AI Trends Weekly Brief: AI Adoption


Expectation for AI Benefits is High, but Current Execution is Low, MIT Study Finds

Artificial intelligence is being adopted so fast in 2017 that MIT is doing a study on it, the AI Index is studying the rate of progress and the Electronic Frontier Foundation is compiling a repository of data points on AI progress.

MIT worked with the Boston Consulting Group to survey more than 3,000 business executives in 112 countries and 21 industries. Many are ambitious, but few are executing, the report found.

More than three-quarters of business executives expect AI to create competitive advantage or new lines of business, but only about one in five companies has incorporated AI in some offerings or processes today. One in 20 companies have extensively incorporated AI into current offerings or processes. Less than 40% of all companies have an AI strategy in place.

“People are worried their competitors will do it if they don’t,” said report coauthor Sam Ransbotham in an interview with AI Trends. Ransbotham is an associate professor of information systems at the Carroll School of Management at Boston College, and the MIT Sloan Management Review Guest editor for the Artificial Intelligence Big Idea Initiative.

The largest companies, with over 100,000 employees or more, are the most likely to have an AI strategy, but only half have one as of now.

The study results should allay fears that AI will lead to huge job loss, at least in the short run. Less than half the survey participants, 47 percent, expect their workforces to be reduced within the next five years, and nearly 80 percent expect employees’ current skills to be augmented. Only 31 percent fear AI will take away some current tasks in their jobs.

“The gap between ambition and execution is large at most companies,” stated BCG senior partner and report coauthor Philipp Gerbert in a press release. “We also found large gaps between today’s leaders – companies that already understand and have adopted AI – and laggards. Leaders not only have a much deeper appreciation of what’s required to produce AI than laggards, they are also more likely to have senior leadership support and a developed business case for AI initiatives.”

In other key findings:

  • Three-Quarters of respondents believe AI will help their companies move into new businesses. Almost 85% believe AI will allow their companies to gain or sustain a competitive advantage;
  • Only about 15 percent believe AI is currently having a large impact on their organization’s offerings and process, but about 60% expect these effects to occur within five years. Most organizations foresee big effects on information technology, operations and manufacturing, supply chain management and customer-facing activities;
  • More than 80 percent see AI as a strategic opportunity and about 40 percent see AI as a strategic risk as well.

The study defines four maturity clusters.

  • Pioneers (19%): Organizations that understand and have adopted AI. These are on the leading edge of incorporating AI into their company’s offerings and its internal processes;
  • Investigators (32%): Organizations that understand AI but are not deploying it beyond the pilot stage. Their investigation emphasizes looking before leaping;
  • Experimenters (13%): Organizations that are piloting or adopting AI without a deep understanding. These organizations are learning by doing.
  • Passives (35): Organizations with no adoption and little understanding of AI.

“Companies in each cluster face their own challenges in further adopting AI,” stated report coauthor and executive editor of MIT Sloan Management Review David Kiron, in a press release. “Pioneers have overcome issues related to understanding their biggest hurdles are grappling with the practicalities of developing or acquiring the requisite talent and addressing competing priorities for investment. Passives, by contract, have yet to come to grips with what AI can do for them. They have not identified solid business cases. Leadership may not be on board. Many are not even aware of the difficulties in sourcing and deploying talent with AI expertise.

What companies reported as their overall understanding of AI varied widely. Some 16 percent agreed their organization understood the cost of developing AI-based products and services, and 17 percent strongly disagreed that their organizations understood these costs. While 19 percent strongly agreed that their organization understood the data required to train AI algorithms, 16 percent strongly disagreed that their organization had that understanding.

Open source tools are making it easier for organizations to learn about AI and get some benefit from it, but “Just having the tool is not enough. So many tools rely on data. Companies need to have that data and the data quality needs to be high, or they won’t get anywhere,” Ransbotham said.

Support for AI investigations from company leadership is important for success.” If they think they can buy a tool and suddenly have AI, they might be disappointed, Ransbotham said. “It takes leadership support to help get through the early incarnations. They are investing in something that needs time to mature. Intuition and experience comes in.”

Also, “As organizations get better at AI, they do more sophisticated analysis, which challenges the organization’s ability to absorb the results. This is a place where people are going to struggle, particularly as AI becomes more pervasive.”

Educational institutions such as MIT are trying to respond to the demand for AI education. “The appetite of companies for talent and people exceeds the supply,” Ransbotham said. “An inevitable gap develops.”

In advice for college students or others interested in learning about AI, “No one ever said I wish I had fewer technical skills. Most companies want more technical skills; they want someone to know A and B, not A or B. How college students allocate the time an energy that is limited, is a challenge they face.”

A broad foundation is needed. “AI involves multiple disciplines: data, statistics, engineering, programming. People will not know every single thing. Have that super being that knows everything will be rare and very expensive.”

MIT BCG plan to make the Adoption study an ongoing research program, to be continually updated.

A copy of the MIT/BCG report can be downloaded here.

AI Index Aims to Chart Real AI Progress

Now some AI observers are trying to develop a more exact picture of how, and how fast, the technology is advancing. By measuring progress—or the lack of it—in different areas, they hope to pierce the fog of hype about AI. The projects aim to give researchers and policymakers a more clear-eyed view of what parts of the field are advancing most quickly and what responses that may require.

