The Human Side of Machine Learning
As enterprises bring machine learning into their organizations, many pundits predict that it will lead to massive layoffs.
Yet, a study SAP developed with the Economist Intelligence Unit, Making the Most of Machine Learning: 5 Lessons from Fast Learners, found evidence that highly skilled employees will be vital during the transformation and beyond. The “people” part of the business isn’t going anywhere. But it will change.
The integration of machine learning will demand completely new ways to define roles and responsibilities, new skills to build algorithms or coexist with them, and perhaps most importantly, a culture able to continuously evolve and learn along with its artificial intelligence capabilities.
Indeed, among the subset of organizations surveyed that are already seeing benefits from machine learning, which we call the “fast learners,” 75% say that they expect to retrain employees as they increase their use of intelligent automation. As Erik Brynjolfsson, director of the MIT Initiative on the Digital Economy, observed, retraining people and redefining work to create a collaborative relationship between employees and machines will determine how well organizations succeed.
Strategy for skills
Keeping up with the machines is part of a larger challenge: digitally transforming the organization to create fallow ground for new technologies such as machine learning, the Internet of Things, Big Data, and analytics to survive and thrive. Avoiding the kind of organizational resistance that plagued previous major technology shifts, such as the enterprise software wave of the early 2000s, will be critical.
Company leaders are responsible for heading off resistance. In that regard, fast learners have a head start. C-level executives at fast-learner companies are engaged with machine learning strategy to a higher degree than in other organizations. Fewer fast learners suffer from a lack of strategic clarity about machine learning—as opposed to previous enterprise software efforts where top executives often checked out.
Having a clear strategy for digital transformation more broadly and machine learning specifically will also be important if organizations are going to attract a new breed of intuitive and inquisitive technologists—people with both programming skills and a deep understanding of both data science and the business—to build their machine learning capabilities. The skills may be hard to find, for fast learners named a lack of available external machine learning expertise as a top challenge.
What’s more, executives must enlist their skilled, non-IT professionals to work with technologists to develop the organization’s machine-learning capabilities over time. For example, accounting firm EisnerAmper, an early machine-learning adopter, hired data scientists, business analysts, and application developers into its enterprise technology group. The newcomers partnered with the firm’s accounting, auditing, and tax leaders to develop new capabilities.
Over time, the machine-learning enterprise will begin to function differently by adjusting business processes, staffing models, and learning and development programs to adapt to the speed and scale at which machines can learn, says Stanton Jones, director and principal research analyst with ISG. Organizations are moving from a focus “in which people are driving a process that is supported by technology,” he says, “to one in which technology is driving a process supported by people.”
Machine learning enables companies to exponentially increase the scale of their capabilities without increasing staffing. People will be involved at a higher level, managing, analyzing, or acting upon the machine learning output.
A human–machine partnership
The exponential value starts to accrue when machines augment and complement human skills. “That’s more of a partnership with machine intelligence,” says Cliff Justice, principal in KPMG’s Innovation and Enterprise Solutions team. “You’re going after new ground. You’re innovating faster.”
At EisnerAmper, machine learning is the engine driving the company’s transformation for the digital era, enabling it to move beyond basic auditing and accounting to becoming a strategic business adviser to its clients. The firm has developed smart auditing tools—software that learns how to learn in order to make the audit process more effective and efficient—and it plans to launch an audit practice fully driven by machine learning.
That will free up EisnerAmper’s practitioners to spend more time providing clients with high-level advisory services and strategic consulting, while offering traditional auditing services at a lower price than competitors.
In our study, fast learners are embracing the organizational and cultural shifts required to succeed with machine learning. Indeed, among the study respondents who have just begun to dabble in machine learning but have not yet seen benefits, only 50% say they are planning to retrain employees for the machine learning era—one-third fewer than the fast learners.
The fast learners have recognized that the value of machine learning comes with the right combination of human and digital labor. That may explain why fast learners say that organizational resistance is less of a challenge than in other organizations.
As a result, they have a head start not just in developing machine learning capabilities, but also in adapting their enterprises for a near future in which the integration of human and machine learning will be a competitive necessity.