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AI: The path to the intelligent enterprise

Augmenting human decision-making at the enterprise level will bring us to the next level in growth.

Joseph ByrumBy Joseph Byrum

Editor’s note: This is another in a series of articles exploring the past, current and future of artificial intelligence and its widespread impact on humans, systems and society.

There are good reasons to be excited about a future guided by artificial intelligence (AI). As we saw in the previous installment of this series, AI has developed over several decades as a means of augmenting human abilities.

The First Wave of AI from 1975 to 1990 never quite lived up to expectations because of the inherent limitations of systems based on deductive logic algorithms. The succeeding Second Wave, which we’re still in, took advantage of massive leaps in sensor and processing capabilities that enabled a transition to more powerful, more flexible learning algorithms, giving us a glimpse of AI’s true potential. DARPA’s Pilot’s Associate is one of the earliest examples of a real-time cognitive engine that matched what’s best about a machine with what’s best about a human. The system relieved combat pilots of the burden of ancillary tasks so that the human could remain laser-focused on making the decisions that count with greater clarity.

We have yet to see the Third Wave of AI, but we can at least anticipate some of its most likely features. We know, for instance, that throughout the centuries, revolutionary technologies have always had a multiplier effect on growth. We saw this in the Industrial Revolution and with the advent of computers, which opened the door to exponential leaps in productivity. To qualify as a new era in AI, the new development really has to deliver the goods.

Don’t expect to see the Third Wave debut in the consumer space – at least, not any time soon. The growth in affordable computing power has inspired every consumer device manufacturer’s marketing department to boast of their implementation of smart technology. The stores are flooded with AI-powered smartphone cameras, AI-inspired wristwatches [1] and even a deep learning toothbrush [2].

Those have little in common with true augmented intelligence systems, and they certainly don’t represent a dramatic leap in capability. Elon Musk put it well when he described what a true advance looks like. “Forty years ago, we had pong,” he said. “Two rectangles and a dot. That was what games were. Now 40 years later we have photorealistic 3D simulations with millions of people playing simultaneously, and it’s getting better every year” [3]. An overpriced electric toothbrush isn’t going to transport society to the next level.

Since all we have seen so far of AI is the equivalent of rectangles and dots, we may tend to look at what’s coming as more advanced two-dimensional shapes. Where we actually will end up in the future will be something much richer – an advance led not by gimmicky AI products, but by joint cognitive systems. That is, it will be led by systems that allow AI to take over tasks suited to automation in coordination with humans who will continue to do what they do best. Both AI and human elements work together in the joint system model of augmented intelligence to create something greater than the individual component parts.

The Intelligent Enterprise

When thinking about what the AI of the future can do, it’s important to think big. And what better way to do that than to consider the intelligent enterprise of the future. Managing a large, multinational corporation is a phenomenally complex task. Resources and talent are spread all over the globe in a rapidly changing business environment presenting multiple regulatory environments and diverse customer expectations.

AI has developed over several decades as a means of augmenting human abilities.

AI has developed over several decades as a means of augmenting human abilities.
Source: ThinkStock

What customers like today they won’t necessarily like tomorrow. Just take a look at the Fortune 500 list from 60 years ago. Nine out of 10 of the list’s original entries have since disappeared through mergers or bankruptcies [4]. Some of today’s top companies, such as Walmart and Apple, didn’t even exist back then.

Technology is often responsible for the changing landscape. Eastman Kodak was a big deal in the days when taking photographs meant developing a roll of film, but the more convenient devices offered by the likes of Apple have rendered that particular business model obsolete. Kodak went from being one of America’s 50 largest to barely qualifying for the Fortune 1000. Likewise, it was Walmart’s logistical prowess and Apple’s skill at designing computing products that drove their success.

Companies that want to enhance their competitiveness know that operations research (O.R.) algorithms are the key to optimizing critical business processes like logistics. O.R. can work wonders, but O.R. solutions are not easy. They require significant effort and custom solutions to implement at scale.

