Santhana’s professional journey began in the Petroleum and petrochemical industry, where he contributed to building Reliance Industries' largest grassroots-level petroleum refinery. 

After earning a quant-focused MBA, Santhanakrishnan worked with GE, Genpact, and Infosys to lead Data & AI as a service line of KPO, Data Technologies, and Management Consulting business divisions.

INDIAai interviewed Santhanakrishnan to get his perspective on AI.

Given your experience in delivering analytics programs that drive value, what are the critical elements of a successful AI-driven transformation for an organization? 

A successful AI-driven transformation relies on three key elements. First is executive commitment and a clear understanding of AI’s potential, especially with generative AI (GenAI) technologies. AI is no longer niche—it can reshape business processes, so strong leadership is essential to driving change. 

Second, developing the necessary technological and people/process-oriented capabilities was important. This includes building expertise in data and AI and supporting people and processes. A key capability is business acumen—using AI to solve specific business problems. It often involves a structured approach called "business translation" or the "techno-functional" capability, which combines technical and business expertise to align AI solutions with business goals. 

The final element is adoption, change management, and value measurement. It focuses on assessing AI initiatives' success—Are they improving business users’ lives? What measurable value are they delivering? Qualitative and quantitative metrics are crucial for evaluating ROI and driving improvements. 

For a successful AI transformation, all three elements—executive commitment, capability development, and adoption/value measurement—must work together. 

What unique challenges and opportunities does AI present in implementing analytics solutions across sectors like Retail, Consumer Finance, and Oil & Gas? 

Some elements across industries are familiar, like the need for executive commitment, technology, people, and processes (both data and AI), as well as change management, adoption, and value measurement. However, industry-specific challenges do exist. 

For example, in sectors like oil and gas, the main challenge is how much data is generated and whether it's usable. Upstream oil and gas systems may produce analog data, which requires digitization—like converting it into digital form using historian databases. While these issues are industry-specific, they generally relate to the digitization of processes. 

The extent of digitization and data availability can be a hurdle in oil and gas, but much of this has been addressed. Today, industries can collect massive amounts of real-time data, such as sensor data from downhole assets. So, many challenges are already being overcome. 

On the other hand, sectors like retail, banking, and finance are data-rich, with plenty of data flowing from customer transactions, e-commerce, and digital platforms. The main issue is integrating external data, such as third-party marketing data. However, these industries face fewer challenges due to their wealth of data. 

How does Tiger Analytics foster a culture of innovation, and what steps does your team take to stay ahead of rapid advancements in AI technologies? 

Fostering a culture of innovation doesn’t follow a specific template, but we focus on a few key principles. We recognize the need for diverse skills to collaborate effectively internally and with clients. It’s like building an aircraft—data scientists are the engine specialists, data platform experts handle the frame, and consultants focus on user experience and translating technical solutions into business impact. Everyone is an equal stakeholder in these significant initiatives, with clear ownership and roles. Still, teamwork is crucial to bringing all the different skills together—data science, engineering, app development, business consulting, and program management. 

We don’t follow a fixed approach. Some companies might lead with a consulting or technology-first mindset, but we’re more flexible. The team structure depends on the problem. If it’s a technical challenge, we’ll focus on technical experts; if it’s business-driven, the team will reflect that. This flexibility allows us to adapt as the project evolves. 

A key aspect of our innovation process is how we co-create with clients. We don’t just take a high-level problem and build something behind the scenes; we work with client stakeholders. In our "co-owned engagements" model, our teams and clients contribute strategically and tactically. This collaborative approach ensures shared responsibility and brings diverse perspectives from our industry experience and the client’s insights, leading to the best possible outcomes. 

What significant changes do you anticipate in analytics consulting over the next five years, particularly with generative AI and machine learning advancements? 

Rather than just an analytics question, this is an inquiry into the changes we already see in the tech space. Technology is becoming more complex and more accessible to deploy, especially with innovations like generative AI. The key is understanding the problems you're solving. Early excitement around small use cases is fine for testing, but businesses now focus on using AI and data to tackle significant challenges. 

One way to approach this is to think of business solutions as products. Take pricing and revenue management—it’s not a small problem, but it’s also not about solving every business process. We can start by focusing on a specific geography, mapping the current process, identifying gaps, and determining which data and insights are needed. Then, we deploy AI to address the problem, measure its impact, and adjust as needed. 

Once a solution is successful, it can be scaled globally and applied to other areas, like Salesforce automation, supply chain optimization, or digital personalization. The key is to treat these as business products built with a deep understanding of business processes and technology. 

The fundamental shift is in how technology is applied, not just its complexity. While understanding the tech is crucial, integrating that technology into business operations drives industry transformation. That’s the change we’re seeing today. 

In Consumer Finance, data privacy and security are paramount. What key considerations should organizations consider when implementing AI solutions in this space, and how do you balance innovation with regulatory compliance? 

It's an interesting question, but I wouldn’t frame innovation and compliance as trade-offs. It's about balancing both. For example, even before generative AI, when we built models for credit card offers, there were strict guidelines to avoid intentional bias. Regulations like the Fair Credit Reporting Act in finance set these rules, but the issue applies across industries where trust is key. Breaching data privacy regulations, for instance, can quickly erode that trust. 

Newer AI models, especially black-box models, require extra caution. Fortunately, significant progress has been made in making AI more explainable and testing for bias, even in generative AI. What’s essential is adopting these frameworks, ensuring teams understand the risks, and following responsible AI practices. 

Addressing the innovation vs. compliance point is not about choosing one over the other. The goal is to stay ahead of regulations. Many businesses had already upheld high standards for consumer data privacy long before GDPR. Similarly, many of our clients take a proactive approach to compliance. 

We also participate in industry groups focused on responsible and ethical AI. Our approach is to stay ahead of regulation and actively contribute to shaping the future of AI. Ultimately, the goal is to maintain the trust of stakeholders—whether clients or consumers—because trust is fundamental in both B2B and B2C. 

As a senior leader, what advice would you give to organizations just beginning their AI journey, particularly those looking to embed AI-driven insights into their core business strategies? 

To answer this, I’d go back to the core elements that need to come together: first, executive commitment and a clear understanding of AI’s impact on your business; second, building internal capabilities—this includes technology, data, processes, and people. While you can rely on external support, the focus should be on developing these capabilities internally. It doesn’t have to happen all at once. Still, it would help if you had a gradual roadmap tailored to your business, starting with identifying the correct problems and prioritizing them based on value and complexity. 

Some problems may be high-value but complex, so it’s important to find the "Goldilocks zone"—moderately complex issues with a significant impact. Solving these builds the experience and confidence to move forward. It is the capability-building phase. 

Finally, creating a culture of adoption is crucial. It involves change management and measuring the real-world impact of AI. Models alone don’t create value if they’re not actively used. You need a strategy to measure results, communicate value, and build comfort with new methods. It can involve an experimental approach, like testing a champion-challenger model to compare old and new ways. The goal is to drive change through measurable impact and clear communication. 

Ultimately, this isn’t just about technology but a mindset shift and new working methods. People are key to making this work, which shouldn’t be overlooked. 

Executive commitment, building internal capabilities, and driving adoption through change management and value measurement are the foundational steps for businesses adopting AI and data in their transformation journeys.