Seats without lift
Usage concentrates in a few enthusiasts while teams lack practical workflows, training and a way to measure impact.
01 // ARCHITECTConsulting & Architecture
Know what to roll out. What to build. What to fix first.
Before you scale ChatGPT, Codex or an OpenAI API integration, find out whether your use cases, data, platform, controls, economics and teams can carry them. We score 6 readiness areas and turn the evidence into a prioritized plan.
45 minutes. No pitch deck, no sales script.
- 01
4 weeks to an executive readout
From kickoff to a decision, with evidence at every step.
- 02
3 paths, 1 plan
ChatGPT, Codex and the OpenAI API assessed together.
- 03
90 days of prioritized next steps
A sequenced roadmap with owners and decision gates.
The problem: the scaling gap
Starting is easy. Scaling is where the hard questions begin.
Teams can buy seats or launch pilots quickly. The harder work is aligning use cases, controls, data access, engineering practices and ownership so early wins survive contact with the enterprise.
Speed without standards
Codex helps ship faster, but review practices, repository access, testing and accountability need to keep pace.
Demos without a path to production
API prototypes work in isolation, then stall on architecture, evaluations, observability, cost or system integration.
Controls arrive too late
Data boundaries, access, human oversight and policy get resolved after teams have already built around assumptions.
The assessment: product fit
3 paths. One joined-up decision.
We evaluate where each OpenAI product belongs in your operating model, and where you need a coordinated approach across them.
01
ChatGPT
Everyday work, at enterprise scale.
Prioritize role-based use cases, workspace rollout, knowledge access, enablement and adoption measures.
02
Codex
Engineering productivity with control.
Assess repository workflows, permissions, review and test gates, team practices and measurable delivery outcomes.
03
OpenAI API
AI capabilities inside your products.
Map integration architecture, representative evaluations, safety controls, observability, reliability and unit economics.
From promising experiments to a decision-ready OpenAI adoption plan.
Why you need a partner
ChatGPT is 5 decision layers. One plan across them.
A choice about product access affects data exposure. A choice about agent autonomy affects review. A choice about use cases affects architecture and spend. We assess those dependencies together.
01 / WORKFORCE
ChatGPT workspace
Which roles and workflows benefit, how people reach company knowledge, and how administrators manage access.
Workspace plansCompany knowledgeApps & connectorsIdentity & adminRole playbooks
Risk without a plan
Broad data access and a license rollout with no change in daily work.
02 / ENGINEERING
Codex workflows
Where coding agents fit in your software lifecycle and what limits, reviews and tests should govern their work.
IDE & cloud workflowsRepository accessAgent instructionsApprovalsReview & test gates
Risk without a plan
Inconsistent repository standards, excessive permissions, and faster changes without dependable quality checks.
03 / APPLICATIONS
API and agent architecture
How models, tools, business systems and human decisions connect in a production workflow.
Responses APITool accessFile searchStructured outputsEvaluations
Risk without a plan
A compelling prototype with no quality threshold, operating owner or safe action boundary.
04 / ENTERPRISE FOUNDATION
Platform and commercial choices
Match identity, data requirements, environment design, model choice, procurement and cost controls to the workload.
Access designData requirementsModel selectionBudgets & logging
Risk without a plan
Security or commercial decisions made after teams have built on assumptions.
05 / OPERATING MODEL
Governance and people
Put ownership, policy, human oversight, training and adoption measures around the work before pilots become a patchwork.
Data handlingHuman reviewTraining & championsSuccess measures
Risk without a plan
Controls written after the pilot and adoption measured only by seat count.
What we score: 6 areas
A score backed by what we can verify.
For each area, we document current and target readiness, supporting evidence, confidence, an accountable owner and the next action. A gap without evidence stays an open question.
01
Data readiness
Data sources, classification, ownership, quality and least-privilege access through approved paths.
02
Use case clarity
Named users, workflow, business baseline, sponsor and the outcome each use case must improve.
03
Platform review
Workspace administration, Codex environments, API architecture, system interfaces and operational support.
04
Security and governance
Policy, permissions, retention requirements, audit needs, human review and agent action boundaries.
05
Evaluation maturity
Representative evals, quality thresholds, adoption signals, latency, usage and cost per successful task.
06
Team and skills
Skills, team playbooks, engineering standards, champions, training and a path for feedback.
How scoring works. We use one rubric across the 6 areas, record the evidence behind each rating and separate verified findings from assumptions. The self-check below is only a conversation starter.
Interactive
Take the self-check.
6 quick questions show which readiness areas deserve the first conversation. Your answers stay in this browser.
Data
Can approved users reach the right data through defined access paths?
Use case
Do your priority AI use cases have a named owner and measurable outcome?
Platform
Can your workspaces, repositories and systems support an approved pilot with monitoring and evaluation?
Security
Are data handling, access, oversight and review rules clear for AI use?
Evals
Are quality, adoption, cost and business results reviewed together?
Team
Do users and engineers have training, guidance and a support path?
Example answers shown. Change them to score yourself. Nothing is stored or sent. This is a directional self-check, not the evidence-based assessment scorecard.
