Ankush Gadi, Director – ‘Director – Business Intelligence and Sales Management at Franklin Templeton India, has been at the forefront of driving AI-led transformations in one of the most highly regulated industries. Twice recognized among India’s Top 50 Most Influential AI & Analytics Leaders, he has built data-driven platforms and intelligence models that are reshaping how asset managers engage with clients, optimize portfolios, and make decisions.

In this exclusive conversation with MartechAI, Gadi shares how advanced analytics and AI are redefining financial services, the tangible impact of AI-driven data products, and why translating analytics into action is the true differentiator in today’s competitive market.

Q1. How do you see the intersection of business strategy, advanced analytics, and technology transformation shaping the future of asset management in India?
The convergence of business strategy, advanced analytics, and technology transformation is redefining the asset management landscape in India.

Business strategy today is increasingly data-driven. Advanced analytics, particularly predictive modelling and machine learning, enables firms to anticipate market movements, optimize portfolios, and personalize offerings at scale.

Technology transformation, especially through cloud platforms, AI, and automation, is democratizing access to insights and accelerating decision-making.

In India, where the asset management industry is growing rapidly and investor demographics are evolving, this triad—strategy, analytics, and technology—is helping firms move from reactive to proactive, from standardized to customized, and from operational to strategic intelligence.

Q2. Can you share examples of AI-driven data products or platform analytics at Franklin Templeton that have had a tangible impact on sales or customer engagement?
At Franklin Templeton, we have built a suite of AI-driven data products and analytics platforms that have delivered measurable impact on sales performance and client engagement. Some of the most notable innovations are:

• AI-powered Lead Generation & Engagement Engine: A predictive analytics solution designed to identify clients with a high likelihood of investing in specific fund categories.
• Impact: Resulted in higher conversion rates using the leads generated through the model.
• Client Segmentation & Personalization: Using clustering algorithms, we segmented clients into behavioural cohorts, enabling tailored marketing and sales strategies.
• Impact: Improved campaign engagement metrics and ROI.
• Sales Intelligence Platform: A real-time analytics layer built on Databricks SQL endpoints and Tableau, integrating CRM, fund performance, and market data to surface actionable insights.
• Impact: Boosted sales team efficiency and strategic alignment.

These initiatives reflect our commitment to building a data-first, AI-enabled culture that empowers teams, enhances client experiences, and drives business growth. Propensity models have become a cornerstone of our sales strategy—turning data into action with precision and scale.

Q3. What role do sales intelligence models play in improving decision-making for financial advisors and customers?
Sales intelligence models are increasingly becoming the backbone of informed decision-making in asset management. At Franklin Templeton, these models serve as a bridge between data and action—empowering stakeholders with timely, relevant, and predictive insights that enhance client engagement.

Sales intelligence models help prioritize outreach by identifying high-potential distributors and clients based on behavioural patterns, investment history, and market sentiment. These models are dynamic as they continuously learn and adapt. By integrating AI and machine learning, it becomes possible to refine predictions, uncover hidden opportunities, and reduce churn. In a competitive and fast-evolving market like India, this intelligence is not just a differentiator—it is a necessity.

Q4. How is AI helping Franklin Templeton personalize customer journeys in a highly regulated industry like financial services?
Operating in a highly regulated industry means every AI initiative must be designed with governance, transparency, and auditability at its core. We ensure this through:

• Model Governance Frameworks: All models go through rigorous validation and documentation using tools like MLflow and internal governance protocols.
• Data Privacy & Security: We strictly adhere to global data protection standards and ensure that client data is anonymized and securely processed.
• Explainable AI: We prioritize interpretability in our models, ensuring that business teams and compliance teams can understand and justify AI-driven recommendations.
• Human-in-the-loop Oversight: Final decisions—especially those involving financial advice—are always reviewed or approved by qualified professionals.

Q5. Being recognized among India’s Top 50 Most Influential AI & Analytics Leaders two years in a row, what do you believe sets your approach apart from others in the industry?
I am deeply honoured by the recognition, and I believe what sets my approach apart is a consistent focus on translating analytics into action. In a space often dominated by technical complexity, I have always prioritized clarity, business relevance, and stakeholder impact.

My philosophy is rooted in three principles:

• Business-first mindset: Start with the problem, not the model.
• Human-centred design: Data products must be intuitive, accessible, and actionable.
• Culture of enablement: I invest heavily in building data literacy across the organization.

At Franklin Templeton, I have had the privilege of leading AI-driven transformations that blend innovation with governance—especially in a regulated industry like financial services.

Q6. Where do you see the biggest opportunities for AI in financial services over the next 3–5 years?
The next 3–5 years will be transformative for financial services, with AI playing a pivotal role across multiple dimensions. I see the biggest opportunities emerging in the following areas:

• Hyper-personalized Customer Experiences: AI enables tailored products and communication using real-time data, boosting engagement and loyalty.
• AI-powered Investment Advisory: Robo-advisors are evolving into hybrid platforms, combining AI and human insight to guide smarter investments.
• Risk Management and Fraud Detection: AI detects patterns and anomalies faster than traditional systems, improving fraud prevention and compliance.
• Generative AI for Decision Support: Generative tools help synthesize data, generate insights, and simulate scenarios for better strategic planning.
• Intelligent Automation: AI automates tasks like underwriting and claims, reducing costs and freeing teams for higher-value work.
• ESG and Sustainable Finance: AI supports ESG analysis and compliance, helping firms align with responsible investing goals.

Ultimately, the biggest opportunity lies in creating a data-driven, agile, and customer-centric financial ecosystem. The key will be balancing innovation with ethical AI practices and robust governance.

Q7. What leadership lessons have you learned while driving large-scale AI transformation within a global investment firm?
Driving large-scale AI transformation in a global investment firm has been both challenging and deeply rewarding. It has taught me several key leadership lessons:

• Start Small, Scale Fast: Begin with pilot projects that show quick wins, then scale with confidence and stakeholder buy-in.
• Resilience and Adaptability Matter: Stay open to feedback, learn from setbacks, and pivot quickly when needed. Invest in continuous learning to keep teams future-ready and innovation-driven.
• Cross-functional Collaboration Is Key: Success in AI requires close teamwork across data, tech, business, and compliance. Lead with purpose but listen and communicate to bring people along the journey.
• Governance and Ethics Are Strategic: Strong AI governance ensures innovation is responsible, secure, and compliant.

Ultimately, the journey reinforced that leadership in AI is not just about driving change—it is about inspiring it. It is about creating an environment where innovation thrives, risks are managed, and people feel empowered to shape the future.

Q8. What advice would you give to young professionals entering the AI and analytics space today?
My advice to young professionals entering the AI and analytics space is as follows:

• Master the Fundamentals, Stay Curious: Build strong skills in stats, data modeling, and coding—but keep learning. Stay curious about emerging tech like generative AI and how it solves real business problems.
• Understand the Business Context: Go beyond the data—learn to connect insights to business goals. Great analysts know how to tell stories that drive decisions.
• Ethics and Responsibility Matter: Use AI responsibly. Be aware of bias and fairness, and ensure your models are transparent and trustworthy.
• Do Not Wait to Lead: Take initiative. Volunteer for tough projects, share ideas, and own outcomes—leadership starts with action, not titles.

In short, be technically sharp, business-savvy, ethically grounded, and endlessly curious. The future of AI is being shaped today and you can be a part of that journey.