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Your Data Isn't as Ready as Your Slide Deck Says

TL;DR

Most AI projects fail because the data is bad: inconsistent, low-quality, unowned, and held together by hope, cron, and a spreadsheet named `final_v7_really_final.xlsx`.

If you walk around enterprise-land in 2025, you get two versions of reality:

  • Slide-deck reality: "We have a modern data stack, a lakehouse, a semantic layer, and a single source of truth."
  • Actual reality:
    • 5 BI tools
    • 12 subtly different definitions of "active customer"
    • 47 critical spreadsheets on someone's desktop called some variant of final_v7_really_final.xlsx
    • And nobody is totally sure who owns the thing that actually feeds the dashboard the CEO likes.

At Causify, we've worked on hundreds of AI and machine learning projects across industrials, hedge funds, healthcare, SaaS, Fortune 500s, tiny startups, and everything in between. Use cases from predictive maintenance and trading to customer support and pricing.

The pattern is weirdly consistent:

The AI initiative is very shiny. The data underneath it is not.

And then leadership is surprised when:

  • The AI pilot slips three quarters.
  • The POC never makes it into production.
  • The GenAI demo that wowed everyone in week one mysteriously dies in week four.

The problem is not "AI is overhyped". The problem is: your data is not in good shape.

Everyone Is Trying to Tell You This (Politely)#

This isn't just us being cranky.

  • Gartner (2025): Through 2026, 60% of AI projects that aren't backed by AI-ready data will be abandoned.
  • Accenture (2024):
    • 61% of companies say their data assets aren't ready for generative AI.
    • 70% struggle to scale AI projects that use their own proprietary data.
  • IDC FutureScape / AI Everywhere (2024): Top barriers to becoming data-driven:
    • Poor data quality (51%)
    • Lack of data automation (50.3%)
  • Forrester (2024): Data quality is now the primary factor limiting GenAI adoption among business users.
  • State of Data Science (Anaconda, 2023): Most practitioners still burn 25–50% of their time on data prep, cleaning, and checks.

Translation:

You can:

  • Buy models
  • Rent GPUs
  • Sign very impressive contracts

You cannot magically conjure clean, consistent, well-governed data out of a swamp.

Your data is what's preventing you from taking advantage of AI. The rest is vibes.

How AI Projects Actually Go Inside a Company#

The official narrative is something like:

  1. Execs decide: "We need AI."
  2. Budget appears for a pilot.
  3. Someone picks a promising use case.
  4. A cool vendor demo happens.
  5. Transformation ensues.

The real sequence is closer to:

  1. Execs decide: "We need AI."
  2. Budget appears for a pilot.
  3. Someone picks a promising use case.
  4. Data/ML team says: "Okay, where's the data?"
  5. Awkward silence.

Then the adventure begins:

  • Tables exist, but:
    • Half the important fields are null in production.
    • Columns are misnamed because "we didn't have time to fix it."
  • Business definitions changed three times, but:
    • Nobody updated the upstream jobs.
    • The slide deck still uses the old definition because it had nicer numbers.
  • Critical data is sitting in:
    • Salesforce with 30% garbage fields and free-text "notes" that mean everything and nothing.
    • CSVs in S3 from a vendor who changed their schema last year, but didn't update the doc.
    • A "temporary" export feeding a brittle cron job someone set up "just for the pilot" two years ago.

The funny part (funny for us, not for you) is that the model is usually the shortest part of the project.

What quietly kills an AI proof of concept:

  • Inconsistent IDs across systems
  • Timestamps in four different time zones and three different formats
  • No clear "source of truth" for core entities
  • Manual, fragile data feeds that quietly fail on weekends and no one notices until Monday's dashboard looks "weird"

By the time you've hacked this into something vaguely usable:

  • Your pilot timeline is wrecked.
  • Your team is burned out.
  • Leadership is asking, "Why isn't this in production yet?"
  • Someone suggests "maybe we should try a different model."

What "AI-ready data" Actually Means (Boring but Necessary)#

When we say AI-ready, we don't mean "we have Snowflake" or "we bought a lakehouse."

We mean boring, unglamorous things like:

1. You Can Actually Find What You Need#

  • There's a catalog or at least a documented set of key tables.
  • People know which datasets are production-grade vs "experiment Bob did on a Friday night."

2. Core Entities Are Consistent#

  • Customers, assets, orders, sensors, trades, etc. have stable, unique IDs.
  • They match across systems in a way that does not require sleuthing or vibes.

3. Data Quality Is Monitored#

  • Basic checks exist: row counts, ranges, null rates, referential integrity.
  • You know when things break.
  • You know who owns fixing them. (Not "whoever notices first.")

