AI-powered initiatives can revolutionise business operations, but up to 85 % of AI projects fail, often due to poor data readiness. Before you dive into AI, here’s why a data-first approach matters:
1. Data is the foundation of AI success
AI systems require clean, consistent, accessible data. Without it, models break. Gartner, Deloitte & others point to poor data quality, bad formats, and governance gaps as leading causes of failure. Imagine building a house on a fault line. AI built on messy or missing data is just as fragile.
2. Email is 75 % of your data, yet it is often the least structured
We know that around 75 % of business-critical data lives in email, often buried in inboxes or shared attachments. Left unmanaged, that data is invisible to AI pipelines, leaving significant blind spots.
3. Storage architecture impacts efficiency and cost
Using Knowledgemill as an example, businesses can de‑duplicate and compress data, reducing storage by as much as 1 TB for every 15 TB archived, helping power smarter, greener IT. This optimisation not only cuts costs but speeds up data ingestion, vital for real‑time AI workloads.
4. Visibility and governance = trust and compliance
A centralised, well-governed repository ensures every email and attachment is auditable, indexed, and securely stored. AI tools thrive on trust, if your data linkages aren’t sound or compliant, models will underperform and may breach regulations.
5. Clean data enables faster iteration and user buy-in
AI projects thrive on agile feedback loops: deploy, iterate, adapt. Yet dirty or siloed data stalls that process. In contrast, well-managed email and content data help you adapt quickly, avoid overloading and bottlenecks, and deliver tangible ROI.
Pre‑AI checklist: data, storage, email management
Use this as a quick guide to avoid the AI failure trap:
- Audit your data sources
Identify where data lives and how it’s stored. Focus especially on email systems and attachments. - Assess quality, formats & governance
Look for gaps in structure, inconsistent formats, or outdated retention policies. - Centralise & optimise storage
Use solutions like Knowledgemill to de-duplicate, index, compress email data, and build a single searchable repository. - Ensure compliance & auditability
Implement robust policies for email retention, disposal, eDiscovery, and access control. - Prepare for AI integration
Confirm your data pipelines are well-defined, accessible, and primed for AI tools.
Key thoughts
AI transformation isn’t plug-and-play. It’s built on high-quality, well-governed, optimised data. Given that up to 85 % of AI projects fail due to data issues, it’s essential to:
- Audit and optimise email and document data
- Clean and structure storage before AI ingestion
- Build centralised, compliant systems to support your AI journey
By doing the groundwork first, your AI initiative moves from risky experiment to data-driven success story. Investing in data maturity, especially around email, transforms AI from a possibility into a competitive advantage.
