Market Notes

Operator memos for applied AI markets.

Short investor-style notes on where AI creates durable operating value, how vertical AI companies earn trust, and how workflow automation markets compare across healthcare, fintech, customer experience, and back-office work.

Memo 01

Where applied AI actually works

The strongest applied AI markets are not the places with the flashiest demos. They are workflows with high manual cost, repeated decisions, visible exceptions, and a clear human review loop.

Workflow gravity matters: if the user already lives in a queue, inbox, call flow, claim file, CRM, or review surface, AI can enter the operating path without asking the user to change jobs.

Review loops create trust: the best early systems keep source evidence, confidence, exceptions, and human decisions visible instead of pretending the model is always right.

Measurable exceptions are a wedge: missing fields, delayed routing, unresolved calls, unworked queues, and repeated handoffs give teams a concrete way to evaluate whether the AI is improving the operation.

The investor question is distribution plus workflow ownership: the company that controls the action layer, not only the model call, has the better chance of compounding product value.

Memo 02

Why vertical AI companies win or lose

Vertical AI wins when the product owns enough of the customer workflow to turn model output into an operating result. It loses when it remains a thin assistant around someone else's system of record.

Data access is necessary but not sufficient; the harder question is whether the product can see the full workflow, the exception path, and the final action.

Trust is a product surface: regulated or operationally sensitive users need evidence, auditability, permissions, and handoff design before they need another chat box.

Distribution usually beats raw model cleverness. The winning company finds a painful workflow, reaches the buyer, and lands inside daily operations before broader platforms replicate the feature.

The diligence lens should test implementation burden, buyer urgency, integration depth, model reliability, and whether the workflow creates proprietary operating data over time.

Memo 03

Market map: AI workflow automation

Healthcare, fintech, customer experience, and back-office operations share the same core pattern: messy inputs, rule-heavy workflows, exception handling, and a need for reliable handoff.

Healthcare workflows often start with documents, calls, referrals, authorizations, scheduling, compliance checks, and routing. The wedge is reducing administrative burden without hiding clinical or operational judgment.

Fintech workflows often start with risk review, customer operations, onboarding, underwriting, compliance, disputes, reconciliations, and internal data quality. The wedge is speed with controls.

Customer experience workflows often start with repeated intents, account changes, billing questions, support queues, and escalation paths. The wedge is automated resolution with observability and safe fallbacks.

Back-office workflows often start with spreadsheets, emails, approvals, reporting, and knowledge gaps. The wedge is converting repeated work into reusable playbooks, data models, dashboards, and agentic workflows.