AI: The Present & Future of Business & Organizational Life

November 2017

There is no doubt artificial intelligence (A.I.) is playing a greater role in business in a variety of sectors than ever before. “[A.I.] is not the future of the workplace, it is the present and happening now,” according to Forbes. A.I. investment also continues to grow: worldwide revenues for cognitive and A.I. systems will reach $12.5 billion in 2017 – an increase of 59.3 percent over 2016 – to more than $46 billion by 2020, predicts research firm International Data Corporation (IDC). Fifty-four percent of the business and IT executives responding to a recent PwC Digital IQ survey said their companies are making substantial A.I. investments today, with that number increasing to 63 percent in three years.

Concentrating on Meaningful Work
Seventy-two percent of those executives believe A.I. will enable humans to concentrate on meaningful work and will be the business advantage of the future. That same survey found that A.I. will amplify management and augment the C-suite by optimizing processes across organizations from business models to operations to strategic decision making. The business executives were most optimistic about A.I.’s ability to increase efficiencies with automated communications and alerts to enable more proactive approaches (70 percent) and to improve big data analytics (59 percent).

Among our Napa Group clients, we see these trends becoming real considerations in strategic conversations in business and entrepreneurial firms, such as ThinkHR, which combines live human resource expertise with innovative online technology. Yet they are also part of the new thinking among our university clients investigating streamlined operations and new client-relationship tools. And, as higher education institutions prepare graduates for careers, the emphasis goes beyond the likelihood of a half dozen jobs; it’s about educating graduates with a “growth mindset” to be ready for expanding their capacities. Lifelong learning will be central to the future of work, including the agility to work in teams made up of humans and machines (A.I.).

How A.I. is Changing Organizational Life
A.I. already is ushering in big changes in the banking, legal and human resources (HR) fields, for example. Wall Street’s biggest firms are using technologies such as machine learning and cloud computing to automate operations, leading a former Citigroup Inc. executive to predict that 30 percent of banking jobs could disappear in the next five years. Bloomberg warned traders to adapt, as banks and investment funds roll out machine-learning software to suggest begs, set prices and craft hedges. Chief Executive Officer Rob May of Talla, an A.I.-powered service desk, predicted A.I. also will have a big impact on the legal field’s basic work and that sector’s jobs will be the most impacted.

In the HR field, business executives responding to the aforementioned PwC survey saw huge potential for A.I. to alleviate repetitive tasks such as 60 percent of HR functions, like benefits, and 37 percent of HR management. Nearly half of the 400 chief HR officers surveyed by the IBM Institute for Business Value recognized the power of cognitive computing to transform key dimensions of HR, such as HR operations, talent acquisition and talent development. A.I. has the potential to reduce human bias, automate a number of the states of the recruitment workflow, enhance and provide feedback about the onboarding experience and improve relationships with existing employees.

A.I. & Business Strategy
One of the enabling factors for machine learning to take hold is large amounts of data, as detailed in an episode of the McKinsey Podcast, which can add to the analytics work a company is already doing. The gathering and analysis of that data also could have a profound effect on business strategy, according to a Harvard Business Review article. A.I. is fundamentally a prediction technology, the authors argued, and as advances in A.I. make prediction cheaper, we will use prediction more frequently and widely. We also will place greater value on complements to prediction, such as human judgment.

Using Amazon as an example, the more data Amazon A.I. collects about its customers – including online and offline behavior, such as what consumers buy at Whole Foods – the greater its prediction accuracy. As prediction accuracy increases, it will eventually cross a certain threshold where it could be in Amazon’s interest to change its strategy and business model. Instead of concentrating on shopping-then-shipping, the new business model could be shipping-then-shopping, something that might already be in the works.

Effects on Everyday Life & Jobs
Clearly there are concerns about A.I. intersecting with culture, part of what Fast Company called “The Great A.I. War of 2018.” These are also competitive business issues, for example in Disney’s and Netflix’s battle for dominance, Carnival’s foray into wearables on its cruise ships, Levi’s Google-powered smart denim jacket and Target’s apparel and accessories line based on apps made by Toca Boca, a Swedish game developer.

There also is trepidation about A.I.’s impact on jobs, part of the longstanding concern about the role of technology. A recent Pew Research study examined Americans’ attitudes about four emerging automation technologies, including workplace automation and computer algorithms used to evaluate and hire job applicants. U.S. adults overwhelmingly expressed worry (72 percent) versus enthusiasm (33 percent) about a future with workplace automation. The same was true about algorithms used for hiring decisions without any human involvement, with 67 percent of respondents expressing worry over enthusiasm (22 percent). Respondents also overwhelmingly supported policies that would limit the scope of automation technologies with regard to driverless cars and workplace automation.

Curiously, the survey respondents said automation would likely disrupt a number of professions but few felt their own jobs would be at risk, with only three in 10 workers saying they thought it was at least somewhat likely their jobs would mostly be done by robots or computers during their lifetimes. Automation currently appears to be having a greater impact on younger Americans ages 18 to 24 (13 percent) than the overall U.S. adult population (6 percent), according to the respondents who said their jobs were eliminated or their wages or hours had been reduced because of A.I. Overall, Americans worry that widespread automation will lead to greater economic inequality (76 percent) and will leave people adrift (64 percent), versus the 25 percent of respondents who said widespread automation would lead to the creation of new, higher paying jobs for humans.

Preparing for the A.I. Future
With A.I. already present in the workplace, our experience suggests several strategic steps will facilitate decisions about preparing for the future of A.I. and capitalizing on its potential. As mentioned earlier, universities, CEOs and workers of all kinds must value and emphasize lifelong learning throughout all career stages. We need to be able to understand the data we have, see what kinds of data we might need and how we can harness and safeguard that data. Most important, there is a great need for creativity, adaptability and complex cognitive skills, as machines increasingly become the “doers” and humans partner and interact with these tools, make decisions that set them on the right course and interpret the results. All this requires education and new skills for the people who will design and manage these technologies.

There is plenty of evidence of businesses transforming themselves to meet new realities of both technology and market demand, and, at the same time, reimagining the involvement of people in these processes. Adapting to change through new solutions that support both people, processes and customers frames the next critical step in preparing for – and implementing – a future that embraces A.I.

Photo montage created using “Binary” by brett jordan and “Brain” by Greg Tee, both licensed under CC BY 2.0

By | 2017-11-21T01:27:57+00:00 November 20th, 2017|Whitepaper|0 Comments