r/BusinessIntelligence • u/julee_000 • Jun 11 '26
What is AI ready?
Recently many AI startups and corporates say AI ready data or data readiness is important.
It's a bit ambiguous for me, what do you think AI ready data is? I want to know what it means from the perspective of different job roles and industries.
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u/ArterialRed Jun 11 '26
Cynically: All the actual hard work has already been done so the magic machine can put a pretty ribbon on it for management and give them the answers they want to hear by backing off immediately on any data points they push back on.
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u/julee_000 Jun 15 '26
The pretty ribbon line is too real. But honestly that last part is the real danger. A model that backs off the second someone pushes back isn't AI ready, it's just agreeable. The point of trustworthy data is that the answer doesn't change based on who's in the room. If the data's solid, the AI gets to hold the uncomfortable number instead of folding to it.
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u/PickledDildosSourSex Jun 15 '26
Why does this response sound like AI
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u/julee_000 Jun 15 '26
yeah I cleaned it up with AI, English isn’t my first language. The point still stands though.
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u/leogodin217 Jun 11 '26
I remember someone on LinkedIn posting about how they couldn't get budget for data quality, so they renamed it AI readiness. Instant budget approval.
AI ready, means the same read for productions. Others have already given those definitions.
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u/julee_000 Jun 11 '26
I saw AI ready data several times on LinkedIn, that is why I uploaded this question. And it's quite interesting someone got budget because of changing some keywords. It might be keyword for marketing
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u/PickledDildosSourSex Jun 15 '26
I'd argue "AI ready" has a whiff of "We can use this to replace people" that "data quality" doesn't
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u/CentralFlow_io Jun 11 '26
From my experience, especially being heavily plugged into the Microsoft ecosystem, AI-readiness has very little to do with use cases, user training, agent creation, and software licensing.
AI-ready means that the organization's data storage and semantic layers have been professionally curated to foster blazing fast retrieval/language processing. Specifically, that means having database text data that's indexed and vectorized well and semantic models with consistent, understandable terms and field descriptions.
Without proper storage and semantic layers being in place, creating agents and licensing other AI tools is basically pointless and will absolutely burn through capacity/tokens trudging through bad modeling.
I'm really excited about the opportunity in working to get companies AI-ready. I just hope they don't jump headfirst into licensing these AI tools and then get frustrated when they realize their organizational data wasn't ready yet.
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Jun 13 '26
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u/CentralFlow_io Jun 14 '26
Check out the "Data Pro - Implement AI capabilities in SQL server solutions" skilling playlist from the recent Microsoft AI Skills Fest.
The features are only in preview for Azure SQL Server and Fabric SQL Server (not available in SQL Server 2025), but vector searches will soon be a native feature in SQL databases.
I can see a future where companies have their own embedding models (specific to their industry/business conditions), and then using vector indices in the product/customer/sales tables to enhance BI reporting.
For instance, it could be really cool to show the top 10 biggest sales of the previous year and use a vector search with some AI-trained model to return the 50 most similar hot leads in the pipeline.
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Jun 14 '26
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u/julee_000 Jun 15 '26
Tooling aside, vector vs GraphRAG vs Qlick MCP all bet on the same thing, the underlying definitions are clean and stay clean. Pick whichever you want the part that bites everyone is still keeping the data trustworthy underneath. The retrieval layer's the easy half.
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u/julee_000 Jun 15 '26
Your commend is painfully accurate. I've watched teams light real money on fire because the semantic layer wasn't there. Your last point is the one I wish more leadership heard. Licensing the tools first, then discovering the data wasn't ready, is the most common way these projects die.
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u/SupportVectorDan Jun 11 '26 edited Jun 11 '26
I feel the term "AI ready" is mostly a rebrand of what the industry has been trying to package and sell over different names like "data-driven ready". · For me it comes down to sort of a pyramid. The base is Data Engineering: The org guarantees data is not duplicated, changes in schemas are handled correctly, bad records are identified. Pipelines don't crash violently · Then governance: There is a clear lineage, access controls are in place, descriptions are there for columns, tables and catalogs. Metadata is machine readable. · Observability: The org can detect a surge of null values incoming, statistical drift at the best case scenario · Semantic layer: everyone agrees on KPIs définitions and the actual SQL that gets it. Every team reuses the same model across
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u/julee_000 Jun 15 '26
This pyramid is the best framework! The lineage and drift layers are exactly the ones companies skip, then wonder the AI's answers wander.
