How to Use AI to Connect the Dots – Not Create More Silos

Too often, AI is now being deployed to solve individual problems in isolation – quietly creating a new kind of fragmentation and resulting in many leaders managing another layer of disconnected systems.

Yet the real opportunity with AI isn’t to add more tools, it’s to connect the dots across the ones you already have. So how do you make this a reality? We spoke to our technology experts to find out.

Stop Deploying Point Solutions in Isolation

What we’re seeing is organizations deploying point solutions in isolation: one vendor for QA, another for virtual agents, another for WFM. Each is best-in-class at what it does – but without a common layer connecting them, agents bear the cost of that fragmentation and customers feel it.

The answer is a platform that works both ways – strong native capabilities for the core experience, and the openness to integrate best-of-breed where it adds most value.

All of it operating from a shared view of the customer, with consistent context and AI surfacing next-best actions based on full interaction history, not just the current channel. That’s where you stop accumulating AI tools and start genuinely connecting the dots.

Contributed by: Rodney Hassard, Head of Product, Applications, Vonage

Extend the Same “Human” Evaluation Framework to Your AI Agents

Where AI can really connect your data points is in the coaching feedback loop. When you’re scoring agents against a dataset that actually represents their performance, and not just a handful of random samples, you can route specific, evidence-based feedback straight into coaching plans.

The coaching conversation goes from “Well, this is what I learned from the three calls I happened to listen to”, to a coaching session that’s grounded in the full picture of how an agent is really performing over time.

And, remember, this loop now has to cover AI agents and chatbots, too. They’re prone to drifting, missing context, developing blind spots… You name it.

The same evaluation framework we use for our human agents needs to extend to AI agents. This gives us one connected view of performance, rather than two separate views running in parallel.

Contributed by: Derek Corcoran, CEO, Scorebuddy

Focus on One Number Everyone Cares About to Deliver Outcomes

Organizations get super excited about the capacity and capabilities of AI. But without due care, AI can just create more noise that you still can’t understand:

• Scorecards balloon to 100 questions – because autoscoring makes it cheap.
• Speech analytics surface 30 sentiment categories – that no one acts on.
• Each department builds its own dashboard – reading the same calls differently.

Instead of getting actionable insights for the business, every team’s just curating its own slice, and silos rebuild on top of the AI.

So, focus on working the other way round:

• Pick one CX number that everyone cares about. Repeat contacts? First-call resolution? Billing handle time?
• Then, point every AI initiative at it. Same definition. Same dashboard. Same focus.

This way you’ll deliver outcomes instead of just another silo.

Contributed by: Steve Nattress, VP Product, Enghouse

Always Ask Yourself, “Does This Reduce Friction for the Agent?”

One of the biggest mistakes I see is organizations building AI initiatives in isolation from the people who’ll actually use them. An AI that flags coaching opportunities but doesn’t feed into the same workflow as your QA process creates extra steps, not fewer.

The question to ask at every implementation stage is: does this reduce friction for the agent, or does it add a new screen to check?

AI should surface the right information, in the right place, at the right moment. When it does, it doesn’t create silos – it actively dissolves them.

Contributed by: Ben Booth, CEO, MaxContact

Start with Your Customer Journey – Instead of Tools

AI has rapidly entered contact centres, but without a clear overarching strategy it risks adding another layer of complexity.

Many organizations start with isolated AI tools for things like virtual agents, analytics or task automation, only to find insights trapped in silos and processes even more fragmented.

The real opportunity lies in using AI to connect data, decisions and experiences to outcomes for customers, colleagues and the company. This needs to span the entire customer journey, contact centre and beyond.

That means starting with journeys rather than tools, treating data as a shared asset, and embedding AI into orchestrated workflows instead of point solutions.

Contributed by: Lewis Gallagher, Senior Solutions Consultant, Netcall

Carry Context Across Channels and Teams

For decades, contact centres have operated in a silo. Agents over here, the rest of the business over there. And when AI arrived, many organizations did what they’ve always done with new tech: bolted it on to one team. The result? A shinier silo.

That’s not the opportunity. Great customer experiences rarely sit inside one department – they pull in sales, product, engineering, and back-office experts. AI should do the same. When it carries context across channels and teams, a refund query stops being a dead end and becomes a resolution.

Think of AI as the sous chef, not a separate kitchen. Its job is to connect the dots your customer can already see – and the ones they can’t. That’s how silos quietly disappear. 

Contributed by: Ben Neo, Head of Zoom Contact Centre and CX Sales, EMEA , Zoom

Bridge the Gap Between L&D and Performance Management

To prevent AI from becoming another isolated tool, organizations must use it to bridge the gap between Performance Management and Learning & Development (L&D).

By integrating Automated QA with AI-driven Training Management, the contact centre creates a self-healing ecosystem. Instead of insights sitting in a supervisor’s dashboard, AI identifies specific skill gaps in real-time and automatically pushes bite-sized, relevant coaching modules or “Knowledge Base” updates to the agent’s desktop.

This “closes the loop” by ensuring that data captured in one system (QA) directly informs the output of another (L&D).

