The gap between AI availability and legal readiness isn’t a matter of technology—it’s a matter of mindset, culture, and skill.

What Legal Leaders Are Getting Wrong About AI—and How to Fix It

by ChatGPT-4o

The article “Here’s What Legal Leaders Worldwide Are Getting Wrong About AI” by Chris DeConti offers a sharp, experience-based critique of how legal departments across the globe are struggling with the adoption of generative AI (GenAI). While enthusiasm and investment are high, meaningful transformation remains elusive. DeConti attributes this to a fundamental misunderstanding: legal teams are focusing too much on access and functionality rather than fluency and mindset. This essay will unpack his arguments, assess their validity, and offer additional perspectives and tips to support the legal industry’s AI journey.

I. Summary of Key Arguments

DeConti outlines several recurring missteps legal leaders make when adopting AI:

1. Confusing Access with Readiness

   • Although 61% of legal departments provide AI tools to most or all of their teams, fewer than 1 in 5 feel confident using them. Provisioning access is mistaken for capability-building.

2. Designing for the Top 5%

   • AI initiatives are built around early adopters, neglecting the cautious majority who need more support and structure to gain confidence.

3. Treating GenAI Like Traditional Legal Tech

   • GenAI is interactive, contextual, and non-deterministic, unlike rule-based legal software. Expecting deterministic outcomes leads to disillusionment.

4. Expecting Results Without Orchestration

   • Without workflows, integration, or clear problem framing, GenAI tools are underused or misused.

5. Running Pilots Without Shifting Mindsets

   • Legal culture prizes certainty, yet GenAI generates probabilistic outputs that require interpretation and judgment. This creates discomfort and friction unless explicitly addressed.

II. Evaluation of the Arguments

DeConti’s central insight—that fluency, not just access, drives transformation—is both timely and accurate. Across industries, similar struggles have emerged as GenAI is introduced: users are overwhelmed, undertrained, and misaligned with the technology’s affordances. In the legal world, where precision and precedent reign supreme, the psychological and cultural hurdles are even higher.

His points about mindset and fluency are especially important:

• Unlearning old behaviors (like expecting binary, rules-based responses) is as important as learning new skills.

• Structured prompting is indeed akin to briefing a colleague—a brilliant analogy that captures the blend of clarity, context, and trust needed to elicit useful outputs.

• Stress testing AI output is essential in legal practice, where hallucinations or biased recommendations could have significant real-world consequences.

However, some arguments could be pushed further or supplemented.

III. Additional Arguments and Tips

1. AI Skepticism Is Rational—but Must Be Constructive

Legal professionals’ skepticism is not a flaw—it reflects their training. But instead of treating it as resistance, leaders should channel it into active engagement: peer-reviewing AI output, documenting errors, and iterating prompts. Think of lawyers as adversarial testers for AI, not passive consumers.

2. The Role of Governance and Transparency

Beyond fluency, legal leaders must develop clear AI governance frameworks. This includes:

• Defining acceptable use cases and “no-go” zones.

• Mandating disclosure when GenAI is used in drafting, research, or negotiation.

• Creating audit trails of prompts and outputs.
  These guardrails foster trust, ensure regulatory compliance, and reduce liability risks.

3. Cross-Functional Collaboration Is Not Optional

DeConti touches on this in passing, but it deserves emphasis: legal teams cannot adopt GenAI in isolation. They must work closely with IT (for security and tooling), data science (for prompt engineering and fine-tuning), and operations (for workflow design). AI adoption is a team sport.

4. Experimentation Should Be Continuous and Documented

Legal teams should treat GenAI use cases like mini-products. Pilot them with a hypothesis, measure impact (speed, accuracy, quality), gather user feedback, iterate, and document lessons. This avoids wasted effort and supports institutional learning.

5. Use Legal Interns and Trainees as AI Champions

Early-career professionals are often more comfortable experimenting with AI. They can serve as internal “AI translators” or prompt engineers, bridging the gap between senior legal minds and new tools. Empowering them builds grassroots momentum.

6. Industry-Wide Sandboxes and Shared Playbooks

Bar associations, legal tech consortia, and law schools should establish safe environments to practice GenAI skills. Sharing prompt libraries, case studies, and redlines can democratize AI capability across firms—especially smaller ones without innovation budgets.

IV. Practical Recommendations

To accelerate effective GenAI adoption, legal leaders should:

• Build AI fluency programs tailored to different roles (e.g., contract analysts, litigation support, IP counsel).

• Use real, high-stakes legal documents in AI training exercises to make learning relevant and contextual.

• Recognize and reward creative AI use internally, turning innovation into a career lever.

• Treat GenAI as a thinking partner, not an automation engine. It supports reasoning, not just formatting.

• Invest in AI literacy across the enterprise, including business units, to foster alignment and integration.

V. Conclusion: The Cost of Misunderstanding

The gap between AI availability and legal readiness isn’t a matter of technology—it’s a matter of mindset, culture, and skill. DeConti’s essay smartly exposes this disconnect and urges legal leaders to reframe their approach. Fluency, not flashy pilots, is the differentiator. Those who understand this will lead legal’s evolution into a faster, more adaptive, and more value-driven function.

The alternative? Legal teams that stall in adoption, underperform in output, and ultimately fall behind their peers—clients and firms alike—who learn how to think with AI.