Enterprise AI roadmap: from use case to an evaluated pilot
An enterprise AI roadmap should make the next investment decision clear: which workflow to improve, what evidence to collect and who can approve a release. For US and UK teams buying custom AI development or enterprise integration, the useful output is a sequence of funded decisions with owners, dependencies and acceptance criteria.
Start with one measurable workflow. Establish the current baseline, check access to data and systems, then evaluate a bounded pilot before expanding. Use the worksheet below in a planning workshop; it is available without a form.
In this guide
Choose a workflow worth funding
Compare candidate workflows by business impact, data readiness, integration effort and the consequence of a wrong output. Record the evidence and uncertainty behind each rating. A high-volume process with available records and a clear reviewer is often a more testable starting point than an open-ended assistant.
Name the process owner and measure the existing service: handling time, rework, exceptions and cost per completed task. Compare against the current process and a simple rules-based alternative. Model accuracy alone does not establish business value.
Do not commit to training a new model before checking whether search, existing models or conventional software can meet the requirement. Separate the experimental work from the API, identity, data and deployment work needed to operate it.
The enterprise AI roadmap worksheet
Create one row of decisions for each candidate workflow. Replace assumptions with measured values before funding the next stage; an unknown dependency belongs in the plan, not outside it.
| Decision | What to write down |
|---|---|
| Business problem | The task, affected users, current friction and the business outcome that would justify investment. |
| Baseline | Current handling time, exception rate and cost per task; sample period, sample size and measurement owner. |
| Data | Sources, access permission, quality gaps, retention and a representative evaluation set kept separate from development. |
| Systems | Source and destination systems, API contracts, authentication, rate limits, environments and dependency owners. |
| Owner | Business sponsor, process owner, technical lead, evaluator and person authorised to approve the release. |
| Risk | Consequences of incorrect or missing outputs; review, escalation, access controls and fallback requirements. |
| Evaluation | Task-level quality, latency, cost and reviewer workload; thresholds agreed before testing. |
| Go / no-go | Evidence required to proceed, conditions that stop the pilot and who signs the decision. |
| Rollout | Limited user group, monitoring, rollback, support owner, review date and operating budget. |
Copy the worksheet into your project brief. It contains blank fields, an evidence log and a decision record; no registration is required.
Four gates from discovery to operation
01 · Scope
Process owner + business sponsor
Deliver a workflow map, baseline and ranked use-case brief. Proceed when the task has a measurable outcome, an accountable owner and an agreed boundary. Pause if the problem or baseline is still unclear.
02 · Feasibility
Technical lead + data / system owners
Deliver a data sample, interface map, access decisions and a small evaluation set. Proceed when representative data can be used and critical system dependencies have owners. Pause if permissions or integrations remain unworkable.
03 · Evaluated pilot
Evaluation lead + process owner
Deliver a test report comparing the pilot with the baseline, including difficult and failed cases. Proceed only against the quality, latency, cost and review thresholds agreed in advance. Record the decision to revise or stop when a gate fails.
04 · Controlled release
Operations owner + release approver
Deliver a runbook, limited rollout plan, monitoring and a tested fallback. Expand after observing the real workflow with users. Keep an owner for incidents, model or data changes and recurring evaluation.
Schedule these gates around access, procurement and evaluation dependencies. A fixed delivery date or a successful demonstration is not evidence that a release is ready.
Worked example: document intake and review
Illustrative planning example, not a G-ATAI customer result: an operations team wants to extract purchase-order references from incoming documents and prepare a review queue. The pilot does not approve payments or write automatically to the system of record.
- Boundary: accept a defined set of document types, extract selected fields and show the source passage beside each suggestion. Missing references and ambiguous matches go to a person.
- Baseline and data: measure manual handling time on an agreed sample. Build a separate evaluation set spanning suppliers, layouts, poor scans and missing fields; prevent near-duplicate documents from leaking across development and evaluation.
- Acceptance: for illustration, require at least 95% exact matches on supported reference fields, no silent approval of missing references in the test set and a 95th-percentile response time below 10 seconds. These are example targets to agree for the workflow, not measured results or universal standards.
- Integration: read documents with restricted credentials, validate output against the destination schema and make retries safe against duplicate records. Test an expired credential, an unavailable API and a changed document format.
- Release decision: compare end-to-end handling time, review burden and cost with the baseline. The process owner approves a limited queue only if the agreed gates pass; otherwise narrow the scope, improve the data or stop.
Questions for US and UK procurement teams
For a US or UK buying team, resolve operating and contractual dependencies before comparing a pilot price. Request written answers that can become acceptance criteria:
- Where will data be processed and stored? Who can access it, how long is it retained and which providers receive it?
- Who owns the code, evaluation data and deployment artifacts? What access and documentation will your team receive at handover?
- Which systems and environments are included? Who supplies credentials, approves changes and owns ongoing cloud or model costs?
- What time-zone overlap, support coverage and escalation process are included? Which responsibilities stay with your team?
- What evidence will security, privacy and procurement reviewers need? Ask your own specialists to determine the requirements that apply to your organisation; do not treat a generic roadmap as approval.
Turn the roadmap into an implementation scope
If the core uncertainty is whether the model can perform the task, use custom AI development services to define the prototype, evaluation set and acceptance test. If the main dependency is connecting an AI capability to existing data and systems, use enterprise AI integration services to scope interfaces, permissions, deployment and monitoring. A roadmap may need both workstreams.
For concrete engineering examples, read the Aesoryx local dialogue engine study for the boundaries of a local dialogue implementation and the AurelQuant reconciliation engine study for a documented reconciliation workflow. These pages state what the source material supports and where measured outcome evidence is still unavailable.
Engineering references
This is a G-ATAI planning guide, not a claim of certification. These primary references provide useful background for risk management and engineering discipline: