Traditional enterprises yet to embrace AI extensively: Infosys

While the potential of AI is lucrative, enterprises are not exactly rolling it out because of several factors. Here, Balakrishna D R (Bali), Head of AI and Automation, Infosys, tells us more. Excerpts from an interview:

DQ: How is Infosys identifying and mitigating the hurdles thwarting AI’s business implications?

Balakrishna DR: While the digital natives are comfortable adopting AI, traditional large enterprises are yet to embrace it extensively. While the potential of AI is lucrative, yet enterprises aren’t exactly rolling it out because of factors such as the absence of a clear strategy, lack of organized data, skills shortage, and functional silos within the organization. As per a Mckinsey study, a mere 17 percent of respondents said their companies have mapped out the potential areas in an organization where AI can succeed.

Organizations who are early adopters of AI, are struggling to accrue the benefits because they have not been successful in scaling and democratizing AI. Also, only 18 percent have a clear strategy in place for sourcing the data that enables AI work.

The dearth of trained talent with AI skills also plays a role in slowing adoption. At the same time, to adopt AI seamlessly, organizations need to take additional measures to ensure better security, governance, and change management.

Having said that, we’re likely to see most of the stated hurdles being overcome as technology evolves at breakneck speed. For instance, data synthesis methodologies are now available to combat data challenges in AI. With the emergence of techniques such as transfer learning and meta learning, reduces the need for high volume data. Aspects like explainability of AI, elimination of bias and ensuring AI is used ethically are becoming mainstream helping in adoption of AI in the enterprise context.

Today, we can automate the steps and processes involved in the life cycle of creating, deploying, managing, and operating AI models which helps scaling AI adoption more widely into the enterprise. Even challenges such as inadequate workforce skills will start to abate over the next few years as more engineers and scientists get trained and become industry ready.

DQ: How is Infosys accelerating digital transformation by scaling with AI across enterprise ecosystems?

Balakrishna DR: AI accelerates and democratizes the decision-making process in an organization reducing the distance between employees and their clients enriching the user experience. The benefits are realized only when the technology is scaled across the organization.

One great example is a leading telecommunications giant that wanted to enable real-time collaboration amongst its employees by breaking data siloes and bringing AI closer to the employees to help them in their decision making. Infosys AI and automation services created an AI marketplace for the company by designing a ‘platform of platforms.’

This machine learning driven platform helps employees’ set-up challenges, compete and collaborate across the organization. It also enables better and faster predictions with humans working alongside AI-powered bots. Several hundred employees are using this platform for forecasting, modelling and creating new experiences for the end-users.

We implemented a Centre of Excellence driven strategy for the discovery and delivery of AI and automation opportunities within a global healthcare equipment manufacturer. The implementation is already delivering more than 30 percent net cost reduction in core processes as well as identifying revenue increasing opportunities in customer interfacing areas.

We worked with a global Oil and Gas company where we offered holistic support in terms of defining enterprise wide AI strategy. We handled the setup and support of Centre of Excellence and initiated common discovery efforts across the organization. We also worked on communication and visibility on use cases across business units, the establishment of a single AI portal for all stakeholders to stay up to speed with developments. These efforts resulted in cost savings of more than 50 percent in core processes.

In another case, we worked with SPARK ANZ through a consulting-led, integrated, process-first approach. We started with small, specific problems that were not among the most complex organizational problems to solve but that were sure to prove and show value.

For example, AI and predictive ML applications in network capacity planning and server patching. Change management to help overcome the employees’ fear of automation was a priority. Also, scaling up was not just about incremental multiplication; it required significant investments in orchestration, management, security, lifecycle management, and governance of checks and balances.

DQ: What consulting-led approaches to AI are taken to achieve top-notch business outcomes?

Balakrishna DR: Infosys’ consulting led approach focuses to find, frame and solve unique business problems that leverage artificial intelligence to create measurable business value. The business value is driven by top down identification of strategic priorities, value driven prioritization, and constant value creation monitoring. It is strategic to approach AI as a journey and not just an immediate destination reached with a few PoCs and pilots.

