The hidden cost of preparing information for AI

Before AI can save your organisation time, how much time are you spending preparing its information?

Making files readable, reviewing duplicates and removing personal and sensitive content all require effort. When that work sits across spreadsheets, manual reviews and separate tools, its cost can be difficult to see.

For organisations introducing Copilot, ChatGPT, Claude or Gemini, information preparation deserves a place in the business case alongside software licences and implementation.

What published customer examples reveal

Two third-party case studies illustrate different parts of the opportunity.

The Royal College of Nursing reportedly reduced its redaction stage from 180 minutes to under five minutes per request using automation. The work concerned subject access requests, rather than preparing information for AI, but it demonstrates the staff effort involved in identifying and removing sensitive content.

Greater Anglia reported approximately US$30,000 in annual storage savings after information cleanup. This included archiving or deleting 1.7TB and recovering another 1.1TB through version cleanup. Building a stronger information foundation for Copilot was among its objectives.

These are supplier-published customer examples with different workflows, not AIAssure results or guaranteed savings. Greater Anglia’s outcomes also came from a broader programme involving business decisions and services. Their value is in showing what organisations can measure: preparation time, unnecessary information and avoidable storage costs.

Why manual preparation becomes harder to repeat

A small collection may be manageable through individual file reviews. As AI reaches more teams and information sources, the same approach can consume increasing staff time or leave issues unresolved.

Scanned information may need a readable text layer. Duplicate copies add review effort. Personal and sensitive content needs careful identification and removal before use. Information owners still need to decide what is current, useful and appropriate to retain.

The job is to create a repeatable preparation process that supports useful AI results while keeping review and ownership clear.

One software tool. Two powerful capabilities.

AIAssure prepares enterprise information before AI uses it, bringing two built-in capabilities into one software tool:

• Optimise makes information readable and removes duplicate and unnecessary files.
• Cleanse identifies and removes personal and sensitive information before AI uses it.

Together, they help reduce preparation effort, avoidable answer errors and information exposure risks. Better information cannot eliminate hallucinations, so testing and verification of important outputs remain essential.

Start the business case with your own information

Published examples establish that the work has a cost. Your own information establishes the size of the opportunity.

In a Free Proof of Value, use a selected volume of your organisation’s content and agree the scope and success criteria. Compare equivalent manual and AIAssure-assisted tasks, including configuration, quality checks, exceptions and rework.

• Measure active staff hours separately from processing time.
• Check readability and the quality of duplicate and sensitive-content decisions.
• Calculate the value of staff capacity released.
• Count storage or processing savings only where charges can actually be avoided.

Staff capacity released is not automatically a cash saving. It becomes a direct financial benefit when it avoids expenditure such as overtime or contractor costs.

How many hours does your team currently spend getting information ready for AI? Request a Free Proof of Value to start measuring the answer.

Case study sources: Royal College of Nursing redaction workflow ↗ and Greater Anglia information cleanup ↗.

Adapted from Stewart Connors’ LinkedIn post. Read the original post ↗