Tech Talks Daily

If artificial intelligence is meant to earn trust anywhere, should banking be the place where it proves itself first?
In this episode of Tech Talks Daily, I'm joined by Ravi Nemalikanti, Chief Product and Technology Officer at Abrigo, for a grounded conversation about what responsible AI actually looks like when the consequences are real.
Abrigo works with more than 2,500 banks and credit unions across the United States, many of them community institutions where every decision affects local businesses, families, and entire regional economies. That reality makes this discussion feel refreshingly practical rather than theoretical.
We talk about why financial services has become one of the toughest proving grounds for AI, and why that is a good thing. Ravi explains why concepts like transparency, explainability, and auditability are not optional add-ons in banking, but table stakes. From fraud detection and lending decisions to compliance and portfolio risk, every model has to stand up to regulatory, ethical, and operational scrutiny. A false positive or an opaque decision is not just a technical issue, it can damage trust, disrupt livelihoods, and undermine confidence in an institution.
A big focus of the conversation is how AI assistants are already changing day-to-day banking work, largely behind the scenes. Rather than flashy chatbots, Ravi describes assistants embedded directly into lending, anti-money laundering, and compliance workflows. These systems summarize complex documents, surface anomalies, and create consistent narratives that free human experts to focus on judgment, context, and relationships. What surprised me most was how often customers value consistency and clarity over raw speed or automation.
We also explore what other industries can learn from community banks, particularly their modular, measured approach to adoption. With limited budgets and decades-old core systems, these institutions innovate cautiously, prioritizing low-risk, high-return use cases and strong governance from day one. Ravi shares why explainable AI must speak the language of bankers and regulators, not data scientists, and why showing the "why" behind a decision is essential to keeping humans firmly in control.
As we look toward 2026 and beyond, the conversation turns to where AI can genuinely support better outcomes in lending and credit risk without sidelining human judgment. Ravi is clear that fully autonomous decisioning still has a long way to go in high-stakes environments, and that the future is far more about partnership than replacement. AI can surface patterns, speed up insight, and flag risks early, but people remain essential for context, empathy, and final accountability.
If you're trying to cut through the AI noise and understand how trust, governance, and real-world impact intersect, this episode offers a rare look at how responsible AI is actually being built and deployed today. And once you've listened, I'd love to hear your perspective. Where do you see AI earning trust, and where does it still have something to prove?

What really happens after the startup advice runs out and founders are left facing decisions no pitch deck ever prepared them for?
In this episode of Tech Talks Daily, I sit down with Vijay Rajendran, a founder, venture capitalist, UC Berkeley instructor, and author of The Funding Framework, to discuss the realities of company building that rarely appear on social feeds or investor blogs. Vijay has spent years working alongside founders at the sharpest end of growth, from early fundraising conversations through to the personal and leadership shifts that scaling demands. That experience shapes a conversation that feels refreshingly honest, thoughtful, and grounded in lived reality.
We explore why building something people actually want sounds simple in theory yet proves brutally difficult in practice. Vijay explains how timing, learning velocity, and the willingness to adapt often matter more than stubborn vision, and why many founders misunderstand what momentum really looks like. From there, the discussion moves into investor relationships, not as transactional events, but as long-term partnerships that require founders to shift their mindset from defense to evaluation. The emotional and psychological dynamics of fundraising come into focus, especially the moments when founders underestimate how much power they actually have in shaping those relationships.
A big part of this conversation centers on leadership identity. Vijay breaks down the messy transition from being the "chief everything officer" to becoming a true chief executive, and why the most overlooked stage in that journey is learning how to enable others. We talk about the point where founders become the bottleneck, often without realizing it, and why this tends to surface as teams grow and decisions start happening outside the founder's direct line of sight. The plateau many companies hit around scale becomes less mysterious when viewed through this lens.
We also challenge some of the most popular startup advice circulating online today, particularly around fundraising volume, pitching styles, and the idea that persistence alone guarantees outcomes. Vijay shares why treating fundraising like enterprise sales, focusing on alignment over volume, and listening more than pitching often leads to better results. The conversation closes with practical reflections on personal growth, co-founder dynamics, and how leaders can regain clarity during periods of pressure without stepping away from responsibility.
If you are building a company, leading a team, or questioning whether you are evolving as fast as your business demands, this episode will likely hit closer to home than you expect. And once you've listened, I'd love to hear what resonated most with you and the leadership questions you're still sitting with after the conversation.
Useful Links
Connect with Vijay Rajendran
The Funding Framework Startup Pitch Deck
Thanks to our sponsors, Alcor, for supporting the show.

