AiThority Interview with Ashok Krish, Global Head, AI.Cloud Advisory & Consulting at Tata Consultancy Services

Hi Ashok, welcome to our AiThority Interview Series. Being a seasoned technology expert, please tell us about your journey so far.  

Thank you for this opportunity to speak with AiThority. I head the AI.Cloud consulting and advisory group globally at Tata Consultancy Services (TCS), the world’s second largest IT services provider. AI.Cloud is a new business unit launched last year as we saw customers reacting to the GenAI hype. The group brings together the best of TCS capabilities and relationships when it comes to the hyperscalers and cloud services with long-term experience in AI/ML across the company. In my role today, I help the AI.Cloud team work across industries to guide companies in adopting an AI-first business strategy, bringing GenAI, cloud, and data into their business value chains.

Prior to AI.Cloud’s creation, I ran the TCS Digital Workplace for 5 years, helping customers implement the future of work and the future of the employment experience. I have been with TCS for over 25 years, joining straight from university, like many TCSers. Working at TCS means being at the forefront of every new trend. When I joined TCS at the turn of the millennium, software was genuinely seen as something that is going to save the world. In my time here, I’ve had the opportunity to be part of every tech wave, the first push to digital, the introduction to mobile, big data, social networking, etc. All these factors have helped to shape business and the internal employee experience.

Who are your customers and how do they leverage your products/ services?  

As the second largest IT services provider in the world, our customers span across every industry, every vertical. Most recently, we announced a deal with Xerox to consolidate its technology services, migrate legacy data centers to the cloud, deploy a cloud-based Digital ERP, and incorporate generative artificial intelligence (GenAI) into operations to help drive sustainable growth.

Customers choose TCS for our domain experience, proven ability to deliver services, deep relationships with the 3 hyperscalers, and growing in-house innovation to make AI relevant for businesses. Our view is that, fundamentally, GenAI represents the exponential tech disruption for knowledge work. However, the true value of this disruption can only be delivered if you have the basics in place. You need cloud storage and services, quality data, large-scale data systems. This is the foundational digital transformation layer. A starting point for our work with organizations today is to help them on their journey to create and manage a hybrid multicloud environment.

With a powerful and responsive IT environment in place, the next step is to start building purpose-built AI models. We encourage clients to prioritize which business functions benefit the most from automation. In every industry, you find specific use-cases around knowledge work, where automation can take the load off humans and enable them to be more strategic, whether that is using AI for drug discovery or for reviewing resumes in an HR department.

You have been with TCS for the past 25 years, as a Tech Leader, what industries do you think would be fastest to adopt Analytics and AI/ML with smooth efficiency?  

The speed and efficiency with which industries adopt these technologies depends on several factors. Broadly, I see industries that are moving forward quickly falling into two categories: those that have historically been advanced in cloud adoption, and those that have the most impactful use-cases, and thus, the most interest and investment in making it happen.

In the first category, industries that tend to be mature on cloud adoption, we see retail, travel, and CPG businesses succeeding in AI implementations. They have already done much of the hard work to build hybrid multicloud environments, host their data in the cloud, and overall ensure that their data houses are in order.

In the second category, we see industries like banking and financial services and healthcare taking advantage of AI. These industries are looking to AI to create major business impact where there is potential to harness huge amounts of data. As a result, they can use large AI investments to leapfrog over legacy issues and get AI in action more quickly.

While some industries will lead – a recent TCS survey identified the industries most likely to complete AI projects as Life Sciences, Communications, Media and Information Services, and Banking, Financial Services, and Insurance – the GenAI era is democratic. There are AI use cases in every industry and GenAI is making it possible for everyone to get involved.

What is the impact of Generative AI on the workplace?  

When we think about conversations around GenAI and the workplace, it’s one of those beautiful things where everyone will underestimate it in the short term and overestimate it in the long term. In general, we see the rollout of GenAI in the workplace happening across three phases of use-cases: Assist, Augment, Transform.

Assist is what’s happening now – in this context, a human uses AI as an assistant to automate some aspects of a task. We see it in marketing, for instance. A marketer can use an AI assistant to get started on a draft for some marketing copy. These small assistants provide value, but they are really a starting point. The real breakthrough comes when AI can solve something completely – when it works alongside a human, not for a human.

Augment is the next phase, where AI is actually doing a lot of the work. This is one of the promises of AI in IT, where 80-90% of issues can be solved by having the right knowledge – something GenAI excels at. The human is still involved but their primary role is to manage the AI, ensure its accuracy, and determine what problems it should be set on.

