For the last few years, let’s call it the honeymoon period, providers have talked about AI in terms of capability: faster search, smarter drafting, better summaries. That chapter is ending. In 2026, the narrative will shift and the users, buyers, regulators, and insurers are going to push back with demands for auditability and evidence.
Like it or not, we live in a regulated world – and if AI is going to creep into everything from our vacuum cleaners to our party planners, it’s going to have to be able to move on from big boi to big boy. This means all of us working market-wide to creating a standardized, repeatable evidence package that lets buyers, regulators, courts, and insurers verify how a system behaved, why it behaved that way, and what companies did, either to prevent it, or to respond when it misfired.
Trust is earned; we need those cramming in the AI to show us why we should allow it. Here’s why I think the market is turning, where the pressure will come from, and what “audit-ready AI” might look like in accountable deployments.
What Will Drive the Shift
1) Enterprise Demand: RFPs and Procurement Can Become a Controls Checklist
Buyers want more than a demo and a sales deck, or an incomprehensible list at a GitHub link. They want clear documentation they can drop into a shared file and hand to counsel. Based on talking to clients, and assessing systems with my team, this should include as a baseline:
- System inventory and use-cases: What models were used, where did the outcomes of each influence the following actions, and how did they impact final decisions.
- Red-team and benchmarking results: Show us the testing. Or at least the testing results. Include expected use plus jailbreak coverage; handling sensitive topics and edge cases; and how filters or systems prompts were adjusted afterwards.
- Bias/impact metrics: It’s not just about compliance for protected class treatment – that’s the floor. So lay that foundation and then describe what your adverse-impact tests showed, describe performance by subgroup, and include mitigation notes.
- Provenance/datasheets: Clear disclosure of training sources, descriptions of licenses, what cleaning or masking has been performed.
- Observability: Performance summaries, confidentiality categories and content management, and role-based access to logs or files.
- Change control: When each of the component models was embedded or updated, with what data, and offer some confirmation of rollback protections.
Buyers should be demanding this clarity: they are accountable to boards and regulators on their own behalf. The practical result is that “show me” requirements can be standardized into RFPs and procurement checklists. Those who cannot supply the credible backend will lose to a vendor who can. And the big tech firms should set the example, create the standards, and lead the way in both providing, and requiring, full accountability.
2) Regulation and Litigation: Rules of Evidence Apply
Colorado’s AI Act, California’s laws, and federal agency guidance (EEOC/FTC) are not hypothetical. They will be used to reshape discovery, forensic investigations behind harms, and other expectations during litigation:
- Impact assessments and audit summaries will be documents parties will demand and courts will expect to see.
- Developer vs. deployer responsibility is the practical, risk-based reality that will split liability across the production chain even (or especially) when there are multiple parties involved in each category. Traditional product safety and consumer protection analysis will be interpreted for new applications and expectations.
- Incident response for AI will look remarkably like cybersecurity IR: what was the issue, who triaged it, how long exposure lasted, what fixes shipped, and when/whether users were notified. But the pool of AI incidents (and the associated harm) is much broader and will require more and different stakeholders to react and respond.
If your system made or shaped a consequential decision (hiring, pricing, eligibility, content moderation) the paper trail around that decision becomes part of the case. Having a robust record doesn’t guarantee absolution but not having one might guarantee liability. And that paper trail almost certainly cannot be created in a vacuum by only one operator. Including, updating, and coordinating to address compliance needs will require cooperation by all.
3) Insurers and Auditors: Premiums and Certifications Will Depend on Competent Assurance
Cyber and tech E&O carriers increasingly ask about:
- Pre-deployment testing (safety/bias/red team) and post-deployment monitoring.
- Guardrails (policy prompts, refusal matrices) and fail-safe design (human review).
- Change management (evaluations, sandboxes, and betas run before pushing new models to production).
- Third-party model controls (zero-retention terms; data segregation; provenance).
We should expect premiums, retentions, and ultimate insurability to hinge on whether you can prove these controls exist and operate.
4) Vendor Differentiation: “Audit-Ready” as a Product Feature
As the flip side of enterprise demand, the sellers who win in 2026 won’t just proclaim model quality; they’ll start promising to ship audit-ready bundles:
- Easily extracted “explainability packets” for each major workflow that cover inputs, retrieval sources, policy prompts, risks, and human-in-the-loop checkpoints.
- Some kind of risk dashboard tied to governance thresholds (“yellow” requires review, “red” blocks release).
- Provenance attestations (standardized versions of model cards and datasheets) embedded in contracts and APIs.
- Incident playbooks with SLA-specified response standards, audit trails, and customer notification templates.
These are not nice-to-have window dressing. They address the real frustrations we see spiking in procurement processes. Vendors who offer to reduce negotiation time and proactively address customer governance demands will find disclosure and auditability are a significant market advantage.
