The future of agentic AI has the potential to transform the enterprise, but that transformation won’t happen by accident.

After a rush of initial investment, many organizations are now right-sizing their expectations as the realities of adoption set in. According to Gartner, by 2027, over 40% of agentic AI projects are expected to be canceled due to rising costs, unclear value, or insufficient risk controls.
But those who can navigate these challenges—and deploy AI in ways that empower, not replace, human talent—will be deeply rewarded.

Adopting AI isn’t about chasing features but building the infrastructure for long-term transformation. At RingCentral, we believe that the foundation should center around voice—the richest, most nuanced data available in the enterprise. With the proper preparation and voice as the centerpiece, companies can infuse AI into operations more quickly and drive value at scale.

So what separates the organizations that realize value from those that cancel their AI initiatives?

Why most enterprise AI projects fail, and how to avoid it

AI agents are driving a fundamental shift in how customer support teams operate. As with past technology shifts, some companies will adapt quickly, others will stall.

While every adoption journey is unique, most enterprises face three core challenges when attempting to deploy agentic AI:

  • Value creation: AI adoption is rising, but many companies struggle to translate it into real-world impact. Too often, AI is bolted onto workflows instead of embedded in ways that accelerate resolution, reduce complaints, or drive personalization.
  • Data mobility and security: Agentic AI demands real-time access to large volumes of data—something many businesses still can’t support securely or efficiently. Siloed systems and fragmented governance are major blockers.
  • Workforce participation: AI adoption only works when the people using it are bought in. That means integrating AI into familiar workflows, minimizing disruption, and reducing the barrier to entry.

Addressing the following can help organizations prepare for AI readiness and avoid pitfalls derailing many initiatives.

Start with the right goals, or risk misalignment from day one

Over the next year, 82% of companies plan to invest in tools for analyzing voice data, according to a RingCentral survey. Yet only half report having clear strategies to implement them.

Ambition alone won’t drive success. Without clear alignment, even the most promising AI initiatives can stall. The organizations seeing real results are the ones turning strategic goals into specific, measurable outcomes from the start.

Take the Detroit Pistons. Their deployment of RingCentral’s AI Receptionist is designed to resolve 50% of inbound calls, a target that reflects more than just efficiency. According to Senior Help Desk Technician Romello Morris, it’s part of a broader effort to transform how their team delivers service and supports fans.

When AI goals are clear and grounded in business outcomes, teams build trust in the tech, unlock early wins, and scale confidently.

Build the data foundation AI needs to thrive

AI is only as powerful as the data that fuels it. And for many organizations, that data remains trapped in silos.

Too often, enterprises equate system integration with data readiness. But integration without accessibility or governance is a half-finished product.

A recent CapGemini study found that 71% of companies have only partially or minimally integrated their data, creating a serious obstacle to AI success.

Even when systems are technically connected, governance and accessibility often lag behind. A modern, dynamic data strategy becomes non-negotiable. Data must be funneled continuously into the right AI systems, with outputs improving in real time. At the same time, governance policies must evolve alongside internal standards and external regulations.

RingCentral’s integrations with leading business software enable real-time data flow across systems. Our Salesforce integration, for example, helps Endeavor Capital deliver more personalized service by making it easy for sales and contact center teams to follow up.

As Regional Manager Jeff Budde said: “Thanks to this integration, our officers can review these auto-generated emails after the call and send them to the customer right from Salesforce.”

This fusion of human insight and machine intelligence unlocks AI’s full potential. But to bring that to life, organizations must invest in their most valuable asset: their people.

Equip your workforce to lead the AI transition

The tech isn’t the hard part. Helping people adapt is.

Success hinges on employees feeling empowered, not replaced. But too often, teams lack clear training or a sandbox to experiment. According to Boston Consulting Group, 62% of executives cite talent gaps as a top barrier to AI maturity.

Upskilling efforts must go beyond theory. They should build AI and data literacy in ways that are tailored to roles and responsibilities. Yet many organizations still find their footing: 63% of workers say current AI training programs could be “significantly improved.”

Bridging the divide between employee needs and management expectations is critical to long-term success.

Companies should prioritize tools with a low barrier to entry, especially those that integrate into systems teams already use. RingCentral’s AI Receptionist and AI Assist do precisely that: they embed directly into workflows, offering real-time support or post-call outputs like summaries and transcripts that enhance agent performance.

As results start to compound, internal momentum builds. Organizations shift from overcoming resistance to keeping up with demand.

AI readiness isn’t one thing; it’s everything

Adopting AI isn’t about flipping a switch. It requires steady progress across people, processes, and platforms. Leaders need to move quickly, but with care. Rushing into untested tools can create risk and erode trust. The most successful organizations start by clearing the foundational hurdles and building a responsible path forward.

Originally published Jul 31, 2025