AI and automation for contact centres.

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Make intelligence accountable.

AI and automation are everywhere in the CX narrative - and mostly nowhere in the actual numbers. Connect’s role is to identify, prioritise and validate AI and automation use cases with real governance and hard evidence, not hype.

We use a structured discovery and governance approach that combines:

Strategic goal alignment.
Operational and performance analysis.
Customer experience insight and sub‑intent discovery.
Stakeholder collaboration and controlled validation.

The outcome is a transformation roadmap where every AI or automation initiative is traceable to measurable value.

“Reason for contact” isn't enough - go to sub‑intent.

Traditional models stop at “reason for contact” and resolution codes. That’s useful, but it misses the reality of a modern conversation: multiple sub‑intents, branching paths, and hidden friction.

Connect’s Data‑Driven Insights (DDI) capability uses our own in‑house AI toolsets, data scientists, conversational designers and business analysts to:

Analyse large volumes of customer interaction data across voice, chat and email.
Apply AI‑assisted clustering, intent and sub‑intent classification, sentiment analysis, and thematic grouping.
Surface repeat contact drivers, friction points and operational inefficiencies that are invisible in standard reporting.

How do you identify and prioritise use cases?

AI and automation use cases should be identified from operational evidence - volumes, handling time, containment, sentiment and known failure modes - then prioritised against business goals, CX impact, feasibility, risk and expected ROI.

Potential AI and automation use cases are identified through a combination of:

Operational performance data: volumes, AHT, containment, complexity.
Customer interaction trends and sentiment patterns.
Known service pain points and failure modes.
Business priorities and target outcomes across key journeys and workflows.

Each opportunity is assessed using operational and customer‑centric measures:

Interaction and case volumes.
Average Handling Time and effort required.
Sentiment and experience performance by topic and subtopic.
Containment rates and failure paths.
Customer journey impact and handoff quality.
Estimated operational cost and potential savings... and more.

Use cases are then prioritised based on a balanced view of:

Strategic alignment to business goals.
Customer experience outcomes and journey impact.
Operational efficiency potential.
Implementation feasibility and complexity.
Risk considerations and governance requirements.
Expected business value and time‑to‑impact.

Human‑in‑the‑loop validation, not black‑box decisions.

We don’t outsource judgement to models - human oversight is built into every stage of AI use case validation.

Connect Business Analysts and operational specialists review AI‑generated findings in the context of your operating model, objectives and CX standards.
Stakeholders from operations, CX, technology and governance challenge and refine proposed opportunities.
Business cases are developed for each use case, with explicit ROI, cost, risk and benefit assumptions.
Recommendations are iterated through structured review, stakeholder engagement and agreed success criteria into a transformation roadmap.

This ensures that AI and automation initiatives are evidence‑based, operationally relevant, governed properly, and aligned to measurable outcomes - not just “labs” or pilot projects that never scale.

Subtopic‑level metrics - where to act, not just what happened.

Calculating performance metrics at topic and subtopic level shows you where to intervene and why it matters financially — not just what happened.

Our DDI approach calculates performance metrics at topic and subtopic level, making it far easier to:

Spot issues and exceptions in specific journeys.
Understand cost per topic and subtopic based on volume and handling time.
Prioritise which journeys to fix, automate or redesign first.

What outcomes can you expect from an engagement.

A typical engagement delivers a set of concrete outcomes:

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Unified intent definition across channels:
Understand why customers are contacting you and what outcomes are being achieved, with intent classification by channel to create a single intent framework across voice, chat and email.
Best resolution path per intent:
Analyse intent versus resolution by channel, linked to satisfaction metrics, to identify the single best resolution path for each intent and sub‑intent.
Intelligent triage and routing for FCR:
Define the optimum triage and routing logic needed to drive First Contact Resolution, not just faster transfers.
Business‑case‑ready journey improvements:
Identify customer journeys that are ready for improvement or automation and build robust business cases with quantified ROI.
Prioritised initiative portfolio:
Prioritise initiatives based on business goals, CX impact and return on investment, rather than anecdote or internal lobbying.
Technology gap mapping:
Identify gaps in technology and data, and plan future improvements to support the AI and automation roadmap.

Find out how we can help your business communicate better.

To discuss your communications challenges and requirements, get in touch with us today.

Connect with us now.

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