What happens when decades of clinical research experience collide with a regulatory environment that is changing faster than ever?
In this episode of Tech Talks Daily, I sat down with Dr Werner Engelbrecht, Senior Director of Strategy at Veeva Systems, for a wide-ranging conversation that explores how life sciences organizations across Europe are responding to mounting regulatory pressure, rapid advances in AI, and growing expectations around transparency and patient trust.

Werner brings a rare perspective to this discussion. His career spans clinical research, pharmaceutical development, health authorities, and technology strategy, shaped by firsthand experience as an investigator and later as a senior industry leader.
 That background gives him a grounded, practical view of what is actually changing inside pharma and biotech organizations, beyond the headlines around AI Acts, data rules, and compliance frameworks.
We talk openly about why regulations such as GDPR, the EU AI Act, and ACT-EU are creating real pressure for organizations that are already operating in highly controlled environments. But rather than framing compliance as a blocker, Werner explains why this moment presents an opening for better collaboration, stronger data foundations, and more consistent ways of working across internal teams.
According to him, the real challenge is less about technology and more about how companies manage data quality, align processes, and break down silos that slow everything from trial setup to regulatory response times.
Our conversation also digs into where AI is genuinely making progress today in life sciences and where caution still matters. Werner shares why drug discovery and non-patient-facing use cases are moving faster, while areas like trial execution and real-world patient data still demand stronger evidence, cleaner datasets, and clearer governance.
His perspective cuts through hype and focuses on what is realistic in an industry where patient safety remains the defining responsibility.

We also explore patient recruitment, decentralized trials, and the growing complexity of diseases themselves. Advances in genomics and diagnostics are reshaping how trials are designed, which in turn raises questions about access to electronic health records, data harmonization across Europe, and the safeguards regulators care about most.
Werner connects these dots in a way that highlights both the operational strain and the long-term upside. Toward the end, we look ahead at emerging technologies such as blockchain and connected devices, and how they could strengthen data integrity, monitoring, and regulatory confidence over time. It is a thoughtful discussion that reflects both optimism and realism, rooted in lived experience rather than theory.

If you are working anywhere near clinical research, regulatory affairs, or digital transformation in life sciences, this episode offers a clear-eyed view of where the industry stands today and where it may be heading next. How should organizations turn regulation into momentum instead of resistance, and what will it take to earn lasting trust from patients, partners, and regulators alike?
Useful Links
Connect with Dr Werner Engelbrecht
Learn more about Veeva Systems
Viva Summit Europe and Viva Summit USA
Follow on LinkedIn
Thanks to our sponsors, Alcor, for supporting the show.

What happens when an industry that has barely changed for generations suddenly finds itself at the center of one of the biggest shifts in modern work?
In this episode of Tech Talks Daily, I'm joined by Kate Hayward, UK Managing Director at Xero, for a conversation about how accounting is being reshaped by technology, education, regulation, and changing expectations from clients and talent alike.
Kate describes this moment as the largest reorganization of human capital in the history of the profession, and as we talk, it becomes clear why that claim is gaining traction.
We explore how AI is shifting accountants away from pure number processing and toward higher-value advisory work, without stripping away the deep financial understanding the role still demands.
Kate shares why so many practices are reporting higher revenues and profits, and how technology is acting as a catalyst for rethinking long-standing workflows rather than simply speeding up broken ones.
We also dig into research showing that pairing AI with financial education strengthens analytical thinking while leaving core calculation skills intact, a useful counterpoint to the more dramatic headlines about machines replacing people.
Our conversation moves into the practical reality of how firms are using tools like ChatGPT today, from scenario planning to preparing for difficult client conversations, while also discussing where caution still matters, particularly around data security and core financial workflows.
Kate also explains how government initiatives such as Making Tax Digital and the digitization of HMRC are changing client expectations and deepening the relationship between accountants and the businesses they support.
We also spend time on the future of the profession, including how hiring strategies are evolving, why problem-solving and communication skills are becoming just as valuable as technical knowledge, and why private equity interest in accounting is accelerating digital adoption across the sector.
Kate rounds things out by sharing how Xero is thinking about product design in 2026, what users can expect next, and why keeping the human side of the profession front and center still matters.
So as accounting moves further into an AI-assisted, digitally native future, how do firms balance efficiency, trust, identity, and long-term relevance, and what lessons can other industries take from this moment of change?
Useful Links
Follow Kate Hayward on LinkedIn
Accounting and Bookkeeping Industry Report
Xero Website
Follow on LinkedIn, Facebook, X, YouTube, Instagram

What does sales leadership actually look like once the AI experimentation phase is over and real results are the only thing that matters?
In this episode of Tech Talks Daily, I sit down with Jason Ambrose, CEO of the Iconiq backed AI data platform People.ai, to unpack why the era of pilots, proofs of concept, and AI theater is fading fast. Jason brings a grounded view from the front lines of enterprise sales, where leaders are no longer impressed by clever demos. They want measurable outcomes, better forecasts, and fewer hours lost to CRM busywork. This conversation goes straight to the tension many organizations are feeling right now, the gap between AI potential and AI performance.
We talk openly about why sales teams are drowning in activity data yet still starved of answers. Emails, meetings, call transcripts, dashboards, and dashboards about dashboards have created fatigue rather than clarity.
Jason explains how turning raw activity into crisp, trusted answers changes how sellers operate day to day, pulling them back into customer conversations instead of internal reporting loops. The discussion challenges the long held assumption that better selling comes from more fields, more workflows, and more dashboards, arguing instead that AI should absorb the complexity so humans can focus on judgment, timing, and relationships.
The conversation also explores how tools like ChatGPT and Claude are quietly dismantling the walls enterprise software spent years building. Sales leaders increasingly want answers delivered in natural language rather than another system to log into, and Jason shares why this shift is creating tension for legacy platforms built around walled gardens and locked down APIs.
 We look at what this means for architecture decisions, why openness is becoming a strategic advantage, and how customers are rethinking who they trust to sit at the center of their agentic strategies.
Drawing on work with companies such as AMD, Verizon, NVIDIA, and Okta, Jason shares what top performing revenue organizations have in common.
Rather than chasing sameness, scripts, and averages, they lean into curiosity, variation, and context. They look for where growth behaves differently by market, segment, or product, and they use AI to surface those differences instead of flattening them away. It is a subtle shift, but one with big implications for how sales teams compete.
We also look ahead to 2026 and beyond, including how pricing models may evolve as token consumption becomes a unit of value rather than seats or licenses.
Jason explains why this shift could catch enterprises off guard, what governance will matter, and why AI costs may soon feel as visible as cloud spend did a decade ago. The episode closes with a thoughtful challenge to one of the biggest myths in the industry, the belief that selling itself can be fully automated, and why the last mile of persuasion, trust, and judgment remains deeply human.
If you are responsible for revenue, sales operations, or AI strategy, this episode offers a clear-eyed look at what changes when AI stops being an experiment and starts being held accountable, so what assumptions about sales and AI are you still holding onto, and are they helping or quietly holding you back?
Useful Links
Follow Jason Ambrose on LinkedIn
Learn more about people.ai
Follow on LinkedIn
Thanks to our sponsors, Alcor, for supporting the show.