The Contact Center As A Service (CCaaS) market was valued at $7.91 billion in 2025, increased to $9.4 billion in 2026, and is projected to reach $18.83 billion by 2030. Most enterprises have already made their move. Platforms are live, AI is in production, and the cloud migration debate that consumed CX leadership teams for the better part of a decade is largely settled.
But a harder and more consequential question has taken its place, and it tends to surface about six to twelve months after go-live
Who is actually governing all of this once the implementation team is gone?
Not who logs the support ticket or which vendor picks up the outage call, but who is genuinely embedded in the environment day to day, keeping AI performance on track, managing platform changes, staying ahead of compliance requirements, and making sure the investment continues to deliver as the business grows and changes. For most organizations, that question does not have a satisfying answer, and in 2026, that gap is starting to carry a real cost.
From roughly 2022 through 2024, enterprise contact centers were focused almost entirely on getting off legacy infrastructure and onto modern CCaaS platforms. That work is largely complete, and buying teams have shifted their attention from debating which platform to buy toward asking what it is actually delivering for the business.
That shift changes what success looks like in a meaningful way. A platform going live on schedule is no longer the milestone anyone is celebrating. The metrics that matter now are ROI, AI containment rates, CSAT improvement, and operational efficiency, and increasingly the gap between a successful deployment and a successful outcome has less to do with the technology chosen and more to do with how it is being run on an ongoing basis.
AI makes this significantly more complex. Conversational AI, agent assist tools, and automated QA have moved from pilot programs to production environments across most enterprise contact centers, and they bring a challenge that does not feature prominently in vendor presentations. AI is not a static deployment the way traditional software tends to be. Models drift over time. Intent classification degrades as customer language patterns evolve and new call drivers emerge that the original training data never accounted for. The gap between deploying AI and actively governing it is where most organizations are quietly losing ground right now.
If you ask an enterprise CX leader who owns AI governance in their organization, you will typically get one of two responses: a long pause, or the name of a committee. Neither answer reflects a genuine ownership model, and that is the core of the problem.
The structural challenge here is not a shortage of talent or investment. It is that the responsibility was never formally assigned to anyone who was set up to carry it. IT manages infrastructure but not CX operations. Contact center leadership manages outcomes but not platform configuration. The CCaaS vendor manages platform uptime but not business alignment. The result is a fragmented accountability model where the most consequential operational decisions, the ones that directly affect customer experience, compliance exposure, and AI performance, end up falling into the space between teams.
The questions that expose this gap most quickly are the practical, operational ones. Who reviews AI model performance on a regular cadence and acts on what they find? Who enforces configuration standards when routing logic gets updated? Who evaluates a vendor's quarterly platform release against your specific integrations and regulatory requirements? Who maintains the documentation of why your environment is configured the way it is, so that institutional knowledge does not walk out the door when key people leave?
In most organizations, these questions get answered reactively and inconsistently, which in a CCaaS and AI environment means finding out that something has degraded from a downstream metric rather than from proactive monitoring. This is precisely the kind of gap that a dedicated, embedded managed services team is designed to close — not by fielding tickets, but by being present and accountable before problems surface.
If 2025 was the year enterprises made their commitment to AI in the contact center, 2026 is shaping up to be the year they are expected to govern it responsibly, and the regulatory environment is moving in exactly that direction.
TCPA, HIPAA, and PCI-DSS have always applied to contact center operations, but regulators are actively extending these frameworks to cover AI-driven interactions in ways that create new obligations. The FCC's February 2024 ruling classifying AI-generated voices as artificial voices under TCPA, combined with the delayed enforcement of the one-to-one consent rule now taking effect on April 11, 2026, signal clearly where regulatory attention is heading. The contact center sits at the intersection of all of these requirements simultaneously.
What makes this harder than a typical compliance cycle is the monitoring gap that already exists in most environments. Traditional quality assurance processes review somewhere between 1% and 3% of total interactions. In an AI-augmented contact center where automation is handling a significant share of customer contacts, that sample size is simply not adequate for meaningful compliance assurance, let alone AI performance management. The enterprises navigating this well have already recognized that post-go-live operations require a dedicated governance model rather than an extension of whatever vendor support contract came with the platform.
The enterprises navigating this well have already recognized that post-go-live operations require a dedicated governance model rather than an extension of whatever vendor support contract came with the platform. A CX Strategist who understands your business processes, a Platform Engineer maintaining your integrations, and a Success Manager running monthly business reviews with you is a fundamentally different operating model than a ticket queue — and the compliance exposure difference reflects that.
It is worth being clear about the distinction between Managed Services as a governance function and Managed Services as a support function, because the two are often conflated and they deliver very different outcomes.
Vendor support is reactive by design. It is built around rotating staff, ticket queues, and accountability scoped to software features and defects. When something breaks, the vendor fixes the software. What vendor support was never designed to do is govern your environment against your business outcomes, tune your AI models on a regular cadence, manage your configuration changes with discipline, or hold itself accountable to the CSAT and containment rate numbers on your quarterly business review.
Governance is a fundamentally different function. It monitors for AI model drift before your CSAT score surfaces the problem. It manages configuration changes with documentation and testing discipline so that every update to routing logic, IVR flows, or agent scripting is traceable back to a business decision. It tracks compliance requirements and updates the environment proactively rather than scrambling after an audit. It reviews operational metrics on a monthly basis and makes adjustments based on what the data shows. And critically, all of this is done by people who know your environment, your integration dependencies, and your business context, rather than whoever happened to pick up the ticket.
The model that works is one where you have direct access to your team through a dedicated Slack or Teams channel, a fixed monthly investment that covers everything with no surprise hourly charges, and a partner who measures success by improved customer experience and reduced operational overhead rather than by tickets closed. As the contact center continues to evolve into both a service engine and a data engine that feeds insight into broader business decisions, the operational model that governs it starts to look less like a support cost and more like a genuine strategic capability.
An effective Managed Services governance model in a CCaaS and AI environment covers five distinct operational functions, each of which addresses a specific gap in the typical post-go-live ownership model.
None of these functions are covered by a standard CCaaS vendor support contract, and none of them fit cleanly into an IT or operations team that is already stretched across competing priorities. All of them become more complex and more consequential as AI takes on a larger share of customer interactions. If you are evaluating what this model would look like for your environment, Condado's Managed Services practice is built around exactly these five functions, delivered by a dedicated team with no rotating staff and no hourly surprises.
If you are not sure your current model is sufficient, it is worth having a conversation with a team that has built this governance layer for enterprises at scale. You can start that conversation with Condado here.
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