Leading the Intelligent Enterprise

To prepare for the next phase of AI, leaders must prioritize assembling the right talent pipeline and technology infrastructure.

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The AI & Machine Learning Imperative

“The AI & Machine Learning Imperative” offers new insights from leading academics and practitioners in data science and artificial intelligence. The Executive Guide, published as a series over three weeks, explores how managers and companies can overcome challenges and identify opportunities by assembling the right talent, stepping up their own leadership, and reshaping organizational strategy.

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Artificial intelligence (AI) and machine learning offer new ways to boost productivity, develop talent, and drive organizational change by enhancing managers’ ability to make the right calls in complex situations.

Augmented intelligence tools have already made an impact for many companies, but the next revolution will happen when every aspect of a business, from top to bottom, is designed with AI in mind. Call this new construct the intelligent enterprise. Like other major revolutions in management, it’s poised to transform industries and organizations for decades to come. To prepare for this next phase, leaders will need to harness machine intelligence for decision-making across the business, assemble the right talent, and recognize the benefits and limitations of AI to shape organizational strategy.

Understanding the AI Advantage

It’s not hard to find examples of the amazing things we can do with artificial intelligence. AI and analytics have changed the centuries-old techniques of plant breeding, helped advance cutting-edge research into disease, and even been used to decipher damaged ancient Greek tablets.

What these achievements have in common is that they are discrete, structured tasks. In each example, algorithms are used to absorb available data, recognize patterns therein, simulate outcomes, and select moves or produce results based on the statistical likelihood of success. In plant breeding, for example, the simple step of designing a trial to see whether your breeding effort has succeeded or failed requires choosing from a set of 1.16 x 1012 possible combinations. Yet, increasing efficiency in this highly complex process through data analytics can save millions of dollars.1

If improving one aspect of one process through data analytics can have a massive payoff, imagine what can happen when an organization takes advantage of AI’s ability to learn, analyze, and optimize across all processes and business functions.

How AI Can Accelerate Leadership

Businesses, particularly large corporations with a global footprint, are complex adaptive systems. No one person, or even one group of managers, can know what’s going on at all levels of an organization consisting of thousands of employees. Even so, the CEO is responsible for keeping the board and shareholders happy, positioning the company for the future, maintaining employee morale, and developing an advantage over the competition — all while turning a profit. Although the CEO relies on an executive team for support across these different functions, he or she ultimately shoulders the blame for bad choices. No wonder most CEOs at large-cap companies don’t last more than five years.2

With so much responsibility, the CEO’s scarcest resource becomes time, and that’s where AI brings the most value to the top job. AI is an ideal tool for observing and gathering the available information touching on business operations. This includes internal reporting data as well as relevant external news stories and analysis relevant to the industry, digested and categorized by natural language processing algorithms. The Reuters news service, for example, uses AI to sift through 700 million daily tweets to spot breaking news that can be handed to a journalist for further investigation.3

The intelligent enterprise must similarly process a mountain of data, prioritizing items according to relevance, which helps to avoid information overload for the leaders reviewing the reports. This gives the CEO maximum awareness of what’s happening throughout the business and the industry so that more of his or her time can be spent addressing issues likely to have an impact on the bottom line.

Moreover, the intelligent enterprise imagines AI systems in every division, department, unit, and group in the organization — human resources, IT, marketing, finance, operations, and so on — so that each of these operations can be optimized with augmented intelligence systems that provide decision support to human employees.

Many HR departments already use a simple form of textual analytics — keyword scoring — to sort through unwieldy stacks of résumés that accumulate whenever a new job is posted. Applications for an accountant position that don’t mention, for example, the required academic credential or license can be tossed out right away. NASA’s AI system performs a deeper analysis that evaluates the context in which the keywords are used.4

In the intelligent enterprise, more-advanced expert systems would use cognitive engines to understand the applications. Moreover, they would not focus narrowly on making the HR manager’s life easier. Each corporate unit’s and division’s systems would exchange information automatically, so the HR system would know when new talent might be needed. It could review past applications and have potential candidates lined up for consideration as soon as any new hiring was approved. In this way, the system would become a key part in advancing the CEO’s goals by ensuring that the company had the talent it needs to execute the overall mission.

