How to Centralize Your Data – Before Scaling AI

In the race to adopt AI, many contact centres overlook a harsh reality: AI is only as intelligent as the data it can access. As such, scaling without a unified foundation leads to “hallucinating” bots, fragmented customer journeys, and more!

So how do you make sure you aren’t building your house on shaky foundations? We asked our panel of technology experts to find out…

Always Start By Working Backwards From the Use Case

To prevent AI from working on partial or incorrect data, an effective approach is to start working backwards from the use case.

Whether the goal is to improve forecasting accuracy, scheduling outcomes, or streamline intraday operations, lay out your data sources to ensure you have everything required to start the project.

Next is to build a trusted, unified data source. That means bringing together interaction history, CRM records, channel data, and performance metrics into a structured environment that AI can reliably access.

Use API-first integrations and data warehouses to standardise and organise information, and incorporate data governance to ensure data quality, ownership, and compliance.

Contributed by: Zaineb Ahmed, Marketing Manager, Peopleware

Treat Your Knowledge Base as a Living System

Static documents create brittle agents. If your SOPs, FAQs, and product guides aren’t regularly updated, agents will confidently surface outdated information – which erodes trust faster than any latency issue.

Build a maintenance process before deployment, not after. The highest-performing deployments treat the knowledge base as an operational asset with a named owner, clear review cadences, and a direct ingestion pipeline from existing documentation systems.

A well-maintained knowledge base is not a setup task – it is ongoing infrastructure. 

Contributed by: Lauren Rothwell, Head of Agents Growth, ElevenLabs

Agree on a Shared Definition of Quality with Regular Scorecard Calibration

It’s fair to assume that most contact centre teams know the value of centralising their data before they scale any AI workflows.

This discussion usually focuses on the systems behind that: which platforms to consolidate, which flows to connect. That’s necessary work, but we must not neglect the layer sitting underneath.

If all that data centralisation is underpinned by inconsistent scorecards, uneven evaluation standards, and ad hoc topic classifications, you’ll end up with centralised noise. AI can only scale what you give it, and if it finds inconsistency it will amplify that across everything it touches.

To centralise data successfully, you need to first centre on a shared definition of quality. That means regular scorecard calibration to keep standards up to date, consistent criteria across interaction types and channels, and a governed framework for classifying topics, signals, and sentiment.

Contributed by: Derek Corcoran, CEO, Scorebuddy

Consolidate Structured and Unstructured Data

One of the biggest barriers to scalable AI is the separation between structured and unstructured data.

Most organizations already centralize dashboards, CRM records, and transactional metrics, but the majority of customer intelligence often lives inside conversations, emails, notes, and tickets.

To scale AI effectively, businesses need strategies that combine both data types into a single intelligence framework.

Structured data explains what happened, while unstructured data explains why it happened. AI becomes far more valuable when it can correlate operational KPIs with the actual customer language driving those outcomes.

Organizations should prioritize platforms and architectures capable of ingesting, indexing, and analyzing conversational data alongside traditional business systems.

This creates richer AI models, stronger predictive capabilities, and more actionable insights across customer experience, quality management, and revenue operations.

Contributed by: Tatiana Polyakova, COO, MiaRec

Make Sure Your Disposition Codes Are Consistent Across the Operation

The goal should not be where the data lives, but what your AI can do with your data. In practice, full centralisation is rarely achievable. Your ACD, your CRM, your WFM platform were never designed to feed a central AI layer.

The smart path is to deploy AI closer to where data already exists: your transcription feeds, your after-call metadata, your customer records.

But this only works if two things are solid first:

• Your interaction IDs need to stitch cross-channel journeys together. Otherwise, your models are learning from fragments.
• Your disposition codes need to mean something consistent. Otherwise, every intent model and VoC analysis you build inherits years of labelling noise.

Fix those two things first and everything else follows. Why? In traditional IT, technical debt slows you down – but in AI, it compounds:

• Every day your disposition codes stay inconsistent, your models are learning the wrong patterns.
• Every month your cross-channel journeys remain unlinked, your AI is building a picture of customers that doesn’t reflect reality.
• Every year you delay structuring your interaction data, you are not just postponing good AI: you are actively training bad AI.

Models built on fragmented, mislabelled data don’t just perform poorly: they learn to be wrong in ways that are hard to undo.

Retraining them later is not a clean reset because you are fighting what the model already learned. And the longer you wait, the more your AI roadmap becomes loadbearing on top of the debt.

Contributed by: Taoufik Massoussi, Product Manager, Enghouse Interactive

Define Clear Ownership for Data Quality and AI Policy Enforcement

Start by identifying the silos that create the biggest gaps in customer context or operational visibility.

Prioritise integrations across the highest-volume interaction channels first, then establish consistent data standards, customer identifiers, and processes so AI models and automation workflows operate on trusted inputs.

Conversation analytics platforms that integrate directly with CRM systems, workforce engagement tools, and BI platforms can play an important role here.

