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Artificial intelligence continues to fundamentally change how we do business, and in the past year, a new innovation has entered the spotlight. AI agents are being adopted at record speed across organizations, from marketing to data management to customer service, with the promise to streamline decisions, engage customers and boost productivity for companies to drive business value.

We’ve seen AI agent launches from companies across all sizes and industries. In May, Google announced it would incorporate AI agents in its searches, while Microsoft also announced a plan to use AI agents to help its users search the web. The use of AI agents is surging across industries, from finance and healthcare to car dealerships.

In fact, Boston Consulting Group predicts that the market for AI agents will grow at a 45% CAGR over the next five years. Gartner has also estimated that 80% of common customer service queries will be resolved by AI agents in less than five years.

But here’s the catch: agents are only as good as the data they run on.

Why Data Still Trips Up AI

No matter the cutting-edge nature of the AI tool or its sky-high promises, one constant remains when it comes to the data they’re operating on: garbage in, garbage out.

Companies racing against competitors to deploy AI agents without taking a step back to evaluate the sources they’re operating on face a major risk—if those agents rely on fragmented or inaccurate data, they won’t perform as expected. Even the most capable AI systems can’t deliver results if they’re built on bad information.

According to MIT Technology Review Insights, 78% of global companies are not ready to deploy AI agents and LLMs. What’s stopping them? Their data is not prepared to support AI. At the core of AI’s success is unified, accurate and real-time customer data.

When AI agents are powered by bad, disjointed data, the consequences can be costly. Last year, Air Canada was forced to reimburse a customer when its chatbot promised a discount that didn’t exist. And, in April, a tech company suffered fallout after a customer service agent’s mistake resulted in a wave of canceled subscriptions.

These types of mishaps can threaten customer loyalty and result in churn. AI agents are only as smart and useful as the data on which they’re built. In order to trust your AI agent, you have to trust your data foundation.

Identity Resolution, Reimagined for Agents

The most essential—and most overlooked—piece of making agentic AI work is identity resolution. Without a clear, accurate view of who the customer is across historically disconnected and fragmented systems, agents are flying blind.

That’s changing. AI agents can now take on identity resolution as part of their function, matching records in real time, continuously refining connections and operating without brittle rule-based systems. Rather than depending on static, one-size-fits-all profiles, agentic identity resolution builds a living picture of the customer, improving with each interaction and fostering enhanced productivity and accuracy.

This means fewer errors, less time-consuming manual data prep and faster time-to-insight for every downstream system.

Getting the Data Foundation Right

Before AI agents can operate effectively, the underlying data must be:

Unified: Data from every touchpoint, ranging from eCommerce and CRM to customer support, should be stitched together into a single, accessible layer that’s usable for marketing and engineering teams alike.

Accurate: Identity resolution must reconcile inconsistencies or duplicates across multiple channels and touchpoints to build a reliable profile.

Contextual: Different use cases need different views. Marketing might need probabilistic profiles for broad targeting, while support needs deterministic, single-session accuracy.

Governed: Access controls, human oversight, feedback loops and consent tracking are table stakes for compliant and trustworthy AI – especially in the wake of evolving privacy regulations.

A modern lakehouse architecture, paired with AI-native tools for identity resolution and customer profile building, can drastically reduce the manual effort required and make real-time, AI-powered decisions viable.

Data as Competitive Differentiator

Often, data quality is treated like plumbing, which is necessary but invisible. But in the age of AI agents, it becomes a competitive asset.

High-quality, agent-ready data enables better personalization, faster experimentation and safer automation. It allows AI to act with confidence, knowing who it's interacting with, what they want and how to best respond efficiently and effectively.

When done right, data doesn’t just support AI - it elevates it.

What’s Next

Agent-based AI is already reshaping expectations for responsiveness, personalization and automation. But the true breakthrough isn’t in the models, it’s in the data.

The companies that invest in a high-quality data foundation now will be the ones who make AI useful, reliable and transformative for not only their operations, but also for the end customer experience. That’s the difference between a flashy interface or a top-notch algorithm and an impactful, scalable solution.

Before you build your next agent, build the data foundation it needs.

We list the best customer experience (CX) tool.

This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

The UK government's designation of data centers as Critical National Infrastructure (CNI) underscores their vital role in national operations and economic stability. This recognition comes with increased scrutiny and highlights the need for robust cybersecurity, especially in the face of escalating global threats.

