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Infosys Applied AI Helps Enterprises To Scale & Future Proof AI-Powered Transformation

Digital giants as well as new-age AI startups and innovators have been harnessing the power of data and growing their AI investments to offer cutting-edge experiences. Leading Information Technology Company, Infosys, is helping enterprises adopt a comprehensive approach and roadmap to scaling enterprise-grade AI for their businesses.

Mr. Balakrishna D R, Executive Vice President – Service Offering Head – Energy, Utilities, Communications Services and AI & Automation Services, Infosys, shared with CXOToday how Infosys is converging the power of AI, analytics, and cloud to deliver new business solutions and perceptive experiences.

1.      How can AI be leveraged to create a sustainable advantage for enterprises?

Artificial Intelligence (AI) is redefining the enterprise ecosystem. Alongside the emerging patterns and data infrastructure, the enterprise world has begun to experiment with the capabilities of AI. This has led more businesses to enhance technologies to a scale. Our customers expect long‑term business value in every engagement and demand innovative solutions to the business problems they need resolved. In a knowledge-led and people-intensive industry like ours, employees expect organizations to provide opportunities to continuously learn and reskill themselves while navigating new opportunities and a northward career trajectory.

It is important to understand the parameters that will define the individual, sustainable, and collective success from the adoption of AI. The strategic objective should be to build a sustainable and resilient organization that remains relevant to the agenda of our clients, while creating growth opportunities for our employees, generating profitable returns for our investors, and contributing to the communities that we operate in using AI. Some of the approaches could be useful.

  • Streamlining Business Operations through AI, taking big advantage of technology by giving AI the mundane & low involvement tasks, and leaving the expertise requiring tasks to humans.
  • Having a technical inclusiveness platform in AI that caters to any kind of disruptions.
  • Another advantage that AI can offer is heightened security in times when data breaches are ubiquitous and, unfortunately, increasingly becoming normal. Thanks to AI’s ability to track patterns, it is now possible for the technology to predict and prevent threats, as well as identify any anomalies with a higher level of accuracy compared to any human reaction.
  • As companies already own significant amount of data about customers, AI can provide them data-driven insights for highly efficient marketing – how to market, who to market to, when and where to market, and even why to market. Amazon is the biggest example for this approach.
  • Organizations need to identify their core capabilities that are aligned to the broader business strategy and can be AI-enabled to deliver enterprise-wide benefits to potentially create competitive advantage.
  • Improving customer journeys and experiences using data, automation, people, and process across the length and breadth of client organization where customer experience is of utmost importance
  • Amplify the capabilities of people to take decisions faster using AI

Infosys’ Applied AI offerings are a response to all the above.


2. How can enterprises futureproof and efficiently scale AI investments while managing reputational risks?

Artificial intelligence adoption is at an interesting juncture – as it graduates from consumer AI, towards enterprise grade AI. This shift also marks the second inflection point for AI. As AI re-shapes businesses, delivers perceptive experiences, and guides decisions, companies around the world are starting to realize that value from AI can bring them the competitive advantage. Enterprises need to think of futureproofing and efficiently scaling these investments, while managing the reputational and performance risks of AI.

To drive the expected results and add value, it’s crucial to see the technology that aligns with company culture, structure to scale up the business. Some of the points through which organizations can futureproof and efficiently scale their AI investments are:

  • Strategy & Clear Vision for AI/Automation Initiatives: Companies need to adopt a strategic framework to understand business impact and prioritize investments in AI initiatives.
  • Establishing Controlled Operating Model: Organizations need to have a standardized way of working, and a way to track business value. Make sure that the right person or people are accountable for the business transformation.
  • Experimenting to Industrialize: Industrialization of AI is not easy; conducting experiments and deciding on the right model creates much challenge for organizations. Thus, using MLOPs, AIOPs for AI/ML Lifecycle can prove to be the right choice.
  • Leveraging the Partner Ecosystem: Organizations must lean on their partner networks and wider ecosystems to maximize the value of their AI investments and partner with many startups and midcap companies to focus on AI.
  • Scaling AI in a Skillful Workplace: AI is becoming a fundamental capability. With it, there is a growing need to nurture full-stack engineers who are equipped to work with AI. A part of this need is filled by easy-to-use tools and the creation of citizen data scientists, as well as through reskilling employees.

To augment the above, enterprises need to invest in their in-house AI solutions and mergers and acquisitions. Enterprises can invest in joint innovation centers. Enterprises can tie up with various universities across the world for trainings, R&D, and joint incubation. To manage reputational concerns while investing in AI, enterprises need to monitor laws and regulations in various countries and make compliance actions.


3.      Tell us about the evolution of hybrid AI, and how India Inc. can leverage upcoming advancements to unlock newer business opportunities?

The hybrid AI thinking dates to 1969 when scientists accepted the fact that neural network-based approach would never be sufficient to imbue machines with genuine intelligence.

Symbolic AI used expert-built ontologies and knowledge representations, decision logic, rules and later extended to statistical models and machine learning based methods. These provided an almost magical differentiation to the pioneers who embraced these technologies. Symbolic approaches were easier to understand and engineer and lent themselves well to regulatory and legal scrutiny. However, these approaches faced a challenge in dealing with the variety and scale of unstructured content.

Neural Networks & Deep Learning are best suited for working with unstructured data such as text, document, audio and video. However, deep learning (DL) provides a narrow field of intelligence in very specific areas to identify patterns and classify. It also needs substantial investments in infrastructure and annotated data to train and execute. There’s also a degree of uncertainty in the outcomes with accuracy levels varying based on conditions in which the DL models execute.

