Archana Joshi brings over 24 years of experience in 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 regular speaker at industry forums and events.
LTIMindtree is a global 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 at 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 two decades of experience in IT Services, I have dedicated my career to driving transformative technology solutions for customers, 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 understand their unique challenges and deliver tailored AI solutions that drive business value. As the head of strategy, I need to collaborate with teams across various departments to promote GenAI adoption and stay informed about new developments to guide my decisions. GenAI processes vast amounts of data to provide actionable insights. This capability is particularly beneficial for a data-oriented leader like me, who values evidence-based strategies.
For example, every morning when I start my day with GenAI-based copilots to help me understand the top items that need my attention or provide insights to create reports that I can share with my team on adoption. In fact, I often say within the team that GenAI-based copilots have essentially become integral members of our team, much like trusted wingmen. They support us by providing valuable insights, automating tasks and keeping us aligned with our strategic goals.
How is Generative AI reshaping traditional IT service models, particularly in industries that have been 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 focus of IT service models is shifting from cost-driven to efficiency- and impact-driven approaches. This means that the value of IT services is now measured by their ability to deliver tangible outcomes rather than just cost savings. This shift is also leading to new types of work in IT services, such as developing custom models, data engineering for AI needs and implementing responsible AI.
Just 18 months ago, these services were not the norm. Even in heavily regulated industries like healthcare and financial services, where legacy systems are prevalent, the value 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 impact by automating things like medical diagnostics, data analysis and administrative work. This is helping doctors and healthcare providers make quicker, more accurate decisions—though adoption remains cautious due to strict compliance and regulatory frameworks. In financial services, GenAI enhances risk management, fraud detection and customer service by automating manual tasks. However, the sector’s adoption is driven by concerns around risk, governance and sensitive data.
Can you share specific examples of how LTIMindtree has successfully integrated GenAI into traditional IT workflows to drive efficiency and innovation?
At LTIMindtree, we have a 3-pronged strategy towards AI. The philosophy of “AI in Everything, Everything for AI, AI for Everyone” underscores our commitment to integrating AI across all facets of our operations and services. This approach ensures that AI is not just an add-on but a core component of our solutions, driving innovation and efficiency.
Customers are looking at AI to improve 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 part of their strategy. Our engineers are focused on integrating AI copilots into their workflows, covering everything from coding, testing, and deployment to software maintenance.
For example, in a transformational move for a Fortune 200 company, we’ve employed GenAI-based copilots to convert large stored procedures into Java, enabling their modernization journey. We recently worked with a large insurance company that wanted 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 some of the ethical considerations enterprises should be mindful of, and how does LTIMindtree ensure responsible AI use?
The evolution of AI is promising but also brings many corporate challenges, especially around ethical considerations in how we implement it.
At LTIMindtree, we have 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. Additionally, it works with teams implementing AI to validate their ethical risk postures.
To effectively implement GenAI, we have 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 focus on high-quality, unbiased data, with measures to ensure data integrity and fairness. Fostering an ethical AI culture involves continuous training on AI capabilities and potential pitfalls, such as AI hallucinations. Finally, regular audits and updates of AI systems are done to address vulnerabilities and ensure 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 continue to roll out new AI tools and platforms, we must ensure they meet our standards and regulations around the technology’s use. In addition to maintaining data quality to provide accurate and unbiased outputs, we are committed to meeting high standards for security and sustainability.
Our platform is built around the principles of responsible and mindful AI. In terms of sustainability, we are aware of the growing energy demand required to support AI models, from training to its continued operation. We have adopted a reduce, reuse and recycle approach to AI to address the carbon footprint and the importance of creating environmentally friendly and sustainable AI practices. Through this process, we focus on reducing the parameters by focusing on smaller, more specific large language models (LLMs) that can efficiently address the needs of enterprise applications while creating a smaller carbon footprint. Additionally, we repurpose data for various applications and use cases to avoid redundancies and reuse mechanisms and prompts that can be used for similar tasks to promote efficiency and sustainability. We are also looking at quantized models to reduce memory footprint, receive faster inference, reduce cost and build sustainable applications.
