Vice President, Growth Markets
Over the past year, generative AI has burst forth from beta builds to the mainstream. It has captured the imagination of people worldwide, to the extent that “prompt” is featured in Oxford’s ‘Word of the Year’.1
From generating sales and marketing collateral with Microsoft Copilot to programming and scripting with Codeium, users are embracing large language models (LLMs) universally. The convenience, speed and utility on offer underscore a tectonic shift as generative AI embeds itself into every enterprise touchpoint and process.
The business world is abuzz with the world of possibilities. According to a Bloomberg Intelligence report, the generative AI market size will grow to $1.3 trillion over the next ten years at a CAGR of 42%, mimicking the early hockey-stick years of Internet spending.2 Meanwhile, the rapid development of LLMs, together with increasingly sophisticated tooling, continue to drive a new technology paradigm, rooted in data mastery, automation, and orchestration.
Given the sizeable opportunity that the generative AI market represents, a plethora of new AI tools are landing on the market every week. Many are undeniably good, while some, as anyone who follows the news would know, have caused much reputational loss for companies with hallucinations, bias, or ethical issues filtering into the public domain.3
The question then becomes, how do you go about using these tools to transform knowledge work safely? The consideration bears more weight for technology services companies like Virtusa, where business models rely on evolving and incorporating genAI into our offerings and frameworks in a safe, secure and reliable way. We believe genAI accelerates productivity and innovation with a technology model that cuts across everything we do, sell and deliver.
With genAI growing so rapidly, does one rush it out or build it out to scale? The question is especially relevant when your clients are some of the largest in the world, where customer scale impacts millions of lives, not to mention operational resilience and reliability among the most mission-critical systems on the planet. At Virtusa, the answer to the “rush it or build it” conundrum was obvious: build. And build, with consideration, with care, with security baked in, and journey to what’s ahead together.
We believe that solutions can’t be slide decked; they aren't PowerPointed; they're engineered. Over the past year, our teams have traversed a multitude of generative AI use cases, from obvious applications like content creation to more complex data-related challenges related to clinical trials in healthcare. These have been looked at through the prism of value they can deliver for our clients, the most common adoption drivers being efficiency gains, customer insight, and revenue growth.
One of the more interesting use cases we’ve worked on is damage assessment for an insurance company, where our work involves refining and deploying large language models to accurately describe the likely liability from the images gleaned at an incident. Pinpointed solutions like this have emerged from working closely alongside client teams to identify how generative AI can be used in their value chain, rapidly prototyping it to realize early successes, and then scaling it for greater scale in a safe and secure way.
Bringing both imagination and execution to the table is critical. Being able to do so, however, requires huge commitments to resource training to stay equipped for the new landscape today and tomorrow. At Virtusa, we think about the execution early on and stay with you throughout your journey to the realized value.
A key piece of the puzzle is ensuring our talent can harness new technologies for client needs. Aligning talent to market demand calls for holistic upskilling across levels: engineers must learn the tools of the generative AI trade, architects must become adept at the risks while solutioning, and leaders must know the nuances of how the technology will behave in an enterprise environment and lay out the art of the possible.
Virtusa has honed its generative AI craft at all talent levels. Having vast experience in traditional AI has only served to accelerate the journey: our AI foundations are built around data mastery, deep learning, machine learning, and building the complex math models that power AI. When generative AI hit the market at breakneck speed, our talent was able to leverage traditional AI skills and pivot towards using LLMs for tasks like sentiment analysis in customer service or prompt engineering for targeted advertising.
Our early experiments were for our own operations at Virtusa, watching, observing, and learning how genAI performed for our organization. We developed and deployed a validation tool to improve job description quality. It worked by enhancing candidate attraction through intelligent job description updates from skills and validations. GenAI was deployed to compare skill demand, availability of talent, and recommend adjacent skill training.
Achieving technological maturity faster requires bringing everyone along on the journey, from coders to leaders. We have held multiple genAI hackathons across our global organization to offer hands-on experimentation and the chance to take risks and opportunities to our freshest talent. Our leaders, too, have scaled up with AI training from leading academia, so we can be truly people-ready for generative AI as we start turning the cogs towards market readiness.
The conversations on generative AI are now turning toward efficiency, engagement models, and orchestration. Our research at Virtusa suggests that 93% of leaders are optimistic that AI will enable them to accomplish more with what they have, driving productivity and innovation.4 They no doubt want to move the needle from experimentation to large-scale implementation. Keeping pace on this journey will require a partner who has the specialized skills that can cut across enterprise domains.
Virtusa set up a Generative AI Center of Excellence (CoE) early on to house varied specialists in different areas of expertise from across the company to be that partner of choice. Our CoE seeds new capabilities for industries from financial services to life sciences, and perfects our platform integrations from Vertex to Whylabs. The CoE also works in synergies with our partnerships for deep tech, crafting highly specialized applications such as using generative AI for clinical trial summarizations and working in tandem with Microsoft Azure teams or Google Gemini. More than 100 proof of concepts have moved to production with significant effort reduction for enterprise modernization work.
Our path to building an AI powerhouse continues because we chose method over madness. Rather than rushing to announce a suite of generative AI capabilities that were untested, unproven, and, frankly, from a cost perspective, unreliable, we carefully plotted the path from imagination to incubation to industry, skilling and scaling the right way.
We strongly believe that genAI will be ubiquitous in the enterprise, perhaps in as little as 12 months. It’s a bold statement but we see the potential and so do our clients. Every business must weigh preparedness over presentation before diving in with their chosen partners. We’re ready to answer the call with preparedness, because Virtusa’s next generation AI capabilities have been engineered. And when it’s engineered, it works the way it should. I look forward to sharing more on our generative AI story soon.
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