Contributed by: Daniel Mertens, WFM Expert, Peopleware

Allow AI to See and Interact with Third-Party Tools

Contact centres must shift from a fragmented “Frankenstack” to a unified Agentic Experience Orchestration (AXO) architecture.

This approach uses a persistent AI layer to eliminate the swivel chair effect, integrating disparate systems into a single, contextual workspace that removes the burden of manual chores from human agents.

By leveraging Computer-Using Agents (CUA), organizations can bridge gaps between legacy systems without brittle APIs, allowing AI to see and interact with third-party tools directly at the interface level. This ensures automation remains integrated within the core operational flow.

Contributed by: Matthew Clare, VP, Product Marketing, UJET

Establish a Unified AI Policy and Governance

As AI adoption expands across the contact centre, inconsistency becomes the fastest path to silos. This is where a unified AI policy creates alignment; clearly defining how AI is used, what data it can access, how outputs are validated, and how decisions are made based on those outputs.

Just as important is governance: who owns AI strategy, who approves changes, and how models are monitored over time.

Without this foundation, AI initiatives tend to drift and diverge. With it, organizations ensure that every use of AI reinforces a consistent, connected approach to performance, decision-making, and continuous improvement.

Contributed by: Tatiana Polyakova, COO, MiaRec

Stop Generating Competing Signals From Separate Data Sets

Contact centres that deploy separate AI vendors for different parts of the customer journey generate competing signals from separate data sets.

Each vendor reads its own slice of the interaction. Insights conflict rather than compound, and the time between data and decision stays long.

A single AI that sits across every customer touchpoint closes that gap. When one AI reads 100% of interactions across every channel, a product gap identified in conversation data reaches the product team the same morning.

A compliance risk surfaces before it becomes a legal exposure. A coaching insight generated from QA scores feeds directly into a personalised plan in an agent’s inbox by Monday morning.

The intelligence moves at a speed that fragmented vendors cannot replicate, because the data was never separated to begin with.

Contributed by: Ashish Nagar, CEO, Level AI

Connect What You Find to What You Do

AI can surface a pattern in seconds. What most programmes haven’t solved is what happens next. In practice, insight often stops at the dashboard: visible to the people who built the report, invisible to the people who could act on it.

The contact centre is unusually well-positioned to fix this – because the people closest to the data are also the people managing the teams.

When AI identifies a recurring issue – a compliance gap, a handling failure across a cohort – the next step should be a coaching action, not another review cycle.

Connecting what you find to what you do about it is the difference between a QA programme that reports and one that improves.

Contributed by: Chris Mounce, Product Training & Enablement Specialist, evaluagent

Don’t Just Plug Technology into an Outdated Framework

AI systems do more than just automate existing tasks; they fundamentally enable entirely new ways of working. However, this progress is often hindered by legacy structures.

To avoid these pitfalls, leaders must look beyond the software. Success requires a fundamental review of your operating model rather than simply plugging technology into an outdated framework.

Attempting to layer advanced AI over stagnant processes ensures that old inefficiencies remain, only now at a higher cost.

For AI to truly connect the enterprise, the organization’s structure, roles, and workflows must evolve. 

Contributed by: Luke Cuthbertson, Head of CX Consulting Practice, Route 101

Anchor Multiple Types of AI to a Customer Data Platform (CDP)

Position AI within an orchestration layer rather than presenting a collection of separate, standalone tools.

The most effective approach is to anchor multiple types of AI to a Customer Data Platform (CDP) that unifies data from disparate systems of record into a single, real-time environment, while preserving the integrity and security of the discrete systems.

CDPs are also evolving into systems of action, based on changes in data. For example, a change to an item of information or a signal from a connected Internet of Things (IoT) device can trigger the CDP to initiate workflows in the CX platform.

Layering AI around the CDP enables hyper-personalisation and intelligent contact at scale.

Contributed by: Martin Taylor, Co-Founder and Deputy CEO, Content Guru

Design for Interoperability From Day One

To stop AI creating new silos in the contact centre, treat it as a shared capability layer spanning channels, data and workflows, rather than point tools.

Every interaction can draw on the same customer context, preventing insight from being trapped in a single queue, bot or dashboard. The payoff is improved CX and efficiency, with fewer repeat contacts and faster resolution.

Next, move beyond AI that only recommends to AI agents that can act, orchestrating routing, resolution and follow-up across CRM, knowledge and workforce systems. 

To scale, embed AI in the core experience and govern it with clear rules for data access, quality and change control. Designed for interoperability from day one, AI becomes a unifying operating model and a single source of truth.

Contributed by: Moritz Fischaleck, Head of Product, AnywhereNow

How is AI Working in Your Contact Centre?

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Click here to join our Readers Panel to share your experiences and feature in future Call Centre Helper articles.

For more great insights and advice from our panel of experts, read these articles next:

• Get Your AI Pilot Off to the Best Possible Start
• Upgrade How You Listen to Customer Feedback
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Author: Megan Jones
Reviewed by: Jo Robinson