Hybrid AI is all about bringing together the best aspects of neural networks and symbolic AI. Combining huge data sets (visual and audio, textual, emails, chat logs, etc.) allows neural networks to extract patterns. Then, rule-based AI systems can manipulate the retrieved information by using algorithms to manipulate symbols.

Some of the opportunities that Hybrid AI can provide to India Inc but not limited to these are:

  • Creating Smart Homes with AIOT and 5G
  • Utilizing computer vision algorithms for facial recognition and optical character recognition (OCR) for KYC in banking.
  • Medical devices with better precision, which can be used for automated surgery, X-Ray, sonography, blood pressure and other health devices.
  • Our chatbots becoming more humane that can understand our queries much better.
  • During accidents, give more precision via video analytics of what happened and how, while providing detailed insights.
  • Better analytics for prediction in retail.
  • Using data analytics, sentiment analysis, and prediction for media


4.      How can hybrid AI be used to bolster democratization of AI?

Hybrid AI has the vast potential to boost up democratization of AI, helping enterprises in scaling their digitalization to achieve business differentiation.

Hybrid AI provides the perfect platform and construct for Human-in-the-Loop implementations that inherently support interpretation and scrutiny for business IT organizations as well as compliance and regulatory authorities.

We can achieve democratization of Hybrid AI by greater adoption of the solutions worldwide across all industrial areas.  With greater adoption comes optimization of the algorithms leading to better precision.

  • Having a library of Hybrid AI solutions, services and platforms per Industry and Domain specific.
  • Uncover actionable insights from their own data estates, open-source data, and curated data exchanges on the cloud to build new AI models and use cases for the organization.
  • Harness the full potential of the Hybrid AI partner ecosystem and create fit-for-purpose solutions, orchestrating offerings from start-ups, partners, joint solutions, data solutions and enterprise security.
  • Analytics model interpretability, bias detection, and continuous performance monitoring are built into various stages of the lifecycle, from development to deployment and use.


5.      How can we de-risk AI, making the technology more explainable, responsible, unbiased, and secure?

Artificial intelligence (AI) is poised to redefine how businesses work. Already it is unleashing the power of data across a range of crucial functions. To remain competitive, firms in nearly every industry will need to adopt AI and the agile development approaches. But they must do so while managing the new and varied risks posed by AI and its rapid development.

Some ways to de-risk AI are:

  • Businesses can rely on rich ecosystem of consortiums and regulatory bodies driving AI directives, for implementations that are compliant with regulatory and security norms
  • Analytics model interpretability, bias detection, and continuous performance monitoring are built into various stages of the lifecycle, from development to deployment and use.
  • Follow the tenets of Responsible AI from development to deployment

Our Infosys Responsible AI part of Applied AI does exactly the above.


6.      How is Infosys enabling businesses to scale enterprise-grade AI?

At Infosys, we constantly endeavor to align with our clients’ priorities, which help us define the problems to be solved. Our Infosys applied AI is an integrated offering converging the power of AI, analytics, and cloud to deliver perceptive experiences and differentiated offerings for businesses. It brings about never-before efficiencies, future-proofs and efficiently scales these investments, while managing the reputational and performance risks of AI.

Further, Infosys applied AI cloud (part of Infosys Cobalt) enables businesses to readily access AI hardware and software, across private and public clouds, as highly contextualized platform services. They can also process AI algorithms locally on any device using Infosys edge AI.

Infosys applied AI maximizes value for our clients through AI, from incubation to industrialization, by addressing real world problems and creating material impact through adoption at scale.

Over the last two decades, we at Infosys have addressed client’s business priorities using our digital technologies, through digital transformation, extreme automation, focus on vertical utility platforms delivering through our 4Es of efficiency through industrialization and automation, effectiveness through analytics and business insights, experience through innovation, and empathy through humaneness.

We differentiate ourselves in the market by navigating clients to experience their next by delivering business value through deep domain expertise and technology prowess.  Our key value proposition lies in our ability to enhance our business model to cater to and meet our client’s requirements.

We enable businesses scale with the following approach:

Discover innovative applications for ready-to-deploy AI solutions across the value chain

  • Power to uncover actionable insights from their own data estates, open-source data and curated data exchanges on the cloud to build new AI models and use cases for the organization.
  • Harness the full potential of the Infosys AI partner ecosystem and create fit-for-purpose solutions in the AI Living Labs, orchestrating offerings from startups and over 30 leading providers of intelligent automation, AI solutions, data solutions and enterprise security.

Future-proof and efficiently scale AI enterprise-wide

Businesses can build their AI cloud, access open-source AI software as a service on their hybrid cloud infrastructure, and harness edge AI capabilities with Infosys applied AI. This can work in tandem with any hyperscale cloud provider’s services providing more choices and future-proofing investments.

De-risk enterprise AI to manage reputational and performance risks

Analytics model interpretability, bias detection, and continuous performance monitoring are built into various stages of the lifecycle, from development to deployment and use. For example, using Infosys applied AI, a machinery manufacturer analyzed warranty claims patterns to eliminate bias, from the data set and process, before reengineering and automating the claims approval process.

AI-powered innovations developed by Infosys in collaboration with the French Tennis Federation for The French Open helped tennis fans enjoy game analytics. Players and coaches could benefit from coaching insights while journalists could access highlights and match synopsis in their natural languages. Learn more

Some of our success stories are here:

  • A medical device manufacturer used automation technologies to not only improve processes, but also connect with customers more closely. Learn more
  • Infosys Autonomous System Platform is driving India’s first-ever project on autonomous buggies – a commercially viable solution for controlled environments. Learn more
  • A leading communication services provider used Infosys applied AI to create a machine learning workbench for data engineers to collaborate, deliver, and industrialize a catalog of real-time enterprise-wide business solutions. Learn more

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