AI is holding up a mirror to professional services
AI is holding up a mirror to professional services. In more and more of our work with clients – and in how we run Garwood itself – we’re finding that it mostly reveals what a firm is already made of. Where the foundations are sound, it builds on them. Where they’re weak, it makes the cracks harder to ignore. It rarely creates something that wasn’t there to begin with.
That pattern ran through our recent six-part podcast mini-series, Unlocking Value: The Tech Disruption Story, which brought together leaders from retail, consulting and technology to discuss how disruption reshapes industries and what separates the organisations that adapt from those that struggle to keep pace.
The interesting thing is that the cracks showing up aren’t particularly new.
Long before AI became the dominant topic in boardrooms, professional services firms were wrestling with many of the same challenges they face today. As organisations grow, expertise tends to accumulate in pockets rather than spreading evenly across the business. Processes evolve to solve immediate problems but aren’t always redesigned as the firm scales, and different teams develop their own ways of delivering broadly similar work.
None of this is particularly unusual, but it creates complexity that can remain hidden until something comes along that puts the operating model under pressure. Technology has a habit of doing exactly that.
Technology rarely creates the problem
Jason Soar, who spent three decades inside Sainsbury’s before advising grocery businesses in the UK and US, described an AI-powered stock management tool that was introduced to improve product availability. Instead, it made things worse.
The problem wasn’t the technology. The data underneath it wasn’t good enough to support the decisions the technology was trying to make, so the new tool exposed a weakness that had always been there.
Professional services firms are beginning to experience something similar.
Many conversations about AI focus on what the tools can do, with less attention paid to what they depend on. Yet the firms making the greatest progress have often spent years improving how the business operates. They understand how work moves through the organisation, trust the information they’re using to make decisions and have created greater consistency in how services are delivered.
AI gives those firms another way to build on work they’ve already done. For firms without those foundations, it can make the gaps much more apparent.
AI amplifies what already exists
Jon Stead, chief executive of CMap, described AI as an amplifier. Organisations can assume that new tools will solve existing problems, but weak data, knowledge that sits with a handful of individuals and informal ways of working don’t disappear when technology is introduced. In some cases, they become more obvious. As Jon put it, if the way a business works is ad hoc, ad hoc is what gets amplified.
Which is why so many discussions about AI eventually move away from the technology itself and onto the business underneath it. The conversation might start with new tools or capabilities, but it rarely stays there for long. Firms soon find themselves talking about the way work gets done, how knowledge is shared across the organisation and whether their systems and processes are strong enough to support what they’re trying to achieve.
Sri Ganesan, founder of Rocketlane, approached the same issue from the perspective of how firms are actually using AI. He sees many services teams making progress with internal operations and the administration around projects, but far fewer changing the work they deliver to clients.
His point was that getting to that next level takes more than access to the technology. Leaders need to understand it well enough to see where working practices could genuinely change, and teams need the time to experiment with those possibilities rather than trying to squeeze that work in around client delivery.
Making expertise more accessible
Knowledge is a good example.
For years, many professional services firms have relied on expertise sitting with experienced consultants. That model can work remarkably well, particularly in smaller firms or specialist teams, but it becomes harder as organisations grow. AI has brought renewed attention to the value of making that expertise more accessible across the business.
Sarah Edwards from Kantata spoke about the ambition of making every consultant your best consultant. That becomes difficult when too much of what makes the firm good at its work sits in the heads of a relatively small number of people.
Firms that have invested in capturing, sharing and codifying what they know have more to build on. For others, AI is bringing into sharper focus just how much of their intellectual capital still depends on individuals.
The challenge of scaling delivery
The same issue appears in delivery.
Many firms still rely on highly bespoke ways of working, with individual consultants developing their own approaches and teams solving similar problems in slightly different ways. That flexibility can be valuable, but it can also make it harder to scale delivery consistently or introduce technology effectively.
Firms that have developed repeatable methods and clearer delivery disciplines have a stronger base from which to experiment with new tools, without having to standardise everything they do.
Why data keeps resurfacing
Data was another theme that kept coming back throughout the series, which is perhaps unsurprising given how much any useful application of AI depends on the information beneath it.
Deb Ashton from Certinia sees many firms under pressure to accelerate their use of AI, but that ambition quickly raises questions about whether the data foundation is ready to support it. Reliable information, connected systems and consistent processes matter because the technology can only work with what the business gives it.
As the tools become more sophisticated, the quality of that underlying information becomes more important, not less.
Different symptoms, the same underlying challenge
The specifics vary from firm to firm. In some organisations the challenge is data, in others it’s knowledge or a delivery model that depends too heavily on individual experience.
Jonathan Corrie‘s perspective was that many firms still haven’t turned their expertise into something genuinely repeatable and scalable, which makes it harder to take advantage of what new technology can offer. A process may appear to work perfectly well until someone tries to automate it and discovers how many manual interventions sit behind the scenes. Knowledge can feel accessible until the business tries to scale it beyond a small group of experienced people.
For a while, firms can work around those things. New technology tends to make the workarounds more visible.
The discussion around AI often centres on adoption: how quickly firms should move, which tools they should use and where they should invest. The conversations in this series suggest that how ready the business is to take advantage of the technology matters just as much.
That readiness comes from work that professional services leaders will already recognise: building reliable information, making expertise easier to share, creating greater consistency in delivery and developing an operating model that can adapt as the business grows.
The technology may be new. The challenge underneath it is not.
Continue the conversation
This article draws on insights from all six episodes of Unlocking Value: The Tech Disruption Story, our mini-series exploring how technology is reshaping professional services and what separates the firms that adapt successfully from those that struggle to keep pace.
Featuring perspectives from Jason Soar, Jon Stead, Sri Ganesan, Sarah Edwards, Deb Ashton and Jonathan Corrie, the series explores everything from AI, delivery and pricing through to operating models, expertise and the future of professional services.