The interconnection between business divisions would also give the CEO a real-time look into company performance. Data from each business unit wouldn’t be filtered by preconceived ideas about what the numbers ought to look like or shaded by department heads putting the best face on the results. The numbers would speak for themselves.

With a clear view of what’s happening, the CEO could swiftly reorient the company, as needed, to remedy problems or take advantage of favorable conditions. Armed with solid information and options weighed by AI simulations, the CEO could formulate multiple potential strategies to deal with the situations that arise. Instead of being based on hunches, emotions, or guesswork, these strategies would be fully informed by the best available data.

Know AI’s Limits

While innovation in AI systems continues to rapidly evolve, it’s not all-knowing — in fact, artificial general intelligence exists only in science fiction. For now, it still falls to the human CEO and executive team to pick the strategy and execute it. But machines and AI systems are incredibly valuable for presenting data and providing options for leaders to consider based on different real-world contexts and goals. For example, sometimes the CEO will want to take a long-shot risk. Or perhaps it’s important to spend money on an initiative that won’t hit certain strategic targets but will improve employee morale. Reality is far too complex for a statistical algorithm to imagine every possibility that leaders might take into consideration.

Experienced CEOs are needed to consider the intangible factors a machine will miss. While the CEO’s primary job is making decisions, the role doesn’t end once a choice has been made. Here, AI tools are essential for monitoring results and evaluating whether the strategy is producing the intended effect. When bad choices are made, it’s important to change course quickly. The continuous cycle of acting and reviewing results is critical for updating or abandoning strategies when necessary to achieve the organization’s goals.

What AI does is enforce discipline on corporate strategies. It continuously, and automatically, evaluates questions like, “Is the plan working?” or “How are the forecasts and projections?”

Constant reevaluation of the company’s direction, in matters big and small, may seem like a waste of time, but it’s an effective insurance policy against complacency. Adaptability allows a business to stay ahead of customer and market needs and avoid becoming the next BlackBerry, Blockbuster, or Borders.

What AI does is enforce discipline on corporate strategies. It continuously, and automatically, evaluates questions like, “Is the plan working?” or “How are the forecasts and projections?” It plots out alternatives — what happens if the company pivots in this direction or that direction? The intelligent enterprise also provides clarity about the goals and objectives of the organization, aligning every business division toward the overall strategy by setting goals (for instance, by having the talent on hand to accomplish the next mission) and tracking progress toward those goals and the end results.

Sometimes overall change is needed, and sometimes it’s not. The intelligent enterprise is a system designed to be ready for either possibility. In a complex market environment, success comes to the companies best able to adapt to fast-changing circumstances. By building adaptability into the structure of the company, AI helps the CEO manage challenges as varied as the disruptions of a global pandemic or the discovery of new technologies.

Companies are investing in AI today, but to achieve the ultimate strategic goals of this investment, organizations must broaden their sights beyond creating augmented intelligence tools for limited tasks. In order to turn this broader vision into reality, leaders must prioritize assembling the right talent pipeline and technology infrastructure to enable the intelligent enterprise of tomorrow.

Topics

The AI & Machine Learning Imperative

“The AI & Machine Learning Imperative” offers new insights from leading academics and practitioners in data science and artificial intelligence. The Executive Guide, published as a series over three weeks, explores how managers and companies can overcome challenges and identify opportunities by assembling the right talent, stepping up their own leadership, and reshaping organizational strategy.

Brought to you by

AWS
See All Articles in This Series

References

1. J. Byrum, C. Davis, G. Doonan, et al., “Advanced Analytics for Agricultural Product Development,” Informs Journal on Applied Analytics 46, no. 1 (January-February 2016): 5-17.

2. “CEO Turnover at Record High; Successors Following Long Serving CEOs Struggling According to PwC’s Strategy & Global Study,” PwC, May 15, 2019, www.pwc.com.

3. “Reuters News Tracer: Filtering Through the Noise of Social Media,” Reuters, May 17, 2017, www.reuterscommunity.com.

4. “Résumés Are Scanned for Keywords by an Automated System,” USAJobs, accessed July 13, 2020, www.usajobs.gov.

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