“This is something that needs to be done in part because there’s so much craziness out there about where AI is going,” says Ray Perrault, a researcher at nonprofit lab SRI International, quoted in an article in Wired magazine. He’s one of the leaders of a project called the AI Index, which aims to release a detailed snapshot of the state and rate of progress in the field by the end of the year. The project is backed by the One Hundred Year Study on Artificial Intelligence, established at Stanford in 2015 to examine the effects of AI on society.

The AI Index will also try to monitor and measure how AI is being put to work in the real world. Perrault says it could be useful to chart the numbers of engineers working with the technology and the investment dollars flowing to AI-centric companies, for example. The goal is to “find out how much this research is having an impact on commercial products,” he says—although he concedes that companies may not be willing to release the data. The AI Index project is also working on tracking the volume and sentiment of media and public attention to AI.

Learn more at the One Hundred Year Study on AI at Stanford.

Electronic Frontier Foundation AI Charting Effort

The Electronic Frontier Foundation, which campaigns to protect civil liberties from digital threats, has started its own effort to measure and contextualize progress in AI. The nonprofit is combing research papers like Microsoft’s to assemble an open source, online repository of data points on AI progress and performance. “We want to know what urgent and longer term policy implications there are of the real version of AI, as opposed to the speculative version that people get over excited about,” says Peter Eckersley, EFF’s chief computer scientist, told Wired.

Some are questioning the value of granular measures of progress in AI. Eckersley of the EFF argues that AI tracking projects will become more useful with time. For example, debate about job losses might be informed by data on how quickly software programs are advancing to automate the core tasks of certain workers. And Eckersley says looking at measures of progress in the field has already helped convince him of the importance of work on how to make AI systems more trustworthy. “The data we’ve collected supports the notion that the safety and security of AI systems is a relevant and perhaps even urgent field of research,” he says.

The EFF launched the progress measurement program in June. A related press release states, “We have drawn data from a number of sources: blog posts that report on snapshots of progress; websites that try to collate data on specific subfields of machine learning; and review articles. Where those sources didn’t have coverage, we’ve gone to the research literature itself and gathered data.“

Learn more at the Electronic Frontier Foundation.

Applied AI Also Charting AI Progress

Cem Dilmegani launched in February 2017, with an initial focus on how AI was being applied to core marketing activities such as  optimizing pricing and placement, optimizing advertising/marketing, personalizing recommendations, collecting and leveraging customer feedback. Dilmegani is a former McKinsey technology management consultant with 10 years of experience in enterprise technology and AI.

Dilmegani stated in a post on LInkedIn about the company’s founding, “While launching we interviewed corporate leaders and all sizes of AI vendors, searched news articles, patents, venture capital financing and more to identify established and emerging AI use cases. We have identified about a dozen fundamental artificial intelligence use cases in marketing.”

Today the focus is more broad, with the site describing the source data as based on over 1,000 references and over 100 use cases “shared online or submitted to the AppliedAI platform by AI vendors/enterprises.”

AppliedAI also describes a mission to “democratize” the AI vendor selection process and bring transparency. A guide to AI startups on the site lists over 500 companies. A guide to use cases is structured by functions and industries such as marketing, IT, operations, fintech and healthtech. Under fintech, for example, you are guides to use cases under fraud detection, regulatory compliance, billing and others. Each lists the total number of public references by Fortune 500 company and total. So for fraud detection, 43 Fortune 500 companies are listed and 50 to 200 total. (As of this writing in September 2017). A drill-down on that leads to coverage of OpenTable, the restaurant reservation service, with SiftScience, which offers digital gift cards.

Learn more at

National Business Research Report Sees 62% AI Adoption by 2018

Based on a survey of 235 business executives conducted by the National Business Research Institute (NBRI) along with Narrative Science and reported in Forbes, 62 percent of enterprises are predicted to use AI by 2018. Today in 2017, 38 percent of enterprises report that they use AI technologies.

The study defines AI technologies as including machine and deep learning, recommendation engines, predictive and prescriptive analytics, automated written reporting and communications, and voice recognition and response.

Here are some other key findings of the survey:

  • 26% are currently using AI technologies to automate manual, repetitive tasks, up from 15% in 2015
  • 20% of those who haven’t yet adopted AI cite lack of clarity regarding its value proposition
  • 58% are using predictive analytics
  • 25% are using automated written reporting and communications
  • 25% are using voice recognition and response
  • 38% see predictions on activity related to machines, customers or business health as the most important benefit of an AI solution
  • 27% see automation of manual and repetitive tasks as the most important benefit of an AI solution
  • 95% of those who indicated that they are skilled at using big data to solve business problems or generate insights also use AI technologies, up from 59% in 2015
  • 61% of enterprises with an innovation strategy are applying AI to their data to find previously missed opportunities such as process improvements or new revenue streams

The availability of large volumes of data—plus new algorithms and more computing power—are behind the recent success of deep learning, finally pulling AI out of its long “winter,” the report suggests. More broadly, the enthusiasm around big data (and the success of data-driven digital natives such as Google and Facebook), has led many enterprises to invest heavily in the collection, storage, and organization of data.

But what is to be done with the data? What is the value of having more data if not in new business insights? To uncover new insights, you need hard-to-find data scientists. Indeed, 59% of the respondents to the survey see the shortage of data science talent as the primary barrier to realizing value from their big data technologies. These companies are now turning to AI technologies to help augment their data science capabilities as partial solution to the talent shortage, Forbes reports.

Startups are emerging to help build a bridge between big data and artificial intelligence, between the generation and collective of massive volumes of data, and applying algorithms to make sense of it. Narrative Science, with its Quill software product, is one of them.

Written and compiled by John P. Desmond, AI Trends Editor