Which Objects Should UC Select to Configure Service AI Grounding
Let me paint you a picture. Because of that, it's a Tuesday morning. Your support team is rolling out a new AI assistant built on your UC platform — Teams, maybe, or another unified communications system — and you're excited. Except the AI is confidently wrong. That said, finally, a tool that answers employee questions instantly. It's making up policy details, citing vacation days that don't exist, explaining benefits that were changed two years ago And that's really what it comes down to. Which is the point..
Sound familiar?
This happens more than vendors would like to admit. And the root cause usually isn't the AI itself. It's what the AI was grounded in — or more precisely, what it wasn't grounded in. Getting the configuration right means knowing which objects to select when you set up service AI grounding. Get this wrong, and you've built yourself a very fast misinformation machine Nothing fancy..
So let's dig into it And that's really what it comes down to..
What Is Service AI Grounding in UC?
Let's be clear about terms first, because "grounding" gets thrown around in AI discussions in ways that can mean slightly different things depending on the platform.
In the context of unified communications, service AI grounding refers to connecting your AI assistant or copilot to specific data sources — knowledge bases, document libraries, FAQs, ticketing systems, org charts — so it pulls from your information rather than just general training data. When someone asks "how do I submit an expense report," a grounded AI answers based on your company's expense policy. An ungrounded AI answers based on whatever it learned during training, which might be completely irrelevant to your organization.
The "objects" you select are those data sources. In most UC platforms, these might include things like:
- SharePoint sites and document libraries
- Confluence spaces or internal wikis
- ServiceNow or similar ITSM databases
- Custom knowledge bases
- FAQ documents
- Org chart and directory data
- Policy repositories
Each object you connect becomes part of the context the AI draws from when formulating responses Small thing, real impact..
Why "Objects" and Not Just "Files"?
You might be wondering why the documentation talks about "objects" instead of just "files" or "sources.Which means they connect to structured data — databases, user profiles, ticket records, real-time availability calendars. " That's because modern UC platforms don't just index flat documents. Also, an "object" can be a document library, a database table, a web service endpoint, or an integrated application. The term covers everything from a simple PDF policy manual to a live connection to your HR system Which is the point..
This matters because the more precisely you understand what you're connecting, the better your grounding will be Worth keeping that in mind..
Why It Matters: The Stakes Are Higher Than You Think
Here's what's at risk when grounding configuration isn't done thoughtfully Worth keeping that in mind. Surprisingly effective..
Trust erosion happens fast. Once your users catch the AI making something up — even once — they'll stop trusting it. And rebuilding that trust is slow. I've seen organizations where employees actively avoid the AI feature because they assume it'll give them wrong information. That's a failure of the grounding setup, and it cascades And that's really what it comes down to..
Hallucinations are embarrassing at best, dangerous at worst. An AI telling someone the wrong safety protocol, the incorrect legal disclaimer, or a fabricated process for handling customer data — these aren't minor inconveniences. In regulated industries, they can create compliance liability. In support contexts, they can create customer-facing errors at scale Simple as that..
Poor grounding defeats the purpose of the AI investment. You're paying for AI capabilities, deploying them to your workforce, and training people to use them — all so the AI can answer questions it fundamentally doesn't have the right information to answer. That's money and effort down the drain.
The flip side is equally important: well-configured grounding makes your AI assistant genuinely useful. IT tickets decrease. New employees get instant answers. Even so, it becomes the single pane of glass for institutional knowledge. Knowledge transfer improves across time zones and departments Less friction, more output..
That's the payoff. But it only comes if you select the right objects.
How It Works: Which Objects to Select
Here's the core question: which objects should you select when configuring service AI grounding in your UC platform?
The honest answer is: it depends on your UC platform and your organization's specific needs. But there are categories of objects that almost always belong in a well-configured grounding setup, and understanding why each category matters will help you make the right calls Not complicated — just consistent..
1. Core Policy and Procedure Documentation
This is non-negotiable. Your AI needs access to the documents that define how your organization operates.
What to include:
- Employee handbooks and policy manuals
- IT procedures and acceptable use policies
- HR policies (PTO, benefits, performance reviews)
- Security and compliance documentation
- Onboarding checklists and new hire guides
Why it matters: These are the documents employees reference most frequently, and they're also the documents most likely to be outdated on personal drives or buried in shared folders. Putting them in the grounding layer means the AI always serves the current version.
Pro tip: Don't just connect the entire library. Connect specific, maintained documents. A five-year-old version of your employee handbook is worse than no handbook at all because it looks authoritative while being wrong.
2. Knowledge Bases and FAQs
Structured knowledge bases are gold for grounding. They're designed to be answers to common questions — which is exactly what your AI should be doing.
What to include:
- Internal IT knowledge bases (how-to articles, troubleshooting guides)
- HR FAQs
- Product documentation for internal use
- FAQ documents organized by topic
- Best practices repositories
Why it matters: Well-written knowledge base articles have the question-and-answer structure that maps naturally to how people query AI assistants. The AI can match user queries to these articles more reliably than it can extract answers from narrative documents Surprisingly effective..
Watch out for: Outdated knowledge bases. If your KB articles haven't been reviewed in six months, you're grounding the AI in stale information. Set up a review cadence before connecting Most people skip this — try not to..
