Next Gen Contact Centres: Technology and Benefits

Next Gen Contact Centre Article Summary

1. Modernisation is not measured by the number of AI features. First, check whether information and actions genuinely follow the customer case.
2. Distinguish between requests you can automate, tasks where AI assists an advisor, and situations that require a human decision.
3. Assess your project based on resolution, repeat contacts, customer satisfaction and total cost, starting with a clearly scoped pilot before rolling it out more widely.

When a customer reports an issue via chat, sends a screenshot by email and then follows up by phone, the advisor may be able to identify the customer—but without the right processes and tools, they may not be able to see which checks have already been carried out or find the attachment. Three channels have been added on top of one another, and the customer journey becomes fragmented.

This is exactly the kind of breakdown a next-generation contact centre is designed to fix. Adding a conversational interface is not enough if the customer record is still incomplete or no one can take over the case smoothly.

An AI assistant can nevertheless deliver tangible benefits. A 2025 study of 5,172 support agents found an average 15% increase in issues resolved per hour when agents used a conversational assistant. The effects varied considerably from one agent to another: this result describes a specific deployment, not a promise that applies to every team [1].

For your project, the key question is therefore: which capabilities will help you handle requests more effectively, and what evidence and safeguards will you need?

What is a next gen contact centre?

Having a customer record pop up as soon as a call comes in is a starting point—and is now standard practice in the industry. It becomes genuinely useful when it also explains why the person is calling, what has already been tried and who needs to step in.

Here, the term “next-generation contact centre” refers to an organisation that connects omnichannel communications, business data, automation and agents’ work to ensure continuity throughout the customer journey. Treat the expression as a way of assessing an organisation, not as proof of performance.

Call centre, contact centre and CCaaS: what is the difference?

These terms describe different things. A contact centre extends customer interactions beyond phone calls. CCaaS, or Contact Centre as a Service, refers to a cloud-based delivery model for contact centre tools.

These categories are not mutually exclusive. A contact centre can use a CCaaS platform while still relying on fragmented processes. Conversely, a mainly phone-based team may already have rigorous customer follow-up processes.

What changes in the customer journey?

Imagine a request to amend an order. The goal is not simply to respond faster: the team needs to retrieve the order, check whether it can still be changed, carry out the permitted action and confirm the outcome.

Continuity must therefore apply to actions, not just conversations.

A complete interaction history solves nothing if the agent still has to contact three departments without knowing which one has the authority to make the decision.

Service quality remains the starting point. An international standard for contact centres also sets out service requirements and performance indicators for in-house and outsourced organisations, regardless of the channels they use [2].

Which capabilities do you actually need?

Take a representative case and move it through your tools. The information that goes missing, the data that has to be re-entered and the approvals that get stuck will give you a more useful list of requirements than a catalogue of features.

Connect channels to your CRM and knowledge base

Start by defining the information needed for someone to pick up a case: verified identity, reason for contact, product concerned, actions already taken, commitments made and next step. Not every person involved needs access to every available piece of data.

Then ask how this information moves between your phone system, CRM and support software. An integration listed in a catalogue does not, by itself, prove that your custom field or attachment will be transferred.

Ringover’s CRM and business-tool integrations, for example, provide a starting point for checking available compatibility. The demonstration should then focus on your specific process.

Apply the same standard to your knowledge base. For a refund procedure, identify an owner, a review date and the conditions under which it applies. A well-worded answer cannot make up for an outdated business rule.

Distinguish AI assistance from automation

An AI tool that suggests a reply to an agent does not have the same level of responsibility as an agent that directly changes an order.

In the first case, the agent can check the suggestion, correct it or disregard it. In the second, you need to define which actions are permitted, the conditions that trigger them and the situations that require human approval.

Set out this boundary for each use case. An assistant might be allowed to check a delivery status but not change an address after dispatch. It might prepare a response to a complaint while leaving the commercial decision to a manager.

The right level of autonomy depends on the risk associated with the action, not just on the system’s ability to understand the request. Also decide what should happen when information is missing: ask a question, flag the uncertainty or hand the case over.

Monitor the quality of interactions

A dashboard showing the number of calls will not tell you why some customers call back. Add qualitative analysis: recurring reasons for contact, procedures that are unclear, missing information and commitments that were not met.

Empower by Ringover’s conversation intelligence software can draw on transcripts, automatic summaries, and detect which topics were discussed. These features can support interaction reviews; they do not remove the need to check important conclusions.

