Think about the last time you contacted a company. You probably didn’t start with “I will call them.” You opened a chat, replied to an email thread, sent a WhatsApp message, or dropped a DM on social media. Customers move between channels without thinking. The gap happens when businesses treat each channel like a separate world.
That’s where AI changes the equation.
An AI-powered omnichannel contact centre helps you respond consistently across voice and digital channels, reduce repeat explanations, and give agents the context they need—without juggling disconnected tools.
Why omnichannel matters in the AI era
“Omnichannel” doesn’t just mean “we have multiple channels.” It means the experience is connected:
- The customer doesn’t have to repeat themselves when they switch from chat to phone.
- The agent sees the same customer history, orders/cases, and prior conversations.
- Routing and handoffs work across channels, not inside silos.
- Reporting shows the full journey, not channel-by-channel fragments.
When you add intelligence to omnichannel, you unlock faster understanding (intent), better prioritisation (routing), better conversations (agent guidance), and better improvement loops (QA + coaching).
The cost of fragmented support
Fragmented support is expensive in ways that don’t always show up on a dashboard—until leadership asks why service quality varies by channel.
Common symptoms include:
- Longer handle times because agents search across multiple systems
- Inconsistent answers across phone, chat, and email
- Limited insight into sentiment, intent, and recurring drivers of contact
- Quality blind spots when QA reviews only a small sample of interactions
- Customer frustration when they have to re-explain the same problem
An omnichannel foundation solves the “systems problem.” AI helps solve the “speed and consistency problem.”
What conversational AI actually means for contact centres
In a contact-centre context, conversational AI typically includes:
- Virtual agents for self-service (common questions, order status, appointment changes)
- Conversational IVR for voice that understands intent, not just keypad menus
- Agent assist / AI copilots that guide agents during live interactions
- Conversation summaries that capture context across chat and voice
- Sentiment and intent detection to flag risk, escalation needs, or upsell signals
The key point: conversational intelligence is not “replace humans.” It’s “reduce friction for customers and workload for agents.”
Building an AI-first omnichannel strategy
1) Start with channels customers already use
List every channel customers use today (and the ones they expect tomorrow): voice, web chat, email, SMS, WhatsApp, social DMs, in-app messaging.
Then answer one practical question: Where do customers switch channels most often?
Those handoffs are your fastest “pain-to-impact” opportunities.
2) Choose a CCaaS foundation that supports omnichannel
A modern CCaaS (Contact Center as a Service) platform helps you unify channels, scale operations, and support remote/hybrid teams. Prioritise:
- Omnichannel routing and queue management
- Reliable voice + digital channel coverage
- Real-time dashboards and analytics
- Security, access controls, auditability
- Integration options for CRM and knowledge sources
3) Integrate your contact centre with CRM for a real 360° view
Artificial Intelligence is only as useful as the context it can access.
When your CCaaS connects to your CRM (like Dynamics 365), agents can see:
- Customer profile + communication preferences
- Case history and current status
- Orders, assets, subscriptions, warranties (as relevant)
- Past conversations across channels
- Next-best actions and knowledge recommendations
This is what prevents “please repeat your issue” moments.
4) Deploy AI copilots for real-time agent support
Real-time AI copilots can assist during conversations by:
- Suggesting replies aligned to policy and tone
- Surfacing relevant knowledge articles
- Highlighting compliance prompts (where applicable)
- Capturing notes and generating summaries
- Helping agents move faster without guessing
Done well, agent assist improves consistency for new and experienced agents alike.
5) Add sentiment + intent analysis where it changes outcomes
Sentiment and intent are most valuable when they trigger action, such as:
- Escalate a high-risk interaction to a specialist team
- Prioritise a queue when frustration signals spike
- Identify contact reasons driving repeat calls
- Spot upsell moments based on intent signals (where appropriate)
6) Automate QA and coaching (without turning it into surveillance)
Traditional QA often struggles with coverage and consistency. AI-powered QA can help you:
- Expand coverage across channels
- Standardise scoring criteria and checklists
- Identify coaching themes (not just agent “scores”)
- Track improvements over time
The best practice here: keep QA focused on customer outcomes and skill-building—not “gotcha” monitoring.
