August 2, 2017 Manufacturing Sector Transforming with AI, Automation

Volume 20 | Issue 3

New AI report from Infosys shows lifelong learning, reskilling viewed as critical to successful digital transformation.

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Every business is exploring ways to increase efficiency and agility by achieving greater operational excellence, while simultaneously seeking to create unprecedented customer experiences that grow revenue and revitalize business. The understanding that this reinvention of processes, infrastructure, solutions and even cultures must be driven by a digital transformation is pervasive.

Digital transformation is advancing in part due to Artificial Intelligence (AI), which is reshaping not just how businesses operate, but the human journey. AI is becoming increasingly part of every aspect of our lives, from simple things such as the way we shop and drive, to more fundamental things like how our homes, automobiles and workplaces amplify us. Business leaders are recognizing the potential AI holds to transform businesses operations with benefits such as radical cost reductions and efficiencies, and opening up new ways to innovate and create revenue.

Industries are beginning to experience the massive potential of AI. A new study commissioned by Infosys, “Human Amplification in the Enterprise,” sought to understand how AI and automation technologies are driving full-cycle digital transformation within manufacturing and nine other industry sectors in the U.S. at companies with 1,000 or more employees and annual revenue exceeding $500 million.

The study revealed that, across all industries, 96 percent of respondents reported they are already underway with or preparing to begin their transformation journey. Yet, nearly three in four organizations say they have not automated all the tasks they believe should be automated.

AI, when integral to the digital transformation journey, greatly amplifies the outcomes of transformation.

Some of the reasons given for not achieving full automation are inadequate focus on streamlining and automating wide-spanning processes especially deeply interconnected operational activities where these are currently adequate to support the immediate mandates of business as usual. Another reason for the want of enthusiasm to pervasively automate is the sheer magnitude and complexity of the legacy operational landscape, even as broader business management concerns continue to demand leadership focus. The study found 57 percent of respondents blame the slow rate of automation on the lack of business and IT alignment, while 44 percent of the respondents report that there is a lack of adequate focus from senior leaders to achieve complete automation.

According to the report, the AI applications that are the leading drivers of digital transformation across industries are machine learning (75 percent), cognitive AI-led processes/tasks (57 percent) and institutionalization of enterprise knowledge using AI (55 percent). In the manufacturing and high tech sector specifically, use of machine learning is higher (79 percent) as is institutionalization of enterprise knowledge using AI (66 percent) and cognitive AI-led processes/tasks (60 percent).

Transformation priorities in manufacturing

More than a third in the manufacturing and high tech sector reported their organization’s first priority for automation is to automate processes. The main reasons are to increase productivity (66 percent), minimize manual errors (61 percent), reduce costs (59 percent) and refocus people’s efforts on non-repetitive tasks that benefit from human intervention (50 percent).

Of the various applications of AI that manufacturing and high tech sector senior level employees want to adopt in the next 12 months, the respondents indicated:

• 60 percent want AI to provide human-like recommendations for automated customer support/ advice;
• 58 percent want AI to process complex structured and unstructured data and to automate insights- led decisions;
• 48 percent want to use AI to create a simulated experience that is essential to a decision-making process;
• 38 percent want to use AI to create a decision-making system in which machine learning allows the system to learn from humans and improve itself;
• 20 percent want to use AI to institutionalize enterprise knowledge.

Investment in employees resulting in innovation

While the research confirms that automation of tasks is a core component of the enterprise’s digital transformation journey, the desired outcomes are not just about achieving operational efficiencies and greater productivity. Enterprises see the potential to redirect their workers’ effort saved through automation toward innovation, and to leverage emerging technologies to shape these innovations.

The Human Amplification in the Enterprise study found that organizations that are reinvesting their people productivity gains into innovation efforts also tend to be more successful—63 percent of the respondents who say their organizations are automating most of the tasks on their agenda also report that their companies were able to develop 20 or more valuable innovations in the past 12 months.

Responses of more than 1,000 IT and business decision makers across a range of sectors highlight what companies say they need to become more innovative.

Two-thirds of the respondents from the manufacturing and high tech sector say employee lifelong learning is extremely important to their organizations. Of the reasons why lifelong learning programs are important, 61 percent report it improves their ability to fit into new roles and jobs, 25 percent say it improves their productivity, and 10 percent say it prevents skills loss when employees with highly specialized skills retire or switch jobs.

Challenges in AI adoption in manufacturing remain

Even though a majority of enterprises in the manufacturing sector are undergoing digital transformation, few have fully accomplished their stated goals. The respondents listed the top reasons their digital transformation goals are difficult to achieve as a lack of data-led insights on demand (67 percent), lack of collaboration among teams (51 percent) and lack of time (40 percent).

When asked about the challenges of adopting more AI-supported activities as a component of their digital transformation initiative, 58 percent of respondents indicate lack of in-house knowledge and skills around the technology, 57 percent mention lack of clarity regarding the AI value proposition and 54 percent say there is a lack of financial resources.

An overwhelming majority of organizations are already undergoing full-cycle digital transformation with the automation of tasks at the center of their collective initiatives. Moving forward, a manufacturer’s competitiveness will be measured in terms of how well their employees are able to do those tasks that automatons cannot do – the tasks that involve human curiosity, creativity and hunger to learn and grow. This also means those organizations that are highly invested in lifelong learning for their employees – to nurture all that is uniquely human in them – will be the those that have a workforce better suited to capitalize on the future of business.

Shanton J. Wilcox is a partner in the consulting practice at Infosys. In this role, he focuses on applying advanced supply chain capabilities to manufacturing and service organizations to integrate and streamline value chain operations.

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