Solving the First-Mile Gap of Unstructured Data Preparation
How to Move Enterprise AI Projects from Pilot to Production
Enterprise AI is stalling before it ever reaches the model. 90% of vertical, function-specific AI use cases are stuck in pilot mode, according to McKinsey, and less than 1% of unstructured data is in an AI-ready format, according to IBM. Vertical AI use cases like claims processing, clinical trial analysis and patient diagnosis depend entirely on the unstructured data layer, and if AI cannot reach the right data reliably, these use cases fail.
This white paper explores why common data preparation tactics like chunk-and-embed and vectorize-and-annotate-everything break down at enterprise scale, and lays out the four requirements enterprise AI needs to close the first-mile gap. Read now.
Download the white paper now to see how leading enterprises are turning petabytes of disorganized data into a governed, AI-ready foundation.
Explore how Komprise Intelligent Data Management closes the first-mile gap, including how to:
- Get one version of the truth with global discovery across every NAS vendor and cloud
- Iteratively tag and classify data down to the exact dataset an AI model needs
- Orchestrate discovery, enrichment and delivery continuously with policy-driven workflows
- Move only what you must, cutting GPU compute and token costs
- Build in governance and audit trails from the first step, not as an afterthought
- Reduce a petabyte-scale estate to a smaller, cleaner, query-ready dataset for AI
The elephant in the room: petabytes of disorganized, unclassified and siloed data obscure the context and intelligence AI pipelines need.
Data Intelligence & Orchestration for AI FAQs
Why are enterprise AI projects stuck in pilot mode, and what does the unstructured data layer have to do with it?
Enterprise AI stalls on the unstructured data layer, not on the model. Vertical AI use cases such as claims processing in insurance, clinical trial analysis in biotech, improved patient diagnosis in healthcare and legal case analysis depend entirely on unstructured data: documents, emails, instrument and research data, images, video, sensor telemetry and scanned records. The scale of the problem:
- 90% of vertical, function-specific AI use cases remain stuck in pilot mode, according to research from McKinsey, which calls this the “Gen AI Paradox”: nearly 8 in 10 companies report using AI, yet just as many report no significant bottom-line impact
- Less than 1% of unstructured data is in an AI-ready format, according to IBM, because it is too large to move, too noisy to trust, and IT has no visibility into how much of it is redundant, obsolete or trivial (ROT)
- Through 2026, organizations will abandon 60% of AI projects that lack AI-ready data foundations, according to Gartner
- Only 7% of organizations say their data is completely ready for AI adoption, according to Cloudera and Harvard Business Review
- Infrastructure, skills and data readiness account for 43% of the biggest obstacles to enterprise AI plans, according to Drexel University’s LeBow College of Business and Precisely
Why don’t common approaches like chunk-and-embed or vectorize-and-annotate-everything solve the problem?
Two tactics dominate early AI efforts, and both break down at enterprise scale because neither asks whether the underlying data has any value in the first place:
- Chunk and embed splits every document into fragments and converts them into vector embeddings without filtering for quality first. It does nothing about noise, size or cost, and only works when the underlying data is already clean, which is typical of a small pilot and rare across a production estate
- Vectorize and annotate everything tags and embeds the entire data estate with AI-generated metadata regardless of whether it is accurate, current or safe to expose. ROT data persists, annotation spend is largely wasted, hallucinations continue, and the resulting vector index can end up larger than the source dataset itself
What are the four requirements of unstructured data preparation for AI?
Enterprise AI needs unstructured data intelligence and preparation that scales to petabytes, spans on-premises and cloud estates, and reduces AI token and compute costs by narrowing the estate to just the right data for each use case. The Komprise Intelligent Data Management platform supports four requirements:
- Global data discovery and intelligence: in-place indexing across every NAS vendor and cloud provider into a single searchable layer, the Komprise Global Metadatabase, without forcing data to be loaded or moved first
- Iterative, custom data tagging and classification: enriching basic file system metadata in repeated passes, tiering off old data, scanning for PII, then extracting domain-specific metadata (for example, DICOM attributes on medical images) to isolate exactly what a model needs
- Policy-driven orchestration: Smart Data Workflows that continuously find new or updated files, apply enrichment tags, run governance checks and deliver fresh datasets to AI platforms on a schedule, with no scripting required
- Efficient ingest, execution and governance: moving only what must move, eliminating 70%+ of unstructured data noise before it reaches an AI pipeline, and retaining a full audit trail of what data went where and who sent it
How does Komprise support global data discovery without moving data first?
The Komprise Global Metadatabase provides in-place indexing into a single searchable layer across the entire hybrid estate. Unlike vendors whose discovery only covers what their own products store, Komprise indexes file and object data across all storage in place:
- Stateless Komprise Observers bring the indexing presence next to the data for performance and security, since the data never has to move
- The Komprise elastic, distributed architecture has scanned hundreds of petabytes without impacting production performance, bringing compute to the data instead of the other way around
- The resulting schema is query-ready and consistent across vendors and storage standards, so an AI team can locate specific files across a hybrid data estate without running a separate query per environment
How does Komprise iteratively tag and classify data for AI use cases?
Komprise AI Preparation and Process Automation (KAPPA data services) delivers project- and industry-specific metadata extraction, such as DICOM attributes in medical images, OSDU metadata for energy or electronic lab notebook (ELN) project codes. The iteration is what sharpens the dataset with each pass:
- KAPPA data services provision compute automatically via Komprise Observers residing next to the data, scaling with parallelism across datasets that may reach billions of files
- Built-in sensitive data classification in Smart Data Workflows identifies PII, PHI and regulated content across the organization
- Komprise integrates with any third-party classification or filtering tool, so IT retains choice and flexibility
- Because the Global Metadatabase persists enriched tags, data intelligence compounds with every pass instead of resetting with each new AI project
How does Komprise keep AI data pipelines governed and cost-efficient?
Komprise Intelligent AI Ingest eliminates 70%+ of unstructured data noise, including duplicates, outdated files and irrelevant content, before it ever reaches an AI pipeline, reducing GPU compute and token costs while improving model accuracy. Governance is built into every step:
- Komprise Transparent File Tables bring metadata to the lakehouse without moving the data, using Apache Iceberg tables with access controls intact, so platforms like Databricks and Snowflake can query unstructured data as if it were structured
- Komprise Deep Analytics curates and eliminates noise before data moves to lakehouses or AI platforms
- Komprise retains a full audit trail of what data was sent where and by whom, a fundamental requirement of AI governance
- Data breaches now cost an average of $5 million, and the share of incidents involving shadow AI more than doubled year over year to 43%, according to IBM, underscoring why governance cannot be an afterthought
- 67% of executives believe their company has already suffered a data leak or breach due to unapproved AI tools, according to Writer’s Enterprise AI adoption 2026 report
What is the cost of getting AI data preparation wrong?
The stakes are high, and the data backs it up:
- Roughly 95% of generative AI pilots delivered no measurable profit, according to MIT Project NANDA’s “The GenAI Divide: State of AI in Business”
- Only 7% of companies have fully scaled AI across their organization, according to McKinsey
- 57% of AI projects aren’t delivering their objectives, and nearly all (94%) struggle to manage unstructured data, according to Nasuni
- Classification is the top challenge in preparing unstructured data for AI, according to the Komprise 2026 State of Unstructured Data Management report
- Companies expect to double the share of revenue spent on AI this year compared to 2025, according to BCG, raising the stakes on getting the data foundation right the first time