What AI Leadership Gets Right, and What It Often Gets Wrong: Lessons from Deependra Chokkasamudra

For today’s CTOs, AI efficiency is critical. Every decision, about tools, workflows, or team structure, has immediate consequences. One misstep can ripple across finance, operations, and customer trust.

For CTOs and technology leaders navigating the accelerating AI revolution, one question looms large: how do you harness tools designed to amplify human capability without letting them become distractions, or worse, liabilities?

Deependra Chokkasamudra knows these stakes well. He has spent nearly two decades answering this question in the trenches. From his start in sales at Dell to consulting, mentoring, and now leading AI-driven growth as Director of Growth at Zvolv, Deependra has seen firsthand what works, and what doesn’t, when implementing tech to drive real business outcomes.

In this conversation, Deependra shares lessons every tech leader needs today: how to introduce AI without chaos, make faster and more informed decisions.

Deependra, your career spans nearly two decades of sales, consulting, mentorship, and now AI-led business orchestration. Your career didn’t follow a conventional path. You began in engineering but moved into sales almost by accident. How did that transition happen, and what did those early years shape for you?

Deependra: When I started out in 2005, I interviewed Dell for a Technical Support role. I had an engineering background, so that felt like a natural place to begin. But there were no openings. Instead, fortunately or unfortunately—they redirected me to sales for the EMEA market. 

That’s how I became an accidental salesman. It wasn’t something I had planned. I began selling to customers in the UK, starting with consumers, pagers and Axims, back in the days before mobile phones became mainstream. From there, I moved into business sales and spent about eight and a half years at Dell across multiple roles. I eventually transitioned into consulting and developed a strong interest in technology-driven sales early on. 

You’ve moved across large enterprises, different geographies, mentoring roles, and now AI-led growth in a startup environment. What patterns did you start noticing across these phases, particularly around people, decision-making, and scale, that influenced how you approach leadership today?

Deependra: After leaving Dell, I spent time with Wipro and a small system integrator, which exposed me to very different operating models and customer expectations. I later returned to Dell in a regional role that looked similar on paper but operated in a completely different market context. That phase reinforced an important lesson for me: the same technology and processes behave very differently depending on market maturity, customer behavior, and decision velocity. I spent close to five years navigating that shift.

During the first wave of COVID, I made a conscious decision to step back. I used that time to upskill and double down on mentoring, something I had been drawn to even earlier in my career. Over the next few years, I worked closely with students and early-career professionals, focusing less on technical skills and more on personal development, communication, and leadership. That experience gave me a much deeper appreciation for how people adopt change, and why many transformations fail despite strong technology.

After that, I moved into startups, including a deep-tech environment. Now, I’ve been helping Zvolv scale. We’re building AI-driven solutions and seeing firsthand the difference between experimenting with AI and operationalizing it. My role is largely growth-focused, identifying where the organization can create real impact, how we scale responsibly, and how technology ultimately translates into measurable business outcomes rather than just technical progress.

At the AI visionary summit, you mentioned that Zvolv doesn’t chase AI aggressively; it’s adaptive. Many CTOs I speak with see AI as a double-edged sword: a potential multiplier but also a risk for productivity if implemented poorly. How are you using AI at Zvolv, and what safeguards ensure it amplifies rather than disrupts work?

Deependra: Let me give you an analogy. Tech is a tool. It has to be leveraged correctly to augment us. Imagine trying to drill a hole in the wall with your finger. We know what will happen, bleeding nails. Maybe worse. That’s why we have hammers.

Now, if someone doesn’t know how to use a hammer properly, they hurt themselves and damage the wall. AI is the same.

Tools don’t fail themselves; humans do if they lack competence or guidance. Whenever a new tool or innovation comes, we need to understand where, why, and how to use it.

From nuclear tech to computers, smartphones, and now AI, humans decide whether it amplifies or destroys productivity. If people claim AI reduces productivity, I haven’t met them yet.

AI leadership and laying the foundation for effective adoption

For sales and marketing teams, AI adoption can be tricky. What foundational steps should tech leaders take before introducing new AI tools? How do you ensure training is effective and adoption drives real value?

Deependra: In today’s world, AI touches every business function. In Zvolv, even expense claims are processed by AI, no humans involved in verification.

We also use AI for sales, marketing, presentation prep, understanding personas, and hyper-personalization. These tools increase productivity, but here’s the paradox: if a task that took 5 hours now takes 1 hour, what do you do with the 4 hours saved?

Productivity isn’t just about speed. It’s about reallocating time to meaningful, value-driven work.

AI can speed execution, but can it improve judgment? How does Zvolv use AI to make faster, smarter decisions, and what can CTOs learn from this?

Deependra: In a small team like ours, AI helps identify who to talk to, which industries to target, and which problems we can solve.

In larger organizations, imagine a tractor manufacturer receiving an order for 10,000 tractors in six months. AI can instantly rank suppliers by quality, delivery history, cost, and reliability. Decisions that used to take months now take minutes. That’s the impact.

Balancing customer needs and product focus in AI leadership

In enterprise sales, customers often ask for multiple features,some critical, some aspirational. But not all of them align with long-term product strategy. From your experience leading sales and working closely with product teams, how do you differentiate must-haves from nice-to-haves? And how do you guide customers without letting feature creep dilute focus or slow execution?

Deependra: Over years of talking to thousands of business leaders, you recognize patterns. Customers often have needs, wants, and desires.

If they’re clear, it’s easy. If not, educate them. Then phase your solution. Year one: must-haves. Later: nice-to-haves. This approach balances customer expectations and product focus.

Data privacy is a top concern. How do you build trust with clients while deploying AI? And how do you train a multi-generational team to use these tools effectively?

Deependra: Guardrails are built into the platform. Guidelines from relevant industries, finance, aviation, fintech, are incorporated.

Training focuses on power skills, not just software features. Across generations, the core principle is simple: understand the customer problem, know your product, and articulate the solution’s impact. That’s the triangle framework we use.

Advice for new tech executives

Deependra, in your experience, not every customer will adopt new technology, even when you provide strong evidence, case studies, social proof, demos. Some remain skeptical, hesitant, or slow to move. How do you approach those customers? Do you try to convince them, or is there a point where you decide that it’s not worth pursuing? How do you balance persistence with pragmatism in these situations?

Deependra: I don’t do business with them. You won’t win every deal. The faster you identify who is not to chase, the better your productivity and focus.

Many new tech leaders try to wear every hat, product, sales, marketing, even operations, often spreading themselves too thin. From your experience, what’s the single most important mindset shift they need to make to lead effectively without burning out? How do they know what to focus on themselves versus what to delegate?

Deependra: If you have the bandwidth, go ahead. But most won’t. One of the most important skills is identifying the right people and delegating effectively. You cannot do it all alone. Over time, trying to do everything leads to exhaustion. Leadership is knowing where to focus, and who can amplify your impact.

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