How AI and ML Help Solve Big Challenges for Professional Services

Much ink has been spilled over the impact of artificial intelligence (AI) and machine learning (ML) on industries, including professional services. Generative AI tools such as ChatGPT have sparked a resurgence in interest—and wary feelings—about AI. But lost in the headline swirl and hubbub are the real capabilities that are useful to firms right now or can be put into action in the near future. 

And these AI and ML capabilities are needed. According to IDC’s Workday Multi-Industry Study, June 2023, only 27% of professional services leaders worldwide believe their industry is thriving and “ready and willing to take full advantage of whatever the future brings.”

For professional services, particularly in the accounting and legal industries, there are many repetitive and manual tasks that are often saddled with paperwork. Essential people are frequently bogged down in administrative tasks that take away from high-value work. 

AI and ML can help address these issues and more. It can drive value creation, such as automating routine tasks to drive cost savings or enabling top-line growth. With value-protection tasks—such as internal audit, risk management, and regulatory compliance—AI and ML can surface anomalies before they cause headaches.

In IDC’s Workday Multi-Industry Study, professional services leaders ranked the top way they are using or considering using AI and ML:

• 41.4% selected “recommendations,” such as resource management, customer payment matching, smart defaulting of expense items.

• 28.9% selected “automation,” such as receipt scanning for expenses, time tracking, auto-skip approvals, self-reconciling accounts.

• 28.5% selected “anomaly detection,” such as journal entries, expense reports, plans, outlier reporting.

This shows a real interest in the various use cases of AI and ML, with only 1.2% saying they are not considering these options. 

In a nutshell, the future of work will be less of a slog. Organizations can use AI and ML to elevate human capabilities and improve the way we work everywhere by making finance and human resources (HR) more intelligent. But for AI and ML to really deliver on the possibilities it offers, it must be trustworthy and take a human-centered approach that augments employees not displaces them. 

“I don’t believe AI will ever disintermediate the need for smart humans, but the kind of work we do will evolve over time,” says Vanessa Kanu, CFO at TELUS International. ”Anything that requires a higher level of complex thinking, relationship-building, spending time with investors and key stakeholders, or how you manage your board, all of those skills are not going to go away because of automation.” 

According to our recent global survey of 1,000 HR, finance, and IT decision-makers, 84% of professional services leaders are feeling the pressure to increase adoption—the highest of all industries surveyed—but many don’t know how to best utilize the technologies.

“It should be a tool, in essence, that helps us work differently, provide insights differently, and free up capacity to partner with our business teams differently,” says Katie Rooney, CFO at Alight Solutions. “Change is hard, especially in finance, but once you help people see the value of how they can do their work differently, it resonates. For me, it’s helped by just honestly getting started and showing what it can be.”

Now let’s look at practical ways professional services leaders can leverage AI and ML. 

Financial Management: Role of AI in Accounting

AI and ML can help finance leaders in professional services to intelligently automate, guide users through tasks, and predict outcomes. The good news is that leading finance organizations are already using AI and ML capabilities embedded in the core of our platform. 

A big need for accounting teams is to reduce incorrect numbers or inaccuracies through anomaly detection, which is challenging with the sheer amount of data, invoices, and reports they manage. One way to address this by using ML is journal insights. It helps surface erroneous journal lines to controllers—dramatically reducing the time and overhead spent by finance teams to close the books.

Journal insights uses ML to detect anomalies in accounting entries by comparing them to other entries for similar transactions. Because these are flagged in real time, people can correct potential reconciliation issues. This enables accounting teams to spend more time on analysis and tackle more strategic initiatives.

According to Kanu this automation helps employees “by making them more efficient and enabling them to focus on more engaging, meaningful work.”

“For me, this is a personal mission because automation is key to unlocking the value from our team members,” she said. “People want to make a difference.”

Another opportunity is supplier invoice automation. ML can deliver intelligent automation in the accounts payable workflow with smart autocomplete and can upload and scan invoices in bulk, identify urgency, and prioritize for processing. It can also intelligently route invoices with potential issues to workers who have shown aptitude at resolving similar questions. 

Supplier invoice automation leverages rules-based work queues and header-level scanning to direct invoices to the right person, and it can also handle invoices that come in via robotic process automation (RPA) or any other means. 

WilsonHCG Chief Financial Officer Ken Bowles says: “Workday allows us to implement more automation as we move into other countries and work with very different clients. For example, in billing, we often use monthly fixed fees, which are almost like a subscription plan, and variable invoicing. With Workday, we can automate our billing schedules so we can be more efficient and still address individual client requirements.”

Optimizing Resource Management

Tighter margins and clients’ increasing sophistication in procuring resources are driving professional services firms to rethink resource management—and the industry is now taking automation more seriously. 

Keeping track of talent has become more complicated in a hybrid work environment with almost 2 in 5 global employees (39%) expected to work hybrid schedules by the end of 2023, up from just 12% in 2020, according to Gartner.

This talent environment demands that industry leaders be able to put the right people on the right projects with ease. But because spreadsheets can’t provide accurate, dynamic insight into available talent and skills, firms tend to staff projects with the same people, time and again.

“New ways of working have highlighted, for a lot of companies, how murky their visibility is on the real skills of their workforce,” says Justin Joseph, Workday’s senior director of product strategy for professional services industries. 

To know how best to use available resources, companies need intelligent resource management software that can provide a holistic, centralized view of people and their skills. And a truly smart resource management application will use ML to pull in data from sources such as resumes, performance reviews, learning systems, and people’s past projects to build a skills ontology that’s both comprehensive and continuously updated. 

Automation and ML can then shed light not only on available people and skills but also on the best combination of people for the project at hand. 

“Machine learning can work through so many more dimensions around who would make an ideal team for a particular project—helping the person tasked with resourcing to make data-driven decisions faster and with greater accuracy and confidence,” says Patrice Cappello, global head of professional services industry strategy at Workday.

Firms can use ML to quickly adjust hiring plans in response to economic fluctuations and other changes, making the most of their billable resources. That’s no longer a nice-to-have, but a must-have.

“Organizations, including our own, are increasingly shifting to skills-based talent strategies to help improve engagement and provide career growth opportunities for their employees,” says Amy Richmond, managing director at PwC. “Working closely with Workday, we are solving a critical challenge by bringing our skills data together, allowing us to have a holistic view of our workforce skills and tailor career experiences that help drive employee success and satisfaction.”

Intelligent resource management unlocks real-time visibility into your workforce and makes project resourcing dramatically more efficient, more effective, and more intelligent. 

“You’ll never take out the human element entirely,” says Cappello. “But machine learning can work through so many more dimensions around who would make an ideal team for a particular project—helping the person tasked with resourcing make data-driven decisions faster and with greater accuracy and confidence.”

In addition, ML can also provide resource scheduling insights. It can help people identify if there are any roles that are “at risk” for a quote and predict which resources to fill on a project based on resource availability, cost, and skills. For the first time, it mixes human capital management, project financials, and skills data. 

“With real-time employee data, we can see who should get a promotion, who has which skills and qualifications, and who might be at risk of leaving,” says Marisol Hughes, executive vice president and general counsel at WilsonHCG. “That gives us the confidence to bring on new clients and projects quickly and address client needs proactively.”