For decades, organizations have poured massive sums of cash into customer relationship management (CRM) systems. I’ve watched the industry’s obsession with CRM drive the very frustrations we as an industry are trying to solve.
They are great at one thing: knowing who a customer is. But they aren’t so great at capturing their current state of mind. Look at it this way: unstructured unified communications (UC) data is the live pulse of the customer, while the CRM record is usually just an out-of-date Rolodex.
Even then, by the time a customer note lands in a CRM, all context—including emotion, a sense of urgency and intent—is already lost.
Conversations capture intent, emotion and other factors in real time before anything is filtered or categorized. This means CRMs don’t reflect what the customer is experiencing in real time; they merely highlight what an agent records after an interaction ends.
That delay destroys context. When data lags, AI goes awry because CRMs simply can’t grasp real-time intent, and no amount of AI layered on top can repair a broken foundation. If a CRM was armed with the right context from the start, it would prevent nightmare scenarios where a customer calls in to cancel a flight, but AI tries to help book another flight.
This isn’t about replacing the CRM. It’s about evolving it, combining its strengths with digital channels, voice and the contact center, with AI acting as the connective tissue.
Gartner calls this experience an omnichannel conversation platform (OCP), formerly known as conversational AI platforms. Gartner also indicates that this high-growth and emerging communications technology market will reach $300 billion by 2027.
This evolution is defined by siloed tools converging into a single platform, with AI acting as the connective tissue to solve increasingly complex customer issues. The reality is, most organizations don’t even know this gap exists. If they do, data is often inaccessible or unintelligible.
Even the most advanced LLM on the market won’t do you any good without real-time customer context and intent.
The CRM Struggle Is Real
Here is exactly where traditional CRMs miss the mark: While they can track names and dates, they can’t read a room to gauge if the climate is good for the upsell or if a customer needs something else.
AI shouldn’t just summarize a transcript; it should have the nuance to tell you when a customer is seconds away from hanging up and you are losing that business. To win, organizations must eliminate the massive silos that cause AI to fail, particularly the fragmentation between communication tools and the contact center.
In high-stakes situations, if an agent is handling a billing dispute but can’t see that the customer recently spoke to a specialist in another department about a related technical glitch that caused the billing issue, the recommended next action probably won’t delight the customer, to say the least.
Communications: The Real Data Goldmine
Chat logs and voice recordings hold high-velocity data streams but often sit and collect dust. RingCentral’s 2026 Agentic AI Trends Report highlights this gap, unearthing that 90% of organizational intelligence remains trapped within unstructured voice and video data.
The real challenge for leaders resides in moving beyond ‘throwing AI at the problem’ and toward true orchestration maturity. In our report, we found that 97% of organizations use at least one form of AI. Some 40% have actually paused an initiative due to patchworking legacy systems together.
Think of orchestration as a coordination layer between AI agents, human agents and disparate systems to share information, extending end-to-end customer context. It isn’t about removing the human from the loop; it is about empowering them to make better-informed decisions.
By working in tandem with humans, organizations can establish a level of trust currently missing from the market, addressing the 38% of users who still cite a lack of trust in outcomes as a primary barrier to AI adoption. It’s a two-step shift: first, converting raw voice and video into metadata; then, bridging the gap between the back office and the frontline. The result? A seamless experience where human agents actually have the context they need, exactly when they need it.
The Remedy to Agent Burnout
When you begin to address data gaps, it doesn’t just resolve burnout; it converts operational friction into a measurable ROI. Having spent the better part of a decade being told that AI is the ‘magic touch’ for the contact center, we still haven’t seen that promise fully materialize.
That frustration isn’t merely on the customer side. Agent burnout has surged in recent years, with many citing a lack of tools and resources to perform their jobs with confidence. They are forced to place customers on hold just to search for answers that don’t exist in their view, while key info resides in a siloed system.
Orchestration isn’t a one-and-done technical upgrade; it’s a long-overdue, well-deserved form of relief for stakeholders on both ends of the phone line.
Getting From AI to ROI
If you can manage to get this right, everyone, internally and externally will rejoice. It’ll also free up human agents to do more impactful work: solving complex problems creatively and with the natural empathy only humans provide.
Get this right and the numbers will follow. We found that organizations that moved beyond the pilot AI phase to fully deploying embedded AI agents reported a 61% increase in productivity and a 49% improvement in customer experience.
The real competitive advantage will not belong to the company with the sharpest LLM, but rather the company with the most robust unified data foundation between its people and data. By unlocking that 90% of untapped data, leaders can stop relying on gut feelings and start running their business on a dataset that actually reflects the customer reality.
Without full customer context, CRMs stop driving revenue and become an expensive Rolodex. You can explore these themes and more in RingCentral’s full 2026 Agentic AI Trends Report.
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