Protecting Decisions That Shape Companies™
Before You Invest the AI —
Have It Reviewed by Someone Who Isn’t Selling It.
AI investments rarely fail in the model. They fail at the decision — committed under vendor pressure, peer FOMO, and assumptions no one in the room is positioned to challenge. Independent review is the one perspective the inside team, by definition, cannot provide for itself.
Serving clients across United States · Canada · Mexico · Central America · South America · Europe
The Problem We Solve
AI Failures Aren’t Technology Failures — They’re Decision Failures
Most AI investments that disappoint don’t fail because the technology didn’t work. They fail because the decision to invest was made under conditions that suppress critical scrutiny: a polished vendor demo, a board pressing for an “AI strategy,” peer companies announcing initiatives, and an internal champion whose career is now attached to the project. By the time the contract is signed, the assumptions that mattered most were never independently tested.
The cognitive forces that drive AI decisions are particularly acute. Novelty bias treats every new model as a discontinuity. Anchoring locks executives onto whatever ROI figure first appears in a vendor deck. False consensus assumes “the technology is ready” because everyone in the meeting wants it to be. Confirmation bias filters case studies to the ones that worked, and quietly removes the ones that didn’t.
The technology has rarely been the problem. The decision around the technology — that’s where the money has been lost.
ID2Solve’s AI Advisory exists for exactly these decisions. Not to question the work your team has done. Not to recommend a different vendor. To add the one perspective that the inside team — by definition, by structure, by incentive — cannot provide for itself.
Delivering True Decision Confidence™
A clear sense of certainty, just before a major commitment, that the decision at hand has been independently assessed and is built on a solid, reliable foundation.
Our Posture
We Don’t Sell AI. We Don’t Implement AI.
We Don’t Take Referrals From Vendors.
Our independence is structural, not aspirational. We have no software to license, no implementation team to staff, no vendor partnership to monetize, and no follow-on integration work that depends on the answer being yes. That structural independence is not a marketing claim — it is the reason our review works.
Vendor influence is most powerful precisely when every person in the room has a reason to want the project to proceed. An independent examiner with no stake in the decision is the only person positioned to see what the inside team cannot.
The technology has rarely been the problem. The decision around the technology — that’s where the money has been lost.
Our Process
Four Things We Examine Before You Commit
Independent AI Decision Review™ doesn’t require months of analysis. It focuses on the four areas that most reliably predict whether an AI investment will scale, stall, or quietly become a sunk cost.
01
Is the Business Problem Real, Defined, and AI-Shaped?
Many AI initiatives are answers in search of a question. We separate the genuine AI use case from the vendor-shaped one — and surface the cases where a workflow change alone would deliver most of the projected ROI without the AI.
02
Is the Company Actually Ready?
AI runs on data, workflows, integration surface, change capacity, and governance. Most AI failures are infrastructure failures dressed up as algorithm failures. We assess all five conditions before commitment.
03
What’s Actually Being Locked In?
Multi-year licenses, data residency, model lock-in, integration debt, vendor dependency. We map the point at which exiting becomes more expensive than continuing — and we put that point in writing, before you sign.
04
What Does Success — and Failure — Actually Look Like?
ROI claims pressure-tested against realistic adoption. Failure modes mapped from patterns we’ve seen repeatedly across industries. Decision-reversal triggers defined before the decision is made.
What’s Included
Every Independent AI Decision Review™ Includes
Depth is calibrated to the stakes; scope is calibrated to the decision in front of you. The following components are mandatory unless the engagement charter formally waives one.
01
AI Feasibility Study
Many AI initiatives are answers in search of a question. We separate the genuine AI use case from the vendor-shaped one — and surface the cases where a workflow change alone would deliver most of the projected ROI without the AI.
02
AI Readiness Diagnostic
Data quality and access, workflow maturity, integration surface area, change capacity, and governance — the five conditions that determine whether AI scales or stalls.
03
Workflow & Operational Optimization Review
Assessment of the underlying processes the AI is meant to augment. Often the workflow needs to be redesigned before AI is introduced.
