Reshaping Business With Artificial Intelligence 10-01

 CLOSING THE GAP BETWEEN AMBITION AND ACTION......

Disruption from artificial intelligence (AI) is here, but many company leaders aren’t sure what to expect from AI or how it fits into their business model. Yet with change coming at breakneck speed, the time to identify your company’s AI strategy is now. MIT Sloan Management Review has partnered with The Boston Consulting Group to provide baseline information on the strategies used by companies leading in AI, the prospects for its growth, and the steps executives need to take to develop a strategy for their business.

Executive Summary

1. Expectations for artificial intelligence (AI) are sky-high, but what are businesses actually doing now? The goal of this report is to present a realistic baseline that allows companies to compare their AI ambitions and efforts. Building on data rather than conjecture, the research is based on a global survey of more than 3,000 executives, managers, and analysts across industries and in-depth interviews with more than 30 technology experts and executives. (See “About the Research.”) 

The gap between ambition and execution is large at most companies. Three-quarters of executives believe AI will enable their companies to move into new businesses. Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage. But only about one in five companies has incorporated AI in some offerings or processes. Only one in 20 companies has extensively incorporated AI in offerings or processes. Less than 39% of all companies have an AI strategy in place. The largest companies — those with at least 100,000 employees — are the most likely to have an AI strategy, but only half have one.

Our research reveals large gaps between today’s leaders — companies that already understand and have adopted AI — and laggards. One sizeable difference is their approach to data. AI algorithms are not natively “intelligent.” They learn inductively by analyzing data. While most leaders are investing in AI talent and have built robust information infrastructures, other companies lack analytics expertise and easy access to their data. Our research surfaced several misunderstandings about the resources needed to train AI. The leaders not only have a much deeper appreciation about what’s required to produce AI than laggards, they are also more likely to have senior leadership support and have developed a business case for AI initiatives.

AI has implications for management and organizational practices. While there are already multiple models for organizing for AI, organizational flexibility is a centerpiece of all of them. For large companies, the culture change required to implement AI will be daunting, according to several executives with whom we spoke.

Our survey respondents and interviewees are more sanguine than conventional wisdom on job loss. Most managers we surveyed do not expect that AI will lead to staff reductions at their organization within the next five years. Rather, they hope that AI will take over some of their more boring and unpleasant current tasks.

AI at Work

2. As Airbus started to ramp up production of its new A350 aircraft, the company faced a multibillion-euro challenge. In the words of Matthew Evans, vice president of digital transformation at the Toulouse, France-based company, “Our plan was to increase the production rate of that aircraft faster than ever before. To do that, we needed to address issues like responding quickly to disruptions in the factory. Because they will happen.”

Airbus turned to artificial intelligence. It combined data from past production programs, continuing input from the A350 program, fuzzy matching, and a self-learning algorithm to identify patterns in production problems. In some areas, the system matches about 70% of the production disruptions to solutions used previously — in near real time. Evans describes how AI enables the entire Airbus production line to learn quickly and meet its business challenge:

What the system does is essentially look at a problem description, taking in all of the contextual information, and then it matches that with the description of the issue itself and gives the person on the floor an immediate recommendation. The problem might be new to them, but in fact, we’ve seen something very similar in the production line the weekend before, or on a different shift, or on a different section of the line. This has allowed us to shorten the amount of time it takes us to deal with disruptions by more than a third.

AI empowered Airbus to solve a business problem more quickly and efficiently than prior approaches (such as root-cause analysis based on manual analysis of hundreds or thousands of cases).
Just as it is enabling speed and efficiency at Airbus, AI capabilities are leading directly to new, better processes and results at other pioneering organizations. Other large companies, such as BP, Infosys, Wells Fargo, and Ping An Insurance, are already solving important business problems with AI. Many others, however, have yet to get started.

High Expectations Amid Diverse Applications

3. Expectations for AI run high across industries, company sizes, and geography. While most executives have not yet seen substantial effects from AI, they clearly expect to in the next five years. Across all organizations, only 14% of respondents believe that AI is currently having a large effect (a lot or to a great extent) on their organization’s offerings. However, 63% expect to see these effects within just five years.

