As former consultants, we appreciate the value of people-powered analytics. Indeed, we grew up using tools like Excel and PowerPoint to uncover and share insight. However, in a world where data doubles ever two years, which is as true for the corporation as it is for the person, the idea that a consultant can do the numbers crunching effectively seems laughable. Indeed, the concept of billing 2,200 hours a year, flying hundreds of thousands of miles to client locations to gather data and developing a hundred-page presentation to help drive corporate strategy seems less viable and valuable than it did 30 years ago.
AI is transforming strategy.GETTY
In a data-driven business world it’s clear that machines are beginning to play, and will play, an ever-larger role in C-suite decision making. AI platforms for marketing, sales, and HR are being financed by VCs and built by incumbent organizations. The best leaders of today and tomorrow will no longer rely on the instincts of a few decision-makers (or the gut as Jack Welch once said) and will instead use insight driven by machine and deep learning solutions. With new competitors changing the market at rapid pace, companies seeking to achieve ‘superstar’ status and dominate the top of the profit and value ladder will need AI to guide the way forward.
Yet most businesses have only just begun to scratch the surface of what is possible with AI, and slow starters are falling further behind. There may be tens of thousands of different adjustments a business can make every day to their tangible (things and money) and intangible (people, knowledge and relationships) assets just like investors do with their portfolios of assets. But most businesses keep doing what they have always done but hope for different results. Why?
The obvious answer is there are only 24 hours in a day (true for even the smartest among us), and the strategists and consultants analyzing the impact of both past and future tests are limited by the time and the power of old technologies. However, modern tools that enable dynamic strategy modeling including product pricing, market strategies, customer segmentation, employee engagement and operational alternatives are no longer limited by human factors. What, then, is actually limiting the corporate leaders in their use of AI to power their corporate strategies – including customer, employee, supplier, and business model?
The new bottleneck is the fact that today’s leaders lack the understanding they need to use AI and data to drive their business. All technology adoption starts with a new idea, then a simple test that is converted into an insight which is confirmed and finally deployed by leaders as the return on investment becomes clear. The more value new technologies generate, the most tests are run, and the faster they are deployed in organizations to drive revenues, reduce costs and improve valuation. AI is needed not to just determine the opportunities for those tests, but also to measure and report on their results. Machines can help organizations assemble and assimilate massive collection of divisional and department data that is collected at the edge and scale it to measure the impact of the thousands of small decisions made every day in an organization. With the help of machines, leaders can begin to see the thousands of experiments being run, either on purpose or by accident, and their collective impact of corporate performance. But to realize this benefit, leaders need to tap into those ideas in an efficient manner using machines.
Automated software solutions to these problems was never possible before artificial intelligence. The quantity of relevant data inside and outside an organization is too much even for the largest teams of analysts at even the best consulting firms—which is why the consulting model has always included the insight of experts who apply heuristics. But machines-based systems can analyze massive amounts data and enable leaders to harness the power of insights like the best investors do today. In effect, AI powered strategies – be they customer, employee, partner, product, market or finance – will turn accidental experiments into machine driven insights. In addition, more ideas will be generated and tested at greater speed and lower cost than the old people-driven models.
Enabling your business to succeed with AI-driven business strategies requires preparation and planning, and a true commitment from the C-Suite. To become AI first leaders, we recommend the following steps:
- It’s all about data. AI is just math layered on top of data. If you don’t have the right data, there is nothing that AI can do to help you. There are two major data inputs: your own data that powers your customer, employee, supplier, and product strategies and external data such as customer, competitor, and investor data. Use your own insight to focus your machines on the most relevant data sets and remember that good data is better than more data.
- Adopt modern business models. Superstar organizations are winning the economic war worldwide. The companies that dominate the superstar category are all platform-based firms—they use two-side revenue models that benefit from network effect and near-zero marginal cost of growth. To achieve this level of performance, leaders need to think about how to connect their suppliers to their buyers and act as brokers instead of primary creators. This business design requires AI to achieve scale in both size, efficiency and capability and platform companies cannot manage growth at scale without strong AI capability.
- AI first leadership. Research has clearly showed that few organizations have AI first leaders or boards, despite the advantages. If leaders really believe that AI is more important and disruptive than the advent of the internet then they need to experiment now before today’s superstar firms become Death Stars (many would say that Amazon already is). Leaders need to be open to new machine-based technologies and to the instrumentation of many of their tried and true business processes.
AI is already changing the way we run our businesses, although for now much of the capability is concentrated within the superstar firms. It’s time for legacy organizations to change the way they run their businesses, empowering machines to make ever more decisions on their behalf – faster, better and more effectively. Investors like Two Sigma and Renaissance Technologies realized the power of data and artificial intelligence years ago. It’s now time for leaders of all types of organizations—private and public, for profit and not for profit—to accept that the world is too complex, and data growing too quickly to rely on humans alone. Leaders should not get lost in the cost reduction discussions of robots replacing humans (although still an important issue). The true promise of machine in the C-Suite is improving how business is run and governance is done.