For a support team, choose a few observable criteria: whether the issue was diagnosed, whether the answer followed the procedure, whether the next step was made clear and whether the case notes are usable. A single overall score is less useful than a specific shortcoming a manager can help the agent correct.

Which use cases should you prioritise?

A frequent request is not automatically a good candidate for automation. First check that the required information is accessible and that exceptions can be identified. The following three situations are illustrative examples, not customer results.

Assist a SaaS support agent

A user can no longer access a feature after changing their subscription. The agent needs to check the permissions associated with the plan, the account settings and the steps the user has already tried.

In an AI-assisted setup, the system finds the relevant procedure and prepares a case summary. The agent remains in control of the interaction and validates the diagnosis before suggesting an action.

The expected outcome is less time spent searching for information, without any decline in response quality. Measure diagnostic time, corrections made to suggestions and repeat contacts about the same incident. If handling becomes faster but customers come back more often, review the answers before expanding the rollout.

Handle a simple e-commerce request

A business customer wants to know the status of a delivery. In this scenario, the assistant verifies the customer’s identity per your procedure, checks the available status and provides the relevant information.

The limit must be explicit: a missing or contradictory status must not turn into an invented delivery date. A dispute about whether an order was received must follow a different process.

The expected outcome is a reliable answer to routine delivery-status inquiries. Track the proportion of requests genuinely resolved and the associated repeat contacts. Do not mistake the absence of a handover for resolution: a customer may leave a conversation dissatisfied and call back later.

Escalate a complex complaint

A disputed invoice contains several line items, a previous commercial commitment and a threat to cancel. You might ask AI to collect the relevant references and prepare the case file, while reserving the decision for an authorised person.

The handover should include the reason for contact, available documents, checks already completed and points that still need a decision. The agent should not have to start the conversation from scratch.

The expected outcome is a more efficient handover, not necessarily a shorter interaction. Measure repeat transfers, time to decision and satisfaction after the case has been handled. Also plan what happens if no agent is available: an arranged callback or written follow-up with a clearly identified owner.

How should you choose the tools and set a budget?

Ask the supplier to handle a request for which information is missing. This test will show how the system deals with its limitations, beyond the scenario prepared for the demonstration.

Test integrations and the handover to a human

Prepare a set of anonymised cases covering your common situations and a few exceptions. Use the same set for every solution, with the same success criteria.

A successful demonstration is only a first step. Assessments of generative AI systems should avoid extrapolating their capabilities from narrow or anecdotal tests; checking the sources produced is also among the recommended safeguards [3].

Set your deal-breaker criteria before comparing proposals. The absence of a secondary feature may be acceptable; the inability to pick up a critical case may not be.

Compare the total cost

Ask suppliers to quote for an identical scope: users, channels, volumes, integrations, storage and support. Clarify every billable unit, what is included, overage charges and optional features.

Add internal costs: data clean-up, procedure preparation, training, administration and quality control. In particular, appoint someone to maintain the knowledge base. Without that responsibility, the budget may appear under control while answer quality gradually deteriorates.

To compare proposals, build one typical scenario and one peak-activity scenario. Also check exit terms: data retrieval, migration support and any associated fees.

Finally, distinguish time saved from money saved. Reducing post-call tasks may free up capacity for customer follow-up without immediately reducing costs.

Check security, privacy, and transparency

An AI assistant connected to your CRM may have access to sensitive customer information. Before deploying it, review the permissions it requires, how its actions are logged, and how access can be restricted or revoked. Clarify where customer data is stored, who can access it, how long it is retained, and whether it may be used to train AI models.

In the UK, your use of customer data must comply with the UK General Data Protection Regulation (UK GDPR) and the Data Protection Act 2018. Depending on the data and how the system is used, you may need to establish a lawful basis for processing, provide clear privacy information, limit data collection to what is necessary, and ensure that appropriate retention and security measures are in place. If the system processes special category data, additional conditions apply.

Call recording and interaction monitoring also require careful consideration. Make sure customers are given appropriate information about recording, monitoring and how their data will be used. Your approach should reflect data protection requirements and relevant guidance from the Information Commissioner's Office (ICO). Recording and monitoring should also be proportionate to a clearly defined business purpose, such as training or quality control. A recorded announcement is one practical way to inform callers that their conversation is being recorded and why.

Automated calls and text messages used for marketing are subject to specific rules under the Privacy and Electronic Communications Regulations (PECR). For example, automated marketing calls that play a recorded message require the recipient's specific consent. The rules for live marketing calls and electronic messages differ, so check the requirements for each channel before launching a campaign.

