and capabilities that you develop now should position you well to move quickly when the next wave of technology hits.

b. Over invest in people not systems: In most instances, there is a real danger that organisations will overinvest in proprietary AI systems that will soon be superseded by widely available platforms and systems. A much better approach is to invest in your people and their capabilities to take advantage of these systems. These capabilities are transferable and can be redeployed as the environment changes.

c. Start simple, experiment: There are two ways that AI might enhance competitive advantage. The first is by increasing the efficiency and effectiveness of what you are already doing. This is a good place for most organisations to start and might involve focusing on things like, for example, “how do we use AI to improve our responsiveness to customers?”

While your competitors will be able to do the same things and any competitive advantages therefore might be temporary, this is a good place to start.

The second way that AI can enhance competitive advantage is by making it possible for an organisation to service a market or a need in a completely different way either in terms of quality, cost or both. This is more challenging but is something that organisations should be experimenting with, seeing how AI might be adopted to help accelerate innovation or to address a market opportunity in a completely different way, at the same time that they are looking to improve the current business model.

d. Like AI, your strategy should learn: An AI strategy that is set in stone and doesn’t evolve is bound to fail. Leaders need to take a lesson out of the generative AI book. The reason that AI is so powerful is that it is always learning from what has happened. The AI strategy needs to be the same, setting values and purpose but being able to evolve as the landscape changes. 

For example, a large bank recently introduced AI into a call centre with the aim of reducing costs. That wasn’t successful. The introduction of AI actually generated greater volumes of calls and additional work that was referred on. You could easily conclude that this was a failed strategy but in this case the additional work generated was because customers were so delighted by their interactions with the AI they were interested in additional products and services. That bank learnt that what it thought was a cost cutting strategy was actually driving revenue growth.

One really valuable experiment is to see what AI does well and the areas that it doesn’t. In one team in an organisation they decided to get AI to do as much as possible for a week: summarising meetings and reports, replying to emails (only internal ones), writing draft policies and putting together presentations. They then went back and reviewed these outputs. 

One conclusion they reached is that they needed to get better at directing the AI so as a team they undertook training in prompt writing and then repeated the exercise. They saw significant improvements but there was still a gap between what the AI produced and the quality of work that they needed to produce. 

This exercise helped people in the team understand where AI added value and the value that human input, knowledge and insight bought and also helped identify where they should focus their efforts.