4. Pipelines Are Automated and Repeatable#

  • No manual CSV uploads as part of your "production" AI workflow.
  • Transformations live in code and version control, not in someone's personal notebook.

5. History Exists and Is Usable#

  • You keep enough historical data with timestamps to train models.
  • You can reconstruct "what we knew at the time," not just "what we know now."

6. Access Is Sorted Out#

  • Security is handled properly (not "just give the vendor admin temporarily").
  • The people building AI can actually query the data without a week-long ticket chain.

None of this is sexy. All of this is required.

For Executives: the Three Lies You're Being Sold About Your Data#

Lie #1: "We already invested heavily in data, so we must be ready."#

What you likely invested in is tools, not in clean, governed datasets that directly power AI use cases.

If any of these are true:

  • Nobody fully trusts the metrics on the main dashboard.
  • Every "simple" data question takes weeks to answer.
  • Data teams spend more time firefighting than building.

...then you didn't actually invest in data. You invested in plumbing, not water.

Lie #2: "We'll fix the data as part of the AI project."#

No, you won't.

You'll:

  • Spend most of the AI budget cleaning and duct-taping data.
  • Under-deliver on AI value because most of the time went into basic hygiene.
  • Conclude "AI doesn't work here" instead of "our data was not remotely ready."

Data foundations are their own roadmap and their own investment.

If you treat them as a side quest inside an AI project, that AI project will suffer. And then, because you're a rational economic agent, you will lose faith in AI instead of losing faith in final_v7_really_final.xlsx.

Lie #3: "We just need the right partner/tool/platform."#

Partners and platforms help. We are one of them. It would be convenient for us if they were the whole story.

But if your data is junk, every partner is swimming in the same junk. Some are just better at:

  • Writing glossy decks, and
  • Hiding the smell for a little longer.

The underlying reality does not change: you can't outsource your way out of fundamentally bad data.

For Engineers and Data Folks: the Red Flags You Already Know#

If you're an engineer, data scientist, or analytics person, you don't need a report to tell you this. You live it.

Red flags:

  • You spend more time chasing data anomalies than building models.
  • Nobody can answer "Which table is the source of truth for X?" without a 20-minute debate.
  • You're afraid to touch certain pipelines because:
    • "That's Sarah's job but she left," or
    • "It just works, don't ask how."

Business definitions are fuzzy in exactly the ways that make AI hard:

  • What precisely is a "churned user"?
  • What counts as "revenue" in this context?
  • Do we treat credits/discounts the same across teams?

Any upstream change can silently break a downstream job...
...and then the AI model's output starts looking "off" and everyone blames the model.

If this describes your week, it's not that you "aren't moving fast enough on AI."
You're paying the data-readiness tax.

Why AI Makes the Data Problem Worse, Not Better#

A quiet hope in many boardrooms was:

"Can't we just point an LLM at our documents and databases and let it... reason around the mess?"

No.

GenAI is extremely good at:

  • Sounding confident
  • Being fluent
  • Making things up over bad or conflicting inputs

It is extremely bad at:

  • Magically inferring which of your 12 definitions of "active customer" is right
  • Reconciling contradictory data sources without guidance
  • Guessing missing context you never stored in the first place

You still need:

  • Clean, well-labeled text and documents
  • Reliable structured data for retrieval and grounding
  • Clear, trustworthy sources the model can lean on

An LLM will happily generate fluent nonsense on top of bad data. Faster.
That is not an upgrade; it's just high-speed wrong.

What We've Learned at Causify#

Across all these projects, a few lessons repeat so often they might as well be laws of physics:

  • If the data is garbage, you cannot take advantage of AI.
  • If data ownership is fuzzy, nothing stays fixed.
  • If you don't automate pipelines, you will never get beyond demos.
  • If you don't start small and specific, you'll spend your time daydreaming about architecture instead of shipping value.

When AI projects fail, the story that gets told is:

  • "The model wasn't accurate enough."
  • "The vendor overpromised."
  • "AI just isn't ready for our industry."

The real story is closer to:

"We tried to build a skyscraper on swampy ground and were very surprised when it sank."

Okay, Now What?#

If you've made it this far and you're feeling slightly attacked: good. That's useful.

At Causify, we built an assessment to help teams figure out where they actually are on data readiness, not where the slide deck claims they are.

Take our questionnaire.

If it leaves you slightly depressed, that's progress. It means:

  • You've stopped believing the PowerPoint, and
  • You've started dealing with reality.

And reality is the only place AI projects ever really work.