Talking Tech & Transformation Series: Interview with Mike Brown

“More than ever, AI is making digital transformation holistic – changing commercial models, operational processes, the way people will add value to customers, and how we manage risk. We will need to be holistic in our approach to digitalisation.”

La Fosse Executive’s Olivia Ogilvie (Head of Research) met Mike Brown (formerly Global Managing Director Digital, Technology, and Transformation at AMS) to discuss several topics currently on the tongues of transformation leaders: digital transformation and automation, the adoption of AI, the evolving capabilities of technology, and change management.

Mike, your experience to date has spanned transformation, strategy, and value creation. Can you talk us through your background in more detail?

I have been fortunate enough to have had a varied career spanning management consulting, senior leadership roles, and being an Operating Partner with a Private Equity fund.

I have also had the chance to lead digital transformations – both developing new propositions and driving productivity enhancement – from strategic conception through to launch and adoption. I’ve done this in businesses ranging from large organisations to smaller but rapidly growing firms.

What have your biggest learnings been across your career in regard to digital transformation and, ultimately, how to drive a successful strategy?

There have been a lot of learnings, not least from things that haven’t gone so well, but two things spring immediately to mind.

Firstly, people sit at the heart of any good digital strategy. They are the customers, the users, the leaders telling the digital story…without having a clear view on what it means for all the people it impacts, any digital strategy and its implementation are likely going to be unsuccessful.

Secondly, although technology moves continuously forward, the drivers of a successful strategy remain largely the same:

• Understanding of where and how value can be created for customers
• Deep knowledge of the processes that deliver that value
• Awareness of what markets and competitors are doing – to ensure that the strategy delivers a sustainable competitive advantage
• Prioritising to maximise return on investment

What is the best way to leverage automation?

Any successful digital strategy has to take into account both today’s reality for the business (and the industry in which it operates, the customer base it has, etc.) and the company’s digital aspirations; does it want to be at the bleeding edge of what technology will enable, a disruptor of its industry, a rapid follower, or even a more measured adopter.

For some, robotic process automation and process mining will be the norm, and therefore leveraging AI could be the next step. For others, introducing master data and a managed workflow might be a significant upgrade. So, there is plenty of value still to be had from what we might call more ‘traditional’ automation. Additionally, while a lot of focus is on generative AI right now, there will still be many use cases best delivered by proprietary machine learning.

Ultimately, most businesses will have to think about the activities they undertake where humans truly add value versus those that can be done effectively by technology, and then design their propositions and Target Operating Models accordingly.

As we know, AI is a huge topic at the moment with huge potential to drive transformative change. Having conducted research across our extensive professional tech leadership networks, we found that 67% of businesses are considering the use of AI in their transformation projects, with a further 44% wanting to utilise the technology to identify inefficiencies, and 42% looking to harness AI for business growth. What would your advice be for any company looking to embark on adopting AI?

On top of sticking to the principles of what makes a good strategy, there are four AI-specific considerations that are top of mind:

1. In the race to adopt AI, don’t lose sight of what makes a good strategy. We shouldn’t do something for the sake of doing it.
2. Being deliberate in decisions about the scale of your AI applications will be critical – are you encouraging individuals to use AI independently, is AI going to be deployed at the team or customer level or are you aiming for AI to learn from and build on company-wide collective capability? We think of human intelligence as individual. In reality, human intelligence is collective (I don’t have to reinvent things others have created). Similarly, AI is going to be most effective if it can draw on the collective knowledge of your business; both data and the judgments and insights of individuals (users, customers, suppliers). This availability of inputs is one of the reasons why consumer-focused businesses (as with many technologies) are often ahead on the application of AI. Of course, unlocking this potential from AI is going to need real investment: planning, coordination, operational and commercial change – and we will likely need effective approaches to scaling-up from individual, team and business-unit AI ‘pilots’.
3. AI and the people using it – our colleagues, our customers, our suppliers – are going to become extremely synergistic. Digital plays a role in eliminating repetitive tasks, reducing errors and rework, speeding delivery, and structuring work. Use cases for AI include summarising, drafting, customer service chatbots and enabling pattern recognition at a level almost impossible for humans to achieve. Any AI strategy, as a result, has to carefully address how humans will interact with the increase in data and insight that both digital and AI will provide. In my view, AI will drive a very significant increase in domain experts (e.g. those people with knowledge and context – those making decisions and judgements every day) playing a direct role in the development of AI applications. Technologists will still have a role, but the days of ‘the business’ handing a brief to the technology team and waiting for outputs to test and deploy will be replaced with users adding to AI capability on an ongoing basis. AI will impact the way that people do their jobs, the skills they will need, the way that they generate satisfaction from how they work, and the products and services they buy. A digital and AI strategy, therefore, has to be centred around a vision for the people as much as the art of what is technically possible.
4. AI presents many opportunities but there are also new forms of risk. Some of these will be teething issues (like the example of the retailer who created a recipe AI use case, but the resulting recipes included dishes that used dangerous and toxic ingredients because the appropriate context hadn’t been provided). Some will be the result of the single mindedness with which AI approaches its optimisation. Ultimately, the impact of AI likely needs more thought than a historic cursory risks and issues slide.

Return on investment is always a leading factor when integrating new systems and infrastructure, and AI is no different. What process do you follow to better gauge risk return to assess whether implementing AI is the right next step?

