What is conversation intelligence?

Conversation intelligence is software that captures and analyzes customer-facing conversations, usually sales calls and meetings. It uses AI to turn raw audio and video into something you can search, review, and learn from.

At a basic level, a conversation intelligence platform:

• Records and transcribes calls
  
• Tags topics, questions, or key moments
  
• Makes calls searchable across the team
  

More advanced systems layer on:

• Detection of objection handling, pricing discussions, or competitor mentions
  
• Metrics such as talk ratios or time spent on specific topics
  
• Coaching views for managers and enablement
  

In practice, conversation intelligence does not magic away the hard work of understanding your sales motion. It does not instantly answer “How do our best reps run discovery?” or “What exactly do champions say when they secure internal buy-in?” It provides raw material and patterns that enable you to answer those questions with less manual effort.

The difference is in how efficiently you can go from: “We think our discovery is inconsistent” to “Here are five concrete behaviors that show up in 80% of our closed-won deals that are missing from most stalled opportunities.”

Some tools stop at the recording and transcription layer. Others offer more opinionated views and workflow integrations. That is where they start to overlap with both AI note-takers and revenue intelligence platforms.

What is revenue intelligence?

Revenue intelligence zooms out from individual calls to view the broader revenue picture.

A revenue intelligence platform brings together:

• CRM data (opportunities, accounts, stages, amounts)
  
• Activity data (emails, meetings, calls)
  
• Sometimes, product usage and billing signals
  
• In many cases, conversation data is one of several inputs
  

It then uses this combined dataset to give you:

• A more accurate forecast
  
• A view of which deals are healthy or at risk
  
• Coverage and conversion metrics across segments and stages
  
• Trends in how your revenue engine is performing over time
  

If conversation intelligence is strongest at answering “What really happened in this meeting?”, revenue intelligence is strongest at answering “What is really happening in our pipeline?”

The user base reflects that. Reps, managers, and enablement typically use conversation intelligence tools regularly. Revenue intelligence is widely used by CROs, RevOps, and Finance, especially during forecast cycles and quarterly planning.

Where AI note-takers fit in

In parallel, a new category has exploded: AI note-takers and meeting copilots.

These tools started from a simple premise: people are tired of taking notes in meetings. They capture audio, transcribe it, and produce summaries, action items, and highlights.

Over time, many of them have added:

• Tags or labels for topics
  
• Suggested follow-up messages
  
• Basic analytics, such as talk time or engagement
  
• Integrations that log notes in CRM and project tools
  
  

For many teams, AI note-takers are their first experience with “conversation intelligence,” even if the vendor does not use that label. They solve an immediate pain point and improve documentation.

Where it gets interesting is when these tools start to talk about “deal insights,” “buying signals,” or “pipeline visibility.” At that point, they are stepping into territory that overlaps with both conversation intelligence and revenue intelligence.

On a comparison table, you might see:

• AI note-taker: real-time transcription, summaries, action items, CRM logging
  
• CI platform: recordings, analytics, coaching, CRM sync
  
• RI platform: pipeline analytics, forecasting, risk scoring, sometimes embedded CI
  

The list of checkmarks can look nearly identical. The meaningful differences show up in the depth of insights, transcription accuracy, speaker diarization accuracy, and the degree to which the system is integrated into your sales process.

Conversation intelligence: strengths and limits

With those definitions in place, it is easier to discuss what conversation intelligence is genuinely good at and where its limits lie.

Better visibility into calls and meetings

The most immediate benefit of CI is simple: you no longer rely on memory and scattered notes to understand what was said in a call.

Managers can:

• Review key moments from important deals.
  
• Listen to how new reps handle tough questions.
  
• Compare how different reps approach the same type of conversation.
  

Reps can:

• Revisit what a buyer actually asked for.
  
• Check the exact language a champion used when describing internal stakeholders.
  
• Prepare for follow-up meetings with a richer context.
  

These tools help facilitate coaching and reduce misunderstandings and dropped balls in complex deals.

A foundation for pattern-finding

Over time, conversation intelligence can facilitate analysis of patterns across many calls.

For example, you might want to know:

• What is commonly discussed in opportunities that reach the proposal stage but rarely stall after that?
  
• How often do reps explicitly confirm the budget and timeline on calls that later slip?
  
• Which discovery questions tend to lead to clearer next steps?
  

Answering those questions still requires thoughtful analysis and, often, human judgment. However, without CI, you would be manually sampling a handful of calls at best. With CI, you have full coverage and a searchable context.

Intersight’s view is that this is where conversation intelligence becomes most valuable: not just in replaying what happened, but in supporting structured ways to learn from it and feed those learnings back into how you sell.

Where CI alone is not enough

At the same time, CI alone does not provide a complete picture of your revenue engine.

You might have excellent visibility into calls but still struggle with incomplete CRM data, still debate the weekly forecast, or lack a shared view of risk at the deal and segment levels.

That is where revenue intelligence comes into play.

Revenue intelligence: strengths and limits

Revenue intelligence is strongest when it has reliable inputs. It cannot fix bad data on its own, but it can make good data far more usable.

Seeing the whole pipeline, not just individual calls

Revenue intelligence lets you see:

• How much pipeline have you actually built against the target
  
• How deals are progressing through stages over time
  
• Where deals tend to stall or slip
  
• Where activity and engagement do not match the story in the forecast
  

It connects data points that are hard to stitch together manually, especially in larger teams or multi-region organizations.

Informing strategic decisions

With a good revenue intelligence layer, leaders and RevOps can:

• Evaluate whether their forecasting methodology aligns with reality.
  
• Identify segments with strong or weak conversion.
  
• Understand which motions or products are driving growth.
  

