How Machine Learning Is Disrupting The Professional Services Industry

Marcus Fischer

Over the last decade, knowledge has become the key driver for productivity and economic growth. Professional services providers like accountants and lawyers have benefited from this strong knowledge economy. These professionals have a combination of knowledge and expertise that makes them uniquely qualified for solving specific problems. Until recently, this industry has been relatively untouched by disruption. Machine learning is changing this equation. Recently, Eric van Rossum, global vice president of the Professional Services Industry Business Unit at SAP joined the S.M.A.C. Talk Technology Podcast to share how machine learning is reshaping the future of the professional service industry.

“Until recently, the professional services industry has been pretty immune to disruption,” says van Rossum on the podcast. “You had this asset or this knowledge or this expertise which kind of sits within the human mind. And you would hire these people at a certain cost. You would add 20% or 30% margin depending on what kind of industry you’re in, and you would position that service into the market space.”

Now, machine learning is disrupting this business model. Repetitive and codified professional services such as auditing, certain legal tasks, and call centers are becoming automated. At the same time, new value-added services are being designed. Machine learning is also helping to predict future workforce needs.

From rules-based automation to machine learning

Using technology to automate rule-based services, like basic auditing, is nothing new. Rule-based workflow automations are stagnant, however. Machine learning is different since the algorithm is able to “learn” as it processes data, accelerating performance and capabilities. For example, machine learning algorithms can cut accounting document review time in half. By the end of 2018, machines will author 20% of all business content, including legal documents and shareholder reports.

There are still a lot of back-office processes where people are involved to steer workflow, which is not predictable. Machine learning can help improve enterprise resource planning, ultimately streamlining this “back office workflow.” This is important for companies who currently struggle with workflow management and resource deployment. Only three out of 10 companies say they can identify and deploy the right resources for the right projects.

“One of the ideas that we’re positioning into the market is what we call intelligent ERP or autonomous ERP,” says van Rossum. “In a completely rule-based scenario, there’s a learning element to it. I think if you leverage intelligently a lot of the data which is in the system, you get much better at predicting the right resource.”

This shift in resource identification and deployment also means that more companies will move from full-time professional hires to contingent hires.

“The decision to staff a project really becomes the right time to staff, the right cost to staff, and then the right skill set to staff as well,” says van Rossum. “Machine learning will help a lot with smartly predicting the right resources.”

New outcome-based billing models

In the professional services industry, most companies charge a flat fee for a list of deliverables or an hourly rate for ongoing consulting. As the S.M.A.C. Talk Technology Podcast points out, these companies are rarely held accountable for the performance or quality of the work delivered.

Machine learning may change this by helping businesses move towards new outcome-based price models, rather than time and materials billing.

There are both positives and negatives to these new models, cautions van Rossum, especially for companies that struggle to correctly productize their services.

“As we go more to outcome-based, the potential to create more margin and more profit is there. But, the downside of that is if you get it wrong, you can take an incredible dive on your margin as well. A lot of these contracts are long term. They are going to be based on some sort of outcome-based model, or a usage-based model. And what these professional services firms will need to start thinking about more … you know a lot of these outcome-based engagements are then actually going to be based on (knowledge-based) products which they drive into the market.

Companies must consider how they productize services, including whether the cost for these services is based on a measurable outcome or result. This is a departure from the dominant pricing model where service is priced based on time or skill level, not a specific outcome.

One key component in outcome-based billing would be customer satisfaction. Factoring in business outcomes to service fees could improve customer satisfaction, strengthening customer relationships and improving retention.

Embracing digital transformation for future growth

Companies that embrace digital transformation will be able to retain and grow existing talent, attract new talent, and protect intellectual property. A smarter, more engaged workforce will give these companies a critical edge in today’s competitive market.

For more information on how digitization is transforming the professional services industry, listen to the S.M.A.C. Talk Technology Podcast with Eric van Rossum.