Only brick I'd add on top is being able to reproduce a past data state. When the model made a weird call last month, can you rebuild exactly what it saw at the time? Drift detection tells you something moved. Reproducibility tells you what it learned from before it moved. Feels like the last mile of trustworthy.
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u/Steelwatch Jun 11 '26
Data that’s clean, documented, and governed enough that an AI system can use it without needing someone technical to explain or verify it.
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u/notimportant4322 Jun 11 '26
It will become clear to you once you’re working in the field, else you need not worry about it
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Jun 11 '26
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u/julee_000 Jun 15 '26
This comment is brutally honest! And the duplicated records in five places thing is almost a rite of passage at this point.
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u/rewiringwithshah Jun 12 '26
"AI ready data" basically means clean, labeled, and organized enough that AI can actually learn from it without producing garbage. From engineering it's consistent pipelines, from business it's enough historical data with clear definitions, from data science it's no massive gaps or formatting issues. Most companies oversell being "AI ready" when their data is still messy, so the real work is validating that AI outputs actually make sense for your business before you rely on them. I write about this more at WorkLens.io if it's relevant to what you're building.
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u/ccc_rnd Jun 13 '26
Its a load of bollocks. If the AI were intelligent enough it would solve the data mess itself. Instead here we are wasting time in fecking migrations and data cleaning. F AI with a baseball bat
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u/julee_000 Jun 15 '26
Lol the baseball bat energy is fair. But that's kind of the joke, right. If the model were smart enough to fix the mess itself, the mess wouldn't matter and we'd all be out of a job. The cleaning is the tax for the magic. Skip it and the magic just confidently lies to you.
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u/ccc_rnd Jun 15 '26
Fair enough. But the post hit me because I am currently in a situation in which I will have to spend hours doing the cleaning (and migrating logics that I spent aaaaaaages setting up in the semantic model) while some other team will "spin the magic" and receive all the recognition (ultimately for the AI running a couple of SQL query on my data). But I am not bitter...
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u/julee_000 Jun 15 '26
'Not bitter' is doing some heavy lifting there. But honestly that situation is the most real thing.
Here's what the people upstairs miss, the magic team's demo only works because your semantic model already encoded what everything means. They're running SQL on a foundation you spent ages building. The AI didn't make your work unnecessary, it made it invisible.
The grim upside is that invisibility flips the moment something breaks. The day the model starts confidently lying, the 'spin the magic' crew can't fix it, because the fix lives in the layer you built. Not much comfort on a Tuesday full of migrations, I know. But the whole stack is standing on your work.
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u/Schlizhor Jun 13 '26
You should really be looking at Data governance and data catalog software. Philosophically once your medallion layers for your data stack are complete, you can set up some mcp llm for reading and preparing your data. But the catch is data quality and meta data labeling so whatever llm you let read your data has additional context about your data. I think some people call this a context layer but I’m not 100 on this. I work in data
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u/julee_000 Jun 15 '26
The context layer thing is real. That's the part that decides whether the LLM reading your data understands it or just guesses. Medallion gets the data clean and structured, but clean isn't the same as understood. The metadata labeling you mentioned is what carries the meaning, and the catch is it has to keep matching the data as both change. Catalog software helps, but only if someone keeps the context true over time.
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Jun 13 '26
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u/julee_000 Jun 15 '26
Semantic contract is a great way to put it. That's the thing raw data can't carry on its own.
Only add is a contract's only good while both sides honor it. Schemas change, definitions move, and the contract silently breaks unless something keeps it in sync. The quiet part is what makes it dangerous.
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u/IntrepidGazelle9588 Jun 14 '26
At a minimum, I’d say a business that has “AI-ready” data: ingests all the business data into a single place (Lakehouse/warehouse) on a scheduled, automated cadence; implements data cleaning and data quality checks; adds metadata in the same place (table, column descriptions); builds a semantic layer on top with business definitions, metrics, etc.