As I mentioned earlier, security is a key concern with the use of any AI tool or application. At LTIMindtree, we have not only prioritized data security and fair usage, but we have made it a cornerstone of our AI strategy. We have also incorporated 50+ best-in-class moderation APIs and responsible AI frameworks from third party providers 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 advantages 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 new avenues for innovation. As the software development landscape evolves, we are leveraging GenAI to automate those repetitive tasks that can bog teams down. This shift allows them to focus more on creative problem-solving and innovation—exactly where they should be.
When we start integrating GenAI into Agile frameworks, there are a few key points we would like to emphasize. First, it is important to understand the nature of AI tools and their potential impact on team collaboration. For instance, Agile teams need to be mindful of the limitations of these tools. They rely on pre-existing data rather than providing real-time insights, so it is essential to validate and refine their outputs.
Our AI native DevOps leverages cutting-edge technology like knowledge graphs, custom SLMs (small language models) along with software development lifecycle (SDLC) agents. This has the potential to achieve 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 need for new talent and skill sets? What initiatives have you led to ensure your teams are equipped for the AI-driven future?
The rise of innovative technologies in the IT industry has highlighted a gap between the skills our workforce currently has and what is needed to thrive in an AI-driven world. GenAI has the potential to completely reshape the daily roles of many employees, so preparing for new skills and roles is essential.
At LTIMindtree, we are taking the lead in this transformation by focusing on upskilling our employees to meet these emerging demands. We have our GARUDA initiative, specifically designed for training and onboarding teams in GenAI and enterprise AI. We recognize that effective training and educational resources are crucial, and we are committed to creating 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 way, we ensure that our teams are not just keeping pace with change but are genuinely equipped to excel in their evolving roles. It is an exciting time, and we are all on this journey together.
In addition to this, we have tied up with seven academic institutions to equip future talent on AI skills. Here we are involved right from curriculum design to administering the curriculum, as well as 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 prepare your workforce for this shift?
In the past, there were distinct roles for creative individuals and technology experts. However, there’s a noticeable shift towards adopting, mainstreaming and scaling innovative content creation techniques, blurring the lines between creativity and technology. This integration is impacting various industries, where the conventional separation between creative roles and technology jobs is gradually diminishing. While promising, this evolution comes with its challenges that indicates a substantial shift of focus on reskilling as an essential for capitalizing on AI’s benefits.
The big conversation now is how to 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 just technology, are at the heart of successful GenAI adoption. It can be a powerful tool for empowerment, even among those who initially perceive it as a threat. Forrester forecasts that by 2030, only 1.5% of jobs will be lost to GenAI, while 6.9% will be influenced by it. Therefore, leaders must prioritize transparency and motivate their workforce about the future of AI in the workplace.
AI is changing job roles across the IT sector, automating everyday tasks, and placing emphasis on strategic decision-making and complex problem-solving. At LTIMindtree, we believe this is a mindset shift and hence have established a dedicated central initiative GARUDA – that focuses on this change adoption. The GARUDA initiative is not just about role-based training and upskilling but also on creating AI ambassadors that can drive this adoption across various layers. We are also working with our HR function to look at impacts on various roles within the organization, along with their career paths and associated rewards and recognition. Today at LTIMindtree we have three levels of upskilling pathways – foundation, practitioner and expert. Over 50,000 of our associates have already completed the foundational skilling initiatives that include concepts of AI to the usage of copilots as well as responsible AI considerations.
What are some of the most innovative GenAI applications you’ve seen recently, and where do you see the technology headed in the next 3-5 years?
We are just scratching the surface of what GenAI can do, and I am thrilled about its potential across the IT industry and beyond. As more sectors jump on board, I find myself particularly excited about their applications to transform human lives.
At LTIMindtree, we have partnered with the UN Refugee Agency to enhance its crisis response capabilities using GenAI. This collaboration aims to accelerate on-the-ground crisis response, providing timely aid and support to refugees in need. The innovative use of technology helps bring hope and relief to vulnerable populations during their greatest times of need. For an American life insurance company, we developed a GenAI solution that translates spoken words in real-time, significantly improving the customer experience. By bridging communication gaps, this technology fosters better understanding and connection between people, bringing us closer together and ensuring that language barriers no longer 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 to add up to $4.4 trillion to the global economy annually, revolutionizing industries and driving efficiency and sustainability, retail, healthcare and life sciences.
The reality is that every workplace will be touched by GenAI in some capacity, becoming a part of our everyday operations. As we continue this transition, I cannot wait to see how it evolves and what innovations will come next.
