Your AI is only as good as what it reads.
Most organizations have already adopted AI. Where it underperforms, the cause is usually one of two things: the content it reads is disorganized, or the people meant to use it have not changed how they work. I address both.
Book a 30-minute callThe problem is rarely the tool.
Gartner projects that at least 30% of generative AI projects will be abandoned over poor data quality, and 57% of organizations say their data is not AI-ready. The most common cause is not the technology. It is the content the AI reads: the documents, policies, and pages your organization has accumulated, its memory of how things work. In most organizations that content is fragmented, out of date, or contradictory, and an assistant can only be as reliable as what sits beneath it.
Adoption compounds the problem. Even an accurate answer has no value if no one thinks to ask for it. The result is a familiar pattern: a capable tool, a real investment, and a quiet return to the old way of working.
Why the work moves through three phases.
These are not three separate services. They are three phases of one problem, and the order matters.
You cannot repair what you have not understood. The first phase establishes exactly why the AI is underperforming: what it connects to, what content it reads, and which gaps are producing the wrong answers.
Adoption cannot be built on an unreliable foundation. When early attempts return poor answers, confidence rarely recovers. So the foundation comes first: the structure, ownership, and lifecycle of your content, so that it stays accurate and current by design.
A sound foundation still fails if the way people work does not change. Once the AI is dependable, the final phase brings people to it: role-based workflows, training, an internal network of champions, and a usage policy, with adoption measured before and after. You can start small, with a single team, and extend from there.
A single framework runs through all three.
Every reliability problem traces back to four gaps in the content your AI reads.
The audit identifies which gaps are present. The foundation build closes them. The enablement work settles people into the system once it holds. One lens, applied across three phases.
Identify your gap in 2 minutesThe right content exists, but it cannot be found.
Several versions compete, with no clear signal of which to trust.
The content is out of date, and the AI repeats it regardless.
Knowledge leaves with the people who held it.
The three phases at a glance.
A three to four week diagnostic that establishes why your AI is underperforming and what to address first. Where it finds urgent failures, a short fix-and-prove sprint can follow.
The work that repairs the foundation: structure, ownership, and lifecycle, so your content stays reliable for people and AI alike, with 90 days of activation so it gets run.
Workshops, champions, a usage policy, and measured adoption, so the tools are genuinely used. Start with a single team, or run the full program.
“Alina’s professional level in the field of Knowledge Management is extremely high… Her mastery of the subject and the way she approached it for such a broad and heterogeneous audience made the result excellent.”
Alina Pukhovskaya PhD · MBA
I am a knowledge governance, information architecture, and AI enablement consultant with 15 years of international experience and a PhD in the field. For four years I rebuilt the IT knowledge management function at KPMG US, a 2,500-person IT organization serving 35,000 employees, with ownership of the ServiceNow and Confluence platforms and the content standards behind them. Since then I have designed knowledge foundations built to serve people and AI in equal measure. I am based in Vancouver.
More about me
Start with a conversation.
The first conversation takes 30 minutes. It is diagnostic, not a pitch: a chance to understand what you are working with and where the problem actually sits.
Book a 30-minute call