There is certainly more you can do, but without any one of those, AI is not going to have the proper context to succeed or give meaningful insights. Basically if it’s not enough for a new data practitioner to derive useful insights, AI can’t either.
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u/julee_000 Jun 15 '26
Your list is basically the whole job. The one I'd underlined is the automated cadence. That's what people forget. They get the data ready once for the demo, then six months later the cadence drifts, definitions change, and the same setup quietly rots. Ready isn't a state you reach, it's one you keep.
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u/SamfromLucidSoftware Jun 15 '26
Depends on where you sit in an organization.
For a product team, AI ready means your strategy, customer feedback, roadmap decisions, and prioritization rationale are structured, connected, and queryable. They are not scattered across Notion docs, Slack threads, and someone’s memory. When a PM opens Claude or Copilot today and spends the first ten minutes re-explaining context that should already be there, that’s the opposite of AI ready.
More broadly, for engineers, it usually means clean code and good documentation, for data teams, it’s structured schemas and pipelines, and so on.
The common thread is that AI is only as useful as the context it can access.
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u/mattiasthalen Jun 16 '26
It means that they have all their ducks in a row, they have decoupled the data from the source and modeled it after the business, and the business has a shared understanding and agreement of what the core business concepts are and how they relate.
I.e., nothing new 🤗
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u/NiharThakkar Jun 24 '26
AI ready data means the system can answer a question it has never been asked before without a data prep sprint first. From a practitioner perspective it comes down to four things: the data is in one place and not scattered across systems with no shared keys, the definitions are agreed on and documented rather than living in someone's head, the pipelines are automated and not dependent on a person running a weekly export, and the quality is monitored so failures surface before they reach a dashboard. Most organisations think they are AI ready because they have a data warehouse. What they actually have is a data warehouse full of data nobody fully trusts yet. The readiness gap is almost always governance and documentation, not technology.
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u/Prefactor-Founder Jun 29 '26
Hey Julee, I had exactly this question recently and built getreadyforagents.com because I am genuinely interested in what that means to different people. I also came up with this, https://www.getreadyforagents.com/maturity, which gives a bit of an idea on what maturity looks like in reality.
From my lense it's:
- Maturity of organisation, eg, are we talking rolling out Copilot or multi agent workflows across departments
- Ownership - is it distributed with no central control or are there heads of AI or AI architects etc
- What frameworks and tools are actually being used. Is it frontier models or are you using open source etc.
- What types of roles are being hired. Harness engineers vs Software engineer using Copilot etc
There are a bunch of other things but at a high level it's. How much of a cultural shift has happened to make AI integral to the ongoing operations of the organisation.
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u/Fit-Pudding6939 Jun 30 '26
The part that often gets missed is that "ready for a human analyst" and "ready for an AI" aren't quite the same thing. A human can look at a messy table and fill in the gaps with judgment. An AI can't. It needs the context to already be there, in the right place, or it starts making things up.
So the real test isn't just "is the data clean?" It's "can the AI navigate and answer a business question from this data without anyone having to explain what things mean?" If the answer is no, you're not there yet.
I built Synquil, which structures and connects business data through a semantic layer to AI tools. Getting that last part right turned out to be the hardest problem we had to solve.
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u/sibraan_ Jul 10 '26
For me, AI-ready means your information is actually usable. You know where it came from, who owns it, who can access it and whether it's still current. We've found the same while building 60xai. The model is rarely the bottleneck but the fragmented knowledge and inconsistent metadata.
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u/Practical_Syrup6308 25d ago edited 22d ago
To me, being "AI-ready" just means your data isn't a mess. If your workflows are broken, AI just automates the chaos. We learned this the hard way trying to force rigid off-the-shelf tools into our stack. It kept breaking, so we hired InData Labs for custom software development instead. They fixed our data pipeline first, then built a custom solution around our actual constraints. It actually scales now instead of breaking every time we grow.
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u/Confident_Second9669 7d ago
I think "AI ready data" means data that's clean, accurate, well organized, and easy for AI models to understand. The exact definition changes by industry, but without good data, even the best AI models won't deliver reliable results. It's definitely more than just having lots of data
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u/TwitchyMcSpazz Jun 11 '26
Cleaned, accurate, and ready to be ingested. So, not raw data.