These are essential questions for budget planning, hiring, and GTM investments.

Where RI without CI falls short

However, revenue intelligence built on incomplete or inaccurate data has a hard ceiling.

If CRM fields are rarely updated after calls, next steps are vague or missing, and stakeholders don’t get recorded, even the best RI engine is modeling on partial truth.

This is why many teams eventually work backward from “Our forecast feels off” to “We need better conversation capture and more reliable deal updates.”

Key differences between CI and RI

Summarizing the distinctions:

• Scope: CI focuses on the content and quality of interactions. RI focuses on the state and trajectory of the pipeline and revenue.
  
• Primary users: CI is most heavily used by reps, managers, and enablement. Leadership, RevOps, and Finance use RI most heavily.
  
• Outputs: CI produces call-level insights and coaching inputs. RI produces pipeline health views, forecast models, and strategic signals.
  

They are not competing in theory. In practice, how vendors package and describe them can be confusing. That confusion increases as AI note-takers move upmarket and RI platforms embed their own CI or meeting-capture modules.

How AI note-takers blur the lines

AI note-takers are worth mentioning again here, because they are often where teams first encounter overlapping claims.

A typical AI note-taker promises to:

• Join calls automatically
• Transcribe in real time.
• Generate a summary and list of action items.
• Generate notes and summaries based on each transcript 
• Make note sharing easy and automated.
  

More advanced offerings highlight:

• “Deal insights” from conversations
  
• Extraction of “buying signals.”
  
• Suggested follow-up emails
• The ability to write the meeting record to CRM. 
• Basic metrics such as who spoke when, and how engaged the meeting was
  
  

On a one-page comparison, these sound very close to “conversation intelligence.” When you add CRM sync and simple deal tagging, they begin to resemble small pieces of “revenue intelligence.”

The key point for buyers is this: overlapping feature descriptions do not mean equal depth or equal impact. Two tools can both say “auto-fill CRM from calls,” yet behave very differently in practice.

Questions worth asking include:

• Does this tool simply paste a paragraph of notes into a field, or does it propose specific structured updates?
  
• How well does it handle our custom fields and sales methodology, not just generic labels?
  
• How often are the suggestions correct and valuable enough that reps accept them?
  

These questions matter just as much for “AI note-takers” as for tools explicitly marketed as CI or RI. Execution is where differences between vendors become apparent.

How the layers work together in a modern GTM stack

Rather than thinking “Do we need conversation intelligence or revenue intelligence?”, it can be more helpful to think in layers.

Layer 1: High-quality conversation capture

First, you need an accurate record of what happens in customer conversations. That can come from an AI note-taker, a purpose-built CI platform, or a meeting-capture feature within a broader system. The important thing is that:

• Calls are reliably captured
  
• Summaries are good enough that people use them.
  
• Key details are not lost between meetings.
  

Without this, the other layers won’t work.  

Layer 2: Translating conversations into structured data

Second, you need to turn the insights from those conversations into structured updates that the rest of your stack can use.

This is where many tools make similar claims, and where approaches differ the most in practice.

Some tools:

• Dump summaries into a notes field and leave it at that.
• Cannot distinguish whether a speaker works for the seller-side vs. the buyer-side 

Most tools on the market do not use any algorithms or heuristics to map meetings to deals, limiting the user’s ability to gain revenue intelligence. 

Few tools today: 

• Suggest updates to specific opportunity, account, or contact fields
  
• Apply consistent logic to next steps, decision-makers, and deal-risks.
  
• Anchor their suggestions to your specific process and CRM fields.
  

Intersight’s philosophy is that this layer is crucial. Conversation intelligence is only as valuable as the actions it enables. That means generating structured, trustworthy updates and guidance that work with your CRM and workflows, rather than sitting on the side as another system to check. 

Layer 3: Aggregating into revenue intelligence

Once conversations and CRM fields better reflect reality, revenue intelligence has a solid foundation to work with.

At this stage, you can:

• Trust your forecast models more
  
• See where risk is concentrated.
  
• Analyze conversion across segments or stages with greater confidence.
  

The better your inputs, the less time you spend arguing about whether the numbers are real, and the more time you spend deciding what to do about them.

Which comes first: CI, AI note-taker, or RI?

The honest answer is: it depends on your current pain.

If your biggest struggle is that no one remembers what was said in calls, or coaching feels like guesswork, then starting with an AI note-taker or CI tool makes sense.

If your biggest struggle is that your forecasts are consistently off, or your board is losing confidence in the numbers, then starting with a revenue intelligence platform might be the right call, with a plan to tighten up conversation capture soon after.

In many organizations, the path looks like this:

1. Start with AI note-taking or CI to reduce friction and improve visibility.
   
2. Realize that structured updates from those conversations dramatically improve CRM hygiene.
   
3. Layer on revenue intelligence once you are confident the underlying data reflects reality.
   

Intersight is focused on making that journey smoother by treating conversation intelligence and revenue workflows as a single, connected system, rather than separate tools that never quite align.

Questions to ask when you evaluate tools

No matter which category a vendor says they are in, a small set of questions can help you cut through the labels:

• What decisions will this actually help our team make?
  
• Whose job gets meaningfully easier if we implement this?
  
• How does this improve the quality and completeness of our CRM data?
  
• How does it integrate into the tools and workflows we already use?
  
• How much effort will it take to see value beyond the first few calls or reports?
  

If you anchor your buying decisions in those questions, “conversation intelligence vs revenue intelligence” stops being a branding exercise and becomes a practical design choice.

You can then decide, with clear eyes, where you want to start and how you want these layers to work together over the next year, rather than chasing buzzwords or overlapping feature lists.