For more than a decade, enterprises have invested heavily in conversation intelligence platforms.
And there’s been return on this investment, too. Across industries, AI is being used effectively to capture conversations, transcribe them, summarize, score, and visualize what’s needed, often in flashy dashboards.
Vendors include CallMiner, Cresta, Five9, Invoca, Medallia, NiCE, Observe AI, Verint, and others, and they’ve built powerful systems to serve this market. They address the goal of helping leaders understand what’s happened across interactionsin sales, service, and support. It’s a fast-growing market: speech analytics hit $3 billion in 2024 and is expected to more than double by 2029 per Grand View Research, with call center applications already yielding tens of billions in annual revenue in 2025.
Yet now numerous CIOs, Chief Sales Officers, and Chief Commercial Officers are running into another problem: how does all this insight help really move the needle?
The problem is that intelligence is no longer the primary constraint.
It is execution.
The Growing Role of Conversation Intelligence and AI Business Automation
Conversation intelligence has already solved many business problems, and not by accident. It grew with contact centers, with increasingly digital customer engagement, and with recognition of the power of customer experience by leaders across businesses.
As companies scale, it got harder to know what customers were really saying and where they were running into issues. Without knowing why deals were stalling, and at what parts in the process, what common objections people were having, and where reps in sales or service might be diverging, it was hard to know where attention was needed. Or where subtle adjustments could yield big benefits.
Conversation intelligence solved this blind spot. Paired with technologies like conversational AI that help companies handle customer contact and outreach at scale, it’s become far easier to capture this kind of intelligence than ever before.
This has meant a successful industrialization of valuable insights, ensuring:
- Interactions are recorded
- Speech gets transcribed and indexed
- Summaries get produced for topics, with tagging for sentiment and intent
- Scoring is applied for quality assurance
- Trends are identified across the process
These solutions have given companies the awareness to know exactly where they need to fix problems, fine-tune, and how best to continue to grow their service and sales effectively.
While learning from every interaction, companies are poised to take the next step seamlessly.
The Bottleneck: Moving AI from Insight to Action
Armed with all this knowledge, the follow-up is obvious: now you act.
But this is where the ball stops.
While thousands of conversations are being made and handled with increasing effectiveness every day—all generating actionable insights—this data is handed to the people within the organization ready to act on it.
But this is a fixed bandwidth, and today it often can’t scale at the accelerated rate of insight.
This produces an execution gap. Where you know what you need to do and how, but you cannot move on it quickly enough.
Study after study flagged this in 2025. AI brings insight and boost efficiencies, and yet companies continue to struggle to get real value. The value they can see, and the value they know they should be getting.
And deploying AI agents built for business operations looks like the obvious solution, but these remain in their infancy.
McKinsey’s state of AI from November points out that nearly two-thirds of organizations are not scaling yet agentic behavior across their organizations. MIT has reported that 95% of enterprise level pilots fail to bring value, and a Carnegie Mellon Stanford study found that despite being faster and cheaper, agents are failing to do autonomous work for most real tasks effectively enough.
That is, AI is helping us understand what’s going on, but it’s failing to capitalize.
Where Conversational AI Outcomes Often Grind to a Halt
With conversation intelligence, you now know where churn’s occurring and what messaging is working best and with whom.
Speed-to-lead (aka lead response time) is one example that can be a strong indicator of whether a lead will actually connect, qualify, and convert to revenue. Based on the elapsed time between first inquiry and meaningful contact, it’s where the rubber meets the road and a classic example of this execution bottleneck.
You can see what’s wanted, when, and where, but you can’t deliver.
In short:
- Organizations now know what happened.
- They know what needs to happen next.
- This gets worked into schedules and timelines, as insights continue to pile up.
So how, then, do we resolve this delay?
Seizing on Actionable Conversational AI Intelligence
Review processes, or human-in-the-loop timelines, are still too long, and too slow. Error rates and exceptions are such that there’s a lack of trust and often inefficient processes that are holding back action.
And as mentioned above, agentic AI remains uneven at more complex or varied tasks, even with benchmarks we’ve covered showing that they can do very well on narrow, well-defined asks.
[For more on the state of AI agents in sales, and research that demonstrates these points, check out our PTP Report on this topic.]
Integration is another problem, with data quality variation, fragmented systems, uncertain ownership, vague or shifting goals, security and compliance concerns all producing potential headaches for companies wanting their AI systems to act.
Closing the Loop with AI Now
Moving from knowledge to action is a recurring pattern:
- Analytics move to decision intelligence.
- Reporting moves to automation.
- Insights move autonomous systems.
Conversations are next. Rather than just asking what we can learn from our interactions, businesses are ready for their systems to determine what the next step is, and, if it’s within their capacity, to take action.
Today, most conversation intelligence stops short of this essential step. They’re made to assist humans, improve consistency, and inform decisions.
But for leaders under pressure to drive efficiency, growth, and consistency simultaneously, insight without execution has simply moved the bind further along, creating a new operational ceiling.
This urgency is not speculative or theoretical.
At PTP, the questions I get from enterprise leadership today have shifted to:
- How do we re-engage tens of thousands of dormant customers without exploding cost?
- How do we qualifyevery inbound lead without hiring hundreds of additional reps?
- How do we enforce consistent messagingacross thousands of conversations in sales and service?
- How do we reduce cost per interaction whileimproving conversion?
The answer is that there are AI systems that can do this today.
That can do more than just observe. It’s AI that can also act, conversation intelligence + execution.
This means systems that don’t just treat conversations only as source data to be analyzed. It means instead using them as live signals that can trigger immediate action.
I understand it’s one thing to say we must implement AI execution systems and another to do it.
So, what do I mean? Rather than do-all agentic systems, I mean conversational AI that:
- Participates in conversations, instead of just fielding them
- Operates on insights in real-time, not after reflection
- Moves autonomously but with very limited possibilities and well-defined guardrails
- Integrates directly with essential systems, instead of serving as an added tool
- Tightens your loops with human handoffs when it’s time to act
Again, this isn’t about replacing people with machines. It’s about removing bottlenecks where today we see actionable insights piling up before companies can move on them.
Operationalizing AI Insight at PTP
At Peterson Technology Partners, we have a unique perspective because we operate at the intersection of AI products and enterprise necessities.
This is why we’ve built a platform that’s not about analyzing conversations that have already ended but instead is geared entirely for key execution from the start.
Our AI agents are active participants in conversations. They exist to:
- Call customers and prospects directly.
- Send and respond to SMS messages.
- Qualify intent in real time.
- Handle common objections.
- Route outcomes to the right teams.
- Directly update CRM and downstream systems automatically.
We implement actual AI workflow execution, so your conversation data becomes a control signal, not fuel for another dashboard or report.
Conclusion: Getting More from Your AI-Driven Business Automation
I’m not suggesting that conversation intelligence platforms don’t bring value. Enterprises need the analytics and oversight they provide at scale.
But these insights alone are not translating into the revenue, efficiency, and experience improvements they could at the enterprise level.
This instead requires scaling AI calling systems, with systems that know when and how to act in safe, effective ways.
It’s a simple point of view, and one that does not require complex agentic AI to automate extensive tasks nor aim to replace humans with machines.
It’s understanding that the future of conversation intelligence is not just knowing what happened. It is automatically doing what needs to happen next.
That is the future we are building at PTP.