They help unify interaction insights, surface operational trends, and create feedback loops for continuous optimisation without requiring organisations to replace existing systems.

Governance is equally important! Define clear ownership for data quality, access controls, lifecycle management, and AI policy enforcement.

The most effective AI implementations typically combine central platform standards with cross-functional accountability across operations, IT, data, and CX teams.

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

Establish Strict Master Data Management Rules to Resolve Conflicts Automatically

The true risk of decentralised data is not connectivity, but conflict. When customer data sits in separate silos, information often clashes. A billing system might show an outstanding balance, whilst the CRM indicates the account is settled.

For an AI model, identifying and resolving these contradictions across separate platforms is incredibly difficult, often leading to inaccurate automation or flawed advisor recommendations.

Organisations can address this through different approaches. Some choose physical centralisation via a data warehouse or lake, creating a single source of truth.

Others opt for virtual centralisation, using data virtualisation layers to leave data in its original home whilst establishing strict master data management rules to resolve conflicts automatically. Whichever architecture you select, resolving data conflicts before deployment is essential.

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

Make Sure Channel, History, and Intent Travel with the Customer

As AI scales in the contact centre, your data foundation decides the ceiling. Get it wrong and you risk multiplying inconsistencies, not insight. A few things I’d recommend:

• Start with the customer journey, not the systems – Map where data lives along the journey, not in your org chart. Silos rarely match how customers actually experience you.
• Unify before you optimise – Bring CRM, contact centre, billing, and workforce data into one connected view. AI can only generate insights from the data it has access to.
• Treat context as a first-class asset –Channel, history, and intent should travel with the customer – not reset at every handoff.
• Govern early – Quality, residency, and access rules are far cheaper to build in than to retrofit.

Do this well, and scaling AI can start feeling less risky and more manageable.

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

Give Teams and Systems Access to Consistent Customer Context in Real Time

The most successful AI initiatives treat AI as a shared layer across the customer experience ecosystem, giving teams and systems access to consistent customer and operational context in real time.

In many enterprise environments, this comes from orchestrating data across existing platforms such as CRMs, contact centre solutions, knowledge bases, and business applications, rather than replacing them.

Equally important is building strong governance, permissions, and data quality standards into AI initiatives from the start. As AI scales across the organisation, consistency, security, and explainability become critical to delivering reliable outcomes and long-term value.

Contributed by: Moritz Fischaleck, Head of Product, AnywhereNow

Don’t Just Try to Rely on ‘Happy Path’ Prototypes

Before introducing automation, you need to map and understand your technology landscape. It is crucial to know where data lives, how it flows, and how well it reflects real customer journeys and business processes – not just ‘happy path’ prototypes.

Critical data from email and document handling is also frequently overlooked, leaving these processes manual, fragmented and at risk of delays and human error.

Using tools to structure, standardise and automate document and data processing can significantly improve data quality and lay stronger foundations for AI adoption.

Finally, engage frontline teams to help uncover unsupported “Shadow IT” tools, ensuring AI models can work from trusted information, delivering consistent and scalable value.

Contributed by: Lewis Gallagher, Senior Solutions Consultant, Netcall

Overlay Multiple Systems with a Data Orchestration Layer

In Customer Experience (CX) and contact centres, the emphasis has traditionally been on integrating into a central system of record, most commonly a CRM or, in some cases, specialist Electronic Health Records (EHRs).

However, the volume and variety of data required for, and generated by, AI services are not suited to a single system of record. Most organisations already operate multiple core systems.

A data orchestration layer is therefore an essential prerequisite for implementing AI services. In CX, this takes the form of a Customer Data Platform (CDP), which overlays multiple systems of record, securely reading and writing to them in situ.

The CDP becomes the repository for AI-generated data at scale and, through built-in event handling, also acts as a system of action, triggering workflows such as a call from a human contact centre agent or updating of underlying data sources. 

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

Eliminate Static and Siloed Data That Only Tells a Part of Your Customer Truth

To move from pilot to production, businesses must centralize their data. Traditional silos like CRM, KB, billing, and shipping tools often leave both humans and AI swivel-chairing between systems without context.

The most effective approach is to centralize your customer data with data lake CDP technology that serves as a single source of truth, ensuring real-time data portability, customer context preservation, and the elimination of static, siloed data that only tells a part of your customer truth (e.g. ticketing).

The future of CX will be won by the providers that leverage agentic AI to orchestrate both customer data and AI. Centralize your customer data first; then scale with confidence.

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

What Have You Tried to Make Sure Your Data is AI-Ready?

In case you hadn’t heard, we’re looking for your real-life AI use cases that you’ve implemented in your contact centre and we’d love to hear more about how you prepared your data ahead of any AI deployments.

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:

• 12 Amazing Things You Can Now Do With Customer Preferences
• How to Use AI to Connect the Dots – Not Create More Silos
• Get Your AI Pilot Off to the Best Possible Start

Author: Megan Jones
Reviewed by: Jo Robinson