For decades, the primary bottleneck in legal and compliance departments was drafting time. Today, Generative AI has solved that problem. Large Language Models can now produce 40-page master service agreements or complex compliance policies in seconds.

But this speed has revealed a much deeper, more expensive problem: The Consumption Gap.

AI has made it effortless to generate documents, but it has not made it any easier for humans to use them. When AI generates documents that are legally "correct" but functionally unreadable, it doesn't save money, it merely shifts the cost from the drafter to the user.  

In the AI era, unreadability is a business friction tax. Here is the forensic math on how unusable output quietly destroys value across the enterprise.  

 1. The Revenue Velocity Gap (SaaS & Commercial Contracts) 

In a high-growth environment, the most important metric is Time to Revenue. AI drafting promises to accelerate this, but unreadable contracts do the opposite.

The Scenario:
A technology firm is closing a €500,000 ARR enterprise deal. The AI-generated contract is legally sound but uses dense, abstract "lawyer-to-lawyer" prose. 

• The Review Cycle: Because the document is hard to parse, the client’s procurement team pushes back on "standard" clauses they don't understand.
• The Legal Bloat: This triggers four additional "clarification rounds." Each round consumes 5 hours of combined time from internal legal, sales, and operations at a blended rate of €300/hr. (Total: €6,000).
• The Delay Cost: The deal is delayed by 6 weeks. For a €500k deal, that represents €57,600 in delayed cash flow and downstream commercial impact.

The Insight: AI "saved" the lawyer 4 hours of drafting time (approx. €1,200), but the resulting unreadability cost the business over €60,000. 

Efficiency is often measured at the point of production, but in contracting, the only metric that matters is the point of execution. When AI-generated drafts prioritize complex legalese to ensure technical coverage, they create a cognitive barrier. This triggers a defensive response: more internal stakeholders get involved, more questions are asked, and more "redlines" are introduced, not because the commercial terms are unacceptable, but because they are obscured.

The €1,200 saved in drafting is a false economy. It ignores the massive opportunity cost of delayed revenue recognition. In a high-growth environment, a four-week delay doesn't just defer cash; it distorts quarterly forecasts, stalls implementation resources, and gives competitors a window to re-enter the conversation. Readability isn't just about "better writing", it is an acceleration strategy for the sales cycle.

2. The Operational Leak (Compliance & Risk Policies)

Compliance is only effective if it is implemented. When AI scales "perfect" but "impenetrable" policies, it creates a massive operational drain.

The Scenario:
A company rolls out a new AI-generated Data Privacy policy to 1,000 employees.

The Friction:

• The Productivity Tax: If the policy is poorly structured, employees will spend more time searching for answers or, worse, guessing. If each employee loses just 15 minutes per month clarifying instructions or correcting errors, the company loses 3,000 hours of productivity annually.
• The Financial Impact: At a loaded cost of €80/hr, that is a €240,000 annual loss in pure human capacity.
• The "Shadow Risk": Unreadable policies lead to inconsistent implementation. A single near-miss incident requiring a formal internal investigation can easily cost €50,000 in management time and legal fees, even without a fine. 

The Insight: Automation didn't reduce risk; it scaled confusion. Clarity is not a "nice-to-have" in compliance; it is a risk-control mechanism.

The primary goal of any compliance policy is behavioral change. However, AI often scales "defensive drafting", the tendency to include every possible regulatory reference without providing a clear path to action. When employees are faced with dense, abstract policies, they don’t become more compliant; they become more paralyzed. They either flood the compliance team with "How do I actually do this?" emails or, more dangerously, they make their own interpretations.

 3. The Institutional Memory Loss (Internal Playbooks)

The third example is the most subtle: The Knowledge Tax. Organizations are using AI to synthesize internal playbooks, the "How we work" documents.

The Scenario:
An organization uses AI to generate its Procurement Playbook for 200 project managers.

The Friction:

• Because the output is legalese or simply too dense to skim, project managers bypass the playbook entirely.
• They rely on "asking a colleague" or "doing what we did last time." 

The Result: The organization loses the ability to implement strategic change. Procurement savings are missed, and legacy errors are repeated. 

The Executive Verdict: Speed is a Commodity; Usability is a Strategy.

If your organization uses AI to produce documents that follow 19th-century drafting conventions, you are simply automating friction.

AI does not question whether a document supports a business decision. It does not care if a sales rep can find the "Liability" cap in under 10 seconds. It simply follows the patterns of the past, faster. 

To win in an AI-driven market, leadership must pivot their focus from Drafting Speed to Consumption Velocity. Most organizations have no idea what their "Unreadability Tax" actually is. They see it as "the cost of doing business." It isn't. It's a manageable expense.

We have developed a diagnostic tool to help you quantify this hidden cost. It turns abstract "confusion" into a concrete financial figure, allowing you to build a business case for clarity.

Don't just draft faster. Perform better.

Stop Guessing. Start Measuring.