Is your data ready for AI?
Enterprise AI projects get stuck because preparing unstructured data at scale is an enormous job. Take 2 minutes to find out where you stand — and what to do next.
Is your data ready for AI?
Question
01 - Data visibility
Emails, documents, PDFs, images, contracts, reports, etc.
02 - Retrieval confidence
Think about result quality, accuracy, and freshness.
03 - AI adoption stage
Choose the option that best describes your current state.
04 - Sensitive data controls
PII, legal, financial, confidential content.
05 - Metadata standards
And do teams actually build on each other's work?
06 - Data freshness
Consider how stale data affects your AI outputs over time.
07 - SME involvement
Think about taxonomy building, tagging, and metadata enrichment.
08 - Data quality assessment
Completeness, freshness, relevance — before it enters AI pipelines.
Is your data ready for AI?
Tier 1 — Foundational • Score:
Your unstructured data is working against your AI.
Your data isn't ready for reliable AI — and it's likely costing you more than you realize. This isn't unusual. Most enterprises are in exactly this position.
- Models are retrieving from noisy, unfiltered data
- Sensitive content may be reaching AI systems it shouldn't
- Every new project is starting from scratch on data prep
- Your team is spending months on work that should take minutes
Tier 2 — Developing • Score:
You’re getting some value, but leaving a lot on the table.
You've made real progress. But gaps in your data foundation are limiting what your AI can do — and inflating what it costs to run.
- Inconsistent data quality is producing inconsistent outputs
- Teams are rebuilding the same pipelines from scratch
- You're sending more data to models than you need to
- Manual data prep is consuming time that should go elsewhere
Tier 3 — Advanced • Score:
Your foundation is solid. Now it's time to scale.
Your AI projects are producing real value. The challenge now is making sure your data foundation can support every use case across the organization without overhead growing with it.
- Manual processes that work for one project won't hold up across ten
- The cost of maintaining data quality will grow as your AI ambitions do
- Your AI is constrained by what your data prep can keep up with