I’ve spent most of this year talking to CIOs who’ve made serious investments in AI, be it intelligent copilots, automation agents or workflow assistants. Almost every one of them tells the same story: they see a burst of early success and then it plateaus or, worse, tanks.

AI can summarize, predict and automate like never before and it’s only going to keep getting better by the day. Yet, one thing that AI systems can’t do is truly understand how your business actually works.

Most enterprise AI systems are programmed to crunch numbers and work with data, but they work mostly in isolation, not knowing the why or for whom they’re solving.

The context chasm between data and interpretation expands, only for well-intentioned AI initiatives to quickly lose traction. So-called smart assistants or agentic workflows end up frustrating end users and IT teams.

That’s why I strongly believe that organizational context — a living and breathing graph of people, processes, systems and policies — is the next frontier of intelligent AI systems. Without it, we’ll only be incrementally improving automation and not driving scaled transformations.

Turning data to intelligence to insight

Over the past decade, we’ve mastered the art of engineering data. Now it’s time to engineer intelligent context behind the data and feed that into AI systems and platforms.

Organizational context is a continuously updated and interconnected layer that understands your enterprise intricately. This includes connecting the dots between people, assets, processes, services, technology, trust, risks, applications and platforms. Details such as:

• User attributes like an employee’s department, location, role, manager and access entitlements
• Device information such as assigned laptops, mobile devices, their configurations, asset health and compliance status
• Application usage patterns of SaaS licenses, entitlements and on-premises apps
• IT infrastructure dependencies between network devices, servers and storage
• Business processes that involve workflows and policies configured around them for different teams

Unlike traditional databases that try to capture the above data partially, the organizational context layer is dynamic. It connects, for example, data from HR systems, ITSM processes, identity providers, network telemetry, SaaS platforms and collaboration tools into one unified layer that constantly tracks the changes and dependencies between them in real time.

What context-aware service management looks like

When deep context is plugged into your agentic AI systems, end-user support becomes more personalized, human-like and intuitive at scale.

Context-enriched support knows that when Sarah reports her Zoom app crashes frequently to the AI assistant, she is from sales working out of London on a MacBook running a specific OS, using a specific Zoom version over a network segment where three other users have already reported similar issues.

This living context enables AI to reason in real time and suggest the next-best actions. It’s context that wouldn’t have been gained in the traditional way until after a lot of back and forth.

Similarly, let’s say an employee requests privileged app access. Context-aware AI can check that they already have an inactive license and their department’s policy pre-approves the tool for client work, so it can quickly reactivate the existing license.

This is a shift from data pipelines to context pipelines, which then gives AI the ability to answer not just what happened, but why and what to do next.

Multiply the time and license costs saved across thousands of requests due to deep org context and the business case becomes obvious: faster resolutions, higher employee satisfaction and lower operational costs.

Context as the trust multiplier

We IT leaders often assume that the biggest barrier to AI adoption is fear of job loss or a technology limitation. In my experience, it’s trust.

Employees and organizations as a whole don’t trust systems that don’t understand them. In fact, research indicates a healthy correlation between trust in AI, ROI from AI initiatives and future AI investments. Higher levels of trust in AI-driven IT systems lead to more usage and investment, which in turn leads to a better ROI.

On the other hand, when AI repeatedly offers irrelevant responses or misses nuances in request handling, people grow averse to AI and adoption stalls.

Organizational context changes that dynamic. When AI recognizes who’s asking, what they’re trying to do and why it matters, users start to rely on it more. Trust builds. Workplace productivity shoots up.

Gartner argues that making context engineering a strategic priority can help organizations keep their AI systems relevant, adaptive and aligned with business goals.

That’s a critical shift from teaching AI how to respond to prompts; teaching it intent turns IT operations into a proactive business enabler.

The CIO’s challenge of unifying context

CIOs building this kind of contextual intelligence need to unify data layers from HR, IAM, device management, security and ITSM data into coherent models. Each of these platforms also needs to have AI-ready architectures that let AI systems consume context safely with clear decision and usage trails. This isn’t about ripping out and replacing what exists but connecting tech that’s already there into a model that enterprise AI can access and comprehend. When tech leaders prioritize tuning their organizational context, the payoff is huge: lesser manual intervention, faster resolutions, smarter automation and a foundation of dependable and scalable AI adoption.

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