Enterprise AI search strategy - how are large companies handling internal + external AI visibility?

We’ve tackled both challenges at [Large Enterprise]. Here’s our architecture:

Internal AI Search:

Implemented federated search with RAG (Retrieval Augmented Generation):

Sources: Sharepoint + Confluence + Salesforce + Internal DBs
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Connectors: Real-time sync with access control inheritance
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Vector Store: Embeddings for semantic search
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RAG Layer: Grounds LLM responses in source documents
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Interface: Natural language query + cited sources

Key results:

• Search time reduced 60%
• Employee NPS for search: 72 (was 18)
• 45% reduction in repeated questions to experts

External AI Visibility:

Different team, different strategy:

• Marketing owns GEO optimization
• Content team restructures for conversational queries
• Using Am I Cited for monitoring across platforms
• Tracking share of voice vs. competitors

The governance layer spans both:

• Access controls (who sees what)
• Audit logging (compliance requirement)
• Human review for sensitive decisions
• Data residency controls

Knowledge management perspective here. The internal search problem is organizational, not just technical.

Root causes:

• Content scattered across 15+ platforms
• No ownership of cross-functional content
• Outdated documentation stays up forever
• Tribal knowledge never gets documented

Technical solution isn’t enough:

We deployed a great AI search platform. Adoption was 30%.

Then we:

1. Assigned content owners for every major topic
2. Implemented content lifecycle (auto-archive after X months)
3. Made content contribution part of performance reviews
4. Created “knowledge champions” in each department

Adoption jumped to 78%.

For external AI visibility:

Same principle applies. You can’t optimize for AI if your content is a mess. Clean up and structure first, then optimize.

Platform selection perspective. We evaluated 8 enterprise AI search platforms.

What matters:

Top platforms we considered:

• Glean (excellent UX, strong connectors)
• Elasticsearch + custom LLM layer (maximum control)
• Microsoft Copilot for 365 (if you’re all-Microsoft)
• Coveo (strong e-commerce + knowledge)

Our choice:

Glean for most use cases + custom Elasticsearch for sensitive data that can’t leave our environment.

Hybrid approach let us move fast while meeting security requirements.

Marketing perspective on external AI visibility.

The challenge:

Our competitors are getting cited in ChatGPT and Perplexity for category queries. We’re not. This is a brand problem, not just a traffic problem.

Our approach:

1. Audit current state - Am I Cited to baseline visibility
2. Content restructure - FAQ format for key topics
3. Thought leadership - Executive content with clear expertise signals
4. Third-party presence - Analyst relations, review sites, Reddit engagement

Metrics we track:

• Share of voice in AI answers (vs. 5 competitors)
• Sentiment of AI mentions
• Citation sources (are we being cited directly or via third parties?)
• Conversion rate from AI-referred traffic

Results after 6 months:

• Share of voice: 8% → 22%
• Direct brand citations up 180%
• AI-referred traffic now 4% of total (growing)

Change management is the hidden challenge.

The workforce shift:

Employees are used to keyword search. AI search is conversational. The mental model change is significant.

What works:

1. Training sessions - Not just “how to use” but “how to think about queries”
2. Champions program - Power users who help their teams
3. Executive sponsorship - Leadership using and advocating
4. Quick wins communication - Share success stories widely

Common adoption blockers:

• “I don’t trust AI answers” → Show source citations
• “My old search worked fine” → Show side-by-side time savings
• “I don’t know what to ask” → Provide example queries
• “It’s one more tool” → Integrate into existing workflows

Target 60-80% adoption within 12 months. We’re at 72% after 10 months.

Data governance framework for AI search.

Policies we established:

1. Data classification - What can AI access? (Public, Internal, Confidential, Restricted)
2. Access inheritance - AI respects source system permissions
3. Retention - How long are query logs kept?
4. Cross-border - Data residency requirements by region
5. Model training - Our data does NOT train vendor models

Implementation:

Audit requirements:

• Who queried what, when
• What sources were used in response
• Was response shared externally?
• Quarterly access reviews

Let’s talk ROI honestly.

Internal AI search ROI:

Average enterprise AI initiative ROI: 5.9% (IBM research)

That seems low, but it’s because many initiatives fail on adoption.

What successful implementations see:

• 60% faster decision-making
• 2-5 hours/week time savings per knowledge worker
• 31% improvement in decision velocity
• Reduced repeated questions to experts

How to calculate:

(Hours saved × hourly cost × employees) - (Platform cost + implementation)

For 10,000 knowledge workers saving 2 hours/week: = 10,000 × 2 × 52 × $50/hour = $52M value

• Platform ($500K) - Implementation ($1M) = $50M+ annual value

External AI visibility ROI:

Harder to measure, but track:

• AI-referred traffic and conversions
• Brand search volume changes
• Share of voice trends
• Pipeline influenced by AI discovery

Start with leading indicators, move to revenue attribution over time.

Looking ahead: agentic AI is coming.

Current state: AI answers questions Next state: AI takes actions based on answers

Enterprise implications:

• AI search becomes AI workflow automation
• Need governance for autonomous decisions
• “What’s our policy?” becomes “Apply our policy”
• Knowledge becomes execution

Prepare now:

1. Clean, authoritative data (garbage in = garbage out)
2. Clear policies (AI needs rules to follow)
3. Workflow integration (not just search interface)
4. Human oversight patterns (when does AI escalate?)

Companies building strong AI search foundations now will transition to agentic AI faster.

Excellent discussion. Here’s our roadmap based on these insights:

Phase 1: Internal AI Search (Q1)

• Deploy Glean for primary search
• Custom RAG layer for sensitive systems
• Access control inheritance from source systems
• Change management program launch

Phase 2: Governance Framework (Q1-Q2)

• Data classification for AI access
• Audit logging implementation
• Human-in-the-loop for confidential queries
• Quarterly access reviews

Phase 3: External AI Visibility (Q2)

• Marketing-led GEO initiative
• Content restructure for conversational queries
• Am I Cited monitoring deployment
• Share of voice tracking vs. competitors

Phase 4: Measurement (Ongoing)

• Internal: Adoption, time savings, decision velocity
• External: Share of voice, citations, AI-referred conversions

Key success factors:

• Executive sponsorship (have it)
• Change management investment (budgeting for it)
• Clean data foundation (work in progress)
• Governance-first approach (non-negotiable)

Thanks everyone for the practical insights. This is exactly what we needed.