Management Consulting’s AI-powered Existential Crisis

We all understand that AI is a disruptive force in the market, but it still takes some convincing for many of us to accept just how deeply AI will affect even highly skilled knowledge-based industries.

Management consulting offers a prime example. A $250 billion industry filled with some of the smartest people on the planet, consulting tends to view itself as an elite, untouchable echelon of the business world. But it is vulnerable to the same market forces that are disrupting services everywhere.

Most skilled services follow a common pattern: Gather data, analyze it, derive insight, and communicate recommendations. And from doctors to drivers, service industries are facing disruption.

To that end, a recent article in The Wall Street Journal caught our eye. It was about Goldman Sachs, where traders and quants have become nearly indistinguishable. The roles have converged, with each skill set becoming essential to the job of trading securities. Adam Korn, a Goldman Sachs managing director, says, “You say ‘trader’ and I don’t even know what we’re talking about. Everyone who comes to sales and trading needs to know how to code.”

This talent shift, a response both to concerns about ethics following the financial crisis and the rise in quant-driven funds, is upending the pecking order of the trading floor and is creating a talent war as Wall Street seeks to lure coders away from Silicon Valley. Rather than hiring traders with deep market knowledge and good instincts who relied on their own human intelligence, organizations are shifting to savvy coders who can create programs to learn the patterns and make decisions independently in response to the latest and greatest data.

As artificial intelligence takes over the capital markets, will consulting be far behind? The similarity between the two lies in their core: data. Just like traders, consultants offer a data-driven service. And, historically, data challenges have ensured the necessity of a human interface to the data. Data is messy. It is hard to process. Key data is often missing or hard to access. These problems created a situation where companies seeking data-driven answers to key strategy questions required experts (consultants) to create, combine, clean, analyze, and interpret data.

But things are changing. For one, companies have seen the light on the value of data and are beginning to gather more of it on their own. Organizations are now amalgams of software-enabled systems (at point-of-sales, managing inventory, tracking production) that dutifully log and store data. The days of junior consultants sitting in parking lots counting customers leaving with shopping bags are quickly coming to an end.

When it comes to analyzing this data, software is also quickly gaining on humans. Strategy consultants often solve the same problems over and over again — product line profitability, for example. Machine intelligence is quickly closing the gap with human intelligence when it comes to these well-trodden paths.

Market leader McKinsey may be well-positioned for this change. While many high-end strategy firms have turned up their noses at technology work, McKinsey invested early with its internal Digital McKinsey capability, and in 2015 also purchased QuantumBlack, an analytics startup with machine-learning experience, to bring new data capabilities to its work with clients.

Overall, however, the consulting industry is at risk. With its deeply embedded business and mental models, many companies will be unable to make the jump — consultants will not be able to leverage machine learning to support their work and will fail to shift their focus to the parts of the job that are human-based, like persuading, mentoring, and coaching.

So how should consulting companies, or any in the services sector, adjust to this new, data- and AI-driven world? Follow the rules below:

1. Accept the fact that all industries are being disrupted by machines and new business models, and consulting will not be spared — no matter how high-end the services. Do not fall prey to exceptionalism. A decade ago, we thought that long-haul driving was more at risk from AI than stock picking, but we were wrong. Consulting is not exempt.
2. Identify where AI can enable and augment your workforce to have a bigger impact, and invest capital in being a leader in that space. Build new platforms with AI technology to drive insights, and let consultants shift on the human aspects of persuasion, motivation, and coaching.
3. Recruit new leaders to your boards and C-suite. If your boards don’t have independent directors who understand AI and platform business models, you probably don’t have a chance to pivot. Don’t fall for the idea that digital innovators lower in the organization can create this type of change. It must come from the top.
4. Determine what data you have, and what data you need to feed proprietary AI. Data is the fuel of business, and consulting companies have fallen behind in quantifying the insights and knowledge that they have given their broad experiences across industries. For example, consulting firms often informally maintain lists of benchmarks that include data points across industries, such as the ratio of HR employees to total employees or the average savings achieved through vendor negotiations in different categories. This data is not extensible or defensible as long as it lives only in partners’ heads.
5. Institute new KPIs that measure what matters in today’s world — the insights that you deliver rather than the hours that you work. Using AI will allow you to better leverage your team for impact. If teams spend less time cleaning data and building models, and more time influencing leaders and creating change, so much the better.

New business models are changing the landscape. The “giving advice” industry is now at risk, too. Consultants, even the most staid and traditional, have a chance to survive in the AI world. However, they need to first change their mental model about what their true value is, and how it can be delivered. No matter what the service, it is shifting to software. Disruption is just a matter of time, and it’s always better done internally than externally.