Generative AI systems introduce additional cybersecurity risks, including attacks on models and data, attempts to extract confidential information, and vulnerabilities in third-party components or services. The UK National Cyber Security Centre (NCSC) recommends addressing these risks throughout the AI system lifecycle, from design and development to deployment and ongoing monitoring [4].

Translate these risks into practical safeguards: apply least-privilege access, separate environments, monitor system activity, test for unauthorised data access or disclosure, require human approval for sensitive operations, and maintain a clear incident-response procedure.

Customers should be able to understand when they are interacting with an automated assistant, what it can do, and how to reach a person when needed. Make these explanations clear and accessible, and ensure that your privacy information accurately describes how customer data is used. Do not assume that a supplier's claim that its platform is compliant removes your organisation's own responsibilities.

Have your legal, security and data protection teams review the proposed use case before deployment. The requirements can vary depending on the data involved, the purpose of the interaction and whether the system makes or supports decisions about individuals.

How should you roll it out and measure the results?

A pilot covering the entire customer service department will be difficult to interpret. Instead, choose one specific contact reason, one team and a controlled data scope—for example, AI assistance for agents handling access issues.

Define a pilot and a baseline

Before launch, observe volumes, handling times, repeat contacts and difficulties within this scope. Keep the same definitions throughout the test. A request marked as closed in a tool should not automatically be counted as resolved without verification.

Document any changes that could affect the results: a new procedure, a product incident, additional staff or a change in case complexity. Where possible, compare groups or periods that are sufficiently similar.

Also define stop conditions: disclosure of information, an unauthorised action or incorrect answers on a critical topic. The pilot should make it possible to suspend a use case quickly without interrupting the entire service.

Train the teams and plan a fallback

Show agents how to check a suggestion, correct a summary and report an error. Their training should include examples of AI getting things wrong, not just successful scenarios.

Set up a short feedback loop: who receives the report, who updates the procedure and who checks that the issue does not happen again? Without an owner, the same errors may remain visible without being addressed.

Finally, test how the team works without the assistant. Agents must be able to find essential procedures and take control again. Automation should not make a routine task impossible when the system is unavailable.

Measure resolution before expanding

Build a dashboard that is limited in scope but easy to interpret. The following indicators can provide a starting point, with definitions adapted to your business.

Do not use a reduction in average handling time as a standalone objective. An agent may shorten a call by shifting the work to the customer or a colleague.

Before expanding the rollout, review the cases that got worse, not just the average. An overall improvement may hide a problem affecting a particular product or type of request. Then expand the scope gradually, keeping the same safeguards in place.

Our final recommendation: start with a journey you can verify

Choose a request that customers still have to explain several times. Map its journey, identify where information gets lost and define what a successful resolution should look like.

You will then have a demonstration scenario, a pilot scope and a shared decision criterion for both business and technical teams. That is a much stronger starting point than a checklist of features.

To assess a solution such as Ringover, bring this case and your constraints to the demo, and ask the supplier to show the integrations, the handover of context and the work carried out after the interaction using your specific scenario.

The most useful next step is not necessarily to automate more. It is to prove that a customer journey actually works better.

Next gen contact centre FAQ

How is a next gen contact centre different from CCaaS?

CCaaS describes a cloud-based delivery model for contact centre tools. The next-generation approach focuses on how those tools are used to ensure continuity throughout the customer journey, support teams and automate certain tasks. Simply subscribing to a cloud platform does not, by itself, guarantee better resolution.

Does AI replace agents?

Not necessarily. You can automate a status check, use AI to assist with a diagnosis and leave a commercial decision to an agent. Define responsibilities task by task rather than treating people and automation as opposing alternatives.

Is it suitable for an SMB?

Yes, provided the scope is proportionate. Start with an observable problem: re-entering information after a call, an inaccessible interaction history or a lengthy search through documentation. The ability to administer the system and maintain its knowledge base matters just as much as its features.

What costs should you expect?

Allow for licenses, usage charges, integrations, training, administration and quality control. Ask for a detailed quote based on your actual volumes and a peak-demand scenario. An entry-level price does not tell you the total cost.

Citations

• [1]https://academic.oup.com/qje/article/140/2/889/7990658
• [2]https://www.iso.org/fr/standard/64739.html
• [3]https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf
• [4]https://www.ncsc.gov.uk/collection/guidelines-secure-ai-system-development

Published on November 12, 2026.