Nearly half of enterprise AI projects end up delayed, underperforming, or failing because the data is not ready. That is not a model problem. It is a foundation problem. A 2025 Fivetran survey put “poor data readiness” at the center of why AI efforts stall, even after companies invest heavily in “AI strategies.”
If you have been in the room when an AI initiative “mysteriously” slips, you know the pattern. The demo works on a curated dataset. Then real-world data shows up. The pipeline breaks at 2 a.m. Metrics disagree across teams. Training data changes without anyone noticing. A model that looked sharp in a notebook becomes unreliable inside the product.
That is why data engineering services sit underneath every serious AI program. Not as an implementation detail, but as the difference between a model that can be trusted and a model that becomes an expensive science project.
One more context point, because content teams ask this in 2026. Google’s own guidance is blunt: the issue is not whether content used generative AI, it’s whether it is helpful, original, and satisfies search quality expectations. The same logic applies to AI programs. It’s not “do we have models.” It’s “do we have dependable data work that holds up under real conditions.”
1) AI is only as good as the data work nobody applauds
AI needs repeatability. It needs traceability. It needs consistent semantics, not “best effort” datasets.
Even in analytics, people routinely cite the 80/20 reality: most time goes into finding, cleaning, and organizing data, not analysis. AI raises the bar further because training and inference are less forgiving than dashboards. A single upstream change can quietly skew features, labels, and outcomes.
Here’s the hard truth: “data quality” is not a single task. It is a system of controls. Gartner frames data quality as “usability” for priority use cases, including AI and ML, and emphasizes ownership, collaboration, measurement, and modern tooling.
This is where data engineering services become the backbone. Not by writing another ETL job, but by creating data that is:
- Observable- You can see when it drifts, spikes, or goes missing.
- Explainable- You can answer “where did this value come from” without detective work.
- Stable- Downstream consumers do not break every time an upstream team “improves” something.
- Auditable- You can prove what data was used, when, and how.
And yes, this is operational work. AI is not a one-time build.
2) Pipeline reliability and throughput are product requirements now
Most teams talk about “pipelines” like plumbing. AI turns them into production systems with uptime expectations.
When AI initiatives fail in practice, the failure mode is often boring:
- Late-arriving data causes training windows to shift.
- Duplicates inflate label counts.
- A schema change drops a feature column, and the model degrades silently.
- A join starts exploding row counts and nobody notices until cost alarms fire.
This is exactly why data engineering services need to include data reliability engineering as a formal discipline, not a side quest.
Reliability checklist that matters for AI
- Freshness guarantees- Define acceptable latency per dataset, per consumer.
- Change contracts-Version schemas, publish deprecation windows, enforce compatibility.
- Data tests- Row counts, null thresholds, uniqueness rules, referential integrity.
- Lineage- Dataset-to-feature-to-model traceability.
- Incident practice- On-call rules, runbooks, and post-incident fixes that remove root causes.
Gartner’s view of data quality programs emphasizes scoping, measurement, and process, not vibes. This aligns with how you treat reliability in software. Data needs the same seriousness.
Common pipeline failures and the AI impact
| Failure pattern | What it looks like | AI impact | Fix that sticks |
| Silent schema change | A column type flips, or a field disappears | Features break or shift meaning | Contract tests + versioning |
| Late-arriving data | Data lands hours late or out of order | Training labels misalign | Freshness SLOs + backfill rules |
| Duplicates | Same entity appears multiple times | Bias in training distribution | Dedup keys + constraints |
| Join explosion | Row counts multiply unexpectedly | Skewed features and higher cost | Cardinality checks + sampling |
| Drift in definitions | “Active user” changes per team | Conflicting labels | Shared metrics layer + governance |
This is not “extra work.” It is the work.
And this is where the phrase scalable data pipelines matters, not as a buzzword, but as a requirement: pipelines must handle growth in sources, frequency, and consumers without becoming fragile. I am using scalable data pipelines here in the practical sense: predictable performance and predictable behavior under load.
3) Designing analytics-ready architectures that don’t fight AI
Too many AI programs are built on data estates that were never designed for decision-making. They were built for transactions.
