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Modern enterprises are investing in advanced contact center solutions to improve customer service and operating efficiency while bringing AI into service journeys that span digital channels and voice. The strongest case is not “more automation” by itself: it is a connected system that gives customers useful self-service, equips agents with relevant context, and makes it possible to reach a person when needed. Survey results show substantial pressure to adopt AI, but also a gap between investment and demonstrated financial returns.
Why are enterprises investing in contact center solutions?
Contact centers are being treated as part of the customer experience and operating model, not merely as telephone infrastructure. Organizations want to improve customer satisfaction, make self-service work more reliably, and manage service operations efficiently. Cloud delivery and AI have expanded the kinds of changes a platform can support, from routing interactions across channels to assisting agents with information and routine work.
Executive pressure is one signal of that shift. In a Gartner survey of 321 customer-service and support leaders conducted in October 2025, 91% said they faced executive pressure to implement AI in 2026. Gartner also identified customer satisfaction, operational efficiency, and self-service success as leading priorities for the year. These are reported priorities and pressure levels among the surveyed leaders, not proof that any particular technology will achieve them.
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What makes a contact center solution “advanced”?
There is no universal definition of “advanced” established by the available evidence. In practice, the term is most useful when it describes how well a system connects channels, customer context, automation, agents, and measurement—not how many AI features appear on a product list.
Connected voice and digital service
Customers may start in one channel and need to continue in another. A capable design keeps the journey coherent across voice and digital interactions, routes contacts to an appropriate destination, and preserves relevant context where possible. Gartner analyst Eric Keller argued that organizations should focus on service journeys across digital and voice rather than standalone chatbots. That makes cross-channel continuity and a workable path to a human important evaluation points.
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Useful context in the agent workflow
Integration matters when it helps an agent understand who the customer is, what has already happened, and what action is needed. In its 2025 Form 10-K, filed in 2026, Five9 describes its own cloud platform as handling voice, chat, email, web, social, and mobile interactions, with integrations such as CRM intended to provide relevant customer data to agents in real time. This is a vendor description of its service, not an independent assessment of all platforms or proof of a particular result.
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AI that supports the service journey
AI can appear in different parts of the workflow: customer-facing virtual agents, agent assistance, workflow automation, call summaries, analytics, and insights. Five9 lists these among capabilities of its platform, including agentic virtual agents and natural-language bots. Such feature descriptions establish what the vendor says is available; they do not establish independent performance, accuracy, or return on investment.
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Cloud delivery and operating flexibility
Cloud contact centers can change how capacity is provisioned and where agents work. Five9 describes its own service as on-demand, with customers able to adjust usage as contact volumes change and agents able to work with a computer, headset, and broadband connection. Those are company-specific descriptions and should be checked against each candidate’s actual deployment model, geographic availability, security requirements, and implementation needs.
Analytics and responsible governance
Analytics can help teams inspect service performance and identify where journeys fail or require human intervention. ISG’s 2025 report announcement identifies generative AI, agentic AI, analytics, omnichannel engagement, and responsible AI governance as trends in contact-center customer experience. Governance should be part of platform evaluation: determine what the AI may do, what requires agent approval, how an escalation occurs, and how the organization will monitor errors and service outcomes.
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What do the adoption and return figures actually show?
