In the latest episode of Digital Banking Podcast, host Josh DeTar, VP of Sales and Marketing at Tyfone, welcomed Barath Narayanan, Global BFSI and Europe Geo Head at Persistent Systems. The episode centered around how artificial intelligence is shaping productivity, strategy, and risk management across financial services worldwide.

Barath shared how AI has moved from a side conversation to the core of every client discussion. He described a clear shift: organizations now focus less on cutting costs and more on boosting output and speed with AI tools. Barath explored how adoption varies by region, with some markets demanding higher productivity gains and others held back by legacy infrastructure and data challenges. He stressed the need for careful governance, drawing on real-world stories where moving too fast led to costly mistakes. He also highlighted the importance of having experts in the loop and building strong guardrails to protect trust and brand.

Throughout the conversation, Barath and Josh examined how evolving technology, data quality, and global perspectives impact AI’s role in digital banking. The discussion underscored that progress in AI adoption is incremental, and that staying relevant means blending bold action with thoughtful risk management.

⚡ Productivity Gains Come From Reinvestment, Not Just Cost Cutting

When adopting AI in financial services, the biggest opportunity lies in reinvesting savings to boost output, not just trimming headcount. Organizations often focus on reducing team size or cost, yet the real value comes when teams use freed-up capacity to deliver more products, features, or services in less time. This approach builds a culture of continuous improvement and helps firms keep pace with fast-moving competitors. By shifting the mindset from cost-cutting to throughput and quality gains, organizations can improve customer experience and business results. The decision to reinvest in innovation, rather than shrink teams, drives growth and helps institutions stay relevant as technology evolves. The lesson: treat AI as a multiplier for talent, not just a lever for efficiency.

⚡ Legacy Tech and Data Quality Shape AI’s Real Value

Legacy systems and poor data quality remain top barriers to realizing the full promise of AI. Many institutions have invested too little in technology modernization, leaving them with outdated platforms that are hard to integrate with new AI solutions. As a result, promised productivity gains often fall short in practice, sometimes delivering single-digit improvements instead of the large jumps seen in pilot projects. Clean, well-structured data is essential—AI is only as good as the information it receives. Without good data pipelines and modern infrastructure, organizations run the risk of faulty results and missed opportunities. Successful AI adoption starts with strong foundations: investing in system upgrades, cleaning up data, and building flexible architectures that can adapt as new tools emerge.

⚡ Governance and Guardrails Are Non-Negotiable for AI in Banking

Strong governance is essential for safe, effective AI adoption in banking. Rushing to deploy tools without clear rules, checks, and oversight puts brands and customer trust at risk. Stories abound of public-facing AI systems that created embarrassing, costly mistakes when left unsupervised. Financial institutions face even higher stakes, since a single misstep can result in regulatory trouble or reputational damage. The solution is to pair every AI initiative with robust controls: expert oversight, clear rules for data use, and step-by-step validation of results. Progress may feel slower, but a measured, incremental approach builds trust and helps organizations avoid big setbacks. In digital banking, moving fast without guardrails is a recipe for trouble—sustainable gains come from balancing bold moves with careful risk management.