Making the case for AI in professional services: Stats, use cases, tips

The rise of artificial intelligence has planted a new inflection point on the curve that tracks our technological progress. Soon, it seems, we’ll be marking developments according to whether they occurred “BC,” before ChatGPT launched publicly in late 2022, or “PC,” as in post-ChatGPT, when generative AI opened the world’s eyes to the seismic impact intelligent technologies can have on how we conduct business and our lives.

That’s certainly true in the world of consulting and across the professional services industries, where firms of all shapes and sizes are building new services and recurring revenue streams around genAI. They’re helping clients integrate digital assistants and other forms of business AI into their operations to enable them to operate and work more efficiently, and capture new insights to support decision-making.

As focused as many consulting firms are on supporting their clients on their AI journeys, and as much as that focus is translating into promising business opportunities, it’s important that leaders pause for a bit of self-reflection and ask themselves an important question: How much more could and should we be doing about AI in professional services? How can we leverage genAI internally to benefit our own business?

Plotting your firm’s internal AI journey 

As the range of large language model-driven AI solutions for business grows, so does the number of potential use cases for AI in professional services.

Open AI’s ChatGPT and other digital assistants like Google’s Gemini are doing the work of socializing and demystifying genAI, to the point where many of us are now comfortable using AI co-pilots in our daily lives, thanks to improvements with natural language processing (NLP) and its constituent parts, natural language understanding (NLU) and natural language generation (NLG).

It can analyze and glean insight from multiple and sequential inputs, then boil that down into understandable language to suggest, for example, which breed of dog might be most suitable for me given my location, lifestyle and other factors.

Now, as our understanding of and comfort level with genAI grows, and as the models themselves mature, the focus is shifting from the horizontal to the vertical: business applications for genAI within specific industry verticals, from finance to healthcare to industrial manufacturing and yes, consulting.

The emergence of digital assistants like Microsoft’s Copilot, IBM’s Watson, SAP’s Joule and others gives consulting and professional services firms prebuilt, ready-to-integrate genAI tools they can embed into their own internal processes, just like many firms have with the intelligent services they offer to clients.

The potential use cases for virtual assistants alone look really promising for their ability to elevate client interactions, provide personalized services and accelerate decision-making processes.

But where inside your firm to look for opportunities to apply genAI? Those opportunities look especially compelling in three areas:

1. Enterprise productivity, including report generation and analysis, intelligent processing of documents — invoices, employee hiring requests, etc., and document screening (reviewing resumes for contract employees to identify specific skills, for example).
2. Client support, including intelligent search, generation of personalized recommendations, case analysis and creation of extracts, automated email responses, and tiered advice with a combination of automated and human support.
3. Code generation. A January 2024 report from IDC predicts that by 2025, GenAI will become a go-to tool for software co-development and that by 2028, 80% of software testing will be performed by GenAI. Firms with in-house development teams, take note. With prompts, GenAI can generate code for users who lack coding skills, enabling them to use low-code/no-code tools to build extensions into standard software.

Uses like these suggest there’s a strong case for embracing genAI in professional services. But you can’t just sprinkle a bit of genAI dust inside your business and expect the insights to magically start flowing.

Because AI depends on fresh, trusted and relevant data to learn and produce value, data quality and accessibility are a critical prerequisite to benefiting from any type of AI. With substandard data, the insights your genAI provides might not be trustworthy.

As with any emerging technology, integrating AI into your business is a journey. Focus on finding AI use cases that align to your business goals and resolve a business issue for you, your clients or partners. Have a plan for strategically piloting genAI within specific areas of your business, then measuring and assessing the results before scaling it more broadly.

And perhaps most importantly, this requires an explorer’s mindset, where you push boundaries, build on successes and learn from missteps.

Ultimately, what you learn along your AI journey creates a cycle of innovation, where internal experiences with AI yield lessons and practices that inform your work with clients, and AI work with clients informs your AI usage internally.

AI in professional services: Stats and use cases

As is usually the case after a technological breakthrough’s initial splash, wide-eyed curiosity has yielded to clear-eyed practicality as business leaders begin viewing AI technology through the more hardened lens of the return on investment it can potentially provide.

These days, the question business leaders in pretty much every industry, consulting included, seem to be asking their preferred generative AI-driven digital assistant is, “What value can you add to my business?”

The short answer, it turns out, is: Plenty.