What “Audit-Ready AI” Actually Looks Like
Put all of the above together, and audit-readiness doesn’t have to be just a slogan. It should be a set of repeatable and provable documentation that comprises an “Evidence Package” to ship with the product.
- Model Bill of Materials (MBOM): This should include base model(s) and version; fine-tuning datasets (categories, licenses, filters); RAG sources and indexing policies; safety classifiers; filter and policy prompts.
- Evaluation Suite & Results: Describe pre-deployment tests for performance, safety, bias/impact, jailbreaks, and hallucination under expected tasks. Also include thresholds and acceptance criteria and known limitations.
- Red-Team Coverage Map: Offer to provide seeded adversarial datasets; generated variants; and coverage maps (what we probed, what we didn’t). Include findings, mitigations, re-testing, and outcome evidence.
- Policy & Guardrails: Provide or describe system-specific policy prompts (e.g., confidentiality, harassment, regulated advice). Describe refusal logic guidance, intentions for escalation to human review, and user-facing notices.
- Observability & Retention: Clarify what the vendor tracks or logs (inputs/outputs/metadata), how long they hold it, access controls by vendor and customer accounts, and legal-hold switches.
- Change-Control Ledger: Provide the accounting for model updates, evaluations, risk sign-off, and rollback handles or kill switches. Include release notes that are useful and understandable by non-engineers.
- Incident Response Kit: Establish detection signals (e.g., spikes in unsafe outputs), triage and containment protocols, notification templates, timelines, and recommend post-mortem structures.
Why Aren’t Companies Doing This Now? (and How to Fix It)
There are four topic-specific arguments (beyond general cost and lack of incentives) around vendors providing some of this information: protecting IP; privacy risks from logging/documentation; bias testing that is too domain-specific; and the difficulty of achieving explainability for foundation models.
When vendors push back on sharing details by claiming it would expose IP or other proprietary methods, they’re often conflating two different things. Buyers aren’t asking for the secret sauce, but they reasonably need evidence of coverage and results measured against meaningful thresholds. The solution lies in structured summaries that describe sufficient details while protecting genuine trade secrets. They can be supplemented by on-site reviews or NDA-protected portals when the cost or complexity justify deeper verification. Similarly, the concern that all logging creates privacy risks assumes an all-or-nothing approach.
Intentional scoping and retention practices can thread this needle: disclosed logs can include minimal, purposeful metadata, delete PII while disclosing model telemetry, and offer customizable retention schedules. The key is explaining these safeguards plainly so vendors and buyers are both comfortable with the protection and the accountability.
The common objection that bias testing is too domain-specific to standardize actually points toward its own solution. Rather than treating domain specific testing as a barrier, vendors could ship impact assessment templates that buyers can adapt to their particular workforce or customer base. Including the scripts that generate tests, and not just the resulting scores, enables buyers to customize and meet their own internal standards and regulatory obligations. Since bias concerns apply to both the underlying algorithmic performance as well as the particular use case, both parties have an interest in making this assessment practically achievable.
As for the claim that explainability is impossible for frontier models, this conflates full weight transparency (which is indeed unrealistic for all audiences) with operational performance transparency (which is achievable and often will be sufficient). Showing inputs, retrieved sources, active policies and guardrails, decision checkpoints, and human override points creates the kind of explanation that should satisfy both governance frameworks, regulatory standards, and courtroom demands, all without exposing the proprietary details of model architecture.
When AI is part of a disputed decision, four questions routinely apply:
- What exactly did the system do? (Inputs, retrieval sources, user context)
- What (technical/policy/contractual) controls should have constrained it? (Refusal prompts, safety layers, business rules)
- What relevant tests were run for this use case? (And did you accept known risks?)
- What did both vendor and operator do when it went wrong? (Detection, containment, fixes, notifications)
With the standardized approach and “audit ready” packages described above, these questions can be answered by both (or all) parties to put together reasonable forensic coverage of what actually happened, when, and where. For those advising clients, or deploying internal systems for use and sale, this is the opportunity to craft the report your future self will want. A combination of policy templates, vendor diligence playbooks, litigation readiness planning, and standards for documentation will ensure future-you has the information you’ll need if/when the day to answer for an AI’s performance arrives.
The Bottom Line
The swell of demand for transparency and accountability is growing. “AI is in everything,” and “I can’t keep up with the AI features being shoved onto us” are laments I hear endlessly. They should be driving more and more enterprises to demand that providers “prove your AI is operating correctly and responsibly!” In addition, regulators, courts, and insurers are all asking for the same thing: understandable and credible evidence. If all this anticipation of accountability makes AI a little more burdensome to include or provide, that’s a feature, not a bug. When AI finally shows up ready to join the grownups, we will all be the better for it.