What you take away
A plan your teams can actually use.
Every recommendation is tied to the evidence, owner, dependency and decision it supports. You leave knowing where to invest, what to govern and what to prove next.
01Scorecard
Evidence-backed readiness scorecard
Current and target state for 6 areas, with evidence, confidence, owner and the decision each gap requires.
02Map
Product-fit and use-case map
5 candidate workflows ranked by value and feasibility, with a recommendation for ChatGPT, Codex, API integration or a different approach.
03Diagram
Current-state architecture
One-page view of relevant data, identity, systems and AI touchpoints, including the boundaries a pilot must respect.
04List
Prioritized fix and control list
Security, data, integration, Codex workflow and operating-model gaps ordered by their impact on the first pilots.
05Charters
2 pilot charters
Defined owners, users, workflow, baseline, quality and value measures, safeguards and exit criteria.
06Plan
90-day roadmap and executive readout
A sequenced plan with decision gates, dependencies, accountable owners and a leadership workshop.
The engagement: 4 weeks
Clarity in 4 weeks. Momentum on day 29.
A bounded engagement with stakeholder interviews, evidence review, working sessions and an executive decision point.
WEEK 01
Align on the opportunity
Set business goals, select candidate workflows, identify stakeholders and agree on the evidence we need.
WEEK 02
Assess readiness
Use interviews, architecture and policy walkthroughs, workflow samples and bounded hands-on checks where approved to examine all 6 areas.
WEEK 03
Score, prioritize and design
Record evidence and confidence for every score, map control and architecture gaps, then shape 2 pilot charters with owners and success measures.
WEEK 04
Decide what moves next
Deliver an executive readout, a sequenced 90-day roadmap and clear go, hold or redesign recommendations.
Why Mach Advisory™
The challenge crosses strategy, architecture and change.
OpenAI adoption is not one purchasing decision. ChatGPT affects how people work. Codex changes software delivery. API integration creates a new production surface. Those choices share data, controls, economics and ownership.
Mach brings those decisions into one assessment so leaders can sequence investments and teams can move with a common plan.
Where can AI create value here, and what has to be true for that value to last?
The question this assessment answers
Powered by our platform CloudTeams™
Context stays on one record, not in a deck
The OpenAI readiness assessment runs on the CloudTeams™ Platform, the AI-powered platform we built for firms that sell expertise. Mach is its first customer.
- Contacts, notes and tasks from discovery stay on one record
- A read-only AI assistant answers questions across it, so nothing is re-explained
- One team carries the work from readout to roadmap
FAQ
Questions before we begin
Straight answers. Still curious? Ask us on a free call.
Who is this assessment for?
Leaders evaluating a broader ChatGPT rollout, engineering teams adopting Codex, or product teams moving OpenAI API prototypes toward production. It is especially useful when several of those decisions need to be made together.
Do we need to have selected OpenAI products already?
No. Product fit is part of the assessment. We begin with business workflows and constraints, then recommend where ChatGPT, Codex or an API integration makes sense.
Will Mach implement the pilots?
The 4-week assessment defines and prioritizes pilots; implementation is a separate decision. The pilot charters and roadmap are written so your team or an implementation partner can act on them.
What access or data do you need?
We agree the evidence request during kickoff. Interviews, existing architecture and policy documents, workflow examples and available usage measures are usually enough to start. Access is scoped to the assessment and governed by your requirements.
What does a typical 4-week scope cover?
A practical starting scope is 1 business unit, up to 10 stakeholder interviews, 2 working sessions, 5 candidate workflows and 2 pilot charters. We confirm boundaries in the discovery call.
Is this an OpenAI vendor evaluation?
No. The assessment starts with your business goals and constraints. It examines where OpenAI products fit, where they do not, and what conditions must be met before you invest further.
How do you score readiness?
We use the same 6-area rubric for each in-scope workflow. A finding records its evidence, confidence, current and target state, owner and recommended action. The website self-check is directional and does not replace that review.
Will you test the products in our environment?
Where access and policy allow, we can use bounded hands-on checks to validate a material assumption. The assessment does not include a production deployment or a full implementation.
Who owns the work product?
You do. Your organization receives the scorecard, diagrams, fix list, pilot charters and roadmap for its own use. Implementation can be handled by your team, Mach or another partner.
OpenAI, ChatGPT and Codex are trademarks of OpenAI. The OpenAI logo is a trademark of OpenAI. Mach Advisory is an independent firm. Product capabilities and availability vary by plan and change over time, so we confirm current details with you at scoping. The scorecard shown is illustrative and is not client data.
Project inquiry
Tell us about your project.
Tell us where you are and what you need. We will tell you honestly whether an OpenAI readiness assessment is the right next step.
- 01Tell us where you are headedA two-minute form. Share the outcome you want and any constraints.
- 0245-minute discovery callWe listen first. No pitch deck, no sales script.
- 03Your readout within one weekWhere the gaps are and which practice will move the needle fastest.
First conversation freeSenior team, not a sales repMutual NDA on request
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Project inquiry // 2 minutes
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