O.R. will be built into the DNA of the intelligent enterprise. At its core will be the use of machine learning to analyze all of the processes of the business with an eye toward discovering optimization opportunities at every step. Perhaps the greatest benefits will be found in the most unexpected places, with efficiencies uncovered where humans assumed it wouldn’t be worth the effort to test alternative methods.

It won’t be the case of robots using O.R. algorithms to call all the shots, rendering humans superfluous. Rather, the intelligent enterprise of the future will rely on intelligent augmentation to manage the complexity of the business environment in all aspects of the business. Instead of merely assisting an individual or a small group of individuals in their decision-making ability, the intelligent enterprise uses data analytics to ensure every decision at every level of the organization is guided by science and not intuition. The difference in scale is important.

Back when Henry Ford was in charge of the automotive enterprise that bears his name, legend has it that he would weigh his bills each month when he wanted to know how much money was needed to take care of accounts payable [5]. It’s safe to say, his decisions weren’t made with precision. They weren’t even made with focus groups to get an idea of what the public wanted. You got the car, in black, and that was that.

Of course, a company that relies on the seat-of-the-pants approach might last for a while as a startup. In a rapidly changing market, a large enterprise can’t survive by putting its bills on a scale and guessing how much money to set aside for payment. What the intelligent enterprise brings to the table is rapid adaptability. By taking in real-time data from all aspects of the business, AI systems will propose changes on the fly to ensure the company is not just on top of the trends, but that it stays well ahead of them. Such an organization serves the needs of customers before they even realize what they want.

That’s rarely seen today. Large organizations aren’t known for their ability to make quick adjustments in tune with the market. Small companies have always had an advantage in this respect, as it’s easier to be nimble when only a handful of people need to adjust their strategy, as opposed to thousands of people.

The intelligent enterprise still depends on individuals – thousands of them – but their decisions will be augmented with AI. Instead of performing repetitive tasks (the machine will take over those functions), employees of the intelligent enterprise will make powerful decisions based on information and statistical probability designed to maximize the chance of success.

Decisions made at the top to change the organization’s direction will instantly filter down and influence strategy at every other level of the organization as algorithms take into account what needs to happen to bring the strategy to life. This means that necessary changes will be managed in the most efficient way possible, and resources will be deployed where they can be put to the best use. Instead of maintaining an irrational attachment to the old way of doing things, the intelligent enterprise can shift as needed to maintain the organization’s goals, whether that means maximizing output, growing market share or boosting quarterly profits.

The Third Wave and Society

Naturally, there are many who worry about the effect an increasing level of automation will have on society. The most pressing concern will be AI’s potential to destroy jobs. There’s no way around the fact that jobs are going to disappear.

That has happened before. Machines in past centuries relieved humans of the need to perform dangerous and back-breaking labor, and future automation has the likely effect of eliminating some of the dullest and more unpleasant tasks that we’re saddled with today.

There are even downsides to eliminating mundane chores. Mindless tasks offer humans an important opportunity to relax. Let’s hope an all-seeing business intelligence doesn’t attempt to forcibly extract 40 hours of maximum performance from every human in the workforce, with no exceptions. It wouldn’t be a fun place to work, and it most likely wouldn’t be productive, either. That’s why the intelligent enterprise needs the human element to succeed.

Time will tell whether the introduction of new technologies, new production methods and a changing social landscape make society better or worse. It’s only natural to wonder what will be left for us if so many human functions are taken over by machines. We’re not likely to know exactly what the future holds. Most likely we’ll find ourselves surprised. We tend to believe that technology revolves around us, when in fact it may well be the case that we are revolving around technology.

Joseph Byrum, Ph.D., MBA, PMP is the chief data scientist at Principal Financial Group. Connect with him on Twitter @ByrumJoseph.


  5. Hoffman, Bryce G., 2012, “American Icon: Alan Mulally and the Fight to Save Ford Motor Company,” page 9, Crown Business.

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