Archana Joshi, Head – Strategy (BFS and EnterpriseAI), LTIMindtree – Interview Series

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Archana Joshi brings over 24 years of experience within the IT services industry, with expertise in AI (including generative AI), Agile and DevOps methodologies, and green software initiatives. She currently leads growth strategies and market positioning for the Enterprise AI service line and the Banking and Financial Services Business Unit at LTIMindtree. Joshi has worked with Fortune 100 clients across various geographies and is a daily speaker at industry forums and events.

LTIMindtree is a worldwide technology consulting and digital solutions company that works with enterprises across various industries to support business model evolution, innovation, and growth through digital technologies. Serving over 700 clients, LTIMindtree provides domain and technology expertise aimed toward enhancing competitive differentiation, customer experiences, and business outcomes in an increasingly interconnected world.

Given your extensive experience in transforming IT services across various organizations, how has your personal leadership style evolved at LTIMindtree, particularly in driving the adoption of Generative AI?

With over 20 years of experience in IT Services, I even have dedicated my profession to driving transformative technology solutions for patrons, be it Agile/DevOps or generative AI (GenAI). At LTIMindtree, my focus is on empowering organizations to leverage GenAI for strategizing and executing their digital transformation journeys. I prioritize customer-centric strategies, working closely with clients to grasp their unique challenges and deliver tailored AI solutions that drive business value. As the pinnacle of strategy, I would like to collaborate with teams across various departments to advertise GenAI adoption and stay informed about latest developments to guide my decisions. GenAI processes vast amounts of knowledge to supply actionable insights. This capability is especially helpful for a data-oriented leader like me, who values evidence-based strategies.

For instance, every morning once I start my day with GenAI-based copilots to assist me understand the highest items that need my attention or provide insights to create reports that I can share with my team on adoption. The truth is, I often say throughout the team that GenAI-based copilots have essentially change into integral members of our team, very similar to trusted wingmen. They support us by providing beneficial insights, automating tasks and keeping us aligned with our strategic goals.

How is Generative AI reshaping traditional IT service models, particularly in industries which were slower to adopt digital transformation?

GenAI is revolutionizing traditional IT service models across all industries by significantly enhancing IT developer productivity. From co-pilots that generate code to synthetic data for testing and automating IT operations, every facet of IT is being transformed. Consequently, the main target of IT service models is shifting from cost-driven to efficiency- and impact-driven approaches. Which means that the worth of IT services is now measured by their ability to deliver tangible outcomes moderately than simply cost savings. This shift can be resulting in latest forms of work in IT services, akin to developing custom models, data engineering for AI needs and implementing responsible AI.

Just 18 months ago, these services weren’t the norm. Even in heavily regulated industries like healthcare and financial services, where legacy systems are prevalent, the worth of GenAI in improving operational efficiency is increasingly recognized.

Our own research at LTIMindtree, titled “The State of Generative AI Adoption,” clearly highlights these trends. In healthcare, we’re seeing GenAI make a big effect by automating things like medical diagnostics, data evaluation and administrative work. This helps doctors and healthcare providers make quicker, more accurate decisions—though adoption stays cautious as a consequence of strict compliance and regulatory frameworks. In financial services, GenAI enhances risk management, fraud detection and customer support by automating manual tasks. Nevertheless, the sector’s adoption is driven by concerns around risk, governance and sensitive data.

Are you able to share specific examples of how LTIMindtree has successfully integrated GenAI into traditional IT workflows to drive efficiency and innovation?

At LTIMindtree, we have now a 3-pronged strategy towards AI. The philosophy of “AI in Every little thing, Every little thing for AI, AI for Everyone” underscores our commitment to integrating AI across all facets of our operations and services. This approach ensures that AI will not be just an add-on but a core component of our solutions, driving innovation and efficiency.

Customers are AI to enhance efficiency across the board. From reducing hours spent on repetitive, time-consuming tasks to scaling operations and improving the reliability of business processes, AI is becoming a core a part of their strategy. Our engineers are focused on integrating AI copilots into their workflows, covering all the things from coding, testing, and deployment to software maintenance.

For instance, in a transformational move for a Fortune 200 company, we have employed GenAI-based copilots to convert large stored procedures into Java, enabling their modernization journey. We recently worked with a big insurance company that desired to automate its data extraction processes. They were facing scalability and accuracy issues with their manual approach. So, our team developed a companion bot, which now helps process multiple documents, extracting critical information like risk, eligibility, coverage and pricing details. This has significantly reduced the time it takes them to file product offers and manage various coverages.