Expectations for Change Across Industries and Within Organizations

Expectations for AI’s effects on companies’ offerings are consistently high across industry sectors. (See Figure 1.) Within the technology, media, and telecommunications industry, 72% of respondents expect large effects from AI in five years, a 52-percentage-point increase from the number of respondents currently reporting large effects. However, even in the public sector — the industry with the lowest overall expectations for AI’s effects — 41% of respondents expect large effects from AI within five years, an increase of 30 percentage points from current levels. This bullishness is apparent regardless of the size or geography of the organization.

Figure 1 Expectations for AI’s effect on businesses’ offerings in five years are consistently high across industries. 

Within organizations, respondents report similarly high expectations for the large effects of AI on processes. While 15% of respondents reported a large effect of AI on current processes, over 59% expect to see large effects within five years. (See Figure 2.) Most organizations foresee sizable effects on information technology, operations and manufacturing, supply chain management, and customer-facing activities. (See Figure 3.) For example:

Figure 2 As with offerings, organizations expect AI to have a great impact on processes within the next five years.

Information technology: Business process outsourcing providers serve as an example of the potential of AI. “IT services, where Infosys plays a big role, has seen tremendous growth in the last 20 or so years,” says Infosys Ltd. CEO and managing director Vishal Sikka.1 “Many jobs that moved to low labor-cost countries were the ones that were more mechanical: system administration, IT administration, business operations, verification. With AI techniques, we now have systems that can do more and more of those kinds of jobs. We are still in the early stages and portions of these activities can be automated, but we will get to the point in the next few years where the majority if not all of these jobs will be automated. However, just as AI technologies automate existing, well-defined activities, they also create opportunities for new, breakthrough kinds of activities that did not exist.” 

Figure 3

Most organizations foresee a sizable effect on IT, operations, and customer-facing activities.

Operations and manufacturing: Executives at industrial companies expect the largest effect in operations and manufacturing. BP plc, for example, augments human skills with AI in order to improve operations in the field. “We have something called the BP well advisor,” says Ahmed Hashmi, global head of upstream technology, “that takes all of the data that’s coming off of the drilling systems and creates advice for the engineers to adjust their drilling parameters to remain in the optimum zone and alerts them to potential operational upsets and risks down the road. We are also trying to automate root-cause failure analysis to where the system trains itself over time and it has the intelligence to rapidly assess and move from description to prediction to prescription.”
Customer-facing activities: Ping An Insurance Co. of China Ltd., the second-largest insurer in China, with a market capitalization of $120 billion, is improving customer service across its insurance and financial services portfolio with AI. For example, it now offers an online loan in three minutes, thanks in part to a customer scoring tool that uses an internally developed AI-based face-recognition capability that is more accurate than humans. The tool has verified more than 300 million faces in various uses and now complements Ping An's cognitive AI capabilities including voice and imaging recognition.

Adoption as Opportunity and Risk

While expectations for AI run high, executives recognize its potential risks. Sikka is optimistic but cautions against hyping AI’s imminent triumph: “If you look at the history of AI since its origin in 1956, it has been a story of peaks and valleys, and right now we are in a particularly exuberant time where everything looks like there is one magnificent peak in front of us.” More than 80% of the executives surveyed are eyeing the peaks and view AI as a strategic opportunity. (See Figure 4.) In fact, the largest group of respondents, 50%, consider AI to be only an opportunity. Some see risks and the potential for increased competition from AI as well as benefits. Almost 40% of managers see AI as a strategic risk as well. A much smaller group (13%) does not view AI as either an opportunity or risk.

Figure 4

More than 80% of organizations see AI as a strategic opportunity, while almost 40% also see strategic risks.  

What is behind these high expectations and business interest in AI? There is no single explanation. (See Figure 5.) Most respondents believe that AI will benefit their organization, such as through new business or reduced costs; 84% believe Al will allow their organization to obtain or sustain a competitive advantage. Three in four managers think AI will allow them to move into new businesses.

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