Infosys’ AI-Driven Approach to Enhancing Productivity and Innovation

CXOToday has engaged in an exclusive interview with Alok Uniyal, SVP and Head of IT Process Consulting Practice, Infosys

Q1. What are the key benefits of a product-led, platform-driven approach in helping companies realize greater business value from their digital investments?

1. Product-led, platform-driven approach helps in embedding customer centricity and innovation in the ways of working of an enterprise and brings in greater focus on business value realization. This is done by identifying key customer journeys, mapping out underlying value streams that orchestrate these journeys, reimagining core business and technical capabilities that constitute the value streams as products and platforms and organizing teams around the value streams and products/platforms. The 3 underlying principles in this approach are – organize for value, speed to value and flow of value.
2. The product and platform teams use agile, DevSecOps, and site reliability engineering practices and related tooling for highly automated and accelerated feature delivery, guided by OKRs.
3. All of this help in realizing key benefits around – impact to top level business OKRs such as revenue, market share, NPS score and impact to engineering OKRs such as velocity, productivity, cycle time, quality and reliability. One of our logistics services clients saw a 2X increase in market capitalization while a telco client could achieve 4X faster time to market.

Q2. What opportunities does generative AI present for businesses in terms of efficiency and customer personalization?

Gen AI offers a wide range of opportunities for businesses to boost efficiency and enhance customer personalization. These include – Gen AI powered Chat Bots and Virtual Assistants optimizing customer service operations, reimagining internal processes in HR, Finance and IT Support, enhancing internal Knowledge Management to increase employee productivity, amplifying software engineering productivity, accelerating drug discovery in pharma industry, improving marketing effectiveness thru targeted campaigns and tailored experiences / recommendations – among others. In an interesting application of gen AI – JP Morgan has launched a GPT4 powered AI tool (called IndexGPT) to create thematic indexes, which identifies investments based on emerging trends – cloud computing, e-sports or cybersecurity etc. – rather than on traditional industry sectors or company fundamentals. With gen AI tools getting better by the day, the use cases will continue to evolve at rapid pace across all parts of business value chain.

Q3. How is Infosys leveraging generative AI to amplify productivity and customer-centric innovation?

Infosys Topaz – an AI-first set of services, solutions and platforms using generative AI technologies, is being successfully leveraged to help clients in above mentioned areas with tangible outcomes. Infosys Topaz drives organization-wide synergies by reimagining user personas, data architecture and engineering blueprints for the future. It also helps build self-supervisory capabilities from harnessing enterprise knowledge with generative AI. A British bank used Infosys Topaz to transform over 2,000 customer service processes to operate in near real-time instead of a week. For a financial services firm, 50% reduction in the time relationship managers were spending on credit review process was achieved. A global home appliances and vehicle components seller was able to reduce its data analytics cost by 50% while increasing accuracy by 25%. A global cosmetics company wanting to enhance its product visibility and cultivate a favorable product perception in e-retail domain was able to effectively harness data intelligence across the value chain in driving higher e-retail sales growth.

Infosys also happens to be one of the largest users of GitHub Co-pilot, the gen AI tool for enhancing developer productivity. Recently we partnered with GitHub in launching the first GitHub CoE at our Bengaluru campus. The center of excellence (CoE) is designed to enhance the speed and efficiency of software development by integrating GitHub Copilot across Infosys’ developer teams and extending its capabilities to the clients.

Q4. What are the key factors organizations should consider when adopting generative AI for better productivity and customer offerings?

Large scale adoption of GenAI will require enterprises to consider and address key factors such as:

1. Responsible AI: Establishing the required AI Guardrails that clearly lay out the dos & don’ts and provide clarity to teams in adoption and usage. While many organizations are running gen AI proof-of-concepts, there is a degree of hesitancy in scaling these due to concerns related to security and ethics. The AI regulatory environment is still evolving and organizations will have to keep a close watch on the same, ensuring that they understand and comply with regulations. Infosys Responsible AI suite of offerings and services, part of Infosys Topaz, is designed to help enterprises navigate the complex technical, policy, and governance challenges related to embedding strong foundations of Responsible AI across the organization. These offerings are also helping Infosys in its AI journey.
2. Change Management: Scaling gen AI adoption will require significant change management across the organization. Coaching and training employees and providing them the required psychological safety will help in addressing their fears and apprehensions and pave the way for adoption. Organizations will have to proactively call out the ‘Elephant in the Room’ – the question around AI taking over jobs! People enablement will be a critical success factor.
3. Business value focus: Gen AI can throw-up many possibilities/use cases and it is easy to get lost in the same! Organizations will need to lay down a prioritization/decisioning criteria that helps in prioritizing the use cases based on clear identification of the problem statement and the associated cost-benefit analysis. Business value articulation of identified use cases will be the key.
4. Choice of LLMs: The LLMs have mushroomed over the last 18 months, both paid and open source. Organizations will have to decide on their strategy with regards to the choice of models, guided by the associated costs and potential benefits. Our view is that a hybrid approach will be better suited – a combination of open source and paid models.
5. Operating Model: Last but not the least, organizations will have to ensure that they have the right operating model, which is conducive to adoption and scaling of gen AI. Multiple elements have to come together to make this a success, such as – product and platform centric approach, human centric design capabilities with a focus on end-user experience, data first approach to design of products & services and a connected data eco-system, AI-driven automation and advanced software engineering practices and AI-enabled digital talent pool. The Infosys Digital Operating Model (DOM) is one such reference operating construct that brings all these elements together to be an AI-first enterprise.

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