AI doesn’t require us to fundamentally rethink our approach to risk and return but, as AI starts to encroach on decision making, we will need to add to our thinking.

For me, there are a few things to think about:

• Unintended decisions driven by a lack of context. These can either be obvious, such as “hallucinations” or a lack of data driving incorrect outcomes (e.g. not considering a third factor such as health and safety). Or, these can be less obvious, for example AI amplifying historic patterns of behaviour (such as hiring non-diverse candidates for roles) without human input to challenge these.
• Pressure for more transparency where decisions are made that impact people, and where AI can make it impossible to provide that transparency.
• Gaming of AI based decision making systems by customers, users, or suppliers who deploy AI to aid them.

There are a range of approaches to mitigating these risks including deploying AI to check AI, human-in-the-loop training and checking. In business services – the sector I have focused most on throughout my career – my favoured approach is to view AI as an aid to human decision-making; using the power of AI to make recommendations and speed up the provision of insight without handing the decision completely over the AI.

Digital and AI are currently stealing the headlines but to what extent, in your opinion, will people, processes, and other core business components continue to play a part?

More than ever, AI is making digital transformation holistic – changing commercial models, operational processes, the way people will add value to customers, how we manage risk. We will need to be holistic in our approach to digitalisation.

Digital and AI, in particular, will change the relationship between the cost of achieving an outcome and the value of that outcome. OPEX will be replaced by CAPEX, time spent will become less important than judgment and context, and productivity should improve dramatically. These factors will necessitate a rethink of many traditional commercial models.

In terms of operational processes – although the focus is on technology – the start point, for me, should be the optimising efficiency by optimising the user experience. It’s crucial to pinpoint the moments that matter, to fully understand the processes people follow and the pain points that come with them, as well as have an ever-deepening understanding of where inefficiency exists (which the data provided by digital systems can enable). These things will be familiar to product developers and lean practitioners.

As I mentioned previously, the people involved throughout the value chain – from customers to sales and marketing professionals to those delivering a product and service – will all be at the heart of any digital/AI driven change. Understanding their wants and needs, including them in the development of digital product, and managing the change impact on them will be as vital as developing the technology itself.

Does large value creation equal large investment?

In my experience, that’s mostly been true historically, but the maturity of digital product development, and the flexibility of AI in terms of setting up meaningful pilots for applications is making it ever easier to innovate at smaller scale. When we add the role AI will be able to play in coding and designing UX, I expect value creation through digital will become less and less linked to large technology investments.

The exception to this is the impact of legacy systems. One factor decision-makers will want to consider where there is considerable technical debt is whether to innovate inside their legacy system environment, or innovate outside of existing architecture and, in the case of the latter, how to then bring the two together.

How do you then evolve existing service propositions, or create new services, to reflect new technologies?

Much of what we have learned about digital product development, the importance of the user experience, the value of personalisation, the ability to test alternatives rapidly, and to adjust to changing customer and user expectations remains as valuable as it has ever been.

In comparing the monolithic development processes of 15+ years ago to today, the biggest learning for me is the value in experimenting: investing in prototypes, seeing what works and then iterating. Real world experimentation will always eclipse anything that could be included even in the best digital strategy. Where we have ended up on each of the major digital transformations I have led is never exactly where we originally thought we would.

Resistance to change can readily derail digital transformation efforts. In your experience, how do you mitigate this?

As the scale and impact of change increase, the tools of change management are becoming increasingly more important.

There is a natural tendency amongst many to view new technologies, such as AI, with some trepidation. Particularly when early use cases have often focused on replacing people with technology. On the other hand, we are seeing a mass of early-adopting individual users creating their own value from use of AI.

To develop the best possible digital transformation outcomes, we need to be able to work with the early adopters as well as those with concerns and understand both parties’ viewpoints. To do this, we need to be genuinely inclusive in capturing all perspectives, and in designing digital products that work for both groups.

More than this, we also need to speak to owners of P&Ls and customer relationships about how value will be created for them, and day-to-day users about how they will be able to focus on activities that generate more satisfaction versus the mundane elements of our working lives.

AI is further complicating this change agenda by creating an uncertain future for some job categories, but also new opportunities for humans to add value. For example, we need human input and domain expertise to provide the data for AI and digital systems to operate, to add context and judgment to AI outputs, to train and monitor AI, and to handle exceptions that AI isn’t equipped for. This will mean that technology focused businesses will increasingly seek to add domain expertise from humans alongside their digital offerings.

For me, digital transformation is a truly holistic endeavour. Creating a vision for technology and for the people we work alongside is key to delivering successful transformation.

Any final words of advice?

We are experiencing a period of massive change – the early impact of technologies that would have seemed in the realm of science fiction only a few years ago. The tendency is to feel the pressure to rush to deliver, be that an application that could set you aside from the competition, a proposition that could cut through with customers, or that could boost your valuation multiple.

It is worth reflecting that sophisticated adoption of cloud technologies and the Internet took years, and early success stories often weren’t sustainable. Taking the time to develop the right strategy for you, value-adding customer propositions, and high impact productivity improving tools, will be better than being first. There is time to try and fail, learn, and try again.

As Bill Gates famously said, “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten”.

To find out more about La Fosse Executive and the end-to-end solution we offer, please contact Olivia Ogilvie.