RESEARCH · GUIDE
PSG EXECUTIVE SERIES · NO. 01
The executive checklist for AI-ready operations.
Six dimensions that determine whether AI will produce measurable results in your business — and a practical scorecard for knowing where you stand before you invest.
PLATINUM STRATEGY GROUP · 2026
WWW.PSG-INC.COM
Most AI initiatives don’t fail on technology. They fail on readiness.
Access to AI tools has never been easier — and the gap between a promising pilot and a production capability that shows up on the P&L has never been more visible. That gap is rarely a modeling problem. It spans data quality, ownership, governance, workflow design, and the organization’s willingness to change how work gets done.
Readiness is not a technology audit. A company can run modern infrastructure and still fail on unclear data ownership, ungoverned usage, or a use case chosen by enthusiasm rather than value. This guide gives you a structured way to see the whole board before committing capital.
THE SIX DIMENSIONS
Strategy & use-case clarity
One measurable business outcome, not a technology ambition.
Data foundation
Accurate, accessible, permissioned, and current enough to trust.
Technology & security
An environment AI can run in without creating new exposure.
Governance & risk
Ownership, review, and rules the business will actually follow.
People & adoption
Skills, incentives, and change discipline where the work happens.
Measurement & ROI
A baseline, a target, and someone accountable for the delta.
How to use this guide. Work through each dimension with the leader who owns it. Score every item 0–4 (0 = absent, 4 = operating at scale). The dimensions are interdependent — strong technology cannot compensate for weak governance — so read low scores as sequencing guidance, not just gaps.
Strategy & use-case clarity
Owner: CEO / strategy lead
One business outcome is defined first. A measurable target — cycle time, forecast accuracy, cost per transaction — not “adopt AI.”
The workflow is mapped before the model. Process, exceptions, handoffs, and approval logic are documented for each candidate use case.
Use cases are ranked by value, feasibility, and data availability — and someone senior has said no to the rest.
An executive sponsor owns the initiative with budget authority and a named operating lead beneath them.
Build vs. buy is a deliberate decision. Prebuilt tools where the task is standard; custom builds only where the workflow is a differentiator.
Data foundation
Owner: CFO / data lead
The data behind each use case passes five tests: accurate, accessible, permissioned, current, and relevant to the decision it supports.
Key metrics have one definition. A KPI dictionary exists, and leadership sees the same numbers from the same source.
Every critical dataset has a named owner accountable for its quality — not a shared inbox.
Cleanup is scoped to what changes the outcome. You are not boiling the ocean before the first deployment.
Sensitive data is classified — you know what AI tools may touch, what they may not, and where that boundary is enforced.
Technology & security
Owner: CTO / IT lead
AI runs inside your environment and access controls — identity, permissions, and logging carry over; no shadow accounts.
Sensitive workloads have a private-environment option where data residency or confidentiality demands it.
Integration paths exist — the systems that hold your workflows (ERP, CRM, ticketing) expose APIs or connectors AI can work through.
Architecture won’t trap the pilot. The first deployment can scale without a rebuild or an unsuitable vendor lock-in.
Security review is part of deployment, not an afterthought — data flows, retention, and third-party terms are assessed before go-live.
Governance & risk
Owner: COO / risk lead
Three questions have named answers: who approves deployment, who reviews output quality, who responds when something goes wrong.
Usage rules fit on one page. What data may be used, which tools are sanctioned, what requires human review — written for the business, not for auditors.
Human review is placed by risk, not habit — straight-through where stakes are low, mandatory review on exceptions and high-impact output.
Output is monitored in production. Accuracy and drift are checked on a schedule, against a documented baseline.
Regulatory exposure is mapped — you know which frameworks apply to your industry and where AI decisions touch them.
People & adoption
Owner: CHRO / operating lead
The people who own the workflow are in the room from design onward — adoption is designed in, not requested after.
Training is role-based, tied to the specific tasks each team performs with the tool — not a generic AI course.
Judgment stays with the team. People know what the tool decides, what they decide, and how to override it.
Time saved has a destination. Hours returned by automation are reassigned to named higher-value work, or they evaporate.
Usage is visible. You can see who is using the capability, where it’s ignored, and why.
Measurement & ROI
Owner: CFO
The manual baseline is documented before deployment — cycle time, error rate, hours — or improvement can never be proven.
Metrics connect to money. Model accuracy matters only insofar as it moves revenue, cost, or risk.
Review cadence exists. Results are examined on a schedule with authority to scale, fix, or kill.
Total cost is honest — licenses, integration, data work, and the team’s time, not just the subscription line.
Someone is accountable for the delta. A named owner reports the measured gain to leadership, on a date.
Reading your score.
Score each dimension 0–4 and total out of 24. The bands below are a planning tool for internal discussion — the pattern of scores matters more than the sum. A single 0 or 1 in data or governance caps what every other dimension can deliver.
A common pattern we see: strong technology, weak governance. The fix is rarely more tooling — it’s ownership, one page of usage rules, and a review cadence the business will keep.
From score to operating capability.
DAYS 1–14
Assess
Run the six-dimension scorecard with each owner. Document the manual baseline for the two workflows that consume the most hours.
DAYS 15–45
Prepare & pilot
Fix the blocking gaps only. Write the one-page usage rules. Deploy one use case with production intent and human review on exceptions.
DAYS 46–90
Measure & decide
Compare against baseline. Scale, fix, or kill — in writing, with the next use case already sequenced.
YOUR MOVE
Want this checklist run against your business?
PSG runs the full readiness assessment as a structured two-week diagnostic — scored, evidenced, and delivered with a sequenced plan. It begins with a complimentary 30-minute consultation.
Michael Yakubin · Founder & CEO
michael@platinumstrategygroup.com
WWW.PSG-INC.COM