With the rapid adoption of GenAI across various sectors, what are among the ethical considerations enterprises ought to be mindful of, and the way does LTIMindtree ensure responsible AI use?

The evolution of AI is promising but in addition brings many corporate challenges, especially around ethical considerations in how we implement it.

At LTIMindtree, we have now an AI council comprising cross-functional experts from AI, security, legal, data privacy, and various industry verticals. This council has established AI assurance frameworks and collaborates with industry bodies on AI regulatory guidelines. Moreover, it really works with teams implementing AI to validate their ethical risk postures.

To effectively implement GenAI, we have now established a set of core ethical principles aligned with corporate values, addressing fairness, accountability, transparency and privacy. This requires executive sponsorship and support from legal and security teams. Next, technical interventions are incorporated into our internal processes that concentrate on high-quality, unbiased data, with measures to make sure data integrity and fairness. Fostering an ethical AI culture involves continuous training on AI capabilities and potential pitfalls, akin to AI hallucinations. Finally, regular audits and updates of AI systems are done to handle vulnerabilities and make sure the accuracy of AI outputs. This comprehensive approach ensures that GenAI is implemented responsibly and effectively, driving business value while maintaining ethical standards.

How does LTIMindtree’s AI platform address concerns around AI ethics, security, and sustainability?

As we proceed to roll out latest AI tools and platforms, we must ensure they meet our standards and regulations across the technology’s use. Along with maintaining data quality to supply accurate and unbiased outputs, we’re committed to meeting high standards for security and sustainability.

Our platform is built across the principles of responsible and mindful AI. When it comes to sustainability, we’re aware of the growing energy demand required to support AI models, from training to its continued operation. We’ve adopted a reduce, reuse and recycle approach to AI to handle the carbon footprint and the importance of making environmentally friendly and sustainable AI practices. Through this process, we concentrate on reducing the parameters by specializing in smaller, more specific large language models (LLMs) that may efficiently address the needs of enterprise applications while making a smaller carbon footprint. Moreover, we repurpose data for various applications and use cases to avoid redundancies and reuse mechanisms and prompts that may be used for similar tasks to advertise efficiency and sustainability. We’re also quantized models to cut back memory footprint, receive faster inference, reduce cost and construct sustainable applications.

As I discussed earlier, security is a key concern with the usage of any AI tool or application. At LTIMindtree, we have now not only prioritized data security and fair usage, but we have now made it a cornerstone of our AI strategy. We’ve also incorporated 50+ best-in-class moderation APIs and responsible AI frameworks from third party providers just like the Nvidia Nemo guardrails and the IBM Watson Governance models. Our platform efficiently manages data while factoring in privacy, security, ethical use and sustainability by leveraging sound governance measures and a well-built framework.

How is GenAI influencing Agile project management at LTIMindtree? What benefits does it bring to Agile teams, and are there any trade-offs?

Integrating GenAI into Agile practices is transforming how teams work. It boosts productivity, streamlines processes, and opens latest avenues for innovation. Because the software development landscape evolves, we’re leveraging GenAI to automate those repetitive tasks that may bathroom teams down. This shift allows them to focus more on creative problem-solving and innovation—exactly where they ought to be.

Once we start integrating GenAI into Agile frameworks, there are just a few key points we would really like to emphasise. First, it can be crucial to grasp the character of AI tools and their potential impact on team collaboration. For example, Agile teams must be mindful of the constraints of those tools. They depend on pre-existing data moderately than providing real-time insights, so it is important to validate and refine their outputs.

Our AI native DevOps leverages cutting-edge technology like knowledge graphs, custom SLMs (small language models) together with software development lifecycle (SDLC) agents. This has the potential to attain 35-50% efficiency in productivity across the Agile-DevOps cycle for an enterprise. It helps an Agile pod during user story creation, sprint planning, code generation to the CI/CD pipelines and subsequent incident management.

With AI transforming the IT industry, how is LTIMindtree addressing the necessity for brand new talent and skill sets? What initiatives have you ever led to make sure your teams are equipped for the AI-driven future?

