Serial Growth Lab
Oct 9, 2025
Inside the Enterprise AI Reality: What the Builders in the Trenches Know That Most Startups Don't
Fifth Third Bank and Great American Insurance Group measure AI by outcomes, not hype: fewer errors, faster cycles, clean audit trails, and funded expansion only after proof.
AI isn't a headline anymore. It's an execution game.
You can feel the change inside large enterprises right now. The PowerPoints about "potential" are gone. The question is simple: what's working, what's not, and who can make it real without blowing up risk controls?
That is where I went with leaders from Fifth Third Bank and Great American Insurance Group during StartupCincy Week 2025. We focused on what it really takes to move AI from pilot to production in two of the most risk-averse industries on earth.
The examples came from banking and insurance, but the patterns apply to any enterprise and to any startup hoping to sell into one.
The shift from strategy to systems
Both leaders agreed: AI is not a tech initiative. It is a business capability that must plug into governance, data, and decision-making that already exists.
At Fifth Third, Jonathan Paul, who leads AI governance, described a move from isolated use cases to a full operating system for responsible AI. That includes productivity tools for employees, real-time monitoring for developers, and automation that shows up on the P&L.
At Great American Insurance, Siôn Williams, Strategic Partnerships, works across 36 divisions, from crop to professional liability. The issue is not ideas. It's prioritization. Where do you find problems at scale inside a federated enterprise?
What great enterprise buyers actually want
Every founder thinks their AI platform is revolutionary. Inside an enterprise, nobody cares.
"The startups that win are never the flashiest," Williams said. "They’re the ones that quietly solve a real problem inside a real workflow."
Paul echoed it. Simplicity beats sizzle. "We see ten vendors for every one we move forward with. The winners understand our environment, where Microsoft, Amazon, and Google already play, and they identify a specific gap we haven't solved."
That principle is universal. Whether you are pitching a bank, a hospital, or a retailer, show you understand how they operate and what their priorities are before you pitch what you've built.
The 60-day rule
The process looks similar in many enterprises. Open demos. A short list. A champion challenge.
If a startup cannot stand up a controlled proof of concept in 60 days, they're out.
"We used to take nine months," Paul said. "Now we ask vendors to prove value in 60 days, with live data, real workflows, and real governance."
For startups, time to proof is a filter. If your tech or your team cannot deliver measurable results inside the real environment within two months, you are not enterprise-ready.
Why simplicity wins
The myth says large companies want full-stack platforms. The reality says they want composable tools that slot into what already exists.
Teams that break through are simple, safe, and specific. One use case. One integration path. One measurable outcome.
Do not sell transformation. Sell traction.
The startup advantage that still matters
The big clouds will cover most of the enterprise AI surface area. The remaining gaps are where startups win. These are domain problems, compliance pain points, legacy integrations, and the "boring" work that makes systems safe and usable.
Close these gaps faster or cheaper than the hyperscalers, and you have a business.
Lessons for every startup selling into the enterprise
Know the workflow. Show how your product fits what exists, not how they should rebuild around you.
Have your house in order. SOC 2, data policies, security review. Without them, procurement stops you.
Prove it in 60 days. Live data beats slide decks.
Land with a sponsor. No senior business owner, no deal.
Be 10x better at one thing. Depth beats breadth.
Treat governance as design. Auditability, explainability, and safety belong in the user experience.
Ship an integration playbook. Most pilots stall here. Help your buyer over the hill.
How enterprises decide to build, buy, or partner
Build when the decision is regulated, the data is proprietary, or the workflow is a differentiator.
Buy when time to impact matters more than ownership and controls are clear.
Partner when a specialist fills a gap the incumbent stack does not, such as real-time guardrails or orchestration across fragmented systems.
This choice is not technical first. It is economic and risk first. Ask what creates a compounding advantage that competitors cannot easily copy.
Governance that enables progress
Banks and insurers measure governance like a product feature. You should too.
Model risk controls that run in real time, not only after the fact. Data handling clarity on storage, retention, redaction, and access. Audit trails that risk teams can follow without a fire drill. Support plans that explain who fixes what when something breaks at scale.
Treat compliance as a design constraint from day one. Show it working while the model runs, not as a PDF at the end.
Integration is the hill most pilots die on
Most pilots don’t fail because the model is weak. They fail because integration was an afterthought.
Agree on identity and data flow before day one. Standardize the data model and event layer as part of the pilot. Ship a short implementation playbook the client can reuse for the next business unit.
Your integration plan is as important as your inference plan.
The economics that unlock the next phase
Budgets are released in phases. Hopes do not get funded. Results do.
Hard returns include cycle time cuts, error reductions, fraud prevention, and loss avoidance. Capacity shifts move hours to higher-value work. Risk reduction shows up as fewer incidents and faster remediation. Phase gates unlock funding only when milestones are met.
The score is not adoption. The score is funded expansion.
Patterns that apply beyond financial services
These patterns show up in healthcare, manufacturing, retail, logistics, and the public sector.
Simplicity beats sizzle.
Guardrails win trust.
Speed is a screen.
Sponsors are oxygen.
Integration is strategy.
Compounding advantage matters. Build where your data and workflow give you an edge. Buy or partner everywhere else.
A universal go-to-market loop for AI startups
Pick a narrow wedge that sits in a high-value workflow.
Package a 60-day proof with governance and integration artifacts included.
Land with a sponsor who owns a business metric and can release phase-two funding.
Standardize the integration playbook so the second unit is easier than the first.
Expand deliberately, one outcome at a time.
The hardest truth
AI adoption is not limited by imagination. It is limited by execution.
Inside these companies, AI is already creating measurable change. Fraud detection is sharper. Underwriting is smarter. Developer productivity is higher. The real lesson is not in the models. It is in the process.
Enterprises win when they move from pilot theater to repeatable playbooks. Startups win when they speak that language and deliver inside those constraints.
The future of AI will be shaped less by those who predict it and more by those who implement it safely, quickly, and at scale.
“The startups that win are never the flashiest. They’re the ones that quietly solve a real problem inside a real workflow.” - Siôn Williams, Strategic Partnerships, Great American Insurance Group
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