---
title: "Custom AI for the CIO"
description: "A CIO portfolio is a collection of AI pilots, vendor platforms, and shadow tools acquired across business units. Most never reach production, and the ones that do rarely integrate with the data wareho"
url: https://impetora.com/for/cio
role: "Chief Information Officer"
audience: "Chief Information Officer"
trace_spine: "Architecture"
author: Impetora
---

# Custom AI for the CIO

> Audience: Chief Information Officer. TRACE spine: Architecture.

A CIO portfolio is a collection of AI pilots, vendor platforms, and shadow tools acquired across business units. Most never reach production, and the ones that do rarely integrate with the data warehouse, identity provider, and observability stack that already runs the enterprise. Impetora designs the architecture that makes the portfolio coherent, vendor-agnostic, and recoverable. BCG and MIT Sloan report that 70 to 85 percent of enterprise AI pilots never reach production, and Gartner forecasts 33 percent of GenAI projects abandoned by end of 2025.

## What CIOs actually care about

### AI portfolio drift

Six pilots in three business units, none of them owned by IT, all of them touching production data. Nobody can answer what is in scope of the next audit.

### Integration with the existing stack

The data warehouse, identity provider, ticketing system, and DMS already work. The new AI cannot be a bypass channel that breaks lineage and access control.

### Vendor lock-in

Foundation models change every six months. Architectures pinned to a single provider have to be rewritten when the contract or the capability shifts.

### Total cost of ownership

Token spend, integration debt, change-management cost, and hand-off training rarely show up in a vendor pitch deck. The first surprise lands in quarter two.

### Pilot to production gap

Most AI demos work in a notebook. Production-grade systems need versioning, observability, rollback paths, and an evaluation suite that runs on every release.

## TRACE pillar focus

For CIOs, the spine is **Architecture**. See https://impetora.com/methodology for the full TRACE framework.

## Use cases

### Document processing automation

Contracts, claims, intake forms turned into structured fields with the source clause cited on every line.

### Internal knowledge AI

Grounded employee Q&A across policies, contracts, SOPs. Permission-scoped retrieval respecting your existing access control.

### Decision support

Recommendations with the evidence chain attached.

### Process orchestration

Long-running stateful workflows across CRM, ERP, ticketing, and document systems.

## What CIOs need from a partner, and what we ship

### Reference architecture

Diagram and written spec for how AI sits inside your existing data, identity, observability stack.

### Vendor-agnostic stack

Foundation-model layer abstracted behind an interface we control. Swap-out cost documented.

### Evaluation harness

Automated eval suite tied to your real workflow, gating promotion to production.

### Regulator-pack

EU AI Act risk classification, ISO 42001-aligned governance memo, technical documentation pack.

### Hand-off pack

Runbooks, incident response, model-version upgrade paths, dependency map. Your team operates without us.

### TCO model

Token spend, integration cost, evaluation overhead, retainer modelled across three years.

## CIO questions, answered

### How does this fit our existing data warehouse and identity provider?

The architecture is built around your existing stack. We integrate at the warehouse and identity-provider layers (Entra, Okta, Ping, federated SAML). AI inference respects existing row-level and attribute-level access control. Audit logs write back through your observability stack.

### What is the swap-out cost if we change foundation models?

The foundation-model layer is abstracted behind an interface we control. A swap is a config change plus rerunning the eval suite, typically two to four weeks of regression testing.

### How do you avoid vendor lock-in?

Open standards for vector retrieval, queue-based decoupling, OpenAPI integrations, OpenTelemetry observability. Where managed components are used, we document the swap path and data-portability terms.

### What does the evaluation harness do?

Versioned suite of test cases tied to your real workflow. Runs every release, gates promotion, reports drift. Grows with human corrections that become regression tests.

### How does this map to total cost of ownership?

We model TCO across three years, shared at scope sign-off and updated quarterly. Surprises are flagged before contract signing.

## Contact

Email: info@ainora.lt
Discovery: https://impetora.com/for/cio#discovery-call