Artificial intelligence can produce tangible process improvements
A recent report released by Infosys, a global technology and consulting company, looked at how artificial intelligence (AI) will be used in manufacturing and other business sectors to improve training and operations.
The study, called “Human Amplification in the Enterprise” discussed the use of AI in manufacturing as well as the barriers to its adoption. Sudip Singh, senior vice president and global business unit head of engineering services at Infosys, discussed the future uses of AI and the state of the digital transformation with Plant Engineering:
Plant Engineering (PE): How do you define the term “artificial intelligence?"
Singh: For the purposes of this research, AI was defined as an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.
PE: How do the survey respondents think AI will help their business efforts?
Singh: Across industries, there is a clear link between an organization’s revenue growth and its AI maturity, and that is also evident in the manufacturing industry. According to our research, 80% of enterprises in the manufacturing and high tech sector are undergoing full-cycle digital transformations to become AI-driven and therefore faster, more agile and more efficient.
These organizations are looking to AI to provide human-like recommendations for automated customer support/advice (60%); want AI to process complex structured and unstructured data and to automate insights-led decisions (58%); and want to use AI to create a simulated experience that is essential to a decision-making process (48%).
PE: There seems to be a disconnection between manufacturers who say they are undergoing a “digital transformation” and the amount of actual transformation. As you analyze the study, where is manufacturing along this journey of digital transformation?
Singh: Even though a majority of enterprises in the manufacturing and high tech sector are undergoing digital transformation, few have fully accomplished their stated goals. This is due to a lack of data-led insights on demand (67%); lack of collaboration among teams (51%); and lack of time (40%). And when IT processionals were asked about difficulties in achieving their enterprises’ full-cycle digital transformation, respondents cited IT misalignment (68%); entrenched resistance to change within the organization (59%); and time constraints (51%).
This suggests there is a disconnect within organizations that undergo these transformations. Until more senior level IT-decision makers buy into the benefits of bringing AI to manufacturing, teams won’t have access to the proper resources to support full-scale implementations, and will therefore deprioritize digital transformation initiatives. Time and again digital transformation projects are abandoned because pockets of an organization are resistant to change, but by getting buy-in from the right advocates, digital transformations can continue, driving innovation forward.
PE: How should AI be used in training the current manufacturing workforce?
Singh: As AI continues to disrupt the current workforce, it’s critical to move past our conventional views of education, and instead shift our focus to holistic, continuous, and lifelong learning. This is a shared view across industries, including manufacturing.
For example, according to our research, respondents say lifelong learning is extremely important to their organizations. Of the reasons for why lifelong learning programs are important, 61% say it improves their ability to fit into new roles and jobs; 25% say it improves their productivity; and 10% say it prevents skills loss when employees with highly specialized skills retire or switch jobs.
Whether AI itself will play a role in training human workers is a facet of AI maturity that remains to be seen.
PE: Is there a sense that there is a generational gap in the way AI is perceived and implemented? If so, how can that gap be overcome?
Singh: The rhetoric around AI has been mislabeled, misunderstood, and largely based in fear. Discussions around AI tend to gravitate toward negative connotations of the technology, but we’re doing a disservice by not focusing on the true value of the technology, which is to amplify human potential.
Organizations can shift the perception of AI by leveraging transformative AI solutions that take work to whole new level, enabling us to do more than we could have ever imagined. In manufacturing specifically, having AI and automation taking over more of the known, well-defined work means we can exercise our human creativity and ingenuity to find new problems and opportunities and create new kinds of products, experiences, and value that do not yet exist.
PE: In your survey, 43% said that making better use of data for business decisions is a difficult goal to achieve. What should manufacturers do to overcome this barrier?
Singh: Organizations will be unable to experience the benefits of AI until they implement information governance strategies that allow them to make better use of their data. However, this is much easier said than done and we continue to see data projects fail because of how companies are organizing their data.
But to capitalize on AI investments, companies need the capabilities to transform insights into actions. Automation will be critical in overcoming this barrier. By eliminating low-level manual processes, organizations can free up human resources, thus amplifying human potential to deliver more value and creativity further up the value chain.
By The Numbers
The Infosys report states, “A majority of enterprises in the Manufacturing and High Tech sector (80%) are undergoing full-cycle digital transformation. (Another) 15% are transforming partially or in pockets and 5% are not currently transforming but will do so in the near future.
Among the AI-supported technologies and uses that respondents said would impact their digital transformation are:
- Machine Learning 79%
- Institutionalization of enterprise knowledge 66%
- Cognitive AI-led processes and tasks 60%
- Automation of decision-making 54%
- Building AI-based applications to amplify
- and improve products and services 53%
- Robotic automation 46%
Source: “Human Amplification in the Enterprise” report, Infosys.