ARC attended Infosys Confluence, which took place in San Francisco the third week of May, 2017. From the opening remarks, Infosys CEO Vishal Sikka talked two major themes: digital workforce skills and artificial intelligence.
In addition to the focus provided by Sikka, Infosys also touted new solutions delivered as part of their system integrator agreement with HP Inc. One included device-as-a-service to support a digital and mobile real-time work environment. The announcement was bolstered by HP’s presence, highlighted by a talk and Q&A given by CEO Meg Whitman. Confluence also provided a chance to get a bit more information on the recently released Infosys Nia, which the company positions as an artificial intelligence (AI) platform.
Infosys Implements A Talent Management Strategy for Digitally-Driven Business
Infosys has spent considerable time aligning its capabilities to meet modern, digitally-driven business needs. This alignment has been both internal and external, with a lens that encompasses people skills, existing solutions, and innovation.
From a workforce knowledge, skills, and abilities (KSAs) perspective, Infosys separates competencies into holistic, new,
and emerging. Detail on those KSAs are in the accompanying visual. As well, they have also looked at how to balance their business efforts into the proper mix of “run” (existing) and “transform” (new) business models, which often present competing priorities. In doing so, they are executing a specific talent strategy that seeks to harmonize internal skills with the pace of customer-driven market change. The term Infosys uses for this direct alignment of all aspects of their business to customers is “zero distance.”
This public commitment to a talent management strategy and improved business balance isn’t lip service. The company indicated they have key performance indicators (KPIs) to measure success. These include improving the utilization of their existing workforce, integrating at least one innovation idea into each client engagement, and hiring new talent. To the last point, the company announced its intention to hire 10,000 American workers over the next two years. As part of that initiative, Infosys is opening four hubs in the United States that will focus on developing cutting-edge technology as well as providing enhanced support for key clients. These hubs are expected to become central to hiring, incubating, and leveraging new workers and skills.
Artificial Intelligence Drives the Infosys Conversation
Sikka and others spent considerable time weaving AI into the event’s narrative. This was certainly done, in part, to boost awareness of the recently released Infosys Nia. Infosys indicates that this AI platform covers capabilities to support data (ingestion, modeling, analytics, machine learning, etc.), knowledge, and automation management. It is not yet clear if these three components are equally developed.
Regardless, it is good to see knowledge as one of the three components. The ability of AI to identify and leverage knowledge is not as yet well understood when it comes digital transformation. This is something I’ve written about before when discussing cognitive analytics and I will cover this topic at the 2018 ARC Forum.
Moving Industrial Companies Beyond Automation
Having an AI platform is one thing, getting customers to leverage its capabilities is another. Infosys seems to have a mix of clients in various stages of adoption of the platform, within some using multiple components and others that are still focused heavily on automation. It is likely that more advanced uses of Infosys AI solutions are occurring with its more consumer-facing customers in retail and finance, for example. This would mirror overall industry adoption trends.
A few examples demonstrate the range of use of AI on the industrial side. One example of a customer moving beyond automation was described to me during my conversation with Nitesh Bansal. Nitesh is the industry head for manufacturing for Infosys. He talked me through a use case undertaken at a high-end motorcycle manufacturing plant. Plant operators couldn’t’ identify underlying causes leading to paint flaking on one very specific section of the motorcycles. It was causing the client too many warranty calls and rework.
Using advanced analytics, Infosys helped the client identify an air quality issue that was related to the adherence of paint on nickel, which is where the problem was occurring. Thus, the client adjusted the paint coating thickness for that area, eliminating the issue and related fallout.
Another example shows how some industrial companies are transitioning toward their use of AI for digital transformation. This use case was discussed during a session entitled “Where will AI take use?”. The premise was that attendees would see three different example of industrial AI. One company, General Cable, presented their solution for cleaning transmission lines for utilities. They are using robots to crawl and scrub the lines.
The AI element in this use case is still a bit down the road. In their next phase, General Cable, will implement AI by using machine learning on the robots to determine where lines need more attention. This will then direct the robot to clean and coat those portions of the lines more deeply.
As Infosys is able to move more of these use cases beyond automation and into seamless integration of AI, their vision for Nia will become more fully realized. When this occurs, the company have validated the zero-distance philosophy.
About ARC Advisory Group (www.arcweb.com): Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.
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About the Author:
Director of Research
Over two decades, Michael has assisted organizations, including numerous Fortune 500 companies, in identifying and capitalizing on growth opportunities presented by the modernization of the energy, technology, and telecommunications industries.
Michael's expertise is in analysis, positioning, and strategy development for companies facing transformational market drivers. At ARC, he applies his expertise to developments related to Industrial Internet of Things (IIoT) and advanced analytics, including machine learning.