Part 1 of a 2-part series that examines lessons learned from professional services firms in innovating with machine learning technology. Read Part 2.
If, as The Economist claims, “software that can learn is changing the world,” then machine learning presents a huge opportunity. My colleague Matt Emmert has already explored how the application of intelligence is lined up to be a key differentiator in the coming years. But now machine learning is taking that a step further, fueling the next generation of business processes and going far beyond digital transformation to give any business a distinct competitive advantage in the future.
Over the past few decades, professional services firms have been on the front line of technological disruptions. In many ways, they are the first responders, channeling their best resources as quickly as possible to support their customers. The exponential pace of technological advancements, along with the pressure to do more with less, has created a window of opportunity for professional services firms to adapt their internal and external processes on the fly – innovating faster than anybody else.
And it’s precisely here, at the meeting point of challenge and opportunity, that we see some of the core trends shaping the industry today: access to innovative new business models, productized knowledge, and embracing the gig economy with talent networks – to name but a few. It’s this inflection point that is propelling firms to jump on board the speeding machine-learning train and making a choice: “disrupt or be disrupted.”
Machine learning’s fast learners
Many professional services businesses are already seizing on the opportunity that machine learning presents. According to Deloitte Global, large and midsize enterprises are about to intensify their use of machine learning, with the number of implementations and pilot projects expected to double from 2017 to 2018. The likes of EY and KPMG have already deployed chatbots to improve efficiency, and there are even more sophisticated, though readily available, opportunities on the horizon, like invisible automation. SAP offers software, for example, that’s designed to automate manually intensive financial processes by learning from accountant behavior.
In a 2017 survey by The Economist Intelligence Unit (EIU), produced in collaboration with SAP, it became clear that a handful of businesses are advancing at pace with this kind of machine learning implementation. The survey calls them “fast learners,” with a clear set of traits uniting them. Namely, senior management understands the strategic value of machine learning, and the business as a whole sees it as a way to stand out from the competition and as an opportunity to generate new revenue streams.
Responding to an outcome-based economy (and the gig economy)
One way in which businesses can be freed up to grow these new revenue streams is through the automation of manual tasks via machine learning. This automation gives employees the time and opportunity to focus on higher-value work and, of course, helps the business to reduce costs and increase profitability. But it also facilitates the generation of the kind of new revenue streams that really give businesses the chance to flourish in an increasingly outcome-based economy. Through machine learning adoption, businesses give themselves the ability to perform advanced analytics on huge data sets and gain insight into the outcomes of the products or services they provide.
Intelligent bid management is a case in point. This is an example of professional services firms using machine learning to build on the past and optimize into the future – and, ultimately, win more bids. Deal intelligence is another. In this case, firms can improve their win rate, close deals faster, and improve pipelines thanks to an intelligent platform that automatically scores deals and ranks them based on propensity to close.
Machine learning also means that professional services businesses can be mindful of the need to adapt to future ways of working, especially in light of the gig economy’s increasing prevalence. In fact, this is an area where machine learning offers a significant benefit, particularly when it comes to recruitment. As businesses find themselves in a “war for talent,” the advantages of a platform that can automatically match top talent to open positions is clear to see. Plus, this kind of platform removes human bias from the process, creating a more diverse workforce (which in itself is linked to productivity). And let’s not forget that machine learning has more benefits to offer throughout the employee lifecycle, from the development of intelligent onboarding to ensuring that employees stay engaged and motivated throughout their careers.
Also forming a large part of the professional services talent network are contingent workers, who are widely utilized in the sector. The challenge here is that it can be difficult to ensure that these workers are sourced and deployed to the right projects effectively. But with machine learning, accessing large data sets about available talent and relying on decision-making embedded in algorithms to match profiles accurately becomes a possibility.
On top of that is the fact that fast learners are more likely to source processes locally and implement initiatives enterprise-wide. Each of these plays its part in successfully integrating machine learning-based technology into the fabric of a business, and using it to gain a competitive edge.
Part 2 of this series looks at some of the factors important to innovating effectively with machine learning.