Digital Problem Solving with Greg Smith from Arthur D. Little
Today we’re delighted to welcome Greg Smith to the show. Greg is the Managing Partner at Arthur D. Little (ADL), a multi-national consulting firm, an acknowledged thought leader in linking strategy, innovation and transformation in technology-intensive and converging industries.
In this interview episode, we talked about the challenges for the consulting business, Arthur D. Little’s key differentiators from other consultancy firms, business case studies of how AI/ML technologies can amplify your business solutions.
Greg, you have a notable career in consulting. Could you provide us with an understanding of your career path that led you to your current position in your current role with the company?
Nearly 20 years ago, I took the plunge into management consulting and technology consulting, having spent until that point all my career towards buying consultancy, which was both a help and a hindrance. I probably overestimated how easy it was, and it turns out that was pretty hard, especially when you hadn’t done it before.
My first venture in consulting was back in the early 2000s with Capgemini, where I joined as part of their technology consulting leadership. From there, I moved to a remarkably similar role with Atos Consulting, another French-owned large managed services system integrator. It was pretty interesting because I won a project with a large logistics company that was a B2C parcel business about to acquire a B2B parcel and a courier business in the second week of my career. I did the technology and integration strategy for the post-merger. And then the Executive Chairman asked me to be the CIO to deliver the strategy, which was my role for the next three years.
Later, my colleague and I wanted to do something different based on the emerging patterns of technology rather than traditional enterprise IT. So, we joined Arthur D. Little to launch a new practice which later became digital problem solving. I’ve been there ever since. I still run the digital problem solving team. But also, as it’s common in consulting, where you tend to pick up a new job every year, but you never give anything back. So in these 6 years, I’ve got a number of hats that I wear.
Managing Partner at Arthur D. Little
What is the most challenging part of consulting business?
Moving from a client role into a consulting role, I noticed a couple of things being different. One of them is context switching. We believe that things that work in one industry can often help unblock a problem in another industry and we deliberately want coverage across as many industries as we can because that provides more insight. That also creates a challenge around context as the client expects you to be an expert in many things in tight timescales. And that ability to flip between contexts is not something you experience on the client-side. There you’ve often worked for many years in one industry, and you know it intimately. It’s not quite the same in consulting.
I want to deliver the best possible outcomes for the clients we work with and find the balance between being driven by the client’s needs and not losing sight of my responsibilities to my business and my staff.
Another challenge is that in helping clients to solve their problems, you’ve got to do it in a way that’s commercially beneficial for your organization. It’s sometimes easy to go the extra mile to deliver an unbelievable result for a client, it has to be balanced between doing an excellent job but not going so far that it starts to directly impact your commercial performance as a business, which is my primary responsibility as a shareholder in Arthur D. Little.
That’s a compelling insight. You mentioned that it’s an essential part of working across industries. Could you tell us which industry is the most inclining to digital information?
I fundamentally believe that every industry has got an opportunity to embrace leading-edge technologies to drive differentiation, optimization, or innovation. I haven’t seen an industry yet that is impervious to being able to be improved by the application of cutting edge digital technologies. Our challenge is often to work with companies struggling to realize a breakthrough opportunity for pre-digital transformation. That’s where consultants can add value by bringing in impetus, insight, and horsepower at a particular point in time.
I could see a framework for different investment strategies on your website, such as acquire harvest, and scout. Could you please elaborate a little bit on these business strategies and how they can help modern enterprises disrupt the market?
Like all consultancies, we have our frameworks, and we’d love to parcel the world up into neatly defined categories. It certainly helps if you have the toolkit of approaches or frameworks of technologies and techniques. But a deep understanding of the business, and the ability to figure out which tool in your toolkit to use in a given circumstance work best. Sometimes, consulting can become slightly too enamoured of its frameworks and lose sight of the fact that a perfectly good tool used for the wrong job is not a particularly helpful tool. The context is often more important than the content.
A brilliant strategy for one business might be entirely inappropriate for a similar-looking business in a similar market.
Greg, how would you define what differentiates your company from the others available in the market?
I’m a biologist by background, so I still tend to see the world in biological terms where different species may thrive in one ecosystem but be predated in another. It’s the same in consulting firms, and there’s no one good and one bad; it depends on what you’re trying to do. Suppose you’ve got a well-understood problem with a well-understood solution. In that case, you want a company that’s delivered that solution many times and knows exactly the steps to go through to deliver a similar product with many shared steps successfully.
