Key takeaways_

• AI works best when it solves specific operational problems for professional services firms.
• The biggest gains come from reducing admin, improving decisions and making delivery more predictable.
• Strong data, connected systems and clear measures are what turn AI pilots into measurable business value.

AI investment is accelerating across professional services. Firms are experimenting with Copilot, AI assistants, agents and automation, and the numbers back it up: generative AI use has nearly doubled in the past year, with 40% of professionals now saying their organisation uses it, up from just 22% in 2025.

Yet ask most leadership teams one simple question — what measurable value are we getting? — and the room goes quiet. The same report found only 18% of professionals say their organisation tracks AI’s ROI, and another 40% don’t even know if it’s being measured at all. Adoption has raced ahead of proof.

In this blog, we look at where AI is genuinely creating value inside professional services organisations today and what we’ve learned from using it inside our own business first.

The state of AI in professional services today_

The AI conversation in professional services has changed dramatically over the past year. The question is no longer whether organisations should explore AI, but how to turn experimentation into measurable business value.

According to Thomson Reuters’ 2026 AI in Professional Services Report, more than 80% of AI users now engage with the technology on a weekly basis, suggesting AI is becoming embedded into day-to-day work rather than remaining an occasional productivity tool.

On the surface, this points to an industry embracing AI at pace. However, the report also highlights a significant gap between adoption and business impact.

Despite growing investment and usage, only 18% of organisations say they actively measure the return on investment of their AI initiatives. Even more concerning, 40% of respondents don’t know whether AI ROI is being measured at all.

This suggests many firms are becoming proficient at deploying AI tools without necessarily understanding whether those tools are delivering meaningful outcomes. Licences are being purchased, pilots are being launched and employees are experimenting with new ways of working, but success is often measured by activity rather than value.

Unlike many industries, professional services businesses live and die by productivity, utilisation, client relationships and the efficient delivery of expertise. AI only becomes valuable when it improves one or more of those outcomes. If organisations cannot connect AI initiatives to measurable improvements in efficiency, profitability, customer experience or employee effectiveness, adoption alone becomes a poor indicator of success.

So, the next stage of AI maturity will not be defined by how many tools firms deploy. It will be defined by how effectively they turn AI into operational improvements that can be measured, repeated and scaled.

What actually works? Five areas where AI is delivering real value_

In order to get outcomes from AI, you need to know where to apply it for the most value. These are some of the areas we’ve seen the most success, from internal use, client successes and wider research.

1. Reducing administrative burden_

One of the biggest challenges facing professional services firms isn’t a lack of expertise. It’s how much of that expertise gets consumed by administration. Consultants, project managers and customer-facing teams spend a surprising amount of time documenting meetings, updating systems, chasing information, creating tasks and maintaining project records rather than delivering value to clients.

This is where AI is delivering some of the fastest and most measurable returns. Rather than replacing professional judgement, organisations are using AI to remove the repetitive activities that surround it. Internal use cases include analysing conversations to identify actions, automatically creating project tasks, capturing requirements from meetings, generating documentation and reducing recruitment administration. These are not headline-grabbing use cases, but they solve a real operational problem: highly skilled employees spending too much time on low-value work.

The result is more time spent with customers, faster execution and greater capacity without increasing headcount. In many cases, AI’s greatest value comes from allowing them to spend more time doing the work they were hired to do.

2. Improving opportunity qualification and sales effectiveness_

Professional services sales processes are increasingly complex. Winning new business requires teams to evaluate opportunities, understand client challenges, research markets, identify stakeholders and prepare tailored responses, often within tight timescales. Yet many firms still rely on manual research and incomplete information when making commercial decisions.

AI is helping sales teams improve both efficiency and decision quality. Rather than replacing relationship-building, it accelerates the work that supports it. Opportunity research agents can analyse prospects, identify risks and surface relevant insights. Qualification tools can help prioritise the right opportunities and reduce time spent pursuing poor-fit prospects. Data enrichment capabilities can improve CRM quality and provide greater visibility into organisations before conversations even begin.

The key distinction is that AI works best when it augments commercial judgement rather than automating it. The most successful implementations help sales teams make better decisions earlier, focus effort where it is most likely to generate returns and enter conversations with stronger context.

3. Making project delivery more predictable_

For professional services firms, profitability is closely linked to how effectively projects are delivered. Small inefficiencies, missed risks or inconsistent processes quickly accumulate and erode margins. The challenge is that many of these issues only become visible once a project has already started drifting off track.

AI is increasingly being used to create greater visibility throughout the delivery lifecycle. By analysing conversations, documentation and operational signals, AI can help identify potential delivery risks, surface actions, automate project administration and improve consistency across teams. Use cases include analysing project discussions for emerging risks, automating task generation, assisting with solution analysis and reducing the burden of time entry and approvals.

Perhaps more importantly, AI helps create a more proactive delivery model. Project teams can identify issues earlier and spend less time managing process overhead. This means more focus on outcomes, improved utilisation and stronger project profitability.

