Pilot framework

Validate one enterprise AI use case before committing to scale

A 45-day pilot is a controlled proof-of-value motion. It is designed to answer whether one AI workflow is valuable, feasible, secure, governable, and worth scaling. It is not presented as a customer case study or guaranteed business outcome.

Enterprise team reviewing an AI pilot roadmap, success metrics, and governance checklist

Best fit

Use this when leadership has a promising AI use case but needs evidence before approving production investment.

Defined scopeGoverned prototypeScale decision

What the pilot answers

The goal is not to overpromise. The goal is to reduce uncertainty with a practical, security-aware pilot design.

01

Business value

What workflow will improve, which stakeholder owns it, and what measurable signal will decide whether the pilot is useful?

02

Data feasibility

Which documents, systems, events, images, or knowledge sources are available, classified, and suitable for AI use?

03

Security and governance

What controls are required for IAM/RBAC, private networking, prompt security, model governance, audit logging, and human approval?

04

Technical architecture

Which Azure, AWS, or GCP services are appropriate for the pilot, and what must change before production?

05

Operating model

Who reviews outputs, who approves risky actions, how incidents are handled, and how monitoring/cost controls are run?

06

Scale decision

What should be built next, what should be stopped, and what investment is required for production rollout?

45-day structure

The timeline is adapted after discovery. Complex data access, legal review, or integration constraints can extend the schedule.

1

Days 1-7: scope and readiness

Confirm use case, business metric, data access, security constraints, governance risks, and pilot acceptance criteria.

2

Days 8-21: architecture and build

Design the AI workflow, prepare the data path, configure cloud services, and build the first controlled proof.

3

Days 22-35: governance and validation

Test output quality, failure modes, access controls, human approval, logging, monitoring, and cost visibility.

4

Days 36-45: decision package

Prepare results, risks, production roadmap, budget view, backlog, and recommended next action.

Responsible AI governance dashboard with approval and audit controls

Included governance controls

Responsible AI review, data classification, prompt security, hallucination risk checks, human approval design, audit trail, monitoring, and cost governance.

Typical deliverables

Pilot brief
Problem statement, user workflow, success criteria, assumptions, and constraints.
Reference architecture
Secure cloud design, AI services, data flow, access model, and observability pattern.
Controlled proof
A limited prototype or working demonstration using agreed sample data and governance controls.
Scale recommendation
Production backlog, risks, cost view, operating model, and go/no-go recommendation.

No fake case studies. No exaggerated AI claims.

This page describes a delivery framework. Pilot results depend on the use case, data quality, integration access, governance requirements, and buyer-side review speed.

Discuss pilot fit

45-day AI pilot FAQ

A 45-day pilot can prove whether one AI workflow has usable data, acceptable accuracy, secure architecture, clear governance controls, user adoption signals, and measurable business value.

A pilot is not a full enterprise rollout. Large-scale migration, unlimited integrations, production SLA, advanced compliance certification, and multi-department change management are scoped separately.

Good fits include document intelligence, internal knowledge assistants, compliance review support, predictive maintenance signals, customer support augmentation, HR screening support, and focused operations workflows.

DigiScience uses access controls, human review, prompt and data controls, audit logging, monitoring, scope boundaries, and success criteria so the pilot remains governed and reviewable.