Commerce Conversations

What Agentic Banking Actually Looks Like, with Drew Sievers

Episode Summary

The conversation covers three main areas: Drew's path from advertising to mFoundry to a stint in investing and back to operating. Drew traces his route from Ogilvy and Saatchi & Saatchi through five years in Japan to founding mFoundry in 2003, before the iPhone existed, to solve mobile banking's two big problems: handset fragmentation and carrier control. The turning point was landing Citi as a client, which pushed him to drop every other vertical (astrology apps, wine guides, Yellow Pages) and bet the company entirely on banking. Within a few years mFoundry had a third of the top 20 US banks as clients before selling to FIS. He's candid that the investing chapter he tried next was his biggest career regret; he now wishes he'd taken months off before jumping back in, which eventually led him back to operating, most recently at Drift. Where agentic AI is actually landing in banking: Drew splits this into near-term and long-term. Near-term, the real activity is in unglamorous, controlled environments: fraud, AML, compliance, reconciliation, onboarding, and customer support, where ROI is provable and regulators are comfortable with augmentation. He cites research showing 88% of finance leaders would allow some form of agentic AI in banking workflows and 71% say AI-initiated connectivity now factors into their choice of bank. Longer-term, he argues the real battleground is orchestration. Whoever controls permissions, identity, and auditability wins. And that banks will eventually serve autonomous software agents, not just people in apps. He also reframes the "AI has to be perfect" objection: human-run banking workflows already run 10-15% error rates, so an AI system that halves costs while improving to 8% error is an easy call for any bank. Could a bank run with a fraction of the headcount, and is AI overhyped: Dan and Drew debate whether a $10B depository institution could be built and run with roughly 50 employees. Drew's answer is yes: building a modern core is no longer the hard part (he points to a Drift team rebuilding a full agentic product in four months versus an 18-month traditional SaaS timeline); the bottleneck is chartering and regulatory approval. On funding, Drew argues that AI remains under-hyped even as capital pours in, and that collapsing barriers to entry mean M&A outcomes increasingly hinge on go-to-market speed rather than product.

Episode Notes

• mFoundry was founded in 2003, pre-iPhone, to solve two problems: roughly 400 incompatible handsets and carriers controlling app distribution and billing.
• The pivot that saved the business: landing Citi as a client convinced Drew to drop every other vertical and focus entirely on banking -- within a few years, mFoundry counted a third of the top 20 US banks as clients.
• Drew's biggest career regret is moving straight into investing after selling mFoundry to FIS instead of taking time off first. His advice to his past self: take three to six months, then bet on yourself as an operator again rather than betting on other founders.
• Near-term agentic AI in banking is landing in the unglamorous places -- fraud, AML, compliance, reconciliation, onboarding, and customer support -- because these are controlled environments where ROI is measurable and regulators are comfortable with augmentation over full autonomy.
• Appetite is real, not just theoretical: 88% of finance leaders surveyed would allow some form of agentic AI in banking workflows, and 71% say AI-initiated connectivity now factors into which bank they choose. (flag: confirm source before publishing)
• The long-term battleground is orchestration -- whoever controls permissions, identity, auditability, and governance across banking data captures the outsized value.
• Reframe on AI errors: human-run banking workflows already carry 10-15% error rates, so an AI system that cuts costs in half while improving to an 8% error rate is a trade banks will make every time.
• A $10B bank could plausibly run with roughly 50 people, per Drew -- the constraint isn't the technology (a core system is "a fairly straightforward ledger"), it's getting chartered and regulated.
• Speed comparison: a team at Drift rebuilt a full agentic AI product in about four months, work that would have taken 18 months on a traditional SaaS timeline.
• Drew's take on AI funding: conversations with veteran investors consistently land on "under-hyped," not overhyped, despite the volume of capital flowing in.
• M&A dynamics are shifting -- competitive barriers to entry have collapsed, so outcomes increasingly hinge on go-to-market speed rather than product differentiation. Expect a small number of massive winners and a long tail of fast flame-outs.
• Founder advice, delivered as a two-part rule: expect the best and worst job of your life, so stay even-keeled -- and never run out of money, because everything else is downstream of that.
• Drew's outlook: the shift plays out over five to ten years, possibly faster, and will be decided by whoever wins the orchestration layer first.