Transform is the future state where the power of AI means the very definition of work needs to be reimagined. In this phase, every industry rethinks job roles and the value of human creativity comes to the fore.

The Generative AI era has the potential to make or break companies. What factors contribute to the difficulty of understanding and deploying AI for operational effectiveness?  

What we’ve learned from over 300 GenAI deployments so far is that there is very high interest in implementing GenAI but difficulties in scaling the services at the level needed to make a true difference. Our AI for Business Study showed that most executives are excited and optimistic about AI, only 4% have used it to transform their businesses.

There are a few issues that organizations need to overcome to understand and deploy AI for true operational effectiveness:

Accuracy of models – the truth is, we are still in the earlier days when it comes to LLMs and small language models. Organizations need to understand that models are not yet fully accurate and need to have consistent oversight and human management. While today there are many things GenAI can do, there is room for improvement. Organizations should not overestimate what LLMs can do. We have a lot of engineering still to do in this space.

Security – as AI is accessing large amounts of data, data security and governance of course must be a concern. New tools, approaches, and training are required in order to ensure that data is secure no matter where it is being accessed or being used.

Change management – rolling out AI applications requires a new change management mechanism. AI operating at this level in the workspace is a completely new thing for most workers. Leaders and managers must deeply think about the impact of introducing a new application. The human/AI is dynamic and different from the human/tool relationship in the past. We cannot rely on old change management systems to approach an all-new challenge.

To help clients manage these challenges, TCS’ Design for AI practice focuses on how to build human/AI interaction systems, how to involve specialists, and how to turn specialists into creative generalists. We can’t have one size fits all approaches to how AI is adopted by businesses. In the medium to long-term, work will be transformed, companies will change how they hire, how they staff, how they train. The number 1 thing that will distinguish companies from each other in the future is how effectively they have changed and transformed to adopt AI while also embracing the human element.

How should young technology professionals train themselves to work better with Automation and AI-based tools?  

Young professionals – of all kinds, not just technology professionals – need to rethink the types of skills they develop, and how they talk about the skills they have. Remember that technology in the past 100 years was augment and replace: machines replaced human hands, the first generation of software replaced analytics people, etc. But now AI and automation is available to everyone as a skill. So, professionals need to rethink the value they offer. The resumes/CVs of the future will not just be about industries and experience, but what AI platforms someone is skilled at using, and what they can accomplish through AU and automation.

In addition to building expertise around using AI platforms, the other aspect young professionals need to focus on are skills that make humans more human. Do not worry about developing deep domain expertise in a single subject. Instead, read broadly, expose yourself to new thinking, and aim to become a creative generalist – an expert at applying creativity and human ingenuity to any problem.

What significant AI tools has the company developed over the last few years?  

TCS has developed a large range of AI tools across industries and use-cases, from products like TCS Optumera™, an AI-powered retail-connected strategic intelligence platform, to ignio AIOps, a solution that offers class-leading observability and monitoring features that derives actionable insights from machine data to TwinX, an AI-powered digital twins business simulation and experimentation platform.

Most recently, TCS AI.Cloud has launched WisdomNext™, an industry-first GenAI Aggregation Platform. The platform aggregates multiple GenAI services into a single interface, making it possible for organizations to rapidly experiment with and adopt AI capabilities while also lowering costs. The platform is the result of our conversations with customers who want to deploy AI in their businesses but need flexibility and lower barriers to entry. Uniquely, WisdomNext allows customers to compare and contrast vendor, internal, and open-source LLM models in one interface.

What are your predictions for AI/ML and other smart technologies heading beyond 2024?  

1 – language models will flatten in terms of size and capabilities. We are reaching the peak of what large language models can do. As a result, language models will become more accurate, faster, and cheaper. Language models will become a baked-in commodity to products and services, and we will see small language models proliferate

2 – closer correlation of AI with green energy. AI is very energy intensive. As we scale its use in business and society, we will need to further scale energy production to support this ecosystem. As a result, I believe we will see faster adoption of solar and wind energy to supplement legacy energy sources and meet the quickly expanding energy demands of AI.

3 – In 5-10 years, quantum as architecture will arrive and deliver a huge leap in AI value. The massive compute power of quantum will create new utility with AI platforms that are extremely responsive, secure, and powerful.

Thank you Ashok, it was fun!