Why 50% of ‘AI Projects’ Turn Into Something Else, And Why That’s a Good Sign

AI is often the fastest way to find the real constraint. Not the final answer.

Here’s a pattern Sigli notices again and again. A team starts with a clear request: “We need AI.” They want new predictive features , automated insights, smarter workflows and a step-change in competitiveness.

Then discovery begins, and the “AI project” quietly turns into something else: data pipelines and data requirements, workflow and model delivery improvements, infrastructure constraints (often security / residency), documentation and operational reliability.

At first, this can feel like scope drift. But in fact, It isn’t. It’s the project doing what it’s supposed to do: finding the bottleneck early, before anyone wastes months building a model that can’t reliably run, can’t integrate, or can’t be trusted.

A simple truth: if your “AI project” becomes a data/process/integration project, it often means you found the real constraint early.

AI isn’t always the end solution. Often, it’s the diagnostic.

AI forces uncomfortable specificity.

The moment you try to ship something real, you have to answer questions like:

• What exactly goes in, and what must come out?
• Where does the data come from, and can we rely on it?
• How often does this need to run?
• What happens when it’s wrong?
• Where in the workflow will people actually use it?

And that’s when you discover the reality: the model isn’t the hard part. The system around it is.

A real example from Sigli's client

In one of Sigli’s case studies, a property data platform wanted to “implement advanced, up-to-date” machine learning to enrich their data and power new customer features.

A typical “AI project,” on paper.

But the work that mattered most, what made the AI shippable, looked like this:

• partnering with the client’s in-house data science team to build new data pipelines and enhance development workflows, alongside ML implementation
• developing dozens of pipelines to streamline data processing and enable expansion of the feature set
• auditing and improving existing ML models that were slow and inefficient, because performance issues were blocking day-to-day workflows
• working under a real constraint: some datasets were confidential, so implementation happened on the client’s on-site servers rather than in the cloud

In other words: the “AI project” immediately surfaced that the real work was data readiness + delivery mechanics + infrastructure reality.

The most common “something else”: AI-ready data

When people say “data quality,” they often mean something broader and more practical:

• data exists, but it’s not usable
• definitions vary (“what counts as on-market?” “what is a prospect?”)
• input/output requirements aren’t explicit
• pipelines aren’t repeatable or fast enough
• key datasets can’t move freely because of confidentiality

That’s why the case study’s three-step approach begins with a review of infrastructure and an audit of existing models plus input/output data requirements and pipelines, before building anything new. And it’s why the delivery centers on pipelines as “engines behind new product features,” not just models. If you want a blunt version: if you can’t move and trust data end-to-end, the smartest model in the world won’t help.

What “AI as a diagnostic” looks like in practice

In the property platform project, the diagnostic showed up as four clear “pivot causes”:

1) Data readiness

The project needed multiple / dozens of data pipelines to make the platform’s insights and features possible at scale. That’s a classic signal that value depends on reliable data movement, not on “more AI.”

2) Workflow reality (performance and iteration)

Existing ML models were slow, and that slowness was interfering with workflows. So the project became: audit, improve, and establish workflows that allow faster feature rollout.

3) Infrastructure constraints

Confidential datasets forced an on-prem approach rather than a cloud-first architecture. That single constraint changes everything: tooling, deployment, monitoring, and iteration speed.

4) Operational debt (often hidden until AI work begins)

The team had to work through a lack of documentation, large/complex datasets, and the usual pain of a tech stack transition. That’s another “AI diagnostic” pattern: AI work exposes the systems you can’t safely evolve yet.

So… what shipped?

What shipped wasn’t “just AI.” Sigli and the client’s internal team delivered infrastructure upgrades including new pipelines, advanced ML functionality, and improved workflows to release new features faster. And those foundations unlocked concrete product outcomes, like expanded feature capabilities including property tracking and market trend analysis for end users.

This is exactly why “turning into something else” is a good sign:

• you don’t get trapped in prototype land
• you build the machinery that makes insights repeatable
• you leave the client stronger, not dependent on a one-off model

A quick checklist: are you building AI, or are you diagnosing a constraint?

If you want to tell early whether an AI project will “turn into something else,” ask:

• Do we have clear input/output data requirements?
• Can we run the data flow end-to-end reliably (even once)?
• Are we constrained by confidentiality / residency (on-prem vs cloud)?
• Are existing models/pipelines blocking workflows because they’re slow or brittle?
• Do we have the documentation and ownership needed to maintain this?

“AI projects turning into something else” is often the moment a team stops chasing the buzzword and starts shipping.

The bad outcome isn’t a pivot. The bad outcome is an AI demo that can’t survive contact with real systems, real constraints, and real users.

The good outcome is what this case study shows: AI work that acts like a diagnostic, and creates durable foundations (data pipelines, delivery workflows, infrastructure choices) that make future AI faster, safer, and actually valuable.