Ask any knowledge manager at a consulting firm how long it takes to sanitise a single client deliverable, and you'll get a familiar answer: somewhere between 4 and 8 hours per deck. That's per deck. A mid-sized consulting firm might produce hundreds of decks per year across its engagements.
The maths is straightforward but striking. If a firm produces 500 deliverables a year and spends an average of 6 hours sanitising each one, that's 3,000 hours — the equivalent of 1.5 full-time employees doing nothing but reviewing slides for client-identifying information. And that's assuming every deck actually gets reviewed, which in practice, most don't.
The real cost isn't just time
The direct labour cost is the visible part of the iceberg. The larger cost is invisible: the knowledge that never gets reused because nobody had time to sanitise it. Every unsanitised deck represents analytical frameworks that can't be shared with other teams, industry benchmarks that can't inform new proposals, and training materials that can't be used to develop junior consultants.
For firms investing in AI and knowledge management platforms, the cost compounds further. A RAG system or enterprise search tool is only as good as the content you feed it. If 95% of your past deliverables are locked away due to confidentiality concerns, your AI investment is running on fumes.
Why firms tolerate the status quo
Most firms have accepted this cost as the price of doing business. Confidentiality is non-negotiable — and it should be. The problem isn't the principle; it's the assumption that manual review is the only way to uphold it.
That assumption made sense when the alternative was crude keyword-matching tools that would either miss critical identifiers or redact so aggressively that the remaining content was useless. But the technology has moved on.
Automation doesn't mean compromise
Modern sanitisation tools can process a full consulting deck in minutes, not hours. More importantly, they can detect the indirect identifiers — the chart annotations, the colour-coded segments, the contextual clues — that human reviewers routinely miss, especially under time pressure.
The ROI case is compelling: reclaim thousands of hours of knowledge management time, increase the volume of reusable content by an order of magnitude, and finally give your AI tools the clean training data they need to deliver real value.
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