Preet Saxena is a seasoned data and AI leader with over 18 years of experience shaping enterprise transformation through analytics, GenAI, and innovation-driven strategy. As Director – Global Data, Analytics & (G)AI at Concentrix, she has led high-impact initiatives spanning solutioning, delivery, and go-to-market strategy across global markets. Recognized among the 10 Most Influential Tech Leaders to Follow and honored with the Women in AI Leadership Award, Preet has built a distinguished career across Concentrix, Fractal, and Evalueserve, spearheading award-winning analytics products and trusted AI solutions. With a reputation for blending strategic vision with hands-on problem solving, she continues to define how organizations harness data to unlock value and meaningful transformation.
You’ve built a career across data, analytics and Artificial Intelligence leadership. What key insights or practices helped you grow, and what advice would you give to early-stage professionals entering this space?
There are few key practices that always helped me along my career. One should Master the fundamentals but stay adaptable. Deep understanding of statistics, data structures, and algorithms is non-negotiable. But the tech evolves fast—whether it’s new ML frameworks or cloud platforms. Leaders who thrive are those who pair a strong technical core with a willingness to learn continuously. For example, knowing Python or R is great, but being able to pivot to tools like PyTorch or Snowflake as they emerge is what keeps you relevant.
Bridge the gap between tech and business and we call this role as Analytics Translators. The best leaders translate complex data insights into actionable business outcomes. They don’t just build models; they solve problems. This means understanding the domain—whether it’s finance, healthcare, or retail—and speaking the language of stakeholders.
And one should cultivate soft skills. Communication, influence, and empathy are as critical as technical chops. You’ll need to convince non-technical execs to invest in your projects or rally a team through ambiguity. Leaders who can tell a compelling story with data—without getting lost in jargon—stand out.
My advice for early-stage professionals is:
Build a T-shaped skillset: Go deep in one area (e.g., machine learning, data engineering) but broad enough to understand adjacent fields (e.g., cloud infrastructure, business analytics)
Get hands-on: Theory’s great, but nothing beats real projects. Contribute to open-source, intern, or freelance to build a portfolio. Even small projects—like predicting customer churn for a local business—can showcase impact.
Network with purpose: Engage with communities on X, LinkedIn, or GitHub. Follow leaders in your field, Ask questions, share your work, and seek mentors who can open doors.
Think long-term impact: Don’t chase shiny tools. Ask yourself how your work creates value—whether it’s saving costs, improving lives, or driving innovation. That’s what gets you noticed.
With Generative AI evolving rapidly, many businesses are rethinking their strategies. From your experience, how are organizations leveraging this disruption to fuel innovation rather than fearing it?
Generative AI is evolving at a remarkable pace, and while this rapid advancement might initially spark apprehension among businesses, many forward-thinking organizations are choosing to embrace it as a powerful catalyst for innovation rather than a threat to their existing operations. From my experience, companies are leveraging this disruption in several impactful ways to drive creativity, efficiency, and value creation. Some of the examples are improving employee productivity, enhancing CX, revolutionizing content creation, accelerating product innovation and most important driving data-driven decision making.
It really needs a shift in mindset. The key to successfully leveraging Generative AI lies in a fundamental shift in perspective. Rather than seeing it as a replacement for human workers, innovative organizations view it as an augmentation of human creativity and decision-making. They’re investing in upskilling their teams to collaborate effectively with AI, emphasizing uniquely human skills like emotional intelligence, ethical reasoning, and strategic thinking that complement the technology’s capabilities.
Moreover, these companies aren’t just adopting generic AI tools—they’re customizing Generative AI models to meet their specific needs. This often involves partnering with AI research teams or building in-house expertise, ensuring the technology aligns with their unique business goals.
While the rapid rise of Generative AI can feel daunting, organizations that embrace it as a tool for innovation are discovering ways to differentiate themselves, boost efficiency, and deliver greater value. As this technology continues to evolve, I believe we’ll see even more creative applications emerge, further blending human ingenuity with machine capabilities. Businesses that stay ahead of the curve—by experimenting, learning, and adapting—will be best positioned to thrive in this dynamic landscape.
Given your work across industries like healthcare, FMCG, and e-commerce, how do you use AI/ML to decode customer behavior and help brands craft impactful, data-driven strategies?
AI and ML are powerful tools for decoding customer behaviour across industries like healthcare, FMCG, and e-commerce, enabling brands to craft data-driven strategies that resonate. Some of the flagship techniques like segmentation, clustering, predictive analytics, sentiment analysis, pattern analysis, churn prediction, recommendation engines, CLTV, etc are still relevant and solves various business problems across the industries.
However, as the amount of data we generate increased; the bigger challenge is to orchestrate the humongous data in a form on which analytics can be deployed. Customers these days leave their digital footprints, and the key is to tie the structured as well unstructured data together to understand deeper patterns and behaviours.
You’ve worked globally across regions with differing regulatory landscapes. As AI regulations tighten, how can companies stay agile and compliant while still driving innovation?
As AI regulations tighten across the globe, companies operating in diverse regulatory landscapes face the challenge of staying compliant while continuing to drive innovation. The key lies in adopting a proactive, flexible, and integrated approach that balances adherence to rules with the pursuit of cutting-edge AI development.
By embedding compliance into the innovation process and fostering adaptability, companies can turn regulatory challenges into opportunities. A flexible framework, expert teams, industry standards, transparency, and agile processes ensure they stay compliant across regions—while still pushing AI’s boundaries. This dual focus keeps them competitive in a world where ethical AI and innovation must coexist.
Leading analytics teams and working closely with senior leadership demands a dynamic leadership style. What values and principles guide your decision-making and stakeholder management?
As a team player or leader, one needs to have values like integrity, transparency, empathy & inclusion and accountability. In today’s dynamic era one needs to collaborate a lot across service lines and partner platforms. So, we should have absolute clarity in our communication and deliver incremental value in fast-moving environments with agility.
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