The approach also emphasizes that for AI to scale successfully, a project must be business-driven and not just an IT initiative run by the CIO’s office. Having a consistent sense of the ROI created from any AI related initiative is crucial. No project should be kicked off, if we are not able to answer the simple question of “How much is this effort going to bring to the enterprise?”

Here are the key tenets of Infosys’ consultant-led engagement on AI:

Start business-inward, not algorithms-outward: Several AI initiatives in the organizations start with a Technology led transformation rather than a business need which needs to be addressed. A deeper understanding of the domain and process is key to arriving at the problem statement, business value followed by the technology implementation.

Focus on the process first: Understand the business process across all aspects of business pain points, priorities, constraints and value maps. Productivity, Decision Making and Quality aspects of the process provide insights to technology roadmap.

Integrated capabilities: At Infosys, our technology services offerings are intertwined with consulting offerings, where the teams bring complementary competencies. While the technology practice brings in AI technology capabilities, the consulting team discovers and identifies the best-fit business opportunities and brings in domain expertise.

Taking a life-cycle management view: The alignment between technology and consulting teams continues throughout the lifecycle of any project— right from implementation, deployment and training, to ultimately driving large-scale adoption in production.

DQ: How is Infosys combining in-house cognitive engines and offerings such as Nia and AssistEdge, and third-party AI tools to transform business value chains?

Balakrishna DR: Infosys has invested in building a strong AI partner ecosystem including partnerships with start-ups, research organisations, and industry. For instance, we have partnerships with universities such as Stanford, MIT, and Indraprastha Institute of Information Technology (Delhi). On the industry front, we have partnerships with Microsoft Azure Cognitive Services and IBM Watson among others. There have also been several investments in start-ups such as Trifacta, TidalScale, and Whoop.

Infosys is known for its consulting capabilities and business outcome focused approaches, with integrated consulting and AI service teams, from the top leadership through to the operational execution layers. This ensures an end to end value creation journey with clients. At the same time, a platform-based approach encourages a robust platform agnostic partner ecosystem that can work with any and every third-party provider.

Infosys not only creates its own in-house platforms (Nia, Edge platforms, Cyber security platform, etc.), but also works with customers to co-create custom AI platforms using some of the best in class open source and licensed software.

DQ: How does Infosys help enterprises leverage big data, advanced analytics, robotic process automation (RPA) to transform themselves into a data-driven intelligent enterprise?

Balakrishna DR: Data is oil, but, only good data can help enterprises to become intelligent. Infosys itself believes in data-driven organization even before Big Data and AI techniques came into picture. We believed on that fact that we believe in God, everything else comes from data.

Infosys has its own IP, AE Discoverer, which helps in mining business process systemically and which is driven though data. Any opportunity to automate or generating insight comes using its output which is data. This approach gives a scientific method for finding opportunities.

Infosys has created an ecosystem of partners, its own IP and academics, which helps client adapt data driven journey faster and easier. We bring various data workbenches, Analytics workbenches and AI models, which can help client get better RoI for their journey and which also becomes Suites for enterprise to build their IT ecosystem to drive data-driven programs.

Our methodology, which is based on design thinking, ensures we help client to not only solve critical problem but help them identify future opportunity and future problem which needs to be solved using experience, innovation and Data.

Infosys offering around enterprise control tower is to show mirror to client which helps them visualize their performance (data driven) and thus enable them to drive action.

DQ: How is Infosys designing and strategizing AI architecture to deliver value at scale?

Balakrishna DR: At Infosys, we believe that to scale AI adoption within the enterprise, it takes three zeroes – zero distance to information and insights, zero disruption to business operations and zero latency to business processes. This means building an integrated approach to problem solving and identification, driving actionable insights across the continuum, and solutions that accelerate adoption.

We work across consulting, technology and operations and rely on three key pillars of AI and automation (as the technology lever), lean methods (as the process lever) and design thinking (as the innovation lever). This holistic approach helps us re-imagine business and processes to create innovative business models.