A 2023 IDC global survey of corporate IT buyers found that organizations recorded an average of 3.5X ROI for every dollar spent on AI. And they’re spending big dollars on AI in search of those returns,  as organizations reported that their planned outlay on AI capabilities for the next 24 months has increased by an average of 23.4%.

By 2027, according to IDC, worldwide spending on artificial intelligence capabilities is projected to exceed $423 billion, with professional services and three other industries expected to be most aggressive, accounting for 60% of global IT spending on AI.

By 2025, the G2000 (the world’s largest companies, essentially) collectively plan to allocate more than 40% of core IT spend to AI initiatives, “leading to [a] double-digit increase in rate of product and process innovations,” IDC predicted.

What kinds of innovations could consulting firms and professional services companies be pursuing with their investments in AI? Which business AI use cases show the potential to provide a firm — and its clients — with the greatest value?

These are questions that firm leaders should be asking as they evaluate their IT spend and overall business strategy in 2024 and beyond. Finding the answers requires a frank evaluation of the ROI business AI can deliver in internal use cases, and in terms of revenue-generation for commercial service and product offerings.

Business AI has been around a while, and there’s a good chance you’ve employed it in using the machine learning-powered capabilities and intelligent automations embedded within the business software inside your firm’s tech stack. These kinds of use cases typically are limited in their applicability and their value to a business.

Now, however, firms are uncovering a huge range of possibilities for using business AI, and genAI in particular, in internal business process applications as well as customer-facing aspects of the organization.

Let’s dig into some of the highly promising applications for genAI in terms of their potential value to a consulting firm.

1. AI as a catalyst for new professional services business models

Innovating on the product and service front with subscription-based and outcome-based services is one area where AI can really move the needle for a consulting firm. According to IDC, by the end of 2024, 33% of G2000 companies will exploit innovative business models to double the monetization potential of genAI.

Instead of traditional fixed-price or per-hour pricing models, firms are exploring development of services built around their unique intellectual property or expertise, with genAI embedded as part of those offerings, then charging a set subscription fee for them, providing the client with cost certainty and the firm itself with a defined, sustainable revenue stream.

A firm with supply chain management expertise could, for example, develop a service designed to curb revenue leakage in transportation/logistics, charging a fee that’s scaled to the actual savings it delivers to the customer.

Here, genAI could work on two levels, providing the firm itself with insight on how to price and structure the service (essentially striking the right balance between risk for the firm and attractive pricing for the customer), while also working as the intelligent engine within the service, providing optimal transportation/logistics pathways based on a huge range of datasets.

2. Intelligent, comprehensive workforce management

Managing internal and contingent/contractor resources, a persistent headache for many firms, becomes a more streamlined and intelligent process with genAI supporting in various areas. With a given set of parameters and the right prompts, it can perform skill-matching, evaluating skillsets, experience, availability, geography, projects pending and in the pipeline, and other factors, then recommend an optimal mix of in-house and contingent resources.

It can recommend an optimal team makeup based on the parameters and priorities of an RFP. It can forecast resource needs and costs based on the project pipeline and prevailing market dynamics, and identify potential skill and resource shortages before they become problematic.

When a project manager identifies a need for a resource with a very specific combination of skills, AI can identify a person whose skillset exactly or approximately matches those skills (adjacent matching). And it can perform intelligent reverse matching for resource managers, recommending a well-suited project placement for a consultant who’s on the bench. With the right prompts, parameters and data sets, genAI can do all this amazingly quickly and insightfully.

3. Elevating client service

Say a client has an accounts payable issue. Instead of having to perform all the manual work that goes into reviewing information about the case and where it stands, then relying on less-than-complete information to make decisions about next steps toward a resolution, genAI can arm service teams with the analysis and information they need about a case, recommend next best options for resolution, then automatically generate communications to the client about the steps being taken.

It can also flag more complicated cases that require deeper human involvement. The result: better overall experiences and outcomes for clients and client service teams alike.

In IDC’s 2023 survey, 41% of IT decision-makers at professional services firms indicated they believed genAI and technologies like ChatGPT would disrupt their organization’s competitive position or business operating model in the next 18 months.

A practical guide for using AI in professional services

Clearly, professional services firms see a solid business case for investing in business AI, including genAI. Indeed, the case for consulting firms to embrace AI within their own businesses (as well as in the services they offer clients) is highly compelling, giving them the means to automate processes, better support clients, boost productivity, improve resource management, and as the foundation for new business models and revenue streams.

