If you’ve ever blindly dug through a junk drawer only to get stabbed by a rogue meat thermometer, you know disorganization is dangerous. Well, it’s the same with AI, only scarier. Because 89% of organizations have the tech equivalent of that messy abyss, only it’s jammed with old scripts, shadow SaaS tools, and mystery service accounts. Yep, only 11% of companies can account for all their data, according to Omdia’s survey of 1,000 IT leaders.
That means everyone else is racing to deploy AI without the visibility and AI data governance it requires. And when you unleash AI on your proverbial junk drawer, you get junk AI that’ll cause far more pain than a stabby turkey probe. Because speed + chaos = a world of hurt.
That’s why we’re about to go full Marie Kondo on the AI acceleration paradox.
Trusted AI starts with cleanup, not speed. Every new AI system multiplies your attack surface because it needs data and identities to function, and it inherits whatever chaos already exists in both. So, without housekeeping, you’re compounding AI operational
risk across the enterprise.
I see this repeatedly in customer environments, and it comes down to three foundational gaps: disordered data, identity sprawl, and platform fragmentation. This creates a context where AI can’t be trusted, traced, or secured, undermining AI security and compliance efforts from the get-go.
And before you assume investing in modern data lakehouse architectures and AI-ready platforms will eliminate the need for any cleanup, the truth is, you’re just dumping your junk drawer into a more scalable environment.
No matter what, trusted AI requires housekeeping. It’s necessary for any effective AI governance framework. And I promise you the results are worth it. So, I’ve broken it down into three manageable steps…
AI housekeeping fix #1: Find, understand, and govern data
Start with data cleanup through simple discovery. Scan everything, including cloud storage, legacy databases, data warehouses, collaboration tools, and yes, the shadow SaaS no one admits they’re using. This is where we find duplicate datasets, abandoned pipelines, and reports with no true owner, which are all key sources of AI risk management blind spots.
Next, add context. Catalog what you find and define it in business terms. What does “customer” really mean here? What data is certified, and what’s just an old export someone keeps copying? Document lineage, so it’s clear how data moves and changes, and flag sensitive fields, so they’re handled correctly. This is how AI learns the difference between data it should trust and data it should ignore, which is an essential requirement for responsible AI.
Then, put rules around it. Set basic data-quality expectations like freshness and completeness, and control who can access what based on real business needs and intended AI use cases. Only data that meets those standards should be considered AI-ready. In modern lakehouse platforms, this is what turns a pile of storage into something AI can safely build on, increasing AI readiness.
AI housekeeping fix #2: From shadow identities to governed access
If data tells AI what to work with, identities determine what it can do. And this is where things can get painfully messy fast. Start by discovering every identity in your environment, and not just people, but also service accounts, workloads, and other nonhuman identities. Most organizations have far more identities than they realize, and many of those have access they no longer need, increasing AI operational risk.
From there, enforce least privilege. Match access to current business needs, remove what’s outdated, and use just-in-time access for sensitive actions, so there aren’t permanent doors left unlocked. Continuous monitoring helps catch odd behavior, like an AI agent accessing systems it’s never touched before. When identities are governed, AI actions are auditable, supporting both AI security and compliance and broader enterprise AI strategy goals. Platforms like Microsoft Entra ID are increasingly used as the central place to make that happen.
AI housekeeping fix #3: Modern platforms that scale AI safely
Now you can focus on platform consolidation and modernization by shifting toward integrated, modern platforms that bake governance, identity, and observability into the architecture. This reduces AI risk management overhead as AI scales.
On the data side, this often means consolidating into a cloud data lake or lakehouse running on technologies like Microsoft Fabric, Databricks, or Snowflake. These platforms support multiple data types, enable semantic modeling, and integrate tightly with governance and catalog tools, which makes it easier to serve trusted data products to both people and AI systems. This is where AI data governance becomes operational.
On the identity side, modernizing onto Entra ID as a primary identity provider reduces the sprawl of disjointed directories and oneoff authentication mechanisms. Legacy connectors remain only where necessary, and all new applications and AI agents are onboarded through a consistent, governed identity plane, supporting responsible AI at scale.
Audit and observability complete the picture. Centralized logging of access, configuration changes and AI actions across data, identity and application layers enables teams to trace decisions, respond to incidents and demonstrate compliance. Without this integrated view, AI operations quickly devolve into firefighting and unmanaged AI operational risk.
So, how long does all this take?
Here’s your AI housekeeping timeline.
The good news is that most teams can move quickly:
Analyze (2-4 weeks): Scan what exists and pick one high-value domain to start.
Organize (6-12 weeks): Govern the data, clean up access, and align platforms to streamline AI risk management.
Operationalize (ongoing): Automate the cleanup, so it stays clean, supporting your enterprise AI strategy.
A few months of spring cleaning beats 18 months of firefighting. And even if you choose the latter, cleanup is still inevitable. Because ignoring those old matchbooks in the back of your junk drawer won’t spark joy. It’ll ignite a full-blown AI dumpster fire. And in the immortal words of Smokey the Bear…
Only YOU can prevent AI dumpster fires.
The choice is yours: do a little organizing now or wait for that junk drawer to spill over into a terrifying episode of Hoarders: IT Edition. And look, I get it. You’re not in the mood to reach back in and get stabbed by some rusty old scissors. You’re already busy, overwhelmed, and nursing a meat thermometer wound. You’re running out of gauze, and you don’t have time for a tetanus shot.
Fine. But in the game of “Would You Rather?” known as life, what’s really more painful? A little decluttering today or dealing with a blazing AI disaster down the line? There’s just no other way around it. Proactive cleanup is quicker, simpler, and 100 percent less likely to end with you covered in Band-Aids and fire extinguisher powder.
Housekeeping: This is the way
Housekeeping is foundational for responsible, fast, trusted AI. It may feel daunting at first, but it’ll pay off quickly, and you’ve got a whole cleaning crew to back you up. Together, we’ll easily clear your path to AI success.