You can often spot it quickly:
- The warehouse is a dumping ground.
- Tables carry business meaning in a dozen half-documented columns.
- Metrics are computed differently in different places.
- Features are built ad hoc inside notebooks with no ownership.
This is where analytics infrastructure design becomes a first-class concern. AI wants the same thing analytics wants, just with fewer excuses allowed.
What does “analytics-ready” really mean?
- Clear semantic layers- Shared definitions for metrics and entities.
- Modeled data- Clean marts aligned to business processes, not source systems.
- Time consistency- Event time, processing time, and reporting time handled intentionally.
- Feature readiness- Reusable feature sets tied to trusted entities.
A good analytics infrastructure design prevents the “model vs dashboard” argument later, because both use the same governed facts.
Also, if your AI strategy includes GenAI, this gets sharper. Retrieval, grounding, and evaluation rely on clean document pipelines, deduplication, chunking rules, metadata integrity, and feedback loops. That is still data engineering, just in different clothes.
4) Engineering for reuse and performance, not one-off heroics
Many teams build features like they build slide decks. Quick, custom, and never reused.
Then the company adds a second model. Or a second product line. Or a compliance requirement. Suddenly every feature has four versions, nobody trusts them, and the cost curve goes vertical.
This is where data engineering services earn their keep: by designing for reuse.
Patterns that reduce repeat work
- Feature stores or feature registries (even lightweight ones): shared computation, shared definitions.
- Golden entities: customer, order, device, product, whatever your business runs on.
- Standard time windows: consistent rolling metrics across teams.
- Performance budgets: query cost expectations per dataset and per consumer.
A practical goal I use: if a feature is useful once, build it quickly. If it is useful twice, formalize it. If it is useful across teams, govern it and monitor it. That is not bureaucracy. It is cost control.
This is also a reliability move. Reuse improves predictability. Predictability improves trust.
And yes, this still comes back to data reliability engineering. If reused assets are not monitored, they become shared failure points. Reliability is what makes reuse safe.
5) Sustaining data platforms after the “launch” moment
AI initiatives do not fail on day one. They fail in month four, when novelty wears off and maintenance shows up.
Sustaining a data platform means planning for:
- Change in source systems
- New privacy rules and audit requests
- New regions and new products
- Vendor shifts
- Cost pressure
- Model monitoring needs that were not in the first scope
Google’s guidance on using generative AI content focuses on helpfulness and policy compliance, not the method of creation. The same mindset applies to data and AI operations: the system is judged by outcomes in production, not by how exciting the initial build looked.
What “sustaining” looks like in practice?
| Area | What mature teams do | Why it matters for AI |
| Ownership | Named owners for key datasets | No orphaned training data |
| SLAs/SLOs | Freshness and quality targets | Predictable model behavior |
| Observability | Alerts + dashboards + lineage | Faster diagnosis |
| Governance | Access rules, audit trails | Lower compliance risk |
| Cost controls | Usage-based chargeback, pruning | No surprise bills |
| Continuous improvement | Regular data “postmortems” | Fewer repeat incidents |
This is where analytics infrastructure design and data reliability engineering meet. The platform must be clear enough to use, and strict enough to trust.
And this is also where scalable data pipelines show their value again. When the number of consumers multiplies, pipelines cannot become a fragile web of dependencies. They need modular design, clear contracts, and operational discipline.
The uncomfortable conclusion most AI roadmaps avoid
If your AI program is struggling, it is tempting to buy new tooling, hire more ML talent, or try a different model family.
Sometimes those help. Often they distract.
A lot of AI pain is data pain wearing a model-shaped mask. Fivetran’s 2025 research points straight at data readiness as the blocker for enterprise AI progress. Gartner’s framing of data quality as a managed program for priority use cases reinforces the same direction.
So if you want AI outcomes that last, start here:
- Treat data engineering services as core to the AI program, not a support function.
- Fund data reliability engineering the way you fund uptime in software.
- Invest in analytics infrastructure design so every team argues less and ships more.
- Build reusable data assets so the second and third AI use cases are cheaper than the first.
- Design scalable data pipelines that behave predictably when usage grows.
That is the backbone. Everything else sits on top.
