Professional Services in the AI age

Generative AI will transform almost every aspect of business operations in the coming years. From data analysis and marketing to optimising operational efficiency and enterprise value (EV), no area will be unaffected.

In our previous articles in this series, we looked at the technologies behind generative AI and explored its impact on business strategy in the context of three strategic frameworks: Porter’s Five Forces, the VRIO framework, and KPMG’s own Nine Levers of Value. In this article, we recap the core competencies of generative AI, explore the reason firm’s engage consultants, and how generative AI might change the consulting profession.

When deployed at scale, generative AI can improve efficiency and creativity across a broad spectrum of business activities. While in the long term, generative AI could replace many roles currently performed by humans, for the time being it’s more likely to be a complimentary tool that will support existing teams, helping to perform tasks quicker and more accurately.

In terms of business performance, AI technology has implications for market leading firms as well as those wishing to improve their market share. Because generative AI is trained on proprietary data, market leaders have the potential to extend their advantage lead if they implement generative AI to support the most value-add/differentiating areas of their business. Conversely, if firms with smaller market share can implement the technology faster and more effectively, they could gain competitive advantage through faster time to market, increased margin, or more responsive customer service. Not to mention the margin-improving potential of quickly automating repetitive, low-level tasks across any organisation, or the new insights that could be derived from using generative AI to spot patterns across data sets. 

In a 1982 Harvard Business Review article, Professor Arthur Turner described a hierarchy of consulting purposes (see Figure 1 below) which are applicable for the wider professional services sector. Despite the 40 years since publication, these purposes hold true today, although specific deliverables are likely to have changed.

It is clear that some of the purposes of modern professional services detailed in Figure 1 could already be delivered by generative AI, such as providing information, conducting diagnoses, and providing recommendations. However, by combining the process and analytical power of generative AI technologies with the experience and know-how of professional service firms, generative AI could act as a force-multiplier, with additional value being delivered at previously impossible timescales.

As explored in our previous articles, asking the right questions is crucial to getting the best output from generative AI. If Henry Ford had asked ChatGPT his infamous question of what his customers wanted ChatGPT’s likely answer would have been ‘faster horses’. If he had instead asked ChatGPT to imagine a future beyond horses, using the latest technology applied to personal transport solutions, the answer would likely have been totally different. 

Prompt engineering

Microsoft describes the skill of asking the right questions as ‘prompt engineering’ - something professional service providers have a lot of experience in. Advisors and consultants, in particular, are trained how to use questions to fully explore a concept, illicit information, and put forward potentially controversial topics in a non-confrontational way. This knowledge can help them use generative AI more effectively for both traditional purposes and additional goals, as defined by Arthur Turner.

Leveraging knowledge and experience

The second area of value could be derived from the data held by professional service firms. Generative AI can analyse privately held data and intellectual property to create competitive advantage. If professional service providers can document and codify their enterprise knowledge in a knowledge management system that is accessible to generative AI bots, it could enable them to achieve improved results in future engagements.

Implementation

While generative AI bots may, superficially at least, compare well against individual advisors in terms of analysis or guidance, professional service firms are likely to have the edge when it comes to implementation. Generative AI might be able to answer prompts and generate implementation plans, but unlike a consulting firm, they cannot currently do the actual implementation itself, engage with diverse stakeholders or adapt plans to the unique culture of a client organisation.

So, for the moment at least, generative AI should be seen more as a force-multiplier to help professional service firms deliver additional value to clients at pace, rather than as an alternative.

People buy from people and improved analytical tools are unlikely to change that. However, the client engagement journey could be quicker, more accurate, and more personal, while providing more specific, meaningful, and actionable advice and services. Consultants and clients could collaborate in real time, leading to faster deliverables built on combined data sets, increasing the competitive advantage, margin, and market share for both client and advisory firm.

KPMG has a successful track record of helping businesses integrate the very latest technology into their operations and could aid in developing generative AI integration and adoption plans. Our Strategy Consulting, Connected Tech, and Digital Ninjas teams can provide expertise and support to help you exploit generative AI quickly, build out test cases, and enhance digital learning across your organisation. Ultimately, we believe in learning with our clients to provide customised solutions that meet the unique needs of you and your business.

Building on this topic, see our next article which focuses on how generative AI could impact the deal lifecycle for buyers and sellers.