Why Contact Centres Cannot Scale AI on Fragmented Systems

MaxContact explores how data fragmentation is limiting AI success in contact centres and what organisations can do to create more connected, scalable systems.

In 2026, one of the biggest challenges facing contact centres is knowing how and where to deploy AI tools and systems.

When integrated correctly, AI could (and should) transform agent workloads by automating routine tasks, freeing up human time and energy for more complex cases.

However, despite more than half of all contact centres reporting that AI and automation are their top technology priority for 2025 and 2026, there remains a risk that AI projects will fail to deliver expected results because too many businesses operate across fragmented systems.

This results in poor, uneven AI usage that cannot scale beyond pilot projects because data is fragmented across multiple touchpoints, creating barriers to cohesive communication.

In a contact centre environment, this can be the inadvertent result of multiple systems, additional plugins and an ever-increasing number of tools, all of which pledge to make the experience more cohesive and intuitive.

The Two Big Problems Caused By Data Fragmentation

Data fragmentation is causing two clear problems for the contact centre industry: it’s becoming a barrier to progress, and it’s increasing agent workloads.

How is Data Fragmentation Blocking Progress?

Contact centres continue to innovate because they recognise that investing in new technology is the way for sustainable business growth.

Senior leadership teams are showing good intentions, a willingness to test new technology, and a desire to invest

substantial sums in their AI implementations, which confirms that the underlying issues holding them back are not a lack of ambition.

Data fragmentation hinders progress because AI pilots need clear, accurate, unified data to succeed and scale.

If systems are built across multiple platforms, or if there are issues with collecting and interpreting data from various pathways, any transformation or AI project will be set up to fail from the very beginning.

AI can only work effectively if it operates on accurate, reliable information; any output will almost certainly be unreliable, unexplainable, or based on false data.

How is it Impacting Agent Workloads?

Ever since AI went mainstream, much discussion has focused on how agents could use automation to handle routine tasks, speed up call enquiries, and free up their time to work on more complex cases.

But despite early wins from routine automation, those benefits have not yet translated into reduced agent workloads. Data from MaxContact’s 2025/2026 KPI Benchmarking Insights Report suggests that more agents are reporting higher workloads than the year before.

There is an ever-growing number of customer touchpoints, from WhatsApp and phone calls to emails and chatbots, which underscores the importance of investing in holistic systems.

Agents need to be able to seamlessly retrieve all conversational records, regardless of where the trail started, without navigating multiple systems.

Right now, agents face a genuine operational risk because systems can inadvertently hold them back from working more effectively.

By having to pull data from multiple touchpoints, they are working across more screens, duplicating data entry, and increasing the risk of errors.

Data Can Only Be Unified When You Know What Caused The Fragmentation in The First Place

Businesses need to recognise that data fragmentation does not happen overnight. It is built up gradually through well-intentioned decisions made by different teams, at different times, without examining any impact on the holistic system.

Isolated Technology Decisions Cause Data Fragmentation.

One of the most common causes of data fragmentation has been the growing use of off-the-shelf plugins, platforms, or specialist tools added to existing systems.

These are usually introduced to quickly solve a specific problem for a team, such as addressing reporting gaps, quality assurance issues, or the implementation of a new chatbot system.

The tools themselves are not the problem; instead, the problem comes from the fact that those decisions have been made in isolation.

Adding a new plugin or tool in one place can affect data collection elsewhere across the wider platform. If different tools have overlapping features, there is an increased risk of duplicate or incorrect data being collected, which can lead to inconsistent outputs if not spotted and rectified.

If multiple tools and plugins perform similar tasks, they must be tightly integrated and regularly tested to ensure a single, trusted data source for AI. This prevents the tech stack from being more complicated than it needs to be.

This Problem is Made Worse by the Rapid Growth in the Number of Customer Touchpoints.

Agents are reporting increased workloads, partly due to the growth in customer touchpoints. Whereas historically a customer could only use a phone number or email address, they are now starting their customer journey in one place (such as a chat tool) and transitioning across an omnichannel pathway that may include email correspondence, personal account logins, text reminders, or personal phone calls.

From the customer perspective, those conversational trails should be seamless, but that can only happen when each channel is connected to a singular system.

Without that, customer records will be held across different interfaces, which not only increases agent workloads but also makes it harder to deliver a consistent experience for each customer.

Data Fragmentation is a Skills Problem as Much as a Systems Problem.

This is a problem that businesses need to prioritise, as the growing digital skills gap concerns every sector, not just the contact centre industry.

The rapid pace of technological advancement is an issue because it contributes to fragmented data, and many organisations lack the internal capability to identify where that fragmentation is happening and how to resolve it.

Modern contact centre systems are highly configurable, which means that internal staff need a higher level of skill to set up, manage, and interpret data.

When agents and internal teams are not properly trained to use the system or to spot anomalies, the system will be used inconsistently, resulting in poor data quality.

From a scaling perspective, it makes it almost impossible to successfully grow AI or automation projects because there needs to be an internal team that can assess the AI outputs, recognise when results are unreliable and know how and when to intervene.

Without those skills, operational complexity increases, and the benefits of AI remain out of reach.

How can contact centres rectify this problem?

Contact centres need to step back and assess their needs before investing in new AI tools or platforms. Understanding where and how data is stored, as well as how agents operate day-to-day, is essential for AI and automation projects to deliver long-term benefits.

A Practical Starting Place is to Look at How Existing Tech Stacks Help or Hinder Existing Agent Workflows.

It’s important to understand how they handle calls and messages, where (and why) they switch systems, and whether they create any workarounds to reduce duplicated effort.

That way, contact centre teams can identify where any AI investment is genuinely needed to help reduce agent workload.

Monitoring user behaviour will also make it easier to see where data fragmentation occurs. For example, are agents making notes in spreadsheets or secondary tools because core systems are slow to react to real-time live customer interactions?

If AI and automation are being implemented to free up agent time by managing routine tasks, then systems need to be designed around how agents work in practice, not just in theory.

That detailed understanding of how an agent works and why it works that way means that any obvious friction points resulting in data fragmentation can be identified.

By addressing those issues, contact centres can create more cohesive systems with unified data that support natural agent workflows.

When that is all in place, not only will AI projects work far more efficiently, but data quality will improve and contact centres will be able to scale far more easily.

For more information about MaxContact - visit the MaxContact Website

Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: MaxContact
Reviewed by: Robyn Coppell