A four stage journey to AI success in industrial manufacturing

Everyone believes in the potential of AI, and yet, many companies fail to realize their AI dreams. Here are four steps from Accenture to help get AI right.
27 April 2018 | 25 Shares

AI can transform the industrial manufacturing environment. Source: Shutterstock

Today’s business leaders and managers are convinced about the potential of AI. However, when it comes to implementing it, there’s hardly any use cases and applications that are groundbreaking.

According to Accenture, AI – particularly when combined with mobile computing and big data analytics – has the ability to transform not only core operations, but also worker and customer experiences, and ultimately even business models.

However, few companies are exploring AI applications in the context of other impactful technologies to create exciting results for themselves.

According to the company’s survey, while 98 percent of organizations said they’ve begun to enhance their offerings with AI, only 16 percent of them have established a holistic AI vision for their business, only 5 percent are committing resources to AI-driven product initiatives, and only 2 percent report that they have begun to leverage AI solutions at scale.

Accenture’s research also highlights the challenges companies face when trying to use the technology: The concerns cited most often were data quality, data- and cybersecurity, deciding between ‘buying vs. making’ AI-embedded solutions, and data sharing and protecting intellectual property.

“The re-invention of industrial products with AI is still in its early stages, and getting it right is anything but easy. However, the successes of the AI leaders in our sample clearly show that it can be done and that the business case for AI in industrial is very strong,” said Eric Schaeffer, a Senior Managing Director at Accenture and Global Lead of its Industrial practice.

The four-stage journey to success

It might seem like different companies in different industries and geographies should each adopt a different approach to AI-driven re-invention, however, Accenture’s research suggests that’s not true.

“Our investigations —a combination of case study research and survey analysis—confirm that all successful companies stay the course of a sequential, four-stage journey,” said the report.

The first stage, according to the report, is to believe. “Digital reinvention starts with conviction. Companies need to really believe in the power of AI to shape the future of their products and businesses, and to bring key stakeholders with them. The good news is that nearly seven in 10 display such conviction,” said the report.

If you’re just getting started on the journey, try to articulate your belief in digital reinvention and share it with stakeholders through internal and external newsletters or public platforms. Make use of opportunities to communicate about your new mission in meetings with suppliers, R&D partners, and the larger ecosystem.

The next stage is to envision. Despite the conviction, the report found that when it comes to creating a commercially viable vision, only 16 percent of survey respondents managed to deliver. In many cases, the CEO took the lead and mobilized teams to start developing the investment and ecosystem strategies to acquire, process and secure the data needed to drive maximum value from AI.

Accenture found that leaders who can envision AI can see the big picture: 82 percent rank enhanced customer loyalty and deeper insights from product and service usage as key value drivers for themselves. The same proportion says that both greater safety and smarter solutions and services will be critical outcomes for their customers.

Once you believe and have communicated your belief to all your stakeholders, you must define the digitally re-invented product you want to build and identify the value to be built and owned, and identify the impact on both top and bottom lines.

In the third stage, the business, together with its leaders and teams, must commit appropriate management and financial resources to two critical areas:

  • gaining the skills needed to ensure frictionless integration of new IT with legacy infrastructures
  • shifting elements of the business model to embed AI

Although only 5 percent of executives Accenture surveyed reach this stage, those leaders are very clear about the reasons for their success. They agreed that strategic and tactical partnerships are key to amplifying the data skills they consider most important.

In order to commit to succeeding with your AI projects, you must commit senior management to securing strategic and tactical partnerships that create value and mitigate risk.

Finally, the last phase of successfully adopting AI intelligently is to execute. Only 2 percent of survey respondents achieve the scale they need to drive market value from their digitally reinvented products.

They do so by working with ecosystem partners to identify the AI components they want to combine with other digital technologies, now and in the future, as part of their customer value proposition. They also take a systematic approach to achieving scale.

To build agile prototyping and production models that actualize efficiencies and experiences, these companies innovate new experiences with start-ups and customers.

They adjust their operating models to accommodate such initiatives as on-the-job training programs and collaborative, co-innovation platforms at speed. And they transform their business models from a product focus to one defined by services that link to the product and make it both more responsive and more responsible.

If you’re working on an AI project, in the final phase, you need to make sure you start innovating with your ecosystem. If necessary, acquire players that can bridge technology and skills gaps. Also develop engaging, on-the-job skilling modules that empower your workforce to develop and service the digitally reinvented product across its lifecycle.