While significant investment is pouring into the UK data center sector, a critical question remains: how much of this addresses the often-overlooked cybersecurity risks associated with Operational Technology (OT) and Internet of Things (IoT) devices?

The Achilles' Heel: OT/IoT Security

Data center operators have traditionally prioritized IT security, safeguarding valuable data from cyber threats by focusing on data center connectivity and server infrastructure. However, critical OT systems responsible for building automation, cooling, power, safety, and physical security often lack the same level of protection and are often not accounted for at all.

These systems, crucial for maintaining the physical functionality of data centers, become interesting targets for attackers if left unsecured, often being used as initial points of access and presence in Data Centre networks. This risk is underscored by the fact that many of these systems are more connected to data center networks and even the internet than security teams realize, while often lacking basic cyber security hygiene like operating system updates, secure credentials, and network monitoring.

Similarly, IoT devices like IP cameras, digital displays, fire suppression systems, and biometric access controls, while enhancing safety and physical security, introduce a complicated additional attack vector for security team to account for. Like OT systems, these devices often use stripped down, embedded operating systems that lack critical cyber security functions making them a relatively easy target for compromise.

Real-World Vulnerabilities

There are now numerous known public examples of OT/IoT vulnerabilities being exploited in data centers and similar environments, and likely many more compromises that are not disclosed or even remain undetected. IP cameras have been hijacked for botnet attacks, launching large-scale DDoS attacks. Building management systems have been compromised for unauthorized activities like crypto mining, impacting system stability and risking failure with dangerous levels of resource utilization.

Even when not targeted for direct impact, OT and IoT devices are often ‘soft’ targets threat actors can use for sustained presence in even otherwise secure networks that have invested heavily in IT cyber security. These incidents highlight the very real dangers of neglecting OT/IoT security. Ignoring these vulnerabilities is like leaving the keys to your data center under the welcome mat.

Bridging the Gap: A Focus on OT/IoT Visibility and Security

Effectively securing OT/IoT environments requires a different approach than traditional IT security. It starts with gaining complete visibility into these often-forgotten systems. Data center operators need to know what devices are connected, how they communicate, and what vulnerabilities they introduce.

This requires specialized tools designed for OT/IoT environments, capable of identifying and profiling industrial control systems, building and IT automation devices, and other connected assets. This likely also requires monitoring wireless communications as well, as many IoT devices are connected via site WiFi networks or IoT connectivity solutions like LoRa or cellular.

Once visibility is established, continuous monitoring and threat detection are crucial. Real-time asset management allows operators to track every connected device, identifying unauthorized or anomalous behavior before it escalates into a major incident.

This includes monitoring network traffic for suspicious activity and implementing anomaly detection systems tailored to OT and IoT protocols. Something as simple as identifying an IoT device like a camera attempting to communicate with the data center server infrastructure could be indicative of a compromised device.

Collaboration and Best Practices: A shared responsibility

While the responsibility for securing data centers ultimately rests with the operators, collaboration between the government and the private sector is still essential. Government initiatives like the NCSC's Active Cyber Defence (ACD) program provide valuable resources for threat identification and response. And, industry collaboration and threat intelligence sharing, as advocated by the World Economic Forum, are crucial for staying ahead of sophisticated attackers.

Data center operators must prioritize OT/IoT security by:

• Asset discovery and inventory: Identify and document every connected OT and IoT device within the data center environment.
• Vulnerability assessment: Assess the security posture of OT/IoT devices and systems, identifying potential weaknesses.
• Network segmentation: Implement micro-segmentation to isolate critical OT systems and limit the impact of potential breaches.
• Continuous monitoring: Deploy real-time monitoring and anomaly detection systems to identify suspicious activity.
• Incident response planning: Develop and test incident response plans specifically for OT/IoT security incidents.

The Time to Act is Now: Don't Wait for a Breach to Wake You Up

As data centers become increasingly complex and interconnected, a holistic approach to cybersecurity, encompassing all of IT, OT, and IoT, is no longer optional – it's a necessity. Don't wait for a breach to expose the vulnerabilities in your OT/IoT infrastructure.

By taking simple, proactive steps, data center operators can significantly reduce their cyber risk and ensure the resilience of these critical facilities. Protecting your data is crucial, but protecting the systems that support your data is equally important. Ensuring that cyber security investment goes beyond IT and accounts for OT and IoT environments is critical to secure the foundation of your data center operations.

We've listed the best software asset management (SAM) tools .

This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro