6 Things Leaders Should Know About Machine Intelligence

• Updated on: 2017-06-23
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6 Things Leaders Should Know About Machine Intelligence
6 Things Leaders Should Know About Machine Intelligence
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How to combine human strengths with algorithmic precision
Machine intelligence—a broad term used to describe the development of a machine's ability to learn, think, and act like humans—has become an increasingly urgent topic of discussion in corporate c-suites, government offices, and newsrooms across the globe. Recent advances in research, computing power, and availability of digital data make it possible to develop algorithms that can perceive, interpret, and take action on information as well as—and sometimes better than—humans.
These advances are evident in diverse applications across sectors, from cars that can drive themselves to software that can identify the earliest signs of disease in x-rays—
many times better than radiologists.
“Recent advances in research, computing power, and availability of digital data make it possible to develop algorithms that can perceive, interpret, and take action on information as well as—and sometimes better than—humans.”
But the reaction around these developments is mixed. Many extol the merits of machines that will free us from work and open us to new realms of human capability. Others forecast a dire future of increasing inequality and widespread unemployment. There are also those that question whether machine intelligence is ready to be put into production, or will live up to its ever-increasing hype.
For executive leaders, the result can be paralyzing: Is it  a business imperative to try and replace employees with machines or would it create an even greater risk in broken trust with shareholders, customers, and the public?
The solution: There is no need to choose either extreme. In fact, organizations  that will be best-positioned to succeed in our still-evolving future will be those that learn to marry our greatest human strengths—creativity, empathy, negotiation, management of others—with those of increasingly capable machines: Collection and processing of data, precision, and scale.
These organizations, or “mathematical corporations,” will complement the best of human leadership with algorithmic insight and precision, enabling them to perceive and shape the future rather than relying on tradition, gut feeling, or past events.
This future sounds promising, but it's difficult to know where to start. Few organizations have a dedicated data science function, and those that do may still lack the expertise needed to perform advanced machine intelligence. Moreover, an organization's data is often messy, complex, and difficult to extract.
Employees may be distrustful of machine intelligence—rightly so given how it is often portrayed—making change management and communications more difficult.
In our new book, The Mathematical Corporation: Where Machine Intelligence & Human Ingenuity Achieve the Impossible , we make the full business case for investing in developing a machine intelligence capability.
Here, we offer six principles for why to get started and how.
6 Things Leaders Should Know About Machine Intelligence
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The why...
1. Human experience + machine insight > gut feeling alone.
Intuition serves leaders well because the mind absorbs and understands more detail than we consciously know. But biases, politics, and wishful thinking sometimes distort our views. Coupling our instincts with insight produced by machines enables us to see surprising possibilities we may have otherwise ignored. This includes everything from enabling us to take a closer look at a job candidate whose alma mater we’d discounted, to identifying a revenue driver we wern't familiar with.
2. Machine models can powerfully augment our mental models.
Machine models are outperforming mental models in a growing array of cognitive tasks. This was evident in the recent defeat of Ke Jie, the world champion in the extremely complex strategy game Go, at the hands of a machine learning program created by Google. Machines’ increasing precision and capability means we can now confidently use machine models to ingest and interpret data for a variety of business purposes, while taking a “trust but verify” approach.
The how…
3. To break through without experience, start by experimenting. 
Many organizations are put off by machine intelligence technologies because they think  the costs, talent, data requirements, and risk of failure is too high. In fact, in this rapidly-evolving space, even the biggest organizations are learning the ropes and making mistakes along the way. When you’re creating a new product or strategy, you can’t beat the odds by insisting your people “do it right the first time.” If you’re cautious about getting started, identify one or a few narrow areas in your business where you can tolerate some risk, and select motivated talent you already have to lead the charge. There are a wide variety of open source tools and platforms they can use to get an early footing. 
4. Complexity is an asset, not a liability. 
Complexity means competitive advantage exists. In straightforward industries with simple rules that always work, it is difficult to find new information that can help a management team make better strategic decisions. But decisions that require fusing information from many parts of the business offer opportunities for mathematical corporations. Organizational complexity and the depth of insight available is a powerful competitive asset that you probably already have.
5. In machine intelligence, you can create value by giving it away.
The development of machine intelligence is  a global phenomenon, bringing together academics, business leaders, and policymakers from around the world. The value of machine intelligence goes beyond short-term returns and cost savings. This technology has the power to fundamentally change how our most important institutions make decisions. It also opens the possibility for us to address some of the world’s most pernicious problems—from income inequality to disease and environmental crises. Even the biggest technology companies are working together and sharing openly to advance machine intelligence through initiatives like OpenAI and The Partnership on AI to Benefit People and Society . Transparency, sharing, and openness will enable organizations to learn and benefit from machine intelligence more quickly, and to take on an important role in the next technological revolution. 
6.     Honesty is always the best policy.
There is no question that as machine intelligence becomes more prevalent in business and society, some jobs as we know them today—particularly those focused on rote tasks and labor—will eventually go away. But this should not cause a sense of doom and gloom, and it will not happen overnight. In fact, studies show that it often takes seven to 10 years after a technology is created for it to be fully integrated in organizations—particularly technology as complex as machine intelligence. Still, leaders must acknowledge this reality with their workforce, and begin to create strategies now on how to help affected employees  develop the skills to transition to new roles. Ultimately, being honest and transparent about the future will help to build trust and buy-in with your employees, customers, and shareholders.
Publication
Discover your blueprint to lead in the machine intelligence era in Booz Allen’s new book  The Mathematical Corporation: Where Machine Intelligence & Human Ingenuity Achieve the Impossible , available now. With unparalleled expertise on our industry-leading team of data scientists, domain experts, and consultants, Booz Allen can help your organization begin its journey of transformation using machine intelligence.
Experts In The Field
Dr. Josh Sullivan, senior vice president at Booz Allen, and Angela Zutavern, a vice president, are radically transforming how Fortune 500 companies, not-for-profits and major government agencies perform by helping leaders shatter long-held constraints and reveal hidden truths in their organizations.