Your AI is giving wrong answers. The tool is not the problem.
You have already rolled out Copilot, ChatGPT, or an internal assistant. It underperforms for one of two reasons, and usually both: the content it reads is unreliable, or your people have no settled way of working with it.
Microsoft Copilot, ChatGPT Enterprise, Google Gemini: they can only surface what is in your content, and only if that content is structured, accurate, and current. And even reliable content changes nothing if no one has a defined way of using the tool. The AI Reliability Audit examines both sides and shows you exactly why your AI is failing, in three to four weeks.
Book a 30-minute call
The AI Reliability Audit
A structured three-to-four week diagnostic that traces your AI's failures to their root, on both sides, and shows you what to fix first.
AI underperforms for one of two reasons, or both: the content it reads is unreliable, or your people have no defined way of using it. The audit examines both — not the tool itself. On the content side, every reliability problem traces back to four gaps, and I find which are hurting you with real evidence: actual questions, and the actual wrong answers your AI returned. On the people side, I read whether each function has a settled way of working with the AI, or whether it simply sits unused. Then I tell you how much of the failure is content, how much is adoption, and what to fix first.
Can your people and your AI actually find the right content, or does the right answer exist but stay buried?
Is there one trusted source, or several competing versions the AI cannot choose between?
Is your content current, or is your AI confidently citing material that is out of date?
Does critical knowledge survive the people who created it, or does it leave when they do?
Not sure which gap is hurting you? to find out.
Two things, end to end:
The content: your platforms, content quality, metadata, ownership gaps, and which of your content the AI is actually set up to read. Your tools are already logging much of this — usage reports, search logs, the questions your assistant could not answer — but almost no one opens those reports and fewer can read them, so the signal sits unused. I open them, compare what your people ask for against what exists, and run real queries against your AI to trace each failure back to its root.
The way your people work: whether each function has a defined way of using the AI, or whether the tool sits unused because no one has been shown how it fits their day.
Three things you can act on:
A Grounding Map: every AI tool you run, what each one actually reads, how it is connected and scoped, and who owns each piece. Most teams have never seen this laid out about their own stack, and it is often where the first surprise is hiding.
An Answer Scorecard: 25 real questions your team actually asks, run against your AI, each answer graded, and the reason behind every wrong one. Your own tool, on your own questions, with the causes attached.
A Fix Path: a clear read on whether the blocker is your content or the way people work, and a prioritized 30 and 90 day plan for what to fix first, driven by real demand rather than editorial judgment.
The audit is the starting point.
The roadmap tells you what to fix and in what order. Where the audit finds urgent failures, a short fix-and-prove sprint repairs the worst of them within weeks and re-runs the exact questions that failed, so you see correct answers where there were wrong ones, measured before and after. From there you can act on the roadmap with your own team, or I can help you build the foundation and get your people using the AI with confidence. The audit stands on its own, and it gives you a clear path forward either way.
What this looks like in practice.
A diagnostic assessment found that the AI-powered internal assistant integrated with their content was consistently underperforming. The diagnosis: the content behind it had not kept pace with rapid headcount growth. No ownership model, no way to distinguish current material from outdated, no single trusted source for the assistant to draw on. The audit identified the four structural gaps driving the AI failure and produced a sequenced roadmap for fixing the foundation the assistant depended on.
Running on rotating intern and volunteer teams, the problem was different: content existed but did not survive the people who created it. Every semester, the incoming team could not find what the last team built and started over. The engagement delivered a full content framework for their Notion workspace, built so that every metadata property, lifecycle state, and naming convention serves two audiences at once — the people navigating the workspace and the AI agents that query it. That dual-audience structure makes the content reliable for AI-driven and automated workflows without bolting on a separate technical layer.
The client assessed the engagement at 100% — the most business-grounded and implementation-ready consulting work the organization had received.
Alina Pukhovskaya PhD · MBA
Knowledge governance, information architecture, and AI enablement consultant with 15 years of international experience. That includes four years rebuilding the IT knowledge management function at KPMG US, a 2,500-person IT organization serving 35,000 employees — where I owned a 3,000+ article knowledge base, led a team of five, and served as business owner of the ServiceNow KM module and product owner of Confluence.
My PhD research focused on knowledge management in nonprofit networks. I have worked across Russia, Germany, the UAE, Hong Kong, Mexico, and Canada, and am based in Vancouver.
Every engagement produces diagnostics grounded in how organizational memory actually behaves at scale — not frameworks built for organizations that do not look like yours.
Security and confidentiality.
All audit work happens within your existing secure environment. I work within your permissions model and do not request access beyond what the engagement requires. Every step is documented, and your proprietary information is protected at every stage.
Don't let your AI become another layer of confusion.
Start with clarity.
The first conversation takes 30 minutes. No pitch. A diagnostic conversation to understand what you are working with, on both sides, and whether this engagement is the right fit.
Book a 30-minute call