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LEADER TALK: IN CONVERSATION WITH Suresh Sundararajan, CEO & Co-Founder- Mindsprint
LEADER TALK: IN CONVERSATION WITH Suresh Sundararajan, CEO & Co-Founder- Mindsprint

July 15, 2025

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"The future isn’t about humans versus AI — it’s about humans with AI. True transformation happens when technology amplifies our potential, not replaces it. End of the day, success really depends on how we use AI as a catalyst, and embed it into the way we solve real-world problems.”

  1. What is foundational to an organization’s pivot to becoming AI-first, from digital-first?

The foundation lies in creating an environment where innovation is cherished and people are encouraged to think without constraints. Often organizations and managers within themimpose artificial limitations to the innovation process. It could be anything from assuming financial constraints to even thinking ‘is it my job’ at the earliest stages of innovation. But real progress, especially with AI, requires the freedom to imagine new possibilities beyond immediate boundaries. AI amplifies human potential for that to happen,we need to create mechanisms that help unearth and unlock that potential.

 

At Mindsprint, we believe AI must be embedded across the company, not confined to isolated projects, but integrated into all our practices, functions and operations. It should have a clear value proposition to our customers, how we deliver outcomes to them. Becoming AI-first is not a technology initiative; it’s a cultural and structural evolution, one that is very important to survive in this industry. It empowers employees to ideate, experiment, and reimagine how work is done, with AI as a core enabler.

 

 

  1. How can an organization’s leadership ensure AI readiness and maturity to become AI-first?

 

Leadership must foster a culture of continuous learning, experimentation, and innovation. Most importantly, they must ensure that people across the organization have the skills and confidence to embrace human-AI collaboration.

 

To support this transformation, organizations must invest in AI-native architectures, scalable data pipelines, and robust MLOps/LLMOps infrastructure. Developing reusable IPs, accelerators, and AI platforms becomes critical to ensure speed, consistency, and differentiation.

 

Equally important is the cultural shift—upskilling teams, enabling hybrid human-agent collaboration, and embedding AI in the flow of work. Moving to AI-first means evolving the operating model toward outcome-driven, cross-functional teams and platform-led delivery—ultimately making AI the engine of business innovation and growth.

 

At Mindsprint, over 95% of our workforce has completed the foundational GenAI certification. This has enabled AI literacy enterprise-wide, allowing teams across functions to understand, apply, and innovate with AI. We have publisheda list of tools for all our employees and are encouraging them to adopt them in their daily work. We will audit and measure our AI maturity periodically. We will continue to offer more programs to reskill and upskill employees in this journey.

 

We have also established an internal AI Council tasked with identifying, scaling, and governing high-impact use cases, ensuring that AI is embedded where it can drive real and tangible outcomes rather than confined to experimental pockets.

 

When advising customers, we advocate shifting the conversation from automating tasks to achieving measurable outcomes, like how we have approached autonomous payment processing, where AI delivers 99% accuracy, 70% faster processing, and enables outcome-based pricing models. AI maturity is achieved when technology translates into tangible business improvements.

 

  1. What do successful AI-first companies do to build and sustain an AI-first culture?

 

The first step is to actively cultivate curiosity, encourage unconstrained thinking, and challenge established norms. Organizations often slip into autopilot, back-to-back meetings, constant emails, and reactive problem-solving—leaving little time or space for long-horizon thinking or bold innovation.

 

Building an AI-first culture means intentionally breaking these patterns. Leadership must lead by example—demonstrating commitment to experimentation, fostering psychological safety, and encouraging teams to take calculated risks without fear of failure. It's not just about introducing AI tools; it's about shaping the environment where those tools can thrive.

 

Listening to frontline teams is especially important. The most valuable AI use cases often come from those closest to customers, data, or operational pain points. Their insights help ground innovation in real-world relevance.

 

Finally, the approach to AI must be centered on augmentation, not replacement. Everything should be anchored in the belief that humans remain at the helm, with AI as a co-pilot. Only when this message is clearly communicated and consistently reinforced will organizations see unified adoption and trust in AI across all levels.

 

 

  1. How can AI-first enterprises measure value and ROI from AI investments?

 

The most common pitfall is approaching AI with a “fear of missing out” mindset — adopting technology without linking it to real business needs. AI investments should always be tied to measurable outcomes, andembedded deeply into client value chains rather than isolated features.

 

At Mindsprint, we assess AI ROI across three dimensions:

  • Productivity Gains: Whether it’s AI-assisted software development, automation, or improved decision-making, efficiency is a direct and tangible measure of productivity gains.
  • Strategic Business Outcomes: AI must help solve customer challenges — enhancing speed, accuracy, and scalability. Our autonomous agent models, for instance, process invoices 70% faster with over 99% accuracy — a clear, quantifiable impact.
  • Growth Enablement: Beyond cost savings, AI should help scale operations, accelerate innovation, and unlock new revenue streams.

 

Ultimately, ROI is not just about technology performance—it’s about how well AI aligns with the organization’s broader vision and delivers meaningful business value. To do that effectively, value measurement must match the nature of the use case: automation should focus on cost savings, augmentation on productivity gains, and innovation on driving growth.

 

  1. What steps can enterprises eager to start on an AI-first journey take to ensure successful outcomes?

 

Despite significant investments in AI, many initiatives fail to achieve their intended outcomes. A key reason behind this is the disproportionate emphasis on technology rather than on actual business needs. In 2025 alone, 42% of companies discontinued the majority of their AI initiatives due to a lack of alignment with core business objectives. Nearly 46% of AI proof-of-concept projectsdid not make it to production.

 

For AI to truly succeed, it must be rooted in genuine business needs. Avoid adopting AI for the sake of it. Identify real challenges where AI can drive meaningful, measurable improvements.

 

It is also important to drive change management from the front, to ensure there is org-wide acceptance of the positive impact AI can bring in. Give teams the space to experiment and foster unconstrained thinking. The most significant breakthroughs often come from employees empowered to challenge routine thinking, experiment, and reimagine processes.

 

Success with AI is fundamentally about mindset, people, and ensuring that every investment is linked back to strategic business outcomes.

 

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