Contact center transformation

Contact center transformation used to mean one thing: moving phones off on-premises hardware and into the cloud. That step still matters, but it's now the starting point rather than the finish line.

Today, the bigger shift is how work gets done once your contact center is in the cloud. AI can support agents during live conversations, give supervisors visibility across far more calls, and, increasingly, resolve routine requests on its own before handing the rest to people with full context.

This guide covers what contact center transformation means now, the three stages most teams move through, what changes for your people, the technology involved, and a practical roadmap for getting started.

What is contact center transformation?

Contact center transformation is the process of modernizing the technology, processes, and roles behind customer conversations so that human agents and AI agents can work across voice and digital channels from shared customer context. It's often called contact center modernization, and it applies to both inbound customer service and outbound sales teams.

In practice, transformation usually combines three kinds of change: moving to a cloud contact center platform, adding AI that helps agents and supervisors work faster and with more context, and introducing AI agents that can take action on routine requests. The goals stay consistent across teams: improve customer satisfaction, gain clearer insight into performance, reduce operating costs, and support revenue growth.

Why contact centers are transforming now

Several pressures are pushing contact center leaders to modernize at the same time:

• Customers expect continuity across channels. A customer who starts in chat and then calls in expects the agent to know what already happened. Separate systems for each channel can make that difficult.

• Cost and staffing pressure. Agent turnover, training costs, and seasonal spikes make it hard to staff for peak demand without overspending the rest of the year.

• Conversation data that goes unused. Many contact centers record and store calls but rarely turn them into decisions about staffing, training, or product issues.

• AI that can take action. AI has moved beyond transcription and summaries. AI agents can now verify a customer, look up an order, or book an appointment, then hand off to a person when a request needs judgment. Gartner has predicted that by 2029, agentic AI could resolve as much as 80% of common customer service issues without human intervention (Gartner, 2025).

Together, these shifts change the question from "How do we move to the cloud?" to "How do we run a contact center where people and AI work together on the same platform?"

The three stages of contact center transformation

Most contact center transformations move through three stages. Each one builds on the last, though teams don't have to finish one before starting the next, and different channels or teams can sit at different stages at the same time.

Stage 1: Build a cloud foundation

The first stage replaces on-premises hardware and disconnected channel tools with a cloud contact center platform. Agents and supervisors can handle calls, chats, and messages from one app on their computer or mobile device, and conversation records sync to the CRM. This stage removes much of the maintenance burden of legacy systems and creates the shared data that later stages depend on.

Stage 2: Add AI that assists

With conversations running through one platform, AI can help in the moment and after the fact. Live transcription and real-time guidance help agents answer accurately. Sentiment detection flags calls that may need a supervisor. Automated summaries and suggested dispositions cut after-call work. And AI-assisted QA lets teams review far more conversations than manual sampling allows.

Stage 3: Introduce agentic AI

In the third stage, AI agents handle routine, structured requests end to end, such as order status, scheduling, and identity verification. When a request needs judgment or empathy, the AI agent escalates to a human agent and passes along the transcript and context, so the customer doesn't have to start over. Human agents spend more of their time on the conversations that need them.

What changes for agents, supervisors, and leaders

Technology is only part of transformation. The bigger change is in how each role spends its time.

Agents

As AI agents take on more routine requests, human agents handle a larger share of complex, emotional, or high-value conversations. Real-time guidance and full context from earlier interactions help them resolve those conversations faster, and automated notes reduce the time spent on after-call work.

Supervisors

Supervisors shift from listening to a small sample of calls toward coaching based on patterns across conversations. Live views of sentiment and call purpose show where help is needed now, and AI-assisted scoring makes it practical to review quality consistently across the whole team.

Contact center and CX leaders

When every call, chat, and AI agent interaction runs through one platform, conversations become a source of conversation intelligence leaders can use to make decisions: which requests to automate next, where training is falling short, and what customers are asking about this week. That's how a contact center can turn interactions into operational insight.

Will AI replace contact center agents?

AI is more likely to change contact center jobs than replace them. AI agents can resolve routine requests, but conversations that involve judgment, empathy, or exceptions still need people. For agents, the way AI is changing call centers shows up mostly as a different mix of work: fewer repetitive calls and more of the conversations that call for a person.

Technology for contact center transformation

Transformation is more achievable than it can sound. Many teams have already taken early steps, like adding chat support or connecting a CRM, without calling it transformation. These are the core technologies involved.

A cloud contact center platform

A CCaaS (contact center as a service) platform replaces on-premises hardware with a subscription-based cloud service. Agents and supervisors can handle voice calls, messaging, live chat, and social conversations from one app, and add or remove agents without new equipment. When the contact center and business phone system share a platform, transfers to experts outside the contact center are simpler too.

CRM and other integrations

Your contact center platform should connect to your CRM so calls, messages, and activity sync to the right records automatically and agents can place or take calls without switching apps. Integrations with help desk tools like Zendesk or Kustomer, collaboration tools like Google Workspace or Microsoft Teams, and industry-specific systems reduce data silos and help agents see the full picture during a conversation. Dialpad offers contact center integrations with tools like Salesforce and HubSpot.

Omnichannel routing

The goal of an omnichannel contact center isn't just to be on every channel. It's to unify the experience across them. When voice, SMS, chat, social, and email route through one system, agents see a customer's full history in one place and don't need to ask them to repeat themselves.

Conversation intelligence and analytics

Real-time dashboards show supervisors queue status and agent performance as it happens. Beyond that, AI can surface patterns across conversations. For example, Custom Moments in Dialpad track how often specific words or phrases come up on calls, such as "refund" or "cancel," and alert managers when those topics spike. AI CSAT estimates customer satisfaction from call transcripts, so teams aren't relying only on the small share of customers who answer surveys.

AI agents with testing and governance

AI agents need access to the systems behind each request, a way to hand off to people with context, and guardrails around what they can do. Look for tools that let you test AI agents against realistic conversations before launch and monitor them once they're live.

Dialpad developed its UCaaS and CCaaS products natively on a shared foundation, so calling, messaging, meetings, and contact center work share one app and one set of customer data rather than being packaged from separate products. Dialpad AI Agents run on that same platform alongside Dialpad Support for contact centers.

A contact center transformation roadmap

You don't have to do everything at once. Many teams find more success starting small, measuring results, and expanding from there. A practical digital transformation roadmap for the contact center often looks like this:

1. Assess where you are now: Document your current systems, channels, and pain points, and identify which requests your agents handle most often. In Dialpad, Skill Mining analyzes human-to-human contact center calls and recommends AI agent skills based on the share of calls each one could handle.

2. Set baseline metrics: Record today's numbers for the outcomes you plan to improve, such as average handle time, transfers, first contact resolution, and customer satisfaction, so you can compare before and after.

3. Consolidate onto one platform: Bring voice and digital channels together and connect your CRM, so customer context is shared across teams and channels.

4. Roll out AI that assists: Start with capabilities agents feel right away, like live transcription, automated summaries, and real-time guidance, then extend to AI-assisted QA and coaching.

5. Pilot AI agents on one or two use cases: Choose high-volume requests with a clear outcome, and test AI agents against realistic conversations before going live. Dialpad's Proving Ground provides a pre-production environment for that testing.

6. Set governance: Define what each AI agent can access and do, and how you'll monitor it in production. Guardian, Dialpad's built-in governance layer, monitors AI agent interactions in real time to help reduce data exposure risk and support safe, reliable performance. Review Dialpad's security and compliance practices with your Legal and Compliance teams.

7. Measure quality, then expand: Track resolution, repeat contacts, and customer sentiment alongside deflection, and use what you learn to choose the next use case to automate.

Common contact center transformation pitfalls

A few patterns can slow transformation down or limit its impact:

• Layering AI onto a fragmented stack: When AI tools sit on top of separate systems for telephony, routing, CRM, and analytics, context can get lost between them, and insights may never reach the people who could act on them.

• Migrating everything at once: Large, single-cutover projects can raise risk and delay results. Phased rollouts by team, channel, or use case are often easier to manage.

• Measuring deflection alone: A high deflection rate can hide a poor experience if customers give up rather than get help. Pair it with resolution and sentiment.

• Skipping change management: Agents and supervisors need to understand what AI will handle, what it won't, and how their work will change. Training and clear escalation paths help adoption stick.

Results teams have seen

In an IDC Business Value Study based on interviews with Dialpad customers, call center agents were 13% more productive and call center managers were 24% more productive after adopting Dialpad.

Individual teams have seen results at different stages of transformation:

• Continental Stock Transfer & Trust replaced a legacy communications platform its provider no longer supported, choosing one platform for both contact center and voice. "We're expecting to save 40% annually with Dialpad," says John Torres, Vice President and Director of Customer Communications. The team also built out its QA scorecard process to score more calls each week.

• Camino Financial replaced a rigid phone system with Dialpad and cut resolution time from days to hours, using Salesforce integration and real-time analytics across its sales and service teams.

• MarketStar, a global sales and marketing outsourcing company, moved its sales organization off an outdated on-premises phone system and now uses AI Live Coach Cards to help reps during live calls. "I'd like to think of it as less of a phone system, and more of a platform for transformation," says Keith Titus, CEO & President.

Contact center transformation by industry

The stages are similar across industries, but priorities differ based on customer needs and regulatory requirements.

• Retail and ecommerce: High volumes of order, shipping, and return questions make retail a natural fit for AI agents, with human agents handling exceptions and upsell opportunities. Western wear brand Tecovas connected Dialpad with its Kustomer help desk so agents see a unified view of each customer during calls.

• Healthcare: Patient access teams prioritize secure, compliant communication. Scheduling, reminders, and intake are common starting points for automation, while clinical and sensitive conversations stay with staff.

• Financial services and insurance: Teams often focus on compliance monitoring, identity verification, and consistent answers to policy and account questions, with AI-assisted QA helping review conversations at scale.

• Travel and hospitality: Round-the-clock coverage matters most. AI agents can handle routine booking changes and questions outside business hours, with teams across time zones covering the rest.

Across industries, the common thread is the same: a shared CX platform and connected systems make it easier to improve both efficiency and customer satisfaction at every stage.