Authors: Glen Greer, Erik Jorgensen
Summary: AI is transforming professional services worldwide, with leaders focusing on profitability, operational efficiency, and smarter AI adoption in 2025.
By embedding predictive, generative, and agentic AI into workflows, firms can reduce manual tasks, empower employees, and deliver measurable value to clients.
Successful adoption requires leadership alignment, strong data governance, workforce upskilling, and a tailored AI strategy—whether local, regional, or global.
In today’s challenging business landscape, professional services leaders must navigate a future in which artificial intelligence has rapidly shifted from an exciting tech dream to an essential business tool. What once was a question of “what is it?” has now become “how can we implement it and, at what scale and when?” Questions that leadership are now pressed to answer. The data shows that leaders are acting, as Certinia’s recent survey of 1,003 services organisations makes clear.
When asked about their key objectives for professional services, leaders ranked “increasing profitability” (62%), “improving operational efficiency” (61%), and “increasing AI usage” (55%). It should be no surprise that “increasing AI usage” entered the top three in 2025, with how quickly the industry has been evolving.
Equally unsurprising is that “increasing profitability” and “improving operational efficiency” were in the same place last year. The question organisations should be asking themselves is “How can we leverage AI to increase profitability and improve operational efficiency?”
Profitability and operational efficiency gains won’t come from AI in isolation; it needs to be embedded within the processes and deliver a clear value-add to the people actually doing the work. It can’t just be a new way of doing things, but has to be truly saving time and adding value.
In professional services, AI is delivering value through three common types that are being widely adopted by organisations, they are:
- Predictive AI – uses historical and real-time data to forecast what may happen. In services organisations, this could involve predicting project margins or forecasting client demand.
- Generative AI – creates content via user prompts and unstructured data. In services organisations, this could involve summarising account activity in Salesforce or writing project documentation. Certinia, for example, is already using generative AI features to help generate client-ready reports.
- Agentic AI – performs multi-step tasks and makes autonomous decisions. In services organisations, this could involve automatically responding to tickets or escalating risks during delivery.
The real opportunity lies in how these AI types can empower people by augmenting their skills, reducing low-value tasks, and allowing them to focus on work that drives real impact. Examples of this are already emerging within professional services software.
Let’s consider Certinia’s PS Cloud Staffing agent (Summer 2025 release): it’s designed to reduce the effort resource managers spend splitting or swapping assignments. Resource managers will be able to chat to an agent and ask what the current billable assignments are for a resource, then ask for alternative resources before finally swapping the resource with just a chat message. This is a strong step in the right direction, but could it get closer to the “digital workforce” that Marc Benioff describes in the Time article “How the rise of new digital workers will lead to an unlimited age” (Time, November 25, 2024)? To truly empower resource managers, could future agents:
- Suggest alternative resources for unplanned absences by monitoring calendars
- Proactively suggest alternate resources to resolve overallocations.
- Suggest new projects for resources rolling off assignments, matching skills with upcoming demand automatically.
- Match resources to opportunities based on their experience, interpersonal skills, and career development plans.
Employees are ready for AI and understand that it will change the way they work. McKinsey suggests “70 percent of employees believe that within two years, gen AI will change 30 percent or more of their work.” (McKinsey, Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential at work). To achieve this adoption, it is important to understand that it is not just a technology challenge. Leaders must put people at the forefront of successful adoption. For organisations to achieve this, leadership needs to take charge. This begins with:
- Leadership sponsorship – AI must be championed and made a strategic priority throughout the organisation.
- Prioritised investments – leaders must balance long-term strategic initiatives with short-term gains. To avoid being left behind, leaders should focus on delivering value immediately.
- Flexible Strategy and Mindset – successful organisations should view AI not merely as a technological upgrade, but as a continuously evolving technology.
After leadership alignment, an AI programme should be split across three pillars:
- Data taxonomy and governance – creating consistent definitions and owners of data across the organisation to ensure AI has clean and trusted data.
- Change management strategy – creating a culture of AI embrace instead of resistance.
- Upskilling of employees – creating confidence in teams to work effectively with AI.
These three pillars create the cornerstone of successful AI adoption, ensuring people, work, and outcomes are aligned.
With leadership now aligned and the foundations in place, the next decision will be which AI solutions best fit their visions and organisation’s business needs. Some may opt for custom-built AI, pre-built AI such as from software firms Salesforce and Certinia, or a hybrid approach.
Custom-built AI offers more control and gives companies flexibility to include unique requirements; however, this approach typically requires a higher investment and longer development timelines. Pre-built AI conversely is faster and more cost-effective to implement, but gives companies fewer customisation options and less control over data. Each approach has trade-offs, and leadership teams will need to weigh them carefully when defining their requirements and formulating their strategy.
Whichever route organisations choose, success will depend on balanced investment across leadership development, strategy formulation, data governance and workforce training. Organisations that take a holistic approach to AI implementation by addressing both the technological and human dimensions will be best positioned to navigate and capitalise on AI’s future in professional services.
