Executives want wins, not another pilot-that-never-ships.
Shore up these four fundamentals to de-risk delivery, prove value faster, and avoid months of expensive rework.
1. Stress test your data architecture
An unprepared architecture doesn't just delay AI — cost and risk multiplies every time you try to expand.
Are your data sources currently centralized, accessible, and clearly organized? If sources are fragmented, siloed, or disorganized, AI projects will stall early and often.
Can you trace lineage clearly through your existing pipelines? If not, you’re not positioned to troubleshoot.
Is your data infrastructure currently able to scale quickly? Taking weeks or months to add new formats won’t cut it.
→ Insider Tip: Pick a high-value AI use case and map how data would flow through your pipelines. Note where sources are hard to access, lineage breaks, or manual work slows you down.
2. Confirm your team's preparedness
Without the right expertise and processes, your AI efforts will face delays, higher costs, and preventable compliance risks.
Do you have clearly defined skills in AI data preparation techniques? You need people who can handle embeddings, sentiment analysis, data chunking, and more.
Have you addressed AI-specific data preprocessing? If your data is set up only for reporting purposes, latency will occur.
Do you have clear DevOps, version control, and governance frameworks? This is a must to manage data quality and consistency.
Do you have data privacy and security controls in place? Implement clear tagging, access controls, and auditability for sensitive data to avoid unnecessary risk.
→ Insider Tip: Do a one-sprint drill on a real use case — and make sure everyone involved knows what they're responsible for when something breaks.
3. Check if validation is truly continuous
Continuous validation keeps AI outputs reliable and prevents you from chasing “model problems” that are really data issues.
Are automated freshness and regression checks part of your daily workflow? If not, you’re immediately exposed to stale or inconsistent data.
Can you detect data drift or unexpected data changes in real time? If not, undetected shifts degrade model accuracy.
Are stakeholder and user feedback loops clearly defined and used regularly to improve data quality? If not, errors persist and erode trust, increasing operational risk.
→ Insider Tip: Pick one high-impact pipeline and verify three things: automated checks run, drift alerts fire, and stakeholder feedback routes to fixes.
3. Verify ownership and accountability are defined
Defined ownership removes bottlenecks and keeps AI initiatives moving forward.
Is there a clearly designated owner accountable today for your data infrastructure and its continuous improvement? Lack of clear accountability creates confusion and operational slowdowns.
Do you currently have a cross-functional governance group regularly managing and clarifying data definitions and metadata? Without this, misalignment and disagreements will slow your AI progress.
Is there a dedicated integration role or team currently bridging your data and AI efforts? If not, silos between data and AI teams may significantly hinder progress.
→ Insider Tip: Run a 48-hour “ownership drill” — request a schema change + access for one AI dataset; wherever it stalls, assign an owner.
Don't spend the rest of 2025 on the wrong work
From open formats to unified semantic layers, the concepts we shared in our 2025 Data Priorities article are still practical areas worth focusing on.