4. Unlocking organisational knowledge_

Many professional services organisations already possess the expertise needed to solve most client challenges. The problem is that knowledge is often buried in documents, project files, email threads and employees’ heads. As organisations grow, finding the right information quickly becomes a challenge in itself.

AI helps firms turn institutional knowledge into a more accessible asset. Knowledge management agents, expertise discovery tools and self-service assistants can help employees locate relevant information, reuse existing intellectual property and access specialist knowledge without lengthy searches or interruptions. Internal examples include knowledge management capabilities, HR assistants and expertise support agents designed to reduce duplicated effort and improve consistency across the organisation.

The benefits extend beyond productivity. Faster access to knowledge can accelerate onboarding, improve customer experiences and reduce the risk of valuable expertise being lost when individuals leave the business. In an industry where knowledge is often the product being sold, helping employees access the right information at the right time may prove to be one of AI’s most powerful long-term use cases.

5. Automating finance and business operations_

Some of the most impactful AI use cases are also the least visible. While much of the attention is focused on copilots and chat interfaces, many organisations are generating the greatest returns from automating core operational and financial processes.

Finance and operations are particularly well suited to AI because they involve structured processes, repeatable tasks and large volumes of data. Examples include automating payables, streamlining billing, accelerating financial forecasting and reducing administrative effort associated with approvals and compliance activities.

The impact of these use cases tends to be easier to measure than many productivity-focused initiatives, making it easier to prove worth and scale.

For more use cases, read our infosheet.

What doesn’t work? Mistakes professional services firms keep making_

For every success story involving AI, there are countless pilots that never progress beyond experimentation. Here are the core reasons why they don’t work:

1. Chasing use cases before fixing foundations_

One of the most common mistakes is looking for AI opportunities before addressing the underlying conditions needed for success. Many firms attempt to deploy AI on top of:

• Poor-quality data
• Disconnected systems
• Inconsistent processes
• Unclear governance and ownership

The result is predictable. AI struggles to access the right information, produces inconsistent outputs and amplifies existing inefficiencies rather than solving them. Strong AI outcomes are usually built on strong foundations.

2. Buying tools without defining outcomes_

Another trap is taking a technology-first approach. Organisations invest in copilots, chatbots or AI platforms because competitors are doing the same, without first agreeing what success looks like.

Questions such as “what business problem are we solving?”, “how will we measure value?”, “who owns the outcome?” and “what process are we improving?” often come later, if at all.

The most successful firms start with a business objective and then identify where AI can help achieve it.

3. Running endless pilots_

Many AI programmes become stuck in a cycle of experimentation. Teams test new tools, run short-term proofs of concept and generate initial excitement, but struggle to move beyond isolated use cases.

Without clear ownership, governance and performance measures, pilots rarely scale into operational capabilities that consistently deliver value. Experimentation has its place, but you need to turn promising ideas into repeatable business outcomes.

What we learned from becoming our own Customer Zero_

As a professional services organisation, we faced many of these challenges: consultants spending too much time on administration, fragmented information spread across multiple systems, inconsistent processes and growing pressure to do more without continually increasing headcount.

Rather than starting with AI, we first focused on creating the right foundations. We consolidated onto a connected Microsoft platform, improved data quality, standardised processes and identified the workflows creating the most friction across sales, delivery, finance and operations. Once those foundations were in place, we began embedding AI and automation into everyday work, from opportunity qualification and project delivery to knowledge management and finance operations.

The results have been significant:

• 90% revenue growth since FY23, demonstrating that growth doesn’t have to come at the expense of efficiency.
• £1.5m+ annual savings through automation, simplification and operational improvements.
• 13% higher win rates by improving the quality of insight available to sales teams.
• Over 90% reduction in time-to-output across key workflows, giving teams more time to focus on delivering value.
• Improved customer satisfaction, including a 16% increase in NPS and a 9.8% increase in customer happiness.

The lesson for professional services firms is simple: AI delivers the greatest value when it’s applied to real operational challenges. Focus on removing friction, improving decision-making and reducing repetitive work first. The technology is important, but the outcomes are what matter.

Best practice: How to successfully implement AI in a professional services firm_

1. Start with operational friction, not AI use cases_

Many AI projects begin with the technology and then look for a problem to solve. The most successful projects work the other way around.

Professional services firms should start by identifying where consultants, project managers and customer-facing teams spend disproportionate amounts of time. Common examples include proposal creation, project administration, knowledge retrieval, reporting and managing handovers between teams. The goal is to identify where work is slow, repetitive or inconsistent, then assess whether AI can help remove that friction.

We found that many of the strongest opportunities didn’t begin as AI projects at all. They started as process improvement initiatives, with AI introduced later to enhance outcomes.

2. Focus on high-volume, repeatable work first_

The fastest AI wins tend to come from processes that happen every day rather than occasional strategic activities.

Tasks such as administrative work, status reporting, requirements capture, information gathering and internal support queries are often highly repetitive and easy to measure. Successfully automating even small parts of these workflows can create meaningful capacity gains across the organisation. Internally, we focused on identifying repetitive tasks, operational bottlenecks and decision-making challenges before layering AI into those workflows.

