Assessing AI readiness with Upland RightAnswers

Most organizations are investing in AI, but few know how ready their knowledge really is. AI is only as good as the knowledge it can trust, find, and reuse.

KMWorld recently held a webinar, The AI Knowledge Maturity Model: Assessing Readiness and Measuring Progress, with Adam Obrentz, principal knowledge consultant at Upland RightAnswers and Mark Mondello, senior director, professional services at Upland RightAnswers, who discussed assessing AI knowledge readiness, benchmarking where your knowledge stands today, identifying priority gaps across content health, governance, discoverability, readability, semantic maturity, and business impact, and planning the remediation work needed before AI scales.

According to Obrentz and Mondello, AI doesn’t solve your knowledge problem, it exposes it. Companies are pouring budget into Copilots, generative self-service and agentic search. Most will fall short of expectations, not because the models are weak, but because the knowledge feeding them is incomplete, unstructured, or ungoverned.

The organizations that win with AI in the next 18 months will be the ones who treat knowledge as infrastructure not as an afterthought, Obrentz and Mondello said.

The problem: Knowledge that is incomplete, hard to read, and not machine-ready stalls AI outcomes.

The program: An objective benchmark, the issues that block performance, and a prioritized roadmap.

The payoff: Higher self-service, faster resolution, lower cost to serve, and trusted AI answers.

As per Obrentz and Mondello, better knowledge quality delivers the following to organizations:

• Improved first-contact resolution: Agents and AI can reach clearer, more complete answers faster.
• Reduced cost-to-serve: More issues are resolved through self-service, chatbot containment, or faster agent handling.
• Higher search success: Agents and users are more likely to find one relevant answer and less likely to abandon or repeat searches.

The AI Knowledge Maturity Model framework consists of human readability, actionability and impact, and AI readiness.

A five-layer value framework connects everyday knowledge quality to the metrics your leadership tracks. This consists of:

• Financial impact: Revenue uplift, lower cost-to-serve, and stronger margins
• Strategic outcomes: Higher NPS, retention, and compliance performance
• Operational KPIs: Faster handle time, higher first-contact resolution, less rework
• User adoption: More self-service use and higher AI answer acceptance
• Technical performance: Fewer ungrounded AI answers from cleaner, AI-ready content

A RightAnswers Knowledge Assessment gives an enterprise five deliverables that turn content quality into measurable AI, service, and cost outcomes, they explained.

1. Quality Assessment: A full audit of content accuracy, structure and readability.
2. Benchmark Score: An objective maturity score showing exactly where you stand.
3. Prioritized Fix List: The highest-impact remediations, ranked and ready to execute.
4. Validation Rerun: A rerun that proves and quantifies your improvement.
5. Executive Review: A leadership session to align on findings, targets, and next steps.

For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.