Effortless 2024 was about how AI and Design put together are redefining SaaS 2.0. DevRev’s flagship conference brought together diverse experts—from entrepreneurs and SaaS leaders to biologists, diplomats, and designers—to explore how AI is reshaping products and customer experiences.
AI’s transformative power took center stage in a compelling session at our event. Dr. Harrick Vin, CTO of Tata Consultancy Services, joined Vikram Seth from DevRev to explore AI’s impact on workflows and productivity.
Their discussion, drawing from Dr. Vin’s extensive experience, offered valuable insights into organizational transformation in the AI era.
Dr. Vin’s career journey
Dr. Vin’s career journey spans academia, startups, and tech giants. From IIT Bombay to a PhD at UC San Diego, followed by 15 years as a professor at UT Austin, he made an unexpected pivot back to India.
There, he became Chief Scientist at TCS in Pune, overseeing their largest R&D center. This diverse background provided a unique lens for discussing AI’s impact on his career and the global business landscape
Efficiency and Gen AI
During the session, Dr.Vin shared some intriguing stats from a LinkedIn post he had read, stating that Tata Group, across 23 of its large companies, had managed to grow revenue by 11%, profit by 35%, and valuation by 45%—all while keeping the headcount relatively flat. This was not just a trend at TCS, but at other major Indian companies like Reliance and Mahindra as well. Eager to know how this efficiency was achieved? The answer lies largely in the role of technology, particularly automation and AI.
The three stages of AI maturity
Dr. Vin introduced a framework that explained the different stages of AI maturity within organizations. These stages highlight how AI progresses from basic assistance to complete transformation of workflows:
1. Assist phase:
In the early stages of AI integration, the technology primarily assists people in their daily tasks. As he explained, “Technology helps people do what they’re doing, maybe slightly faster, improving productivity by automating part or all of the tasks.” This is where most organizations are today, using AI to handle repetitive tasks or to speed up certain processes.
2. Augment phase:
The next level of AI maturity, which he identified as where the majority of progress is being made, is the augmentation phase. AI not only assists but also enhances the decision-making process. He noted that in many fields, there’s a huge gap between the average worker and elite performers—up to 100 times the difference in productivity. AI, when used effectively, can help bridge this gap by improving the decisions workers make, rather than just speeding up the tasks they perform.
3. Transform phase:
The final phase is transformation, where AI fundamentally rethinks and redesigns entire value chains. He provided an example using customer service:
“The assist phase uses AI for sentiment analysis and report generation; in the augment phase, AI helps an agent with next best actions. But in the transform phase, we ask, ‘Why is the call coming in the first place?’ Can we reimagine the contact center to be near-zero inbound contacts, turning it from a cost center into a value center?”
The 100x gap in terms of the quality of work produced by the average worker and elite performers persists despite the increase in automation, because decisions—rather than tasks—are still driven by the tacit knowledge that people carry. The problem is that the tacit knowledge of today becomes obsolete quickly, and the elite of today may be the average of tomorrow.
These three stages, according to Dr.Vin, represent the journey organizations must take as they evolve with AI. Some are still in the assist phase, while others are moving toward full transformation, redefining value in the process.
Automation vs. Upskilling: Finding the balance
One of the key challenges organizations face as they adopt AI is balancing automation with upskilling their workforce. Dr. Vin noted that while automation reduces the effort required for certain tasks, it simultaneously requires a shift in the role of employees. He emphasized that the future of work will be “inherently hybrid,” where humans and machines collaborate closely. He explained that people’s roles would evolve from “doers of work” to those who train and supervise machines, engage in critical thinking, and drive creativity.
This shift demands significant organizational change, not just technological advancement. He pointed out that AI adoption is “not just a tech problem—it’s an organizational change management problem.” As machines become more capable, the roles of employees must adapt continuously. The biggest challenge for organizations, he explained, is not the technology itself but managing this change.
The shrinking half-life of skills
Dr. Vin noted that the time it takes for a skill to lose half its value has decreased from 30 years to just six years—and it’s shrinking fast. This rapid pace of change means that workers will need to be retrained far more frequently. In a company the size of TCS, this means retraining around 100,000 people every year, making the organization one of the largest “universities” in the world.
However, the challenge isn’t just about retraining large numbers of people. It’s about personalized learning.
As he put it,
It’s not about saying let’s train everybody on prompt engineering because that may or may not be the right thing to do.
Instead, the goal is to create personalized learning journeys that fit the unique needs of each employee, leveraging AI to help tailor these experiences.
AI project success: Overcoming the challenges
The reality is that many AI projects are still failing to deliver on their potential. In fact, Dr. Vin and Vikram discussed statistics showing that only around 15% of AI pilots go into production and deliver value. He attributed this low success rate to two main factors:
1. Misaligned objectives:
Many AI projects are initiated by tech teams, which can lead to a scenario where, in Dr. Vin’s words, “we have a hammer and the hammer is going around looking for nails.” In other words, teams are applying AI without a clear business value in mind.
2. The complexity of building solutions:
AI solutions often require blending a variety of techniques, and knowing when not to use AI is just as important as knowing when to use it. He explained, “Building high-quality solutions is still very much an art form,” and democratizing AI can lead to misuse if not handled carefully.
The key takeaway here is that successful AI implementation requires alignment across all parts of the organization—business, technology, risk, compliance, and security teams must work together to define and execute a clear strategy.
Looking ahead: The importance of learning to earn
As the session drew to a close, Dr.Vin shared his thoughts on the future of AI and the skills needed to thrive in an increasingly automated world. He emphasized that the ability to “learn how to learn” will be one of the most critical skills for the future workforce. As he pointed out, the “why” and “what” of work will remain human-driven, while the “how” will increasingly be handled by machines.
This shift raises important questions about the future of education. Will traditional four-year degrees focus on teaching specific skills, or will they shift toward teaching students how to learn and adapt in a world where the half-life of skills is so short? He suggested that colleges might need to focus more on critical thinking and problem-solving, which will become more important than learning specific skills.
Preparing for the hybrid future
The session provided an overview of the challenges and opportunities presented by AI. From the three stages of AI maturity to the balance between automation and upskilling, it’s clear that organizations must evolve rapidly to keep pace with technological advancements. Dr. Vin’s insights highlighted that while AI offers tremendous potential, its successful adoption requires careful alignment of technology with business strategy, continuous learning, and organizational change management.
In the end, the future is hybrid, and success will depend on how well we can learn, adapt, and collaborate with machines.
Forward-thinking organizations are leveraging AI to redefine their operational workflows and improve workforce productivity. This transformation is reshaping how industry leaders approach business agility and organizational autonomy.
See how DevRev is helping revolutionize operational workflows and enhance workforce productivity. Book a demo.
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