{"id":61182,"date":"2026-04-03T08:00:25","date_gmt":"2026-04-03T15:00:25","guid":{"rendered":"https:\/\/newrcblog.wpengine.com\/us\/en\/blog\/?p=61182"},"modified":"2026-04-13T16:03:31","modified_gmt":"2026-04-13T23:03:31","slug":"why-ai-adoption-isnt-translating-into-impact-8-takeaways","status":"publish","type":"post","link":"\/us\/en\/blog\/why-ai-adoption-isnt-translating-into-impact-8-takeaways\/","title":{"rendered":"8 takeaways from Beyond the Pilot: Navigating the Agentic AI Revolution session on why AI adoption isn\u2019t translating into impact"},"content":{"rendered":"

In the latest Beyond the Pilot: Navigating the Agentic AI Revolution session, Mike Egli, CX Transformation Practice Leader at RingCentral, and Melody Brue, VP and Principal Analyst at Moor Insights, brought into focus a critical issue enterprises are facing today, highlighted in the RingCentral Agentic AI Trends 2026 report<\/a>:<\/p>\n

Adoption has surged across industries. In fact, 97% of organizations are now using at least one form of AI, and 86% report having an AI strategy in place. Yet the expected shift in performance has not materialized at the same pace.<\/em><\/p>\n

Organizations aren\u2019t questioning whether AI works\u2014they\u2019re questioning why, despite near-universal adoption, it hasn\u2019t fundamentally changed how their business operates.<\/p>\n

What\u2019s driving the disconnect beneath the surface? Here\u2019s how Mike Egli explains the gap between AI adoption and impact:<\/p>\n

<\/a>AI adoption\u2019s reached scale, but it\u2019s not embedded in the workflows that drive the business<\/h2>\n

AI is present across organizations, but it is rarely positioned where work actually moves.<\/p>\n\n\n\n
\u201cWhen we see those projects go forward, they didn\u2019t really integrate. Integrate means a lot of things. There are systems integrations, like did it tie into your CRMs and your EHRs, but did it integrate into your business processes, too?\u201d<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

In most cases, AI is introduced at the point of interaction. It assists employees or surfaces information, but the surrounding workflow remains intact. Work still relies on manual handoffs, delayed decisions, and follow-ups that depend on someone taking action. AI sits on top of the workflow instead of inside it, limiting its ability to influence how work progresses.<\/p>\n

<\/a>Organizations are overestimating their AI maturity because outcomes are unclear<\/h2>\n

The visibility of AI has created a perception of advancement.<\/p>\n\n\n\n
\u201cAI is phenomenal, but there\u2019s a bit of an illusion around the maturity of organizations actually getting to outcomes with AI.\u201d<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The absence of clearly defined outcomes complicates that assumption. Many organizations can\u2019t trace AI deployments to specific improvements in performance. There\u2019s no consistent framework for measuring whether AI has reduced friction, improved responsiveness, or increased revenue.<\/p>\n

This ambiguity allows activity to be interpreted as progress. It becomes difficult to distinguish between experimentation and execution. Without outcome-based evaluation, maturity is inferred rather than demonstrated.<\/p>\n

<\/a>Lack of Agentic AI implementation<\/h2>\n

Earlier automation models were built around containment. Systems were designed to perform specific functions within predefined limits. Once a task was completed, responsibility shifted back to a human operator.<\/p>\n\n\n\n
\u201cAgentic AI offers something really unique that we haven\u2019t necessarily had before. Before, things had to be very prescriptive and very defined, and the guardrails you had to put around them were fairly strict. With agentic AI, it\u2019s very different. Even after conversations at certain points in a customer\u2019s journey, we can have awareness and drive action and workflows.\u201d<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Agentic AI allows systems to respond to context as it emerges and to initiate actions that extend beyond a single task. During a customer interaction, multiple processes can be set in motion across systems, with each action informed by the same underlying context.<\/p>\n

The significance lies in continuity. Work no longer pauses at the boundary of a task. It progresses across functions without requiring manual coordination at every step. This transforms AI from a tool that assists work into a mechanism that carries it forward.<\/p>\n

<\/a>The scale of improvement reveals how inefficient legacy processes have been<\/strong><\/h2>\n

Organizations are reporting improvements that would have previously been considered outliers. Productivity gains and efficiency improvements are no longer incremental.<\/p>\n\n\n\n
\u201cWe\u2019re seeing this massive acceleration of impact that we\u2019ve never seen before. We were happy five or ten years ago, if you could make a 2 to 4 percent improvement in business efficiency and bottom line. You see that agentic, for the first time, can hold the ball and run with it. It can build better customer experiences and drive immense change.\u201d<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

These results expose the structural limitations of legacy processes. Many workflows were designed around constraints, such as fragmented systems, delayed information flow, and reliance on human intervention for coordination.<\/p>\n

The data already shows what is possible. By deploying AI agents,<\/p>\n