How AI is transforming business advisory

Ask most accountants for advice, and what you get is a look backwards. The year closes, the numbers get reviewed, and months later, someone tells you what you should have done differently. That's not a criticism of any one firm. It's how the entire professional services industry has worked for decades, and until recently, there was no real alternative. It's a familiar frustration for any business leader who's ever asked a professional advisor a straightforward question and been told to wait for the next scheduled review.

Andrew Fahey has spent over two decades inside that industry, and he's now CEO of Briars Group, an outsourced accounting, tax, payroll, and HR provider working with businesses expanding into new countries across more than 100 regulatory environments. He joined Geeks CEO Lindsay Jessup on a recent episode of the Innovation Room to talk about AI for global expansion, what happens when AI stops professional services from being retrospective by design, and what it actually took inside his own business to get there.

Andrew doesn't pull punches about his own industry. Accounting, by its nature, works backwards. A year closes, the numbers get compiled, and analysis happens months later, often six to nine months after the period it actually relates to. By the time advice arrives, it's advice about a problem that's already happened, which Andrew calls, only half joking, a bit of an oxymoron.

There's also a structural reason it's stayed this way, and it goes some way to explaining why AI business advisory has been so slow to take hold industry-wide. Time-and-materials pricing has never rewarded speed. The slower an engagement runs, the more it can be charged for, and firms have built entire pricing models on selling variations of the same advice repeatedly rather than solving a client's problem once and moving on.

Andrew put it plainly when describing his own sector from the outside in. Professional services like accounting, law, and HR consulting have historically operated on a time-build basis, and some firms have deliberately taken longer to deliver work because that's how they get paid more for it. Seen that way, an industry built on billable hours has had very little commercial incentive to move faster, regardless of what the technology allows.

What AI changes, in Andrew's words, is that it totally flips the script. Instead of reviewing what happened after the fact, real-time data and predictive analysis let advisors run scenarios as conditions change, sensitivities around tax and regulatory shifts, revenue and cost movements, even environmental factors, instantaneously rather than in a quarterly review. A tax rate changes in one of the hundred-plus countries Briars operates in, and instead of that showing up in a report months later, the impact on a client's numbers can be modelled the same day. That's the kind of AI-powered advisory that was simply impossible to deliver at scale before, no matter how skilled the advisor giving it.

“It's such a liberating enabler, the ability to shape and model performance for your client and then advise them in a way you'd never been able to do before.”

— Andrew Fahey, CEO, Briars Group

That's not a small shift. It's AI in professional services finally doing what the industry has claimed to offer for years, proactive advice, delivered while there's still time to act on it, rather than a polished explanation of what already went wrong. Andrew is blunt about what that means for an industry with, in his own words, a bad reputation for taking the money and giving little back. AI-powered advisory, done properly, is the thing that actually earns that trust back.

Briars didn't start with a grand AI strategy. Andrew broke the business down into its component parts, accountancy, HR consultancy, payroll, and advisory, and looked for where AI automation in professional services could remove genuinely repetitive work first. Three areas stood out as the most immediately useful, all inside the parts of the business that had never been particularly exciting to talk about.

• Sentiment analysis on internal communications. Every email and call now runs through a sentiment analysis tool, flagging tone issues before they become a client problem. One example Andrew shared: an out-of-office message from a Scandinavian team member was flagged as unusually abrupt, a tone that would have read as normal in that culture but risked being misread elsewhere.
• Predictive delay and capacity planning. Across accounting schedules, payroll calendars, and country-specific public holidays, AI now helps predict where delays are likely before they happen, rather than after a deadline's already been missed.
• An FAQ automation engine. Briars is building an interactive knowledge base from years of client requests and how they were answered, so advisors spend less time repeating routine answers and more time on the advice that actually needs a person.

None of these are dramatic on their own. Together, they're a genuine example of AI for accounting and tax done practically, freeing up the people Briars calls global account managers, the client-facing quarterbacks managing complex, multi-country delivery, to spend their time on the client relationship itself, rather than the administrative work sitting underneath it.

Andrew's approach to rolling this out inside Briars is arguably more useful than the technology itself. The instinctive fear anyone hears when AI enters a professional services business is that it's there to automate people out of a job. Andrew tackled that directly, framing the whole effort as a broader change programme rather than an AI project, and being explicit with staff about where their value actually sits.

None of this happened by accident. Andrew has spent over a decade running change programmes inside professional services businesses, long before AI was part of the conversation at all, and he's candid that this rollout benefited directly from lessons learned well before this specific technology existed. It's a reminder that adoption skill, not the tool itself, is usually the thing businesses are actually missing.

He also made a deliberate, counterintuitive choice about where to start. It would have been easier, and cheaper, to begin with a role AI could realistically replace. Instead, Briars started with its global account managers, arguably the hardest role to automate, because a strong result there would be visible to the entire business and build trust rather than suspicion.

Lindsay made a similar observation from her own work across other businesses: done well, this kind of AI adoption doesn't just make people faster, it visibly levels them up, with junior team members starting to operate like seasoned ones in day-to-day interactions. That's a very different story to the one most people expect to hear about AI and professional services headcount.

For a business actually expanding into new markets, the practical takeaway isn't really about picking the right AI tool. It's about the questions worth asking a professional services partner before signing anything.

• Is the advice you're getting retrospective or real time? A partner still working from a review six months after year-end is offering yesterday's answers to today's problem.
• Does the partner understand cultural nuance, not just language? Sentiment and tone read differently across regions, and getting that wrong can quietly damage a relationship long before anyone notices.
• Is AI adoption being treated as a change programme or a tech rollout? The businesses getting real value from this are the ones bringing their people along deliberately, not bolting on a tool and hoping.

That last point echoes what we've written about more broadly in AI and cross-cultural trust in global expansion, and it's a similar principle behind Geeks' own AI Adoption Framework. The technology is rarely the hard part. Bringing people along with it is.