AI may be the future of IT operations—but bad data will break it before it begins.
As agentic AI sweeps across the enterprise, promising autonomous decision-making, operational efficiency, and innovation at scale, one critical detail is often overlooked: most organizations' data isn’t ready.
According to Gartner, 99% of I&O-led investments in agentic AI will fail by 2028—not because of the AI itself, but because the underlying IT asset data is incomplete, inconsistent, or siloed.
Before deploying intelligent agents across your infrastructure, there’s one foundational question every IT leader should ask:
Can you trust the data those agents will rely on?
To understand why data readiness is so critical—and so often overlooked—it helps to look at how agentic AI works, and how different it is from the AI most organizations are used to.
According to the World Economic Forum, agentic AI has the potential to “transform industries, enhance efficiencies, and tackle societal challenges.” Gartner further confirms that vision, claiming, “AI agents are poised to disrupt the IT operations ecosystem by breaking down product silos. Consumption of future ops functionality will occur via collaborating, cross-discipline agents that will produce superior outcomes.”
However, challenges still remain. Without strong data to feed agentic algorithms, solutions keep falling short of expectations. According to Gartner research, by 2028, 99% of I&O-led investment in agentic AI without system data and operating model improvements will fail to achieve sustainable return on investment.
This is something I&O teams can't accept–especially when agentic AI removes the need for human intervention and allows them to explore new solutions and more heavily invest in those that seem most favorable for their enterprise.
In this blog, we will explore the critical importance of establishing a foundation built on accurate, complete IT data that supports AI initiatives and empowers IT teams to leverage technology that transforms their operations.
The AI Opportunity and the Data Dilemma
Understanding why data matters so much starts with understanding what agentic AI is—and how it differs from other, more familiar types of AI.
Agentic AI refers to autonomous or semi-autonomous software entities that perceive, decide, act, and achieve goals with little to no human intervention. These agents combine large language models (LLMs), natural language processing (NLP), machine learning, reinforcement learning, and knowledge representation to operate independently in digital and physical environments.
But to operate effectively, these agents need context. They rely on underlying infrastructure data—about assets, dependencies, and relationships—to make accurate decisions. Without that foundation, even the most sophisticated AI will produce flawed or risky outcomes.
The Benefits of Agentic AI
Unlike generative AI, which reacts to prompts to produce content or insights, agentic AI is proactive. It can make decisions, take actions, and solve problems on its own. These agents are designed to operate through sequences of logic and learning—constantly adapting as they interact with systems, data, and users.
When implemented with the right foundation, agentic AI can offer I&O teams significant benefits. These agents operate continuously, scanning and analyzing massive volumes of data at machine speed. This allows IT teams to redirect human resources toward higher-value initiatives and strategic decisions.
This technology is also designed to perform specific tasks and work in the background of their programs. The algorithms can be built to meet the unique needs of the programs they serve, and that specialization increases their effectiveness across I&O functions.
As I&O teams use agentic AI over time, they will typically see the technology’s impact in three key areas.
1. Accelerated Innovation and Strategic Differentiation
AI agents support cost efficiency by performing tasks in seconds that once took days. They also unlock strategic differentiation—testing new ideas, optimizing operations, and enabling tailored responses to specific user or business needs. This flexibility allows teams to focus more precisely on high-impact segments and initiatives.
2. Enhanced Productivity and System Performance
The beauty of AI agents lies in their autonomy. As they seamlessly enhance technology platforms, workflows, and assets to necessary levels, they also enable better system performance, increase visibility into IT infrastructures, and reduce waste in the event of demand spikes.
3. Greater Resilience and Customer Experience
According to Gartner, the top challenges facing I&O teams include aligning with business goals, building critical skills, and improving customer experience. Agentic AI helps address these gaps by using existing infrastructure and assets to better interpret workload demands, support real-time service delivery, and adapt to shifting priorities—enhancing both resilience and end-user satisfaction.
These outcomes are powerful—but they all depend on one critical factor: the accuracy, completeness, and trustworthiness of the data that powers the AI.
The Hidden Risks of Inaccurate IT Asset Data
Most I&O teams don’t have the clean, complete, and consistent asset data they need to support agentic AI. Even the most advanced models will fail if the information they rely on is inaccurate, fragmented, or out of date.
And yet, many organizations continue to depend on legacy systems—like static CMDBs—that were never designed to keep up with today’s dynamic IT environments. The result? A growing gap between AI ambition and operational reality.
Running agentic AI on poor-quality data can lead to:
- Incorrect Actions: AI agents can take the wrong actions when based on outdated or misleading data—resulting in faulty decisions, unnecessary alerts, or misrouted workflows.