The rise of revolutionary technologies within the IT industry has highlighted a spot between the abilities our workforce currently has and what’s needed to thrive in an AI-driven world. GenAI has the potential to completely reshape the every day roles of many employees, so preparing for brand new skills and roles is important.

At LTIMindtree, we’re taking the lead on this transformation by specializing in upskilling our employees to fulfill these emerging demands. We’ve our GARUDA initiative, specifically designed for training and onboarding teams in GenAI and enterprise AI. We recognize that effective training and academic resources are crucial, and we’re committed to making a culture of continuous learning.

Our training strategies include data-driven adaptations, real-time online learning, advanced reinforcement learning, transfer learning and feedback loops. This manner, we be sure that our teams aren’t just keeping pace with change but are genuinely equipped to excel of their evolving roles. It’s an exciting time, and we’re all on this journey together.

Along with this, we have now tied up with seven academic institutions to equip future talent on AI skills. Here we’re involved right from curriculum design to administering the curriculum, in addition to equipping the professors via train-the-trainer approaches.

How do you see the role of human talent evolving in an increasingly AI-driven workplace, and what steps are you taking to organize your workforce for this shift?

Previously, there have been distinct roles for creative individuals and technology experts. Nevertheless, there is a noticeable shift towards adopting, mainstreaming and scaling revolutionary content creation techniques, blurring the lines between creativity and technology. This integration is impacting various industries, where the traditional separation between creative roles and technology jobs is step by step diminishing. While promising, this evolution comes with its challenges that indicates a considerable shift of concentrate on reskilling as an important for capitalizing on AI’s advantages.

The massive conversation now’s how one can make this GenAI change stick and scale. Here’s where change management becomes crucial. It requires a structured approach and a dedicated team to oversee the AI adoption process. People, not only technology, are at the center of successful GenAI adoption. It may be a robust tool for empowerment, even amongst those that initially perceive it as a threat. Forrester forecasts that by 2030, just one.5% of jobs can be lost to GenAI, while 6.9% can be influenced by it. Due to this fact, leaders must prioritize transparency and motivate their workforce in regards to the way forward for AI within the workplace.

AI is changing job roles across the IT sector, automating on a regular basis tasks, and placing emphasis on strategic decision-making and sophisticated problem-solving. At LTIMindtree, we imagine this can be a mindset shift and hence have established a dedicated central initiative GARUDA – that focuses on this modification adoption. The GARUDA initiative will not be nearly role-based training and upskilling but in addition on creating AI ambassadors that may drive this adoption across various layers. We’re also working with our HR function to have a look at impacts on various roles throughout the organization, together with their profession paths and associated rewards and recognition. Today at LTIMindtree we have now three levels of upskilling pathways – foundation, practitioner and expert. Over 50,000 of our associates have already accomplished the foundational skilling initiatives that include concepts of AI to the usage of copilots in addition to responsible AI considerations.

What are among the most revolutionary GenAI applications you have seen recently, and where do you see the technology headed in the following 3-5 years?

We are only scratching the surface of what GenAI can do, and I’m thrilled about its potential across the IT industry and beyond. As more sectors jump on board, I find myself particularly enthusiastic about their applications to remodel human lives.

At LTIMindtree, we have now partnered with the UN Refugee Agency to reinforce its crisis response capabilities using GenAI. This collaboration goals to speed up on-the-ground crisis response, providing timely aid and support to refugees in need. The revolutionary use of technology helps bring hope and relief to vulnerable populations during their biggest times of need. For an American life insurance company, we developed a GenAI solution that translates spoken words in real-time, significantly improving the shopper experience. By bridging communication gaps, this technology fosters higher understanding and connection between people, bringing us closer together and ensuring that language barriers now not hinder effective experiences.

Looking ahead, Agentic AI will enable autonomous task performance and decision-making. By 2027, industry-specific models will dominate, synthetic data use will rise, and energy-efficient implementations will grow. Multimodal models integrating text, image, audio and video inputs will enhance capabilities, driving significant economic impact and innovation. GenAI is poised so as to add as much as $4.4 trillion to the worldwide economy annually, revolutionizing industries and driving efficiency and sustainability, retail, healthcare and life sciences.

The truth is that each workplace can be touched by GenAI in some capability, becoming an element of our on a regular basis operations. As we proceed this transition, I cannot wait to see the way it evolves and what innovations will come next.

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