3. Fix your data foundations before scaling AI_

AI depends on access to accurate, trusted information. If your systems are disconnected, your data is inconsistent or ownership is unclear, AI will struggle to deliver reliable outcomes.

Before introducing AI at scale, we focused on consolidating systems, improving data quality and creating stronger governance across the business. This created a stable foundation where AI could operate on accurate, governed information rather than fragmented datasets.

4. Simplify processes before automating them_

AI can remove inefficiency, but it can also amplify it.

One of the most important lessons from Customer Zero was that automation should follow process improvement, not replace it. Before implementing AI, review how work is completed today. Identify unnecessary handovers, manual steps and duplicated effort.

Once a process has been simplified, AI can help accelerate it. Trying to automate a broken process often creates a faster version of the same problem.

5. Measure business outcomes, not AI adoption_

Many organisations track AI activity, but far fewer track business value.

Licences deployed, prompts created or training sessions completed won’t tell you much about the actual impact of AI. Focus instead on outcomes that the business actually cares about, such as time saved, improved utilisation, increased win rates, faster reporting cycles, reduced operational costs or better customer experience. Decisions about scaling AI should be based on observable improvements in quality, speed, capacity or commercial outcomes.

6. Keep people at the centre_

Professional services firms are fundamentally built on expertise, relationships and judgement.

AI should be used to augment these strengths, not replace them. Our approach involved employees in identifying pain points, testing solutions and shaping use cases, helping ensure AI supported day-to-day work rather than disrupting it. Humans remained responsible for context, decision-making and customer relationships, while AI focused on removing repetitive effort and improving consistency.

7. Move beyond individual productivity gains_

For many firms, AI adoption starts and ends with tools that help individuals work faster.

While these can deliver value, the biggest opportunities emerge when AI becomes embedded into business processes. This might mean improving opportunity qualification, identifying delivery risks, automating operational workflows or enhancing knowledge management.

The objective should be to improve how the organisation operates. That’s where the most significant gains in efficiency, scalability and commercial performance tend to be found.

The future of AI in professional services_

The next competitive advantage in professional services won’t come from having access to AI.

That’s because AI is rapidly becoming the norm. According to Thomson Reuters, the majority of professional services firms expect AI to become a core part of their workflows over the next five years. And the firms that will pull ahead will the ones that have built the operational foundations needed to turn AI into measurable business outcomes.

That means:

• Better data that employees and AI can trust.
• Better processes that can be standardised, automated and scaled.
• Better governance that allows innovation without creating risk.
• Better integration between systems, people and information.
• Better adoption that turns AI from an experiment into part of how work gets done.

This is where many organisations will succeed or fail.

In other words, the conversation is shifting. The winners won’t be the firms asking, “How do we use AI?”. They’ll be the firms asking, “How do we become easier to improve, automate and scale?”.

Five questions every professional services leader should ask now_

Before investing in another AI tool, take a step back and ask these crucial questions:

1. Which process is creating the most friction today?

Is it proposal creation? Project reporting? Time entry? Knowledge retrieval? Customer service? The best AI initiatives start with a specific operational challenge, not a technology trend.

2. What is that friction costing the business?

If a process is slow, manual or inconsistent, can you quantify the impact? Consider lost billable time, delayed decisions, margin erosion, reduced productivity or customer experience challenges. If you can’t measure the problem, it’s difficult to measure the value of solving it.

3. Is our data ready for AI?

AI relies on trusted information. If your data is fragmented across systems, inconsistent or difficult to access, AI is unlikely to deliver reliable outcomes. Before asking whether you’re AI-ready, ask whether your data is.

4. Do our people know how AI fits into their daily work?

Successful AI adoption is as much about people as technology. The strongest use cases are often those that remove repetitive tasks, improve decision-making or help employees work more efficiently, rather than completely changing how they work.

5. How will we measure success?

Many organisations measure AI adoption. Far fewer measure business outcomes. Before launching any AI initiative, define what success looks like. Will it reduce administration? Improve win rates? Increase utilisation? Improve customer satisfaction? Create operational capacity.

If you can answer these five questions confidently, you’re already ahead of many organisations still focusing on the technology rather than the outcome. And that’s often the difference between an AI pilot and an AI success story.

See the real impact of AI in a professional services organisation_

AI in professional services absolutely works. The evidence is becoming harder to ignore.

But the biggest gains aren’t coming from headline-grabbing demos, experimental pilots or organisations racing to deploy the latest AI tool. They’re coming from firms that are using AI to solve genuine business problems: reducing administration, improving decision-making, automating repetitive work, protecting margins and helping employees spend more time delivering value to customers.

Our own experience as a professional services organisation reflects that reality. The biggest improvements didn’t come from AI alone. They came from combining AI with better data, connected systems, streamlined processes and clear business objectives. Once those foundations were in place, AI became a powerful accelerator for growth, efficiency and customer experience.

Download our Customer Zero guide to learn how Infinity Group simplified systems, improved data foundations and embedded AI across sales, delivery, operations and finance to achieve measurable business outcomes.