04
Root Cause Analysis on Prior Pilots
If your organization has stalled or quietly retired a previous AI initiative, we examine why — and whether the same root causes are present in the current proposal.
05
Innovation Strategy Alignment
We test whether the proposed AI investment fits the broader portfolio of strategic bets, or whether it is being pursued in isolation against priorities that matter more.
06
Vendor & Contract Pressure Test
Total cost over the contract life, exit terms, data ownership, IP and model rights, performance guarantees, and the realistic cost of switching — examined as an experienced acquirer would.
07
Cognitive Bias Assessment
Formal review of the decision-making process for the biases most acute in AI investments: novelty bias, vendor anchoring, false consensus, confirmation bias, sunk-cost reasoning, FOMO.
08
Cross-Risk Map
How this AI decision interacts with cash position, talent capacity, leadership bandwidth, regulatory exposure, customer-data obligations, and existing technology commitments.
09
Pre-Mortem Analysis
Structured exercise that imagines the project has failed two years from now and works backward to the proximate causes — surfacing failure modes the inside team finds it hard to name in the present tense.
10
Formal Position & 90-Day Follow-Up
One of five clear recommendations: proceed, proceed with safeguards, redesign, delay, or stop. A scheduled return at 90 days to examine how the decision is holding up against the indicators we defined.
When Problems Actually Start
The AI Decision Risk Timeline
Most companies engage AI advisory help at Phase 4 or 5 — when the project is already struggling and the capital is already committed. The leverage point is between Phase 1 and Phase 2, before the contract is signed.
PHASE 1
Vendor Demos & Internal Excitement
Demos look impressive. ROI projections circulate. An internal champion forms. The board asks why the company doesn’t “have an AI strategy yet.”
↳ ID2Solve Steps In Here
The Right Time to Review the Decision
We review the decision before the contract is signed. This is where prevention costs the least, the option set is widest, and the inside team is still positioned to make changes.
Phase 2
Contracts Signed, Capital Deployed
The vendor is selected. Multi-year terms are agreed. Integration budgets are approved. The project becomes politically committed before it is operationally proven.
Phase 3
Reality Catches Up
Data quality issues surface. Workflow changes turn out to be larger than the deck suggested. Adoption is slower. Integration takes twice as long. The ROI window slips.
Phase 4
Quiet Cost Escalation
Additional licenses, additional integration work, additional change management. The original ROI case is no longer being measured against. The internal champion has moved on or grown quiet.
Phase 5
The Project Is “Paused” or “Evolved”
It is rarely declared a failure. It is paused, repurposed, or absorbed into something else. The capital is gone. The vendor relationship remains.
Every AI decision is reviewed eventually. The only question is whether you review it before or after the contract is signed.
What You Walk Away With
What an Independent AI Review Delivers
Capital Protected From Premature Commitment
Decisions made before vendor pressure peaks, with the option to delay or restructure still on the table.
Higher Probability of Successful Implementation
The investments that proceed are those whose underlying conditions actually support success. The ones that don’t are caught early.
Blind Spots Surfaced
Assumptions the inside team didn’t realize it was making, vendor claims it wasn’t positioned to challenge, and integration realities the proposal under-described.
Risk Reduced and Quantified
A formal map of what could go wrong, the magnitude of each failure mode, and which failures are reversible.
AI Decisions Aligned With Real Business Objectives
An explicit test of whether the proposed AI investment serves a top-five business priority — or competes with priorities that matter more.
Board-Ready Documentation
A written record of the decision, its assumptions, its alternatives, and its safeguards — the kind of record successors and acquirers expect to find.
Decision-Reversal Triggers Defined
The leading indicators that would tell you, within 90 days, whether to continue or to revisit — set in writing before the decision is made, when judgment is clearest.
Independence on the Public Record
When the AI initiative is announced, the company can credibly state the decision was independently reviewed — a meaningful signal to boards, investors, and regulators.