Dialpad vs. Mitel: communications and contact center evaluation guide

What to consider when evaluating communications and contact center platforms

Dialpad and Mitel represent different approaches to communications infrastructure. Mitel has a long history in enterprise communications, with its core MiVoice Business platform supporting on-premises and hybrid deployments, and Mitel CX as its AI-powered contact center product. Dialpad Connect and Dialpad Support are cloud-native platforms designed to bring AI, voice, and digital channels together in a single system.

Organizations evaluating both are often weighing existing infrastructure investment and deployment flexibility against cloud-native AI capabilities, time to value, and long-term operational simplicity.

This guide covers some of the criteria worth examining as you evaluate where your communications and contact center infrastructure is headed.

Deployment model and migration path

One of the more meaningful differences between these platforms is how they approach deployment. Mitel's MiVoice Business supports on-premises and hybrid configurations, which can be an advantage for organizations with existing Mitel infrastructure or specific compliance requirements. Dialpad is fully cloud-native.

Questions to ask:

• Which deployment model fits your organization's current infrastructure and compliance requirements?

• If you are moving from an on-premises or hybrid environment, what does the migration path look like and what does vendor support include?

• How does the deployment model affect the speed at which new features and AI capabilities become available?

• What is the total cost of ownership across the full deployment, including implementation, professional services, and ongoing administration?

AI architecture

How AI is built into a communications platform has become a meaningful factor in evaluations. The depth of that integration, and how early in the interaction it activates, has real implications for what agents and teams can act on.

Questions to ask:

• At what point in the interaction does the platform's AI activate, and what can it act on in the moment?

• Is AI built into the core platform, or delivered through a separate product or third-party partnership?

• How does AI inform routing decisions, agent support, and supervisor visibility during live interactions?

• Does the platform draw on proprietary data to inform its AI, and if so, how does that affect accuracy and relevance?

AI agents

AI agents are a common part of contact center evaluations, and what vendors mean by the term can vary.

Questions to ask:

• Can the AI agent handle multi-turn conversations and reach resolution for appropriate issue types?

• When an AI agent escalates to a human agent, what context carries over, and is it enough for the agent to continue without asking the customer to repeat themselves?

• How can teams configure, test, and validate AI agent behavior before deploying to customers?

• How does the platform support ongoing optimization of AI agent performance over time?

Conversation intelligence

Contact center and communications platforms generate significant volumes of customer conversation data. Where platforms can differ is in what they do with it, and when.

Questions to ask:

• What percentage of interactions does the platform analyze, and how does that affect what teams can learn at scale?

• At what point do insights become available, and how quickly can teams act on them?

• How can agents, supervisors, and business stakeholders access those insights?

• How does conversation intelligence connect to coaching, quality management, and workforce workflows?

Communications and contact center in one system

If your evaluation includes both internal business communications and customer-facing contact center, it is worth understanding how each platform delivers that combination.

Questions to ask:

• Are communications and contact center capabilities part of one system, or delivered through separate products?

• How deeply are those capabilities integrated, and what does that mean for data sharing and context continuity?

• What does administration look like across both areas, and can changes be made without significant IT involvement?

Channel coverage and cross-channel context

Most platforms in this category support voice and digital channels. A more important question is whether those channels share context and how AI capabilities apply across them.

Consider whether a customer who contacts via different channels across separate interactions would need to re-explain their situation each time. Ideally, agents should have a unified view of prior interactions regardless of channel.

Questions to ask:

• Which channels are natively supported, and which (if any) are added through third-party integrations?

• Can agents see a complete interaction history across channels?

• Do AI capabilities apply across voice and digital, or does coverage vary by channel?

Reporting and operational visibility

Reporting needs to serve multiple audiences: supervisors managing performance in real time, analysts identifying trends, and business leaders making decisions about staffing and CX strategy.

What to evaluate:

• Real-time dashboards. Supervisors need visibility into what is happening in the moment across queues, agents, and channels.

• Historical reporting granularity. Look for the ability to segment by queue, agent, team, and interaction type. Granular segmentation can help identify what is driving performance.

• AI-generated insights. Platforms that surface patterns and recommendations proactively give teams more time to act on them.

CRM and business system integration

Communications and contact center platforms connect to CRM, ticketing, workforce management, and other systems. When evaluating platforms, look at integration depth rather than just the list of supported tools.

• Bidirectional CRM integration. Agents should be able to see customer data from the CRM during an interaction. It is also worth evaluating whether call data and outcomes write back to the CRM, and how that affects agent workflow.

• Pre-built vs. custom. Pre-built integrations with platforms like Salesforce and Zendesk help reduce implementation time. If connecting to your core systems requires significant custom development, factor that into total cost and timeline.

Security and compliance

Communications and contact center platforms handle sensitive customer information, and compliance requirements vary by industry and geography.

Look for:

• Data handling and residency. Where is data stored, and does the platform support data residency requirements for the regions where you operate?

• Compliance certifications. Common certifications include SOC 2, ISO 27001, and GDPR readiness. For healthcare use cases, HIPAA-ready infrastructure may be required.

• Role-based access and audit logging. Can access to recordings and transcripts be restricted by role? Is there an audit trail for administrative actions and data access?

Dialpad's security and compliance posture is documented at the Dialpad Trust Center.

Disclaimer: This comparison is based on publicly available information as of the date of publication. Features, pricing, and capabilities of third-party platforms are subject to change. This content is provided for informational purposes only.

Updated July 2026