“AI initiatives at many organizations are too small and too tentative,” wrote Babson professor Tom Davenport and Deloitte principal consultant Nitin Mittal in “Stop Tinkering with AI,” a recently published HBR article. The article is adapted from their book All-in on AI: How Smart Companies Win Big with Artificial Intelligence which was published earlier this year.
AI technologies have significantly advanced over the past few years. But, while leading edge firms are placing AI at the center of their business strategies, a number of recent surveys continue to show that the majority of enterprises are still in the early stages of AI experimentation and deployment and risk being left further behind. For example, the latest Deloitte survey on the “State of AI in the Enterprise” reached out to over 2,800 executives from advanced economies and found that 28% of respondents were deploying AI at scale and achieving high outcomes, but 46% were still in the early stages of deployment with no significant outcomes.
In another recent survey, Accenture reached out to over 1,600 C-suite executives of the world’s largest companies and found that only 12% had the strategic and operational AI capabilities needed to achieve superior growth, while the majority of firms, 63%, were still at the experimenting stage and had only average AI capabilities.
This is not surprising. As we’ve learned over the past two centuries, even after achieving broad acceptance, it takes considerable time, - often decades, - for transformative technologies like AI to be widely embraced across firms and economies. These technologies have great potential from the outset, but realizing their potential requires major complementary developments including business process redesign, innovative products, and services, new business models and strategies, the re-skilling of the workforce, and a fundamental rethinking of the overall organization.
In their HBR article, Davenport and Mittal cite a 2019 survey of more than 2,500 executives by the MIT Sloan Management Review and the Boston Consulting Group that found that while 90% of respondents agree that AI represents a business opportunity for their companies, significant challenges remained. Seven out of 10 companies reported minimal or no impact from their AI efforts, and among the 90% that had made some investments in AI, fewer than 2 in 5 had achieved any business gains in over the previous three years.
“AI initiatives at many organizations are too small and too tentative. They never get to the only step that can add economic value — being deployed on a large scale. Testing the waters may deliver valuable insights, but it probably won’t be enough to achieve true transformation. A pilot program or experiment can take you only so far.”
On the other hand, the authors point out that the broad market acceptance of AI is relatively recent, i.e., no company was powered by AI a decade ago, “so all those that have been successful had to accomplish the same fundamental tasks: They put people in charge of creating the AI; they rounded up the required data, talent, and monetary investments; and they moved as aggressively as possible to build capabilities.” These companies have gone all in on AI for their own transformation.
According to the authors, all-in-on-AI firms comprise less than 1% of large companies. It wasn’t easy to find enough, but they identified around 30. The common thread of these firms is that they’re at the far end of the scale in leveraging AI technologies, including spending, strategizing and implementing. After studying the journeys these companies took to reap the benefits of their AI investments, the authors identified several key actions that helped them become successful AI adapters. Let me discuss a few of these actions.
Fundamentally rethink the way humans and machines interact in work environments
AI is essentially a prediction technology. Its key economic impact is to significantly reduce the cost and expand the number and variety of applications that rely on predictions. The opportunities for AI to improve the quality of decisions in business, government, scientific research, and just about any other area of human endeavor is boundless.
“Most successful AI adopters had significant analytics initiatives underway before they moved headlong into artificial intelligence, … which is why mastering analytics is crucial to AI adoption,” wrote the authors. “But what exactly does mastering analytics mean? In this context it requires a commitment to using data and analytics for most decisions, which means changing the way you deal with customers, embedding AI in products and services, and conducting many tasks — even entire business processes — in a more automated and intelligent fashion.”
The article cites the example of Seagate Technology, the world’s largest disk-drive manufacturer. To automate the visual inspection of the silicon wafers used in disk-drive hearts, multiple microscopic images are taken throughout the wafer fabrication process. Using the data provided by these images Seagate created an automated systems that allows machines to find and classify wafer defects directly. Introduced in late 2017, the use of these automated systems has grown extensively across the company’s wafer factories saving millions in labor costs and scrap prevention. Visual inspection accuracy, originally at 50% now exceeds 90%.
“Data is the foundation of machine-learning success, and models can’t make accurate predictions without large quantities of good data. It’s fair to say that the single biggest obstacle for most organizations in scaling up AI systems is acquiring, cleaning, and integrating the right data. It’s also important to actively pursue new sources of data for new AI initiatives.”
Focus on applications that will change how employees perform and how customers interact with your company
“No matter what your reason is for harnessing AI, we recommend identifying one well-defined, overarching objective and making it a guiding principle for your adoption.” Successful AI adopters, like those described in the HBR article, worked hard to determine which daily workflows and widely used business processes can most benefit by leveraging AI-based automation and began integrating AI into them as soon as possible.
For example, improving customer service by integrating AI into existing customer-service workflow requires in-depth, on-the-ground knowledge of how those processes work and how they can be specifically improved with AI, so make sure that you involve the appropriate line employees who have that expertise.
Consider deploying AI systematically across every key function and operation to support new processes and data-driven decision-making
“Once you’ve internally tested and mastered AI across a specific workflow, you’ll want to become more aggressive in deploying it throughout the organization. Rather than designing one algorithmic model for one process, your goal should be to find a unified approach that can be replicated across the company.”
Doing so takes great leaders who understand what AI can do for their companies, strategies, business models, processes, and people. “But the greatest challenge leaders face is creating a culture that emphasizes data-driven decisions and actions and makes employees enthusiastic about AI’s potential to improve the business. In the absence of that kind of culture, even if a few AI advocates are scattered around the organization, they won’t get the resources they need to build great applications, and they won’t be able to hire great people. And if AI applications are built, the business won’t make effective use of them.”
AI should eventually transform every aspect of your business.
“We believe that companies with the most aggressive AI adoption, the best integration with strategy and operations, and the best implementation will achieve the greatest business value,” wrote Davenport and Mittal in conclusion. “We also believe that AI — applied strategically and in large doses — will be critical to the success of almost every business in the future. Data is increasing at a rapid pace, and that’s not going to change. AI is a means of making sense of data at scale and of ensuring smart decisions throughout an organization. That’s not going to change either. Artificial intelligence is here to stay. Companies that apply it vigorously will dominate their industries over the next several decades.”
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