As with any technology or digital system, AI comes with risks that organizations planning to or already using the technology would be wise to address in order to protect their investments. They include:

• A lack of explainability with an AI model
• Bias in the data or algorithm that skews outcomes
• Production of suspect information
• Displacement of human workers
• Cyberattack

However, these risks shouldn’t discourage consulting organizations from exploring AI use cases inside their business. Rather, they would be wise to approach AI as they would any new technology: with an open mind and a well-thought-out, tactical approach in how they invest in and deploy it, conducting a thorough cost-benefit analysis and risk assessment as part of the process.

As your firm embarks on the AI journey, it’s important to realize that not all business issues warrant an AI solution; a traditional software solution might suffice. Where AI is the best solution, focus on identifying use cases that are technically feasible, map to a well-defined business need or problem, align with your business goals, and project to add enough value to the business to justify the investment.

In the IDC survey, North American corporate IT buyers reported that AI use cases involving the automation of IT tasks provided the highest return on investment, followed by use cases that yield product and service innovation.

After identifying AI use cases, the next big task is to find a technology partner or partners that can provide you with what you need to build out those use cases. If yours is a deeply resourced firm with a large in-house stable of AI-savvy developers (few consulting firms fit this profile), then maybe all you need to source externally is an open-source AI framework with a large language model.

Many firms, however, likely will need a multifaceted set of AI capabilities that includes a variety of models to test, along with storage and computing/processing capacity, data resources and other tools, all accessed within some kind of development platform or environment. Some may prefer a plug-and-play, off-the-shelf type of industry-specific solution that maps to professional services-related use cases.

Here’s where thorough due diligence in evaluating what you need, as well as the provider(s) of the AI platform, models or off-the-shelf AI solution — the security measures they have in place, their commitment to ethical use of AI, etc. — is a must, keeping in mind that you may end up relying on more than one technology vendor in your AI journey.

In evaluating use cases and specific AI products, it’s also important to consider how readily the use case and product can scale and be applied elsewhere in your business. The more scalable and adaptable an AI solution is (without sacrificing the quality of outcomes), the lower the marginal cost associated with your AI investment will be.

Also as part of the process, be sure to keep AI ethics/governance, as well as staff AI training needs, front of mind. Having a clear, comprehensive AI ethics and governance policy, one that people throughout the organization understand and follow, is essential. For ideas about what to include in such a policy, try a Google search to see what other companies are doing.

When the cost-benefit and risk analyses are complete and you’ve laid the fundamental groundwork, including assessing the state of your data and taking the necessary steps to ensure it’s fresh, comprehensive, trustworthy and readily accessible to train your AI models, it’s time to put AI to work inside your business.

Start small—sooner the better

One piece of advice here: As ambitious as your ultimate plan for using AI might be, it’s wise to start with a narrow proof-of-concept project to test the waters. Integrating business AI into a professional services firm carries substantial expense and risk, so you want to be confident the technology can deliver the outcomes you expect, and do so cost-effectively, with a manageable level of risk.

Say, for example, the use case involves a workforce management solution embedded with a virtual assistant to help match in-house resources to specific projects. In the proof of concept (POC), evaluate the quality of the user experience and of the recommendations the virtual assistant provides based on the data the solution’s underlying model used, and how it was prompted.

Then refine the model, along with the prompts and the data it uses (with help from your technology partner, which could be the provider of the AI solution and/or a third-party implementation specialist). Keep iterating until you get the outcomes you expect for the use case, or until it’s evident the AI solution isn’t the right fit for this use case.

You also want to be sure the AI solution, its underlying model, and the output it produces are readily explainable, not only to mitigate potential legal, security and compliance issues, but also to inspire trust in the technology and enable auditability to ensure model performance doesn’t drift or degrade over time.

Ultimately, these POCs can lead in surprising new directions, revealing unexpected possibilities for iterating an AI model to add value in other use cases within your business. So be alert to opportunities to apply AI capabilities in areas you initially may not have contemplated using them.

“By deploying applications that help enhance productivity and personalize processes, organizations can use Generative AI to accelerate the pace of their business, evolving an experimental investment into an established value driver,” the Deloitte AI Institute said in a report released last September.

The sooner your firm starts responsibly experimenting with business AI, the sooner it can start stepping on the accelerator.