What Innovative CEOs and Leaders Need to Know about AI
Artificial intelligence (AI) is a rising global imperative. Enterprise software companies are rushing to incorporate AI functionality into its product offerings. Venture-capital funding is pouring into AI startups globally. AI is a geopolitical movement with many countries putting it as a top priority. Top-ranked MBA schools are including AI in the curriculum. AI can be found in neuroscience, life sciences, health care, financial services, esports, art, science, entertainment, and many more industries. Forward-thinking companies are starting to realize returns on their investments as early adopters of AI. It’s not a question of whether or not AI should be incorporated in your company, but rather when it should be implemented. Where is AI in the technology adoption life-cycle? Where is AI being used and how? Here is an executive summary of what a few of the leading global management consulting companies have to say about how artificial intelligence will impact businesses and economies worldwide.
McKinsey Global Institute
In a McKinsey Global Institute (MGI) discussion paper published in September 2018 titled “Notes From The AI Frontier – Modeling The Impact Of AI On The World Economy,” the estimated impact of AI is $13 trillion additional economic activity worldwide by 2030. MGI is led by three McKinsey & Company senior partners — Jacques Bughin, Jonathan Woetzel, and James Manyika, the MGI chairperson.
In the report, MGI estimates that 14 percent of the global workforce, up to 375 million employees, may need to change jobs due to AI automation. The occupations most likely to be automated with AI are data collection, data processing, and jobs that require “performing physical activity and operating machinery in predictable environments.”
MGI predicts that the AI adoption rate by companies over time will resemble an S-shape curve — initial adoption will be slower due to the requisite learning involved, then expand rapidly as competition and “improvements in complementary capabilities” increases. Interestingly, MGI predicts a significant first-mover advantage for companies who are early AI adopters. Companies that fully deploy AI throughout the enterprise over the next five to seven years may double their cash flow, whereas the long tail of laggards may experience a 20 percent decrease in cash flows by 2030.
In the Harvard Business Review January-February 2018 edition, Thomas H. Davenport and Rajeev Ronanki advise a highly pragmatic versus “moon shot” approach to AI implementations based on a Deloitte Study of 152 cognitive (AI) projects. Davenport is a senior advisor at Deloitte Analytics, a research fellow at the MIT initiative on the Digital Economy, and a professor at Babson College. Ronanki is a principal at Deloitte Consulting focused on cognitive computing and health care innovation.
The authors view AI as “performing tasks, not entire jobs.” Out of the 152 AI projects, 71 were in the automation of digital and physical tasks, 57 were using algorithms to identify patterns for business intelligence and analytics, and 24 were for engaging employees and customers through machine learning, intelligent agents, and chatbots.
In the Harvard Business Review article, a 2017 Deloitte survey of 250 executives who were familiar with their companies’ AI initiatives, revealed that 51 percent responded that the primary goals was to improve existing products. 47 percent identified integrating AI with existing processes and systems as a major obstacle. When it comes to employment impact, within “the next three years, 69 percent of enterprises anticipate minimal to no job loss and even some job gains.” Early adopters of AI in the enterprise are reporting benefits — 83 percent indicated their companies have already achieved “moderate (53 percent) or substantial (30 percent) economic benefits. 58 percent of respondents are using in-house resources versus outside expertise to implement AI, and 58 percent are using AI software from vendors. Only 20 percent of those surveyed are developing AI applications themselves “from scratch.”