Artificial intelligence pilots often fail, not because models are weak, but because enterprise workflows are disjointed. That presents a major hurdle when companies try to realize ROI, because enterprises want to scale what works, not run more tests.
A 2026 RingCentral agentic AI study found that most organizations today have some level of AI adoption. That study also noted that some 97 percent of organizations use at least one form of AI, and nearly 90 percent say they have an AI strategy in place.
These numbers are surprising because the same report found that many organizations have called off AI initiatives, particularly when moving from pilot to production, where operational challenges begin to surface. Most companies can't figure out which workflows to automate or, if they do, they lack the institutional knowledge to pull it off.
Even today, many AI success stories still revolve around pilots and demos. And measurable ROI is often painfully slow or never fully realized. Forrester Research found that only 10 percent to 15 percent of pilots make it into production. Moving beyond the trial phase of AI to production is only the first hurdle.
Deploying AI across global operations is even harder due to legacy infrastructure and fragmented data environments. These systems also often struggle to talk to each other. As a result, 40 percent of organizations have paused or called off at least one AI initiative, RingCentral data found.
Deloitte's 2026 "State of AI in the Enterprise" report emphasized that moving from pilots to scale requires governance frameworks, unified data strategy, and workforce readiness, not just better models.
Fragmentation: The Real Failure Point
Enterprise workflows span multiple systems, from unified communications/customer experience platforms, CRM databases, ticketing tools, and operational software. When context doesn't move cleanly across these systems, that's when AI deployments struggle to scale and we hit the aforementioned Pilot Paradox.
Agents might summarize conversations or recommend actions, but they often cannot reliably execute them by retrieving the correct CRM record, updating a case, or triggering the next workflow step.
When workflows remain fragmented, automation stalls, and employees and customers repeat steps across channels, creating a frustrating and costly inefficiency.
Nearly half of stalled AI projects cite integration complexity, while one-third cite internal resistance or unclear ROI. There is a viable fix, and it comes in the form of process orchestration.
Enterprise leaders aren't looking for more sophisticated bots; they need systems that can operate reliably in the real conditions of enterprise infrastructure. On the ground, real-time conditions equate to messy data, multiple systems, and employees who can't wait for IT tickets to move tasks along.
The orchestration layer here is critical: It connects AI agents, people, and enterprise systems, keeping work flowing smoothly instead of getting stuck in silos.
Treating this solely as an IT project misses the point.
Scaling AI represents an operating model shift. IT maintains architecture and security, but operations teams own the workflows and performance metrics that determine whether automation actually works.
Voice AI as an Enterprise Input Layer
Many enterprise workflows begin with a conversation, not a form. This is why capturing voice interactions allows organizations to structure intent, urgency, and context that static automation often misses.
This is where agentic voice AI becomes most effective by translating unstructured conversations into actionable workflow inputs. Traditional automation relies heavily on structured inputs, such as forms and fields. Conversations provide richer signals, helping systems understand intent and take actions that humans would normally handle.
With voice acting as an input layer, orchestration systems can route requests, trigger workflows, document interactions, and even escalate issues in real time.
This shift also reinforces the role of operations teams, who understand how workflows actually function across the organization. Security remains equally important. Voice data and conversational intelligence can be processed within enterprise infrastructure. This ensures sensitive information remains internal rather than flowing into public models.
With proper controls, including permissions, audit trails, and retention policies, conversational inputs can drive automation while maintaining trust and compliance.
CIO Considerations
To survive beyond the pilot phase, leaders should focus on operational readiness over model performance.
Teams can analyze call transcripts and chat logs to detect bottlenecks, automate repetitive tasks, define orchestration, prioritize workflow integration, ensure reliability, capture operational signals, and measure lasting outcomes.
While AI pilots show potential, workflow orchestration ensures value translates into production and ROI. Not addressing workflow fragmentation carries serious consequences across every level of the organization: wasted investment, lower employee productivity, poor customer experiences, and missed revenue opportunities.
CIOs who don't prioritize an orchestration-first mentality run the risk of being left behind by competitors that have long come aboard.
Staying ahead is simple and boils down to how successfully companies can map out these end-to-end workflows that connect AI with people/systems while establishing operational metrics. This is a winning combination to ensure AI pilots deliver measurable ROI at scale and never stall.
John Finch leads product marketing at RingCentral, where he drives global go-to-market strategy, positioning, and messaging for the multi-product Agentic AI Customer Communications portfolio. He has held product marketing leadership roles at Zendesk, Dialpad, Serenova (LiveOps), and Genesys, launching enterprise SaaS contact center solutions into the market.