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 described connecting AI work with governance and the bank’s operating environment. The emphasis was on building a repeatable path beyond isolated use cases.
Siôn Williams discussed the challenge of finding and prioritizing useful problems across Great American Insurance Group’s different businesses.
What great enterprise buyers actually want
Every founder thinks their AI platform is revolutionary. Inside an enterprise, nobody cares.
Williams emphasized startups that solve a specific problem inside an existing workflow.
Paul’s point was complementary: understand the environment the buyer already operates, including its major technology providers, and identify a gap that remains.
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.
Agree on a time to proof
The discussion emphasized a controlled proof of concept with a clear scope, a sponsor and a decision at the end.
A 60-day proof was one approach discussed in the session. The important part is agreeing on a realistic test, the access it needs and what the result will establish.
For a startup, time to useful evidence matters. The schedule should reflect the customer’s requirements and the work being tested, rather than a universal definition of enterprise readiness.
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. Understand the customer’s security, data and assurance requirements before the pilot.
- Agree on the proof. Set a timeframe, suitable data and a result the buyer can evaluate.
- Land with a sponsor. No senior business owner, no deal.
- Be meaningfully better at one thing. Demonstrate the improvement against the buyer’s current approach.
- Treat governance as design. Auditability, explainability, and safety belong in the user experience.
- Ship an integration playbook. Help the buyer understand what connects, who owns it and how it will be supported.
How enterprises decide to build, buy, or partner
- Build when internal capability and control are the best fit for the decision and workflow.
- 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 can’t 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. Evaluate buying or partnering for the rest.
A practical go-to-market loop for AI startups
- Pick a narrow wedge that sits in a high-value workflow.
- Package a bounded proof with the required governance and integration work 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.
The lesson I took from the session was about process: define the problem, understand the environment, make the controls visible and prove something the buyer can use.
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.
From the portfolio
AlignAI gives regulated enterprises one operating path from initiative intake through risk review, approval, and production, with the evidence and ownership behind every decision intact.
Does your AI operating model make the safest valuable work easier to put into production?
