Most companies test their AI-generated answers once before they launch and then move on. The answers might still look right today, but nobody’s gone back to check. That’s because testing just once is the only option they have, even though the knowledge base keeps changing, AI models get updated, and customers keep asking new questions…
Most organizations already run SharePoint, and it earns its place: it’s a strong platform for storing documents, building intranets, and supporting day-to-day collaboration. But storing content is not the same as managing knowledge. Organizations now need to give accurate, consistent answers to the people they serve and to the staff who serve them, increasingly through…
“AI agent governance” is a broad discipline that spans tool and action permissions, agent identity and authorization, security constraints, real-time monitoring of agent behavior, and the knowledge or content the agent relies on. This article focuses on one specific layer within that stack: knowledge governance, or controlling what an AI agent knows and is allowed…
Quick answer: AI chatbots give wrong answers for a handful of predictable reasons: outdated or missing knowledge, conflicting content, unstructured source material, weak retrieval, LLM hallucination, and no feedback loop to catch errors. The fix is rarely a better model. It is trusted, governed knowledge, strong grounding, and continuous optimization. When an AI chatbot gives…
Quick answer: AI content readiness is a measure of whether your customer service content is structured, trusted, and governable enough for generative AI to use it accurately. Content a person can read is not automatically content an AI can safely answer from. Assessing and improving readiness is what separates a reliable AI deployment from one…
Enterprises may need to supplement SharePoint with governed, AI-ready knowledge platforms to enable accurate, compliant AI agent responses at scale. SharePoint served a generation of enterprise document management. However, as organizations connect AI agents to their knowledge repositories, they come across structural problems. Today’s enterprises require platforms that enforce accuracy, enable compliance, and speak the…
84% of health insurers report using AI and machine learning across product lines including prior authorization, utilization management, and disease management, according to a 2025 National Association of Insurance Commissioners (NAIC) survey across 16 states. That scale of adoption is reshaping a high-volume, high-complexity industry but also creating a new and largely unnamed risk. Compliance…
Generative AI has quietly collapsed the seventy-year separation between instruction and data. The companies that recognize this, and build the engineering discipline to match, will own the decade. A few months ago, the chief information officer of a health insurer with more than $20 billion in annual revenue made a remark that has stayed with…
Banks and credit unions are sitting on an underleveraged growth asset. For years, the banking contact center has been measured almost entirely on efficiency with key metrics including handle time, first-call resolution, and cost per interaction. The primary goal was to manage a high volume of inbound service requests, and the technology available in past years did not offer much beyond that. Now AI is handling…
Should you build or buy a knowledge management system? For most organizations, buying a commercial knowledge management platform is more cost-effective than building one in-house. Hidden costs, including security, AI development, scalability, integration, and ongoing maintenance, cause in-house builds to exceed original budgets. Commercial platforms spread these costs across many customers and continuously invest in…