At ADL, in digital problem solving, we are attracted to unique problems that don’t have an obvious solution. When you start the project, you might not fully know what the solution is, but you know how to find out what the components might be, and you’ve got the experience to assemble those components into a solution. To have that model, you need highly experienced, very skilled teams who can bring different elements, backgrounds, and experiences. In that sense, our model is more like a law firm, where you’ve got a relatively low ratio of consulting staff to partners. The partners are very much hands-on in delivery and the commercial side of the business. And that is great if you want to work with unique and complex problems. We’re also good at having an open mind throughout the project and listening to external and client inputs as part of that process. That’s where we’ve carved out our niche. That’s how we differentiate from some of the purely technology-focused or much larger businesses.
What types of people are you trying to bring to your organization to deliver on the promise?
We want fundamentally curious people who finish the projects and think, “If I had to do that again, what would I do differently because the world’s moved on?” We want people who have got both the humility and the ability to work as part of teams.
A big part of our model is open consulting. We don’t have access to all the skills we might need unless it’s a small project. Thus, we partner with complementary organizations to create a team to work on the client’s goal. We set the vision for the open consulting ecosystem and let everyone get on with their particular specialism. We take full responsibility for the deliverable, but we don’t hide the fact that we’re working with consulting partners. Clients like the fact that the best experts with the best skills have been brought in. But they also want to know that they don’t have to manage five different stakeholders within the project team; they want to know that ADL is accountable for delivering the solution.
Maybe you could share an interesting case study where you have helped solve your clients’ problems?
We were contracted by a mass transit operator in an Asian city that had a massive logistics problem with delays. One of the significant causes of delays was trees falling onto the line disrupting the service. The question we were asked was, ‘Is it possible to predict which trees will fall on the line using artificial intelligence and machine learning?’
We thought this is a good challenge where AI and machine learning can find patterns and insights that humans alone can’t see. We started with historical data about which trees have fallen, why and when, and what might have caused it. As we got deeper into the problem, we realized there were many dimensions to this problem, and not all of them were to do with the data that was available to the organization. So, we were considering topology, geology, soil types, atmospheric conditions, and microclimates. As a result, we ended up layering 15 different external data sets into the model that took a graphical representation matching the physical environment. Then we added new data sets to see which add value to our predictive model or a future-looking risk model.
That was a project that epitomized the individual contexts combined with internal and external data that were applied to an assertive scientific experimentation approach. We tested many hypotheses, let the data speak, and then pulled all that together into an actionable strategy. The project’s critical part was to build an AI and machine learning model that you can interpret into a dashboard and a decision-making framework. Five data layers helped to pin down the probability of where a tree falls, and then you go through the classic AI experience of trading your model on historical data to try and see how well it’s tracking to reality.
That must have been a massive amount of work?
It would have been a lot more work a few years ago. It’s amazing how the technology, techniques, algorithms, and models available out of the box help to shortcut heavy lifting. They’ve made it easier, but it’s still a significant challenge.
Could you tell us about the technology stack that you as a company will invest in to continue delivering the best result for your clients?
Firstly, we have to be technology agnostic. Often, there is a pre-existing decision, a specific context, a particular technology that a client has invested in, and looking to exploit that investment. Therefore, we have to work around that.
We avoid some classic enterprise IT technologies if it’s our choice because we don’t believe that’s where the innovation is, we don’t think that that’s where the smarter solutions are.
Another thing that is important about being technology-agnostic is that things change. What might have been the lead in technology four years ago may not still be the lead today. We’ve got many examples where our technologies have been superseded by better offerings in just a couple of years. Thus, if we’re to offer the best advice, we need to check what’s happened over the previous three years because maybe the world’s moved on, and you need to do a quick refresher.
What lessons can you distil from your experiences of helping enterprises to hit the reset button on their development?
We try not to hit a big reset button. There are many examples where you have to redo something that isn’t going to succeed. But it’s more satisfying when you can take what’s already there but put it on a better course to success and augment that rather than stopping and changing fundamentally.
Greg, thank you so much for your time. It’s been a great pleasure to talk to you today.
Stay tuned for more great interviews coming your way!