Reported adoption is not the same as proven business value. The figures below come from separate surveys with different populations and methods; they should not be combined as though they describe a single cohort.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall| Finding | Survey and qualification | What it can tell a buyer |
|---|---|---|
| 91% reported executive pressure to implement AI in 2026 | Gartner survey of 321 customer-service and support leaders, conducted October 2025 | AI adoption had strong executive urgency among respondents; the result does not establish effectiveness. |
| Approximately 3× greater likelihood of using third-party GenAI than company-provided chatbots for service issues | Gartner survey of 3,566 B2B and B2C customers, conducted February and March 2026 | Customer service strategies need to account for behavior beyond a company’s own chatbot. |
| 24% demonstrated positive financial returns across their AI use cases | Separate Gartner survey of 1,303 senior leaders, conducted January through April 2026 | Positive returns were not demonstrated across AI use cases by most respondents in this survey. |
| 35% of contact centers already used agentic AI | Deloitte Digital 2026 Global Contact Center Survey, conducted in late 2025 and early 2026 | Agentic AI was already in use in a portion of surveyed centers; adoption alone does not show whether it is effective. |
| AI-mature centers had 85% greater contact-center profitability than low-maturity peers | Deloitte Digital’s reported comparison in its 2026 survey release | This is an association between maturity groups, not evidence that AI caused higher profitability. |
Gartner also reported that service and support leaders allocated a median 12% of their 2025 budgets to AI, the highest median allocation among the 10 business functions assessed in its survey of 1,303 senior leaders. Read alongside the 24% return finding from that same survey, the figures suggest a practical discipline for buyers: treat spending and successful outcomes as separate measures, and require evidence from the organization’s own deployment.
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What should I look for in a cloud contact center?
Evaluate the design against actual customer journeys and operating constraints. A feature checklist is useful only when it answers how service will work in practice.
- Channel and journey coverage: Confirm which voice and digital channels are supported, whether a customer can move between them without losing essential context, and how the customer reaches a person.
- Integration and context: Identify the CRM and adjacent systems that must connect. Ask what customer information agents see, when it appears, and what happens when an integration is unavailable.
- AI scope and control: Separate customer-facing automation from agent assistance and back-office workflow automation. For each, establish permitted actions, review or approval requirements, escalation rules, and governance responsibilities.
- Deployment and fit: Validate cloud, on-premises, or hybrid requirements; capacity changes; geographic availability; security and compliance needs; and the implementation or service-provider support required. Do not assume that one vendor’s deployment claims apply to another.
- Measurement plan: Record a baseline and define how the organization will measure customer satisfaction, first-contact resolution, customer effort, self-service completion, agent workload, operating cost, and financial return. The cited surveys do not establish universal benchmark values for these measures.
Can AI improve customer service without replacing human agents?
Yes. AI can handle selected routine tasks or help agents find information, summarize interactions, and follow workflows while people remain responsible for cases requiring judgment, empathy, exceptions, or customer preference. That is a design choice, not an automatic property of an AI-enabled platform.
Make the handoff explicit. Decide which issues can be resolved through self-service, which require agent involvement, and what information must pass along when a customer changes channel or escalates. Gartner’s finding that customers expect a human option when AI is used makes this more than an internal staffing decision: it is part of the service experience customers encounter.
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How should an enterprise judge whether an upgrade is worth the investment?
Start with a defined service problem rather than a general mandate to deploy AI. For example, an organization might target a specific journey where customers repeat information, self-service fails to resolve a common issue, or agents lack timely customer context. The target should be narrow enough to measure and important enough to justify implementation effort.
- Map the current journey. Document entry channels, transfers, repeat contacts, escalation points, systems used by agents, and the outcome customers need.
- Choose the smallest relevant capability set. Determine whether the problem calls for better routing, an integration, agent assistance, self-service, analytics, or a combination. Avoid buying features that do not address the mapped failure.
- Set baseline measures and guardrails. Record relevant customer and operating measures before launch. Define acceptable service quality, human escalation, error handling, and oversight alongside any efficiency goal.
- Run a controlled deployment. Introduce the change to a defined journey, team, or volume, and compare results with the baseline while accounting for changes in demand or policy.
- Review both outcomes and unintended effects. Check whether customer effort, satisfaction, resolution, workload, and cost moved in the intended direction. Expand only when the observed benefits and risks justify it.
Deloitte Digital’s reported profitability difference between AI-mature and low-maturity contact centers may be useful context, but it does not establish that adopting AI produces that result. Likewise, Gartner’s investment and return findings come from distinct surveys with their own populations. An enterprise’s investment case should rest on its own measured service outcomes and costs, not a headline adoption statistic.
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