3. Org Chart and Directory Data
This one surprises some people. They think of grounding as "documents only," but structured directory data is invaluable.
What to include:
- Organizational hierarchy and reporting structures
- Department and team listings
- Role-based information (who handles what)
- Contact information and escalation paths
- Location data for distributed organizations
Why it matters: Questions like "who approves expense reports for the marketing team" or "who's the director of engineering" require structural data, not documents. Without this grounding, the AI either guesses or says it doesn't know.
4. Integrated Service Management Data
If your UC platform integrates with ITSM tools like ServiceNow, Jira Service Management, or Freshservice, you can often ground the AI in live ticket and knowledge data.
**What to
deserve special attention because they combine structure with the need for strong governance. Real-time data — like open incidents, recent changes, or in-progress deployments — can give the AI awareness of what's happening right now across the organization, but only if access controls are enforced properly.
What to include:
- Active incident and request catalogs
- Change calendars and planned maintenance windows
- Known-error databases linked to current issues
- Service catalog entries for common request types
- Historical ticket data for trend analysis (with appropriate retention policies)
Why it matters: When the AI knows there's an active outage in the email system, it can proactively inform users instead of helping them troubleshoot an issue that's already being resolved. When it can see that a change is scheduled for a specific system, it can warn users about potential disruptions. This shifts the AI from reactive to genuinely helpful.
Governance caveat: Real-time data is riskier than static documents. A grounding source that pulls from ITSM should respect field-level permissions, exclude sensitive ticket content, and have a clear data refresh policy. You don't want the AI surfacing details from a confidential HR ticket to an employee who shouldn't see it.
5. Collaboration and Communication History
Some UC platforms can index content from collaboration tools — chat channels, meeting recordings, project spaces. This is powerful but also the most sensitive grounding category Simple, but easy to overlook..
What to include:
- Public team channels and project spaces
- Meeting summaries and shared notes
- Decision logs and project documentation
- Cross-functional announcements
- Archived discussion threads with clear value
Why it matters: Much of an organization's actual knowledge lives in conversations, not documents. The decision to launch a feature, the rationale behind a policy change, the context behind a reorg — these often exist only in chat history or meeting notes. Grounding in this content lets the AI answer "why" questions, not just "what" questions Most people skip this — try not to..
The hard part: Privacy and consent. Employees typically have very different expectations about who can access their chat messages versus published documents. Before grounding in collaboration data, you need clear policies on what's in scope, how long content is retained for AI use, and how employees can opt out. This isn't just a legal question — it's a trust question. Get it wrong, and people will stop saying anything useful in writing.
6. External and Vendor Documentation
The grounding layer doesn't have to be purely internal. If your AI assistant helps employees interact with third-party tools, vendor documentation can be a legitimate grounding source.
What to include:
- SaaS tool user guides and API documentation
- Vendor support portals and known-issue pages
- Contract summaries (where appropriate)
- Integration guides for connected systems
- Compliance frameworks relevant to your industry (SOC 2, HIPAA, GDPR references)
Why it matters: "How do I reset my password in the CRM?" "What's the rate limit on the analytics API?" "Is this vendor SOC 2 compliant?" — these are all questions employees ask constantly, and the answers live outside your walls. Grounding in vendor docs means the AI can answer them accurately instead of inventing plausible-sounding steps.
Watch out for: Licensing and freshness. Vendor documentation often has usage restrictions on redistribution, even via AI. And external sources change without notice, so you need automated freshness checks rather than relying on point-in-time imports.
Building a Grounding Strategy That Lasts
Connecting sources to your UC AI is not a one-time project. It's an ongoing program that needs the same care as any other critical data infrastructure.
Start with a grounding audit. Before you connect anything, inventory what already exists. Walk through the categories above and identify the canonical sources — the ones people should be using, even if they often don't. That gap between "should" and "actually" is your biggest opportunity It's one of those things that adds up..
Prioritize by impact, not volume. Connecting every document in every SharePoint site sounds thorough but usually makes things worse. Start with the high-frequency, high-stakes sources: the ones that answer questions people ask every day, and the ones where a wrong answer would actually cause harm And it works..
Assign ownership explicitly. Every grounding source needs a named owner — someone accountable for keeping it current, reviewing access, and retiring it when it's no longer relevant. "Everyone is responsible" means no one is.
Build feedback loops. The best grounding systems get smarter over time because users flag when the AI gets something wrong. Those flags aren't just noise — they're signals that point to gaps in your grounding sources or quality issues in the underlying content.
Plan for the long term. Sources drift. Organizations change. A grounding layer that was perfect in 2024 might be dangerously incomplete in 2026. Build review cadences into the system itself: quarterly for fast-moving content like FAQs, annually for stable documentation like policies.
The Real Goal
Grounding isn't about making your AI sound smart. It's about making your AI be accurate — consistently, accountably, and in a way that scales with your organization. In real terms, the UC platform gives you the reach: every call, every chat, every meeting. The grounding layer gives you the foundation: the right information, in the right context, with the right controls And it works..
When you get both right, the AI stops being a novelty and starts being infrastructure. It becomes the thing people trust to get answers, the thing that handles the routine so humans can handle the exceptional, and the thing that makes your UC investment actually pay off And it works..
The technology is ready. The question is whether your content is And that's really what it comes down to..
