data flowing in and out of an ai processor

Why operational truth matters more than visibility

Over the last several years, organisations have invested heavily in visibility, automation and artificial intelligence. Yet many still face a common challenge: They have more dashboards than ever, but getting a reliable answer often requires manual investigation, reconciliation and validation before action can occur. The problem is rarely a lack of data. More often, information lacks the shared context people need to understand it and act.

This challenge becomes even more important as organisations introduce AI into daily operations. According to McKinsey's State of AI survey, 23% of organisations are already scaling agentic AI in at least one function and a further 39% have begun experimenting — roughly two-thirds in total. Yet use remains narrow: In any given business function, no more than 10% of respondents say their organisations are scaling AI agents. McKinsey's separate research on scaling agentic AI? Eight in 10 companies cite data limitations as a roadblock to scaling agentic AI.

The lesson is increasingly clear. Successful AI initiatives depend less on deploying new technologies than on establishing a reliable operational foundation. As AI becomes more involved in decisions, the question is no longer whether organisations have enough data. It is whether they have a data authority that allows people, workflows and AI systems to work from the same reality. For IT and Security leaders, that reality includes assets, users, devices, vulnerabilities, service history, security posture, compliance signals and operational relationships, brought together in context so teams can move faster from insight to remediation.

That distinction matters. AI can surface insights, explain relationships, identify patterns and recommend next steps, but enterprise decisions still require governance, accountability and human judgement. Without a shared system of record, AI may simply make fragmented data easier to access without making it more reliable. With one, organisations can ground intelligence in current, governed and verifiable information.

This is where the Ivanti Neurons Platform establishes the foundation for AI-powered operations. As the system of record and data authority for IT and Security, it connects data and relationships with automation and AI while preserving transparency and human oversight. The next evolution of unified asset intelligence is not simply seeing more. It’s making operational truth easier to reach, interpret and act on with confidence.

Why dashboards alone are no longer enough

For decades, operational visibility has depended on dashboards, reports and specialist expertise. These tools remain valuable, but modern environments are becoming too dynamic, too interconnected and too complex for every answer to require manual investigation.

When a security leader wants to understand which unmanaged endpoints present the highest risk, or an operations leader wants to identify devices affecting service performance, they often need information spread across multiple systems, teams and workflows. This challenge reflects a broader organisational reality. According to PwC's 2026 Digital Trends in Operations Survey, 94% of organisations with siloed or partially integrated operating structures expect to move toward more connected, networked operating models, yet only 41% operate that way today. Organisations increasingly recognise that information exists across the enterprise, but transforming fragmented information into trusted context that people, workflows and AI can rely on remains difficult.

The challenge is no longer collecting information. The challenge is reducing the distance between a question and a trusted answer that can drive meaningful action.

As organisations expand automation and AI-assisted workflows, that distance matters. Every manual lookup, spreadsheet reconciliation or context-switch slows response times and creates opportunities for inconsistency.

The analyst view is moving in the same direction. Gartner anticipates that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% the year before. Its infrastructure and operations research expects the interaction model itself to shift from command lines and scripts toward "prompt engineering, policy definition, and workflow orchestration," as teams turn to agentic AI they can direct in natural language. The implication for buyers is direct: Gartner notes that "enterprise buyers will deemphasize buying more new tools or dashboards" and "want better outcomes," because "adding more AI features often creates more cost, not better outcomes."

This is why AI-powered intelligence is emerging as the next interface for enterprise operations. The shift is not about replacing dashboards. It is about giving people faster access to relevant information, useful insights and recommended next steps so they can respond more effectively.

Instead of searching across disconnected systems, teams increasingly expect to ask:

  • Which unmanaged endpoints present the greatest operational risk?
  • Which locations have the highest concentration of non-compliant devices?
  • What changed in our endpoint environment during the last week?
  • Which remediation actions produced the biggest improvement in compliance?

Teams increasingly expect systems to do more than provide answers. They want AI to explain what changed, identify priorities, recommend next steps and show the evidence behind them. In that sense, Neurons AI is not simply a chatbot layered over operational data. It is an orchestration layer that applies enterprise context, connects systems and workflows and helps teams move from evidence to governed action. In this way, AI becomes the interface to operational truth, making answers available when decisions are being made while people retain judgement and control.

What makes AI-powered operational intelligence trustworthy

AI-powered experiences deliver more than a simpler interface. They help teams interpret information, identify priorities and reach sound decisions. As AI-native capabilities evolve and integration standards expand how systems exchange data, the system of record becomes even more important as the data authority that keeps insights, recommendations and workflow guidance grounded, governed and explainable.

Without a shared system of record, AI can amplify inconsistency by drawing on fragmented or conflicting sources. Teams may receive different answers or priorities depending on the available data and how it is interpreted.

As AI takes a greater role in decisions, trust becomes the defining challenge. Leaders need confidence that insights and guidance are grounded in reliable information, aligned with policy and supported by clear accountability. Governance is not a separate consideration; it is part of every interaction. That also means AI should be additive and governed by customer choice. Organisations need control over where AI-assisted capabilities are used, how they are enabled and how core workflows continue to operate as AI adoption matures.

