2024 Insights: The changing face of Professional Services
According to the Professional Services Global Market Report 2023, this global market grew from $6,023 billion in 2022 to $6,382.56 billion in 2023 and will reach $7,770.09 billion in 2027.
Whilst audit, compliance and management consultancy remain strong revenue streams, another core driver of this growth is increasing demand from organisations trying to build effective business models in a dynamic world, changed forever by the pandemic, climate-change, war and AI.
Globalisation has been replaced by regionalism and many integrated supply chains are now broken and fragmented. At the same time, consumers demand seamless, real-time excellence from their suppliers with products and services personalised to them and precisely meeting (even predicting) their needs as they interact through digital apps, platforms, networks and portals.
The rise of command-line models has allowed organisations to explore techniques such as generative AI, but what, more widely, will be the impact of Artificial Intelligence on the sector, as more complex models move out of development and into production?
How are professional services organisations changing?
Professional services organisations are defined by key characteristics:
- High knowledge intensity.
- Domain experience with industry and business unit specialisation.
- Repeatable processes and workflows.
- A professionalised workforce, often trained internally to their own specific standards Formal grading levels and role structures.
- Low capital intensity.
Traditionally, these characteristics would define an organisation with high-barriers to new entrants, who may not have the knowledge and experience at scale to offer comparable services and cannot deliver this knowledge with an effective service-structure, all of which may take decades to build.
Fast forward to today, and the ability of Artificial Intelligence to learn and perform not only simple low-level repetitive tasks but also to perform high-value, professional cognitive roles. For example, the chart below shows how well a single AI model from US research company Open AI performed across a range of professional and academic exams across multiple disciplines, including law, where it passed the American Bar Association Exam, Medicine, where it passed the Medical Assessment exam and Biology, where it passed the US Biological Olympiad. It even passed the Sommeliers professional wine exams, and more recently achieved a distinction in the Harvard MBA final year thesis. All these achievements from a single AI model that is one year old.
Professional services organisations, with repeatable processes based on high knowledge intensity, have a great opportunity to embed Artificial Intelligence into these processes to enrich their offerings. Those that do not may struggle to compete.
Let’s take audit, for example: a rules-based and regulated compliance exercise in analysing and presenting data to agreed formats. Where AI and Machine Learning (ML) will have real, tangible impact on accounting is by drastically cutting down the number of repetitive tasks, such as curating and aggregating data, leaving experienced auditors free for more strategic insight projects and higher-order analytical tasks.
Since AI and ML can be used to efficiently support the audit process through the analysis of large data sets and the identification of patterns, outliers, and anomalies within financial statements, it can turn this analysis into recommendations for business performance improvement. State-of-the-art AI can now learn GAAP rules and apply changes to GAAP almost instantaneously to restate accounts, with natural language annotation to the changes made and their effects.
Similarly with insurance, the actuaries that model and price risk are turning to new data sets and AI models to change the industry. New driver insurance, for example, was based on highest risk for the first year as no history was available. But by reviewing data from a small telemetry box in the vehicle, an hourly data set including features such as driving in rush-hour, driving in the dark, cornering and braking speeds etc, mean the data is modelled to individualise the risk profile and change the policy cost monthly; rewarding good driving with lower premiums each month.
What does the roll-out of applied Artificial Intelligence mean for professional services organisations?
“Professional services organisations can now use data as a proxy for experience, meaning that their Artificial Intelligence models become more expert with every client engagement”
Professional services organisations are engaged by their clients for regulatory compliance reasons such as independent auditing or legal oversight, but it is increasingly value-added services that their clients are commissioning.
organisations operate in increasingly uncertain business environments, requiring expertise and knowledge leadership to navigate to success. In an Artificial Intelligence driven world, what does this mean for the established Professional services sector? Here are the top drivers and trends of future success:
1. Talent recruitment and retention
Professional services organisations will increase their requirements for staff with knowledge of advanced data science and artificial intelligence skills, both to support their internal business roles and to support their clients. This transformation has already happened in investment banking, where typically 40% of staff are now technical staff, predominantly data scientists and software engineers, building and maintaining data models, algorithms, and pipelines.
Given the global demand for technical staff, in addition to external recruitment, professional services organisations will need to upskill and reskill internally, based on their skills gaps and client project demand. Internal training and organisational culture programmes will become more data centric.
2. Partner dynamics and new skills, when Artificial Intelligence may be the smartest entity in the practice
Analysis of the partner ratio in the six biggest providers averages out at one partner for every 22 staff. Typically, partners manage client relationships, mentor their teams and embody the ethos, culture and ethics of the business. In future, they may have responsibility for a new team member: their proprietary AI platform, a super-smart “colleague” who learns 24 hours per day and surfaces insight from big data sets in seconds.
Indeed, a key feature of applied Artificial Intelligence is the fact that it has a different relationship with time than human workers and so technically its billable hours look very different to most knowledge workers.
So, a key skill for practice leads is ensuring that the algorithms are fed with clean and unbiased data, kept optimised, updated as regulations change and are interpretable and explainable. Staff need to be trained in new techniques, such as data literacy, data governance, how to shape and frame problems as data-problems and then how to tell stories using data, insight and narrative to effect organisational change.
3. Increased role of systems thinking, not just at the client-level, but at the industry level
Systems thinking allows us to understand complexity by analysing an entire system, organisation, market or industry, rather than the traditional technique of breaking it down into its constituent parts, such as finance, HR or supply chains.
This way, we understand relationships, interconnectedness, mechanistic models, causality and data-flows and can ultimately apply Deep Learning techniques capable of building models of these complex systems through reinforcement learning. These techniques are already being used to model economies, financial markets, oil refineries and people flows through travel networks. They will increasingly be used to model, simulate and optimise individual organisations and the markets they serve.
4. Building and servicing clients through Knowledge Platforms
“In the future, most of the data and knowledge in the world, will be created by machines and will reside within machines”
Yan LeCun, Prof. Deep Learning and Mathematical Science, New York University
Within professional services organisations, knowledge and expertise is acquired by individuals through specialisation as they progress through their careers, and clients effectively gain access to this knowledge through engagement. As organisations digitise and develop new project delivery modalities, knowledge will become increasingly centralised and platform based, with individuals internalising knowledge within a knowledge platform.
This is a key requirement for training and developing applied Artificial Intelligence. The main benefit here is the concept of “information asymmetry”, where systems hold significantly more knowledge than any individual, and these platforms learn exponentially as more information and knowledge is internalised.
In future, professional services will see the rise of proprietary AI-driven knowledge platforms that form part of service delivery in client engagements, effectively acting as knowledge stores of best practice and effective implementation techniques, with governance and compliance based on legal and organisational rules. These will form a repeatable revenue generator as organisations sell the platform expertise to clients and also help clients to build their own knowledge platforms, allowing the two knowledge platforms to communicate based on rules and governance requirements. Ultimately, audit can be a largely automated process, with the anomalies and exceptions being flagged for human review.
5. Professional services organisations as the gold-standard of Human Intelligence and Artificial Intelligence co-working to deliver success.
There is a real opportunity for the professional services sector to lead on best practice as their clients search for partners to help them redefine and fully embrace the opportunities of data and artificial intelligence. Most clients are exploring the art of the possible and are at the start of retraining staff in the soft data skills of data literacy, data leadership and data governance to prepare them as data citizens in future roles.
Professional services partners that demonstrate from their own experience how data, AI and human-workers can jointly maximise success, will outperform their competitors in a market where AI will contribute $15.7tn to the global economy by 2030.
