Legal Teams Can Tap AI to Redesign Workflows, Focus on Revenue

When generative artificial intelligence went mainstream, legal work was quickly identified as prime for AI transformation due to its textual, repetitive, and pattern-driven nature.

Like-minded general counsel from diverse industries invested early in generative AI, secured access to tools, launched pilots, and began training their teams—and are now increasingly recognizing the need to build fluency across the whole legal department. Without them, lawyers wouldn’t gain the necessary exposure and confidence to experiment and drive adoption.

But after all that activity and investment, a question lingers: Why hasn’t AI delivered measurable cost savings or impact for most legal departments? According to a recent MIT study, as many as 95% of generative AI pilots deliver no measurable return on investment. So what’s going on, and how can leaders start seeing the impact that matters?

Use Case Chase

Legal teams are stuck chasing the perfect AI use case, asking, “What’s our killer application that will unlock ROI?”

This mirrors the early prompt era when people treated clever ChatGPT prompts like magic spells. But just as prompts weren’t where real value lived, neither is chasing the perfect use case. Part of the problem is that adoption itself is uneven. Within the same department, one lawyer may be experimenting with agentic workflows, while another hasn’t touched AI at all. No single use case bridges that divide.

Real impact comes from building the mindset, workflows, and training that allow many use cases to succeed. There is value in sharing use cases to advance the profession and show the art of the possible, but they can only truly advance the business when paired with the right upskilling and adoption environment.

Through my focus on AI enablement and leadership, legal AI impact happens along two dimensions. Both matter, both require attention, and both go beyond the narrow search for the perfect use case.

Scaling Efficiency

Despite a reputation for tech avoidance, legal professionals are actively engaging with AI. The MIT study found that workers at over 90% of the surveyed companies are using AI at work despite the fact that only 40% of those companies have sanctioned access.

The appetite for usage is there, but most legal AI “success stories” are personal. A lawyer saves 30 minutes summarizing a document. Someone drafts a faster email or finds clauses across a portfolio more quickly. These examples are real, but they don’t translate to departmental impact or cost savings.

That’s because fragmented personal gains don’t scale. To change the economics of a legal function, efficiency must be embedded into workflows, not scattered across individuals.

What does that mean in practice?

• Integration into business context and workflows. Moving beyond personal chatbots toward AI that supports how legal work is delivered day to day.
• Workflow redesign. AI handles intake, initial analysis, and issue-spotting before a lawyer ever touches it, reimagining entire workflows around AI capabilities.
• Agentic systems or automated workflows. AI agents draft fallback language, propose negotiation positions, and handle routine correspondence, with lawyers in review rather than doing everything from scratch.

When AI is embedded at this level, the savings become systemic. Contracts move faster, reviews scale to thousands of documents, and legal bandwidth stretches without adding headcount. That’s how efficiency starts to show up on the bottom line.

Embedding AI directly into legal workflows can accelerate reviews and reduce turnaround times, but it only delivers lasting impact when outputs are verifiable and trustworthy.

Expanding Strategy

Efficiency alone is fragile. Gains can vanish under verification burdens, and shaving time off repetitive tasks won’t change the perception of legal as a cost center. Using AI to add capability, not just to subtract workload, is the bigger prize.

This means treating AI as a thought partner, not a paralegal, tackling high-value challenges.

• Scenario testing. Asking AI to play the counterparty in a negotiation, exposing weaknesses in arguments before they go to the table.
• Strategic brainstorming. Using AI to generate alternative deal structures, risk mitigation strategies, or compliance frameworks that broaden the option set.
• Context synthesis. Feeding AI a stack of regulatory updates, litigation precedents, and internal policies to generate cohesive strategic recommendations.

Using AI only to redraft emails is like buying Excel to make shopping lists. The real value comes from tackling strategic, high-stakes problems—not just the routine ones. This can be a scary proposition, but you’re not ceding control—you’re gaining inspiration to enhance your own experience and judgment.

And while this expansion dimension is harder to measure in minutes saved, its ROI can be even more significant. One better argument that wins a case, one sharper risk assessment that prevents a costly dispute, one creative deal structure that accelerates revenue—these are the kinds of outcomes where AI can repay its investment many times over.

AI doesn’t just accelerate reviews; it can also uncover insights at scale. For example, a risk scan across thousands of contracts might cut review hours while also surfacing patterns that help leadership make smarter, more strategic calls. That’s the combination legal leaders are really looking for: efficiency they can measure and expansion they can feel in better decisions.

Why Both Matter

Efficiency and expansion are required for AI to deliver real impact in legal. Efficiency delivers steady compounding gains when scaled into workflows; expansion delivers step-change wins when AI amplifies judgment and strategy. Together, they unlock the impact leaders expected when they first invested in AI.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

Alex Denniston is director of insights and innovation at Factor, where he focuses on generative AI’s impact on legal operations.

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