- Increased Bias: Poor data introduces systemic bias into AI agents, reinforcing flawed patterns and compounding errors over time.
- Loss of Specialization: Without quality data, agents become overly generalized—unable to adapt to edge cases or new conditions reliably.
- Limited Scalability: When foundational data is untrustworthy, agentic AI can’t scale reliably across systems or environments.
- Service Disruptions: Inaccurate asset or dependency data increases the risk of unexpected outages and operational slowdowns.
- Security Gaps: Poor visibility into assets and their configurations opens the door to vulnerabilities and missed threats.
- Cost Overruns: Recovery from AI-driven errors caused by bad data often requires manual fixes, rework, and delayed projects—draining time and budget.
These aren’t edge cases—they’re everyday realities for teams relying on systems that weren’t designed to maintain accurate, federated, automation-ready data.
Why IT Asset Data Is Still Failing AI
Despite major investments in IT tools and automation, most organizations still struggle to access and maintain trustworthy asset data. Siloed systems, manual processes, and legacy architectures make it difficult to deliver the kind of real-time, accurate, and enriched data that agentic AI requires. To move forward, I&O leaders must rethink their approach to data management—starting with a clear understanding of what’s holding them back.
Disconnected Systems and Siloed Data
Many organizations have adopted a wide range of best-in-class tools—each solving a different problem but rarely speaking the same language. As a result, IT asset data lives in disconnected systems, each with its own definitions, update cycles, and blind spots. Even when I&O leaders can access the data, it’s often incomplete, duplicated, or stale—making it unfit for automation or AI.
These siloes not only obstruct cross-functional visibility and impact analysis, they also create friction for basic operational needs like security audits, compliance checks, or infrastructure optimization.
Inadequate and Outdated CMDBs
CMDBs were designed to serve as the “single source of truth” for IT asset data—but in today’s dynamic environments, they often fall short. Manually maintained and disconnected from real-time operational systems, most CMDBs contain outdated, duplicated, or incomplete information.
Gartner warns that these gaps can seriously undermine IT service management—leading to inaccurate impact analysis, delayed resolution, and inefficient change management. In fact, they predict that by 2028, large enterprises using AI-powered, real-time discovery and dependency mapping will see 30% fewer outages than those still relying on manual CMDB updates.
The takeaway is clear: maintaining data accuracy through static systems and periodic updates is no longer sustainable.
Legacy Tools That Can’t Keep Up
Many legacy IT systems were built for a different era—before ephemeral assets, multi-cloud environments, and real-time automation. They often lack integration capabilities, require manual upkeep, and run on outdated or unsupported software.
Instead of supporting agility, these tools introduce friction: slowing down data updates, increasing reliance on overworked admins, and making it nearly impossible to establish a current, connected, and complete view of IT assets.
Despite these challenges, there are several steps that organization leaders can take to assess and improve their data readiness and enable I&O teams to build out the agentic AI programs necessary for IT transformation.
How to Build a Data Foundation AI Can Trust
Building a data foundation for AI doesn’t start with new tools—it starts with a new mindset.
Most IT organizations have been conditioned to think of asset data as something to store in one place and update manually. But in dynamic environments, that model breaks down fast. Static CMDBs, spreadsheet audits, and patchwork integrations simply can’t keep up with the velocity and complexity of modern infrastructure.
The only sustainable way to support agentic AI is with a foundation that is:
- Federated, not centralized – pulling data from where it lives, rather than forcing it into a single system.
- Normalized and triangulated across systems – so conflicts, duplicates, and discrepancies are resolved at the source.
- Continuously enriched – with new context and attributes that make the data more useful to automation.
- Synchronized in real time – so that updates flow across systems, not just into a dashboard.
- Auditable and trusted – so teams have confidence that the AI isn’t acting on guesswork.
Building a strong data foundation isn’t a one-time effort. To ensure long-term trust and accuracy, organizations need continuous feedback loops—systems that detect changes, surface anomalies, and trigger corrective actions automatically.
Fuel Agentic AI with Data You Can Trust
The benefits of agentic AI are real—but so are the risks. Inaccurate, incomplete, or outdated asset data will derail even the most ambitious AI initiatives. For IT leaders, success depends not just on adopting intelligent agents, but on feeding them the kind of data they can actually trust.
That requires a shift: from static repositories to federated, normalized, continuously enriched data that flows across systems and reflects the real state of your environment. This is the foundation that makes AI not just possible—but powerful.
Want to understand what’s at stake and how to get ahead of it? Download the Gartner report Innovation Insight: Agent-Native I&O to explore why 99% of AI investments will fail without better data, and what leaders can do to build a resilient, automation-ready data foundation today.