Where Dynamics 365 fits (and why it matters for AI)
When your contact centre is connected to Dynamics 365, you’re not only connecting systems—you’re connecting context.
That matters because many of the most useful outcomes depend on CRM signals:
- Personalised support based on customer history
- Faster resolutions based on case + knowledge context
- Better routing based on skills, workload, priority, and customer value
- More accurate summaries because the AI can anchor to real records
If your service teams already live in Dynamics, bringing channels and artificial intelligence into the same flow reduces context switching and keeps work consistent.
Region-specific considerations (US, Canada, Australia & New Zealand)
Omnichannel + Artificial Intelligence is global, but rollout details change by region—especially around privacy and customer expectations.
United States
- Build for multilingual support if your customer base spans multiple languages.
- If you record calls or use automated transcription, align your processes with consent expectations and sector rules (especially in regulated industries).
Canada
- Many organisations need bilingual (English/French) experiences—think language detection, routing, and knowledge coverage.
- Keep privacy compliance front-and-centre when using Artificial Intelligence on customer interactions (data retention, access controls, and transparency).
Australia & New Zealand
- Customer expectations often lean toward fast, digital-first responses alongside strong escalation paths to humans.
- Privacy obligations should be baked into Artificial Intelligence design (data minimisation, storage choices, and clear operational controls).
AI governance checklist for contact centres
Before you scale, treat governance as part of delivery—not paperwork at the end:
- Data boundaries: what data AI can access (and what it must never access)
- Human oversight: when AI suggests vs when humans must decide
- Accuracy controls: monitoring, feedback loops, and content updates
- Bias checks: watch for uneven outcomes across accents, languages, demographics
- Security controls: role-based access, audit trails, encryption
- Customer transparency: clear messaging about AI use where appropriate
This is how you keep Artificial Intelligence helpful, safe, and trustworthy.
A practical pilot plan (without boiling the ocean)
A strong starting pilot usually looks like:
- One channel (often voice or web chat)
- One use case (top 3–5 contact reasons)
- One team (a queue with measurable volume)
- Clear KPIs (AHT, FCR, CSAT, repeat contacts, escalation rate)
Once the workflow is stable, scale to adjacent channels and use cases.
KPIs to measure AI impact (choose what fits your goals)
Operational:
- Average handle time (AHT)
- First contact resolution (FCR)
- Transfer and escalation rate
- Queue time and abandonment rate
Quality:
- QA score consistency across channels
- Coaching completion and improvement trends
- Policy adherence (where relevant)
Customer:
- CSAT / NPS (where you track it)
- Customer effort (repeat explanations, repeat contacts)
- Sentiment trends over time
FAQs: AI in omnichannel contact centres
What is the difference between omnichannel and multichannel?
Multichannel means you offer multiple channels. Omnichannel means those channels are connected—shared context, shared history, and consistent service.
Will AI replace contact-centre agents?
In most real-world teams, Artificial Intelligence reduces repetitive work and improves consistency. Humans remain essential for complex cases, judgement calls, and relationship-based service.
What is agent assist AI?
Agent assist Artificial Intelligence supports agents during live conversations with suggestions, knowledge prompts, summaries, and guidance—without taking over the interaction.
How do I keep AI accurate in a contact centre?
Use strong knowledge management, restrict Artificial Intelligence to approved sources, monitor outputs, and create a feedback loop from agents to continuously improve answers.
What’s the safest way to start with conversational AI?
Start with one channel and a small set of common use cases, measure outcomes, then expand. Governance and privacy controls should be included from day one.
Conclusion
Omnichannel support powered by Artificial Intelligence is no longer a “nice-to-have.” It’s quickly becoming the baseline customers expect.
If you want a practical, region-aware roadmap—covering CCaaS selection, Dynamics 365 integration, conversational intelligence design, and governance—Osmosys can help you plan and implement an omnichannel contact-centre approach that fits your industry.