This is where a system of record becomes essential. More than a repository, it serves as the organisation's data authority: the source that people, workflows, automation and AI rely on to understand what exists, what matters, what should happen next and whether outcomes were achieved.

Ivanti's own research underscores the payoff. In the 2026 AI Maturity Report, 57% of IT professionals said AI has improved knowledge sharing across IT and Security, 53% reported that data is easier to share, and 50% have created joint workflows that span multiple teams — results that climb significantly among those using AI more extensively. The same research reminds us that nearly 90% say siloed data negatively affects IT operations, which is precisely the gap a system of record closes.

Within the Ivanti Neurons Platform, this foundation supplies the governed information that dashboards, workflows, automation and AI rely on. This platform-first approach also creates a common foundation for agentic experiences, where MCP-enabled tools, shared AI services and governed data access support multiple AI-powered workflows across the platform.

By keeping people and systems aligned to the same source, organisations can improve consistency, strengthen accountability and reduce ambiguity. Responsible scale also depends on how AI accesses information. Neurons AI is designed to retrieve the right governed context for the task through efficient, tool-based access patterns rather than relying on broad, unnecessary data transfer into AI models. That helps organisations improve efficiency and scale AI-assisted workflows with greater control over operating cost.

"AI is only as effective as the data that powers it, and Ivanti's strength in data authority, autonomous workflows and governance is a critical differentiator in the market." – Dennis Kozak, CEO, Ivanti

Four leadership practices for making operational truth accessible

1. Establish a trusted operational foundation

Before expanding AI-driven experiences, leaders must establish confidence in the underlying data. They should create a shared foundation that governs asset, endpoint, configuration and relationship information across IT and Security and serves as the system of record for decisions. When teams work from the same source, discussions shift from validating information to solving problems, multiplying the value of every automation and AI initiative.

2. Create visibility into outcomes, not just activity

Many dashboards focus on activity metrics. Leaders should prioritise outcome metrics.

Examples include:

  • Reduction in unmanaged endpoints
  • Improved remediation speed
  • Increased policy compliance
  • Reduction in duplicate or conflicting records
  • Higher rates of verified automation outcomes
  • Decreased operational risk exposure

Outcome-oriented metrics create stronger alignment because they connect operational improvements directly to business value and help teams understand whether insight is translating into action.

3. Give teams AI-powered access to trusted operational truth

Organisations making the greatest progress with AI reduce friction between questions, understanding and decisions by making governed information accessible to technical and non-technical stakeholders alike. AI reduces manual investigation and helps people interpret relationships, focus on what matters and apply broader operational knowledge.

Anchored in the system of record, AI supports human judgement rather than replacing it. Teams can use these capabilities to identify unmanaged endpoints with the highest risk, understand changes in compliance, uncover relationships among assets, services and business operations, surface remediation guidance, or retrieve relevant information through conversational experiences.

Together, these capabilities turn raw data into intelligence that supports better judgement. As they evolve, AI can extend guidance across systems and workflows while humans remain accountable and the Ivanti Neurons Platform keeps every interaction anchored to governed truth. The result is broader participation, faster alignment and clearer priorities.

4. Make governance auditable, not assumed

As AI becomes more involved in operational decision-making, governance can no longer be treated as a separate process or a policy document that lives outside the workflow. Organisations need visibility into:

  • The data used to generate recommendations
  • The policies influencing decisions
  • The actions taken
  • The resulting outcomes

Governance creates accountability, improves trust and helps ensure AI-assisted workflows remain aligned with organisational objectives.

From trusted context to action

When trusted context becomes easier to access, organisations spend less time searching and more time acting. Endpoint teams can identify compliance issues faster and respond with greater confidence. Operations teams can make decisions using current information rather than outdated reports. Security teams can understand exposure in context and prioritise more effectively. Compliance leaders gain access to verifiable evidence without extensive manual effort.

Most importantly, people no longer need expertise in every system to benefit from the intelligence within them. Operational truth becomes a shared organisational capability rather than a specialised technical resource. As the foundation matures, teams can interact with their environment more naturally while maintaining the oversight and accountability required for enterprise-scale AI.

Ready to build what lasts

The future of enterprise operations will be defined by how effectively organisations turn operational truth into decisions and decisions into coordinated action with people in the loop and clear controls over how those decisions and actions are made.

As AI-native experiences mature, the organisations that pull ahead will be those that treat operational truth as a living, shared capability rather than a reporting exercise. They will interact with their environments in natural language, delegate governed action to AI with confidence and make decisions from a single source of truth that every team, workflow and agent can trust. That is the destination unified asset intelligence points toward, not just better visibility, but a fundamentally more responsive, accountable way of running IT and Security.

This is also what makes the platform future-ready. As agentic workflows mature, organisations will need AI experiences that can use the right tool, data source or AI capability for the task, while extending through shared services rather than creating disconnected AI experiences. A governed system of record gives those future agents the context and control they need to act responsibly.

Want to see how a governed system of record makes AI the interface to operational truth for your teams? Explore the Ivanti Neurons Platform to assess where your operational context stands today.