An enterprise AI roadmap that ranks what to build, fix and stop funding

The roadmap is the final output of the AI Audit, a 90-day plan built from its findings: every use case marked quick win, strategic bet or kill, the data work each one needs, and the stack it runs on, with an owner on every item.

Book a Discovery CallSee the AI Audit
Use-case registerSample
  • Customer support chatHubSpot inbox, live order dataQuick win
  • SEO content pipelineKeyword to publish-ready draftQuick win
  • Purchase-order approvalsSAP, with a human approval gateStrategic bet
  • Chatbot over unstructured file sharesData not ready, low returnKill

Ranked by value, effort and risk.

What a 90-day AI roadmap contains

Six parts, written so your engineers can start on the first item.

Register

Every use case, ranked

Quick wins, strategic bets and kills, each scored on value, effort and risk, and on the data it needs.

Data

A gap analysis per use case

Which records exist, which systems can be written back to, and the data work each item waits on.

Integration

Workflows mapped to your systems

Each multi-step workflow traced through the APIs of the systems it reads and writes.

Governance

Checkpoints set before go-live

Guardrails, human-in-the-loop approvals and an escalation path for every workflow, so nothing ships without an owner.

Stack

The technology stack, specified

Five layers inside your own cloud, from systems of record to the tools your team already works in.

Milestones

Dates, metrics and owners

Sequenced milestones to the first production-ready workflow, each with a success metric and a named owner.

Four phases, in the order they run

Diagnose, design, build and go live. Each phase closes a gap that stops pilots from reaching production.

  1. 01
    Diagnose

    Operational audit and prioritization

    We map your workflows, find the repetitive processes worth automating and rank use cases by value, effort and risk.

    • The use-case register
  2. 02
    Design

    Data infrastructure and readiness

    Your systems are checked against what a real-time, autonomous workflow needs, and the data strategy is designed around the use cases at the top of the register.

    • Gap analysis and data strategy
  3. 03
    Build

    Agentic integration

    Autonomous, multi-step workflows planned into the systems they act on, on self-hosted n8n in your cloud.

    • Workflows wired into your systems
  4. 04
    Live

    Governance and risk

    Oversight, compliance guardrails and human-in-the-loop checkpoints set before anything goes live, built into the workflow from the first sprint.

    • The first production-ready workflow

Quick win, strategic bet or kill

Every idea on the register gets one of three verdicts. The kills matter as much as the wins, because they release budget for the rest. How the audit measures value

Quick win

Data ready, systems open, process stable

These are built first. In the sample, customer support chat on a HubSpot inbox with live order data.

Strategic bet

High return, more to put in place

These follow once the groundwork is in place. In the sample, purchase-order approvals in SAP behind a human approval gate.

Kill

Data not ready, return too low

These come off the plan and out of the budget. In the sample, a chatbot over unstructured file shares.

Why most AI plans never reach a first build

95%

of generative AI pilots show no measurable P&L impact

Source: MIT NANDA, The GenAI Divide: State of AI in Business 2025

Pilots stall where they meet the real operation: data nobody prepared, failure nobody owns, value nobody reports. Each figure maps to the phase that closes it.

42% feel strategically prepared but unsure of their data, risk and talent infrastructure

Phase 2 checks data readiness against what each use case needs before it is scheduled.

4% of organizations report AI value at board level

Value is tracked from the first workflow against the audit's ROI models, and every system we ship reports its own results.

No owner, no return to report

The 95% maps to phase 4, which names an owner and an escalation path for every workflow before deployment.

Sources: Deloitte, State of AI in the Enterprise; Deloitte, AI Transformation Predictions 2026.

The stack your roadmap specifies, inside your own cloud

Orchestration runs as self-hosted, containerized n8n, so there is no per-seat licensing and your data does not leave your environment.

Your cloud, your data stays here

Layer 5

Where your team worksNo new software to learn

  • CRM inbox
  • Website chat
  • Slack
  • Email
Layer 4

GovernanceSet in phase 4, before go-live

  • Guardrail agent
  • Second-agent check
  • Human-in-the-loop
  • Escalation paths
Layer 3

Agents and modelsModel chosen per task

  • Multi-step agents
  • Multi-model routing
  • Grounded in your knowledge base
Layer 2

OrchestrationSelf-hosted, containerized

  • n8n, self-hosted
  • Dev, pre-production, production
  • Error alerts to Slack and email
Layer 1

Your systems of recordRead and written through their APIs

  • SAP
  • Odoo
  • HubSpot
  • Zendesk
  • Your data warehouse

A quick win, shipped: GoKickflip's content pipeline

The SEO content pipeline on the sample register is already in production for GoKickflip. See all case studies

GoKickflipE-commerce and SaaS, content pipeline
$700$12.11
Cost per publish-ready article

From 4 to 5 articles a month to 43, each 29 minutes from keyword to publish-ready

A multi-agent pipeline on n8n runs about 33 agent executions across six or more models for each article: research, brief, then content generation. Built and shipped in 30 days.

98%lower cost per article
10xmonthly output
$358Ka year saved at equal volume
Read the GoKickflip case

From day one, the Ovidius team moved fast, thought big, and executed with precision.”

Renaud TeasdaleRenaud TeasdaleCEO, GoKickflip

Enterprise AI roadmap questions

Ask us something else

What does an enterprise AI roadmap include?

A ranked use-case register, a data gap analysis, the integration plan, governance checkpoints, the technology stack and dated milestones with an owner and a success metric on each.

How do we get a roadmap from Ovidius?

Through the AI Audit, whose final output it is; we do not write a roadmap without the audit. The audit takes 4 to 8 weeks, costs $10,000 to $100,000 and delivers ROI models for your top three to five initiatives alongside the roadmap. The fee is credited toward your first build.

How are use cases ranked on the register?

By value, effort and risk, weighed against data readiness. Value covers cost saved, decision speed, process speed and new capability, and our enterprise AI strategy page explains how the AI Audit sets it.

Who owns the items on the roadmap?

Every item names an owner. On our side, Jason, our COO, runs the audit, integration and delivery, and Maciej, our CTO, owns the architecture and the build.

What happens after the first 90 days?

Shipped workflows move to AI managed services, run and extended in weekly sprints you direct, and the next items on the register are scoped with you.

The steps either side of the roadmap

Before

AI Audit

Eight phases across strategy, operations, data and systems, ending in this roadmap.

See the AI Audit
Next

AI pilot

A strategic bet tested as a working prototype on your own data, in 2 to 6 weeks, with the fee credited toward the build.

See the AI pilot
After

AI managed services

Workflows monitored after launch, with failed runs alerted to Slack and email.

See AI managed services

Put your first workflow on the board

Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.

Owen, Maciej, Jason, Oskar and Ben