Ask a company about its accepted contract terms, and you'll likely be handed a playbook of policies. However, probe deeper into the rationale behind these terms, the conditions for exceptions, and the interplay between different clauses, and the clarity often dissipates. This murky realm of corporate legal judgment, although critical, remains largely unspoken and resides predominantly in the minds of seasoned attorneys.

For years, humans have tacitly filled these gaps with their personal insights and experiences, but the advent of artificial intelligence (AI) is set to make these omissions glaringly apparent. AI, with its capacity for rapid data processing and enforcement of set rules, lacks the ability to intuit the nuanced decision-making process that human lawyers consider routine.

Consider a scenario where a lawyer evaluates a supplier's request to use company data. Officially, the policy might strictly prohibit the use of confidential information for unrelated purposes. Yet, the lawyer's actual decision-making process involves a nuanced analysis: What type of data is involved? Can it be anonymized? What are the security measures in place? These are just a few of the considerations that seldom make it into the written policy.

The challenge with AI in legal contexts is its reliance on explicit, documented knowledge. Without a clear definition of factors like "material risk" or "reasonable protection," AI systems struggle to make informed decisions. They either adhere too rigidly to the rules, make uninformed guesses, or constantly require human intervention.

This problem isn't confined to contracts. It extends across various facets of corporate governance, where vague terms and undocumented exceptions are common. Experienced lawyers navigate these waters with ease, understanding the implications of combining certain provisions and recalling past exceptions and their justifications.

The push towards AI in legal processes necessitates making more of this implicit knowledge explicit. This means clearly defining what constitutes acceptable risks, outlining when exceptions can be made, and determining which circumstances require escalation to human oversight.

However, this transition is fraught with challenges. Documenting these nuanced judgments could reveal inconsistencies in how policies are applied, uncover approved exceptions that contradict stated positions, and highlight reliance on customary practices rather than risk-assessed decisions.

To prepare for AI integration, corporate legal teams need to start by scrutinizing recurring decisions. Identifying which factors influence different outcomes and documenting the rationale behind routine exceptions will not only refine AI applications but also enhance consistency in human decision-making.

While AI has the potential to significantly advance legal processes, its initial role will likely be to highlight the extensive judgment calls that companies have yet to formally record. This revelation, though uncomfortable, is a necessary step towards more transparent and consistent corporate legal practices.