Customer relationship management (CRM) tools have long served as the enterprise’s source of truth. If the data is clean and the fields are updated, they can tell an executive what is in the pipeline, what is likely to close and what revenue should look like next quarter. And more often than not, entire forecasts, compensation plans and board conversations rest on the assumption that what is inside the system reflects reality.
But anyone who has sat through a late-stage deal review knows how fragile that assumption can be.
The moment a deal truly changes rarely shows up as a field update. It happens behind the scenes, when events change before systems catch up, like a buyer hesitating on a call, even though the deal still looks healthy in the CRM.
That disconnect is why a growing number of enterprises are experimenting with conversational AI, not as a replacement for the CRM, but to see what CRMs routinely miss.
The Problem With The CRM
CRMs are excellent at structure. They capture names, dates, deal sizes and business stages with consistency. However, why they struggle to capture is context — the subtle signals that shape outcomes in real time.
“CRMs are systems of record, making them only as solid as the manual data humans feed them,” says Carson Hostetter, executive vice president and general manager of AI and customer experience (CX) solutions at RingCentral. In practice, that means critical nuance is often filtered, summarized, or lost entirely by the time it reaches the system.
Hostetter’s critique is not that CRMs are obsolete. It’s that they are late, often reflecting what a human decided to type in after the conversation, not what it vactually revealed.
A CRM may show an account as stable while a sales call reveals uncertainty about budget approval. It may reflect confidence while a customer conversation signals fatigue or second thoughts. Those gaps matter more as sales cycles stretch and buying committees grow.
Industry analysts have been tracking the industry shift in similar terms, even if they label it differently. In a Forrester blog post on the evolution of sales technology categories, the firm describes how modern revenue platforms are increasingly trying to “unify conversation intelligence, pipeline analytics and forecasting,” into a single workflow experience, precisely because structured systems alone lag behind how decisions actually unfold.
That is the real pressure point here. Enterprises are not chasing conversational AI because they need better documentation of sales calls. They are doing so because they need warnings about deals at risk, before those deals show up as problems in the CRM.
Early Signals, Mixed Proof
There is no shortage of enthusiasm around conversational AI. Enterprises are deploying tools that analyze voice, chat and video interactions to surface patterns humans may miss, from changes in sentiment to shifts in engagement.
RingCentral’s internal research, conducted with Opinium and scheduled for public release this year, suggests that organizations using AI agents are already seeing gains in productivity, customer experience and workflow speed. Those results echo what many enterprises report anecdotally: Conversation analysis can improve coaching, reduce manual work and create more consistent follow-up.
What remains harder to prove, however, is whether those insights actually translate into better revenue forecasts. Many vendors imply that better conversation intelligence leads to better forecasts. But fewer provide public, independently verifiable evidence that conversational AI improved forecast precision in a way finance leaders would bet on without hesitation.
That challenge is not unique to RingCentral or to conversational AI. It reflects a broader enterprise AI reality. Gartner has repeatedly warned that while AI adoption is widespread, many projects struggle to move from pilot value to system-level impact. In a recent outlook, Gartner predicted that over 40% of agentic AI projects could be abandoned by 2027 due to “escalating costs, unclear business value or inadequate risk controls.”
In other words, while it’s easy for enterprises to find patterns in conversations, trusting those patterns when real money is on the line remains a challenge.
Augmenting, Not Replacing, The CRM
Despite the hype around AI agents, enterprises are not racing to abandon their CRMs. Instead, they are testing how conversational intelligence can feed into existing systems without breaking them.
Hostetter is explicit on this point: Conversational AI is not meant to displace systems of record. It is meant to inform them. Structured data still anchors forecasting and reporting, but conversational data adds a layer of immediacy that CRMs were never designed to capture.
RingCentral’s research leans heavily on this point, arguing that fragmentation is the constraint holding back broader AI impact, not a lack of interest. Its survey structure also matches where enterprise adoption pressure is building. Leaders want reliability more than novelty — systems that behave predictably and integrate cleanly, not tools that merely sound human.
That preference aligns with Gartner’s warning about cost, value and risk controls, and it also aligns with how the market for conversational AI is expanding. IDC, for example, forecasts conversational AI software services growth to more than $31.9 billion in revenue by 2028, with a quoted CAGR above 40%, underscoring that enterprises are spending here because they expect it to become foundational.
The Takeaway
Still, the point is not that voice AI is magical. It is that conversation carries the “why” behind a decision, and enterprises are tired of systems that only store the “what.” If conversational AI becomes valuable to forecasting, it will be because it helps teams detect shifts early, connect that signal to the pipeline and route it into decision-making without turning every meeting into an argument about whether the AI can be trusted or not.
The enterprises testing conversational AI are not looking for certainty. They are looking for honesty — a system that can tell them when their assumptions about a deal no longer match reality. If that is all conversational AI delivers, it may be enough. And that’s because the alternative is not a better forecast. It’s another quarterly miss that nobody saw coming, even though the signs were there all along.
