Irving Wladawsky-Berger

How to Support the Widespread Adoption of AI

“Artificial intelligence is reshaping business - though not at the blistering pace many assume,” notes Building the AI-Powered Organization, an article in Harvard Business Review by McKinsey partners Tim Fountaine, Brian McCarthy, and Tamim Saleh in its opening sentence.  A number of studies agree that AI is the biggest commercial opportunity for companies and industries over the next 10-15 years.  Two recent reports by McKinsey and PwC respectively concluded that AI has the potential to incrementally increase global economic output between now and 2030 by an additional $13 to $15 trillion.

“Yet, despite the promise of AI, many organizations’ efforts with it are falling short,” adds the HBR article.  Surveys with thousands of executives, showed that most firms are only using AI in ad hoc pilots or applying it to a single business process.  Only 8% of firms are engaged in practices that support widespread adoption.

Why the slow progress?  “At the highest level, it’s a reflection of a failure to rewire the organization.  In our surveys and our work with hundreds of clients, we’ve seen that AI initiatives face formidable cultural and organizational barriers…  at most businesses that aren’t born digital, traditional mindsets and ways of working run counter to those needed for AI.”

To support the widespread adoption of AI, companies must make three fundamental shifts.

Despite Their Huge Upside Potential, Why Do Most Platforms Fail?

“For anyone who follows the world of business, it is now common knowledge that the most valuable firms on the planet and the first companies to surpass the trillion-dollar mark in value (albeit temporarily) are platforms,” note Michael Cusumano, Annabelle Gawer, and David Yoffie in their recently published book The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power.  “Moreover, in a recent list of more than two hundred current and former ‘unicorns’ - startups with valuations of $1 billion or more - we estimated that platforms made up between 60 and 70 percent.”

What do we mean by platform?  The book offer a simple definition.  “Platforms, in general, connect individuals and organizations for a common purpose or to share a common resource.”  A platform strategy differs from a product strategy in that it relies on an external ecosystem to generate complementary innovations and/or business transactions.  The effect is much greater potential for innovation and growth than a single product-oriented firm can generate alone.

Platforms are all about network effects.  The more products or services a platform offers, the more users it will attract, helping it then attract more offerings, which in turn brings in more users, which then makes the platform even more valuable.  Moreover, the larger the network, the more data is available to customize offerings to user preferences and better match supply and demand, further increasing the platform’s value.

The Future of Work in the US: A Tale of Multiple Americas

The US economy looks good by almost any measure.  Unemployment is at historical low levels, forcing employers to raise wages and become more aggressive about hiring and training workers.  There are more job openings than unemployed people to fill them.  Inflation remains low.  A number of recent studies have concluded that over the next decade, our technology-based economy will create a significant number of new occupations which will more than offset declines in occupations displaced by automation.  Not only is the US economy doing well in the present, but despite our increasingly smart machines, automation fears appear to be unfounded.

Why then are so many Americans so anxious about the future?  Why do they feel left behind despite our growing economy?  Why is the country so polarized?

A recent McKinsey report, The Future of Work in America: People and place, today and tomorrow, sheds light on these very important questions.  For the past several years, McKinsey has been conducting research on the impact of automation technologies on the future of work at a national level.  This new report is radically different, based on a detail analysis of more than 3,000 US counties and over 300 cities.

Overall, the study found that while in aggregate the country is doing well, there are actually multiple Americas, each with widely different economic patterns and on sharply different trajectories, some of which are doing quite well and face an exciting future, while others are not in good shape and risk falling further behind.

The report is organized into five main sections, and includes a number of tables, graphs and maps.  Let me summarize the key findings of each of the sections.

The State of AI in the Enterprise

A few months ago, Babson College professor Tom Davenport gave a talk on the state of AI in the enterprise at the annual conference of MIT’s Initiative on the Digital Economy.  His talk was based on two recent US surveys conducted by Deloitte, the first one in 2017 followed by a second in 2018.  Davenport was a co-author of both reports.

The 2017 survey was focused on the responses of 250 US executives who were leading the applications of AI in their companies.  The larger 2018 survey reached out to 1,100 IT (46%) and line-of-business (54%) executives from US-based companies (64% at the C-level) and 10 different industries.  All of these respondents were early AI adopters compared with their counterparts in an average company, - 90% were directly involved in their company’s AI projects, and 75% said that they had an excellent understanding of AI.

Davenport started his talk by summarizing the key findings in the Deloitte surveys:

• 20-30% of enterprises are early adopters, having implemented at least one AI prototype or production application;
  
• Many projects are in pilots but some are already in production;
• Relatively simple low hanging fruit projects prevail over more ambitious and complex moon shots;
  
• Only 24% cited “reducing headcount through automation” as one of their top AI priorities;
• The great majority of respondents believe that AI leads to moderate or substantial changes in job roles and skills;
• Implementation, integration, data issues and talent top the list of challenges faced by early adopters;
  
• Further AI growth is inevitable.

The Digitalization of the American Workforce

“Over the past half century, wave after wave of digital innovation has ensured that digitalization - the diffusion of digital technologies into nearly every business and workplace and pocket - has been remaking the U.S. economy and the world of work,” said the Brookings Institution in Digitalization and the American Workforce.  The digitalization of everything has both increased the potential of individuals and societies while contributing to widespread anxiety about its impact on jobs and economic inequalities.  “And yet, for all of the evidence that big changes are underway, surprisingly little data exists to track the spread of digital adoption.”

The Brookings report quantified the spread of digitalization by analyzing the changes in the digital content of 545 occupations between 2002 and 2016 based on O*NET, the most comprehensive data on US occupations sponsored by the US Department of Labor.  O*NET includes highly detailed, task-level information on hundreds of occupations, and includes quantitative measures of knowledge requirements, skills, tools, education, training and work activities. In addition, the report collected information on the occupations’ particular industries, pay, growth rates, geographical locations and distribution across educational and demographic groups.

Each of the 545 occupations, - which encompassed 90% of the US workforce, - was assigned a digital score between 0 and 100 based on the required digital skills.  High digital occupations are those with scores over 60.  They include software developers - with a score of 91; computer system analysts - 79; and financial managers, - 61.  Medium digital occupations scored between 33 and 60, and include lawyers - 58; automative service technicians - 55; office clerks - 55; and registered nurses - 55.  Low digital occupations have a score below 33, and include security guards - 31; restaurant cooks, - 18; construction laborers - 17; and personal care aides - 14.