The impact of generative AI on the talent and operating models of professional services firms

The widespread adoption of generative AI and analytical automation poses a risk to the career progression for early career professionals who traditionally learn and develop their skills through hands-on experience with data analysis, modeling and forecasting. With generative AI taking over these tasks, emerging professionals may find it challenging to gain necessary experience and expertise, potentially limiting their career growth. These limitations also pose a threat to the sustainability of the firm, underpinned by a healthy flow of talent through the ranks. While the industry has seen other technologies that significantly improved productivity (e.g., personal computers, electronic mail, spreadsheets), the incorporation of generative AI will redefine the industry’s competitive landscape in multiple ways, as we will explore later in this article.

Investment in comprehensive upskilling and reskilling programs and rethinking of the firm’s career development philosophy will provide early career professionals alternative avenues for acquiring content knowledge. Learning and development programs will need to emphasize the complementary aspects of human and AI-generated reasoning in maximizing delivery and solutioning efficiency. Firms may also want to enhance mentorship programs to connect learning and career growth more directly to ensure that there is a healthy supply of quality talent through the pipeline at every level. These investments in upskilling and reskilling employees could bring high ROI to the firm, especially with the prospect of de-leveraging operating models and wider operating margins.

With the massive power of generative AI in aggregating and synthesizing data, highly specialized knowledge that defines a firm’s competitive advantage may become commoditized in the future. It may only take AI models a few weeks to match or exceed the technical and industry knowledge that would otherwise take humans decades to accumulate. Proprietary data or specialized knowledge may no longer provide the same competitive advantage as in the past.

Co-creation with generative AI may become a core competency for career growth within a professional services firm. Professionals at all career levels will need the ability to prompt the AI for most relevant information and understand, interpret and effectively utilize the generated outputs. This requires a deep understanding of generative AI algorithms, their limitations and how to better train algorithms to improve quality of insights. Additionally, professionals must develop skills in data storytelling and visualization to communicate the insights derived from AI-generated outputs effectively to clients and stakeholders. A culture where generative AI is positioned as an augmentation to human ideas and creativity will ultimately lead to more innovative solutions for clients.

While generative AI can automate certain tasks, it cannot (at least for now) replace human creativity, empathy and critical thinking. Successful professional services firms will deemphasize technical specialization and prioritize the development of human-centric skills that complement the capabilities of generative AI, many of which are skills that define a great professional services practitioner: strategic thinking, problem-solving, relationship-building and effective communication. Firms that focus on the qualities of being their clients’ trusted advisor and meaningful client interactions will gain competitive advantage over those that seek to differentiate themselves with technical detail and proprietary data.

Generative AI algorithms are trained on vast data sets, and biases or inaccuracies present in the data can inadvertently be reflected in the generated outputs. Addressing such potential biases (and the resulting wrong decisions) requires oversight and specialized knowledge. Failure to address these new risks may result in ill-informed recommendations provided to clients and higher professional liability premiums. In worse cases, clients may pursue litigations if ill-informed recommendations lead to significant harm to their businesses.

Safeguarding proprietary knowledge will be another important emphasis for professional services firms. Until legislative efforts catch up with the technological development, firms may risk losing proprietary data or knowledge if it is used in public AI algorithms; case in point, the entertainment industry is still wrestling with the economic rights to AI-generated content based on art and music created by human artists.

Successful firms will establish new guidelines and quality review processes to minimize bias and data inaccuracies when using generative AI to develop insights for client deliverables. They will explore appropriate risk mitigation and transfer strategies in light of new professional liability risks emerging from use of AI-generated insights in client deliverables. Firms may explore more expansive quality assurance tactics. Strong data confidentiality protocols will ensure that the firms’ proprietary data and knowledge do not become available to public AI algorithms. Navigating these uncharted waters will require robust governance frameworks to ensure ethical use of AI and to drive a more holistic culture of responsible AI utilization, including comprehensive training on AI ethics and what can or cannot be fed to AI algorithms.

Fundamentally, automation allows professional services firms to de-leverage. The leverage model is often the heart and soul of the economics of a professional services firm, and it varies significantly by type of service. Some firms ideally operate with a pyramid, where each partner generates work for eight to 10 associates at various levels. In more technical environments, the leverage model could be as low as one partner to one associate. Firm profitability depends largely on how well the leverage model is optimized given the nature of work. Development and adoption of generative AI allows firms to de-leverage so they can accomplish more client work with the same staffing structure, meaning wider margins. Generative AI may also create new domains of services that do not exist today, providing new revenue streams and increased profitability to firms effective in combining innovation and commercialization.

One possible outcome is a downward pressure on professional services pricing. With more work done with fewer resources, some disruptive players may seek competitive advantage through lower fees. However, firms that seek longer-term growth will seize the opportunity to differentiate themselves in ways explored above, such as better client experience and higher-quality client deliverables using better trained AI algorithms. Firms may be tempted by the wider margins to distribute the additional profit among partners; however, responsible firm stewards with long-term vision will instead make sound investments to deepen the firm’s competitive edge.