Satisfying Customers Through AI, Humans and Machines

July 9, 2018
While automation is important, businesses should also consider building strong interactions between artificial intelligence, humans and machines to create an organization that is nimbler and more responsive.

Industrial manufacturers focused on fully automating their operations as an end goal might be limiting their growth potential. Forging strong interactions between artificial intelligence (AI), humans and machines to create an organization that can be nimbler and more responsive to the rapidly changing market could be a better option.

In today’s industrial market, driven by disruption and demand for ever-more innovative, personalized digital products and services, applying automation alone will not be enough to sustain success. Human input and creativity remain the fuel of innovation and maximizing AI will become increasingly critical to gain a competitive advantage in the digital age. Companies using this approach are significantly outpacing many of their peers in terms of performance.

One major industrial conglomerate is using AI to enhance its power plant maintenance processes to analyze and predict the lifespan of equipment if no repairs are made. And a leading automaker is pairing humans with machines in its autonomous car project to solve the many challenges of driving a vehicle—from plotting the best route to keeping passengers safe. This critical work also is contributing to the creation of an entirely new market.

Despite these examples, however, many companies still have a narrow view of AI capabilities. Research conducted by Accenture with more than 1,000 business process professionals to help develop insights for a new report and for the new book, Human + Machine: Reimagining Work in the Age of AIshows that only 9 percent of the companies surveyed are extracting the full value of AI. Moreover, just 39 percent are maximizing the potential of collaboration between people and machines to improve performance, job satisfaction and retention. In a business environment that will only become more challenging, companies that are not pursuing this approach should seriously consider it.

Reinventing success

Three key elements define the blueprint for developing an effective AI, human and machine business model. They include:

  1. Rethinking business processes: Part of maximizing the power of AI involves reinventing the organization’s business and operations processes. The use of AI machine-learning algorithms and real-time data can transform current systems into self-adapting, self-optimizing processes that generate continuous improvement, are less deterministic, and are more responsive to today’s disruptive business environment. The ability of machines to act as agents of change will also unlock new roles and ways for humans and machines to work together. This approach is becoming more prevalent among leading companies, with AI applied to multiple processes across entire enterprises.
  2. Maximizing data use: Essential to responsive, self-adapting, self-optimizing processes is the data that drives them. Equally important is leveraging information that the organization collects during regular business activities, but does not typically use. Known as dark data, reinvented processes that employ machine learning can find hidden value in such data, enabling companies to find solutions through the unused information, make better decisions and offer new products and services. Eighty-two percent of those surveyed say that machine learning-enabled processes help them address unsolved problems through dark data that they previously were unable to tap.
  3. Optimizing the human factor: Success in AI is inextricably linked to investment in people. It will be to the advantage of organizations to unlock the full potential of their workforce in today’s digital world by retraining workers to maximize their creative skills and judgment as part of interacting with machines.

Workers, for example, who can act rapidly on the opportunities and situations that machines discover in real time, whether a sales lead or maintenance issue, can play an integral role in self-changing, data-driven processes—contributing to enhanced performance and growth. They also will be critical to assessing the need for operational improvements and safety.

Decide the future

Industrial companies face a window of choice—whether to pursue total automation or elevate the AI, human and machine equation. Customers with ever-evolving expectations for more personalized digital products in an industrial market changing at hyper-speed suggest that the latter might be better suited for sustained success.

>>Brian Irwin, [email protected], is managing director, automotive and industrials, North America, and products industry X.0 consulting for Accenture. Paul R. Daugherty, [email protected], is chief technology and innovation officer for Accenture.

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