Human-Centered AI Is Crucial Piece of Successful Implementation

The Bottom Line

• The legal industry’s ultimate success with artificial intelligence will depend on how we support the humans who work alongside it.
• For change to succeed, we must cultivate curiosity and creativity—the ultimate transferable skills.
• Framing AI investment through a risk management lens helps protect companies and clients from potential compliance issues.

What feels like uncertainty about the effects of artificial intelligence can be reframed as possibility. After three decades of leading legal teams through technological transformations, from paper discovery to AI-powered review, I’ve observed that organizations that focus on employee development during technological upheaval consistently achieve better outcomes than those focused solely on efficiency gains.

Generative AI is transforming how lawyers research, draft, review, and analyze legal documents. Yet for all the technological sophistication (and limitations) of these tools, the success or failure of AI implementation ultimately will depend on the most analog factor: how well we support the humans who must learn to work alongside AI.

Familiar Disruption Patterns

In e-discovery’s early days, some lawyers voiced fears that technology would be the end of substantive lawyering. Some had an allergic reaction to the suggestion that technology could improve the legal process.

Those concerns sound familiar to the ones today about generative AI—job displacement, the commodification of legal expertise, and the end of lawyers. While AI’s generative capabilities present more fundamental questions about the nature of legal work than previous technologies, the underlying anxiety about professional value and job security remains consistent.

When we created our e-discovery practice more than 20 years ago, we took what I called the “Field of Dreams” approach, slightly tweaking the 1989 baseball movie line: “If you build it, [they] will come.” We built a specialty practice of attorneys and technologists adept at e-discovery and litigation technology, invested in advanced technology, and created repeatable processes focused on defensibility.

We didn’t pressure reluctant lawyers to embrace technology out of fear. Instead, we let results speak for themselves.

The feared end of substantive lawyering never materialized. Instead, technology freed lawyers to focus on higher-value legal analysis and strategy. This pattern repeated through every tech transition I’ve witnessed; resistance gives way to adoption when people see that technology augments rather than replaces core professional capabilities.

AI Transformation Pillars

Based on decades of navigating technological change, I’ve identified four pillars for successful AI adoption that prioritize human flourishing alongside operational efficiency.

Pillar 1: Psychological safety through mindful change. Insights from modern neuroscience show that uncertainty activates our threat-detection systems. But if we approach change mindfully, we can reframe uncertainty as possibility.

An effective approach involves awareness and perspective. When we encourage a perspective of possibility that doesn’t dismiss the reality of challenges, people can become curious rather than defensive and are more willing to collaborate.

With purpose-built generative AI for e-discovery, we’re seeing this play out in real time. The technology is woven into our collaboration, and people start to see it as a team member rather than an obstacle or burden.

Successful change requires creating space for people to notice resistance without immediately trying to fix or dismiss these natural responses. This means acknowledging what’s changing while articulating what remains constant: the fundamental value of legal judgment, relationship-building—both with clients and colleagues—and strategic advocacy.

Pillar 2: Cultivating curiosity and creativity as the ultimate transferable skills. After observing hundreds of attorneys navigate tech disruptions, I can confidently say the most successful adapters share two crucial traits: curiosity and creativity.

Curious people ask questions such as: What patterns does this AI tool see that I might be missing? What would happen if we combined this with another system we already use? How does this algorithm decide what’s relevant?

This mindset of treating technology as a collaborative partner rather than a threat is proving to be something people can strengthen with practice and experience.

Recently, our team tackled a challenging document analysis project involving years-old scanned images with handwriting. When the first generative AI tool got us partially there, our curiosity kicked in, and we used a second AI tool to complete the task. This kind of adaptive problem-solving—moving fluidly between different technologies to achieve the goal—exemplifies the skill set e-discovery professionals have been demonstrating for decades.

Curiosity and creativity aren’t just traits; they can be developed. Attention to cultivating a lifelong learning mindset, in which professionals stay intrinsically motivated to learn and adapt, pays dividends.

Leaders should foster curiosity in their AI training programs, celebrate creativity, and reward experimentation. Equally important is finding effective ways people can share knowledge and learn from each other’s discoveries.

Leaders should also create space for people to make mistakes. Trial and error are part of innovation, and cultivating a culture of fearlessness (not recklessness) is also a fundamental building block.

Pillar 3: Experience-driven learning that works. From my experience, traditional corporate training, especially related to tech changes often fails because it prioritizes demos and formal instruction over hands-on experience. Research by the Center for Creative Leadership demonstrates that effective learning follows a 70-20-10 model: 70% from challenging experiences, 20% from relationships with others, and 10% from formal training.

When implementing technology-assisted review 2.0 a decade ago, we faced pushback. Traditional training sessions weren’t driving adoption; we shifted to experiential learning.

Instead of explaining how TAR 2.0 worked in theory, we showed a few handpicked attorneys how it accelerated the review process by serving up the most relevant and important documents first. They saw, in real time on their matters, that it wasn’t replacing human judgment but improving efficiency. The attorneys became advocates through hands-on experience, not formal training.

This same “show, don’t tell” approach drives faster generative AI adoption. Because generative AI tools are more intuitive and less technical than previous legal technologies, people can experience benefits immediately, and we are seeing dramatically higher adoption rates.

And when people have places to share use cases, prompts, and ideas for applying generative AI to their work, such as Microsoft Teams channels or other collaborative platforms, we’re seeing creativity blossom.

Pillar 4: Frame AI investments through a risk management lens. When building our e-discovery team, the business case wasn’t centered on implementing tech solely for efficiency. The legal industry faced serious risks and challenges as the universe of potential evidence expanded to electronically stored information.

We responded by focusing on defensibility to protect our clients and our firm from potential sanctions.

This risk management approach resonated with firm leadership, who understood that quality is key. A single discovery sanction could do more reputational damage than any technology investment cost. We invested in training skilled e-discovery attorneys and professionals and leveraged advanced technology to do things right, not just more cheaply.

Today’s e-discovery AI adoption should follow the same playbook. Frame AI investment as risk management such as:

• Quality risk: While AI can create more efficient work products across attorney teams and legal matters, validating AI outputs and ensuring rigorous process checks is critical.
• Competitive risk: Clients increasingly expect AI-powered efficiency. Without it, firms lose market position to more technologically sophisticated competitors.
• Talent retention risk: Empowered talent wants to work with cutting-edge technology. AI investment becomes talent investment, helping firms retain and attract the best minds.
• Regulatory risk: Early adoption allows firms to develop compliance frameworks and AI governance approaches that align with their values and ethical obligations even before regulations mandate specific approaches.

Human Investment Matters

According to a 2024 survey, legal professionals are experiencing burnout and career uncertainty, with conditions worse than before the pandemic.

This is a complex, multilayered challenge. A human-centered approach to the current AI transformation will contribute to more sustainable well-being and advantages that extend beyond tech capabilities.

Teams that feel supported through change become more committed and innovative—I’ve seen this firsthand. When purpose-built generative AI tools for e-discovery became available for testing in 2023, our team members were at the starting blocks well before the race began. Stable teams maintain stronger connections during tech transitions, and firms known for employee development attract better talent and clients.

The most successful AI implementations share a common characteristic: They prioritize human flourishing alongside operational efficiency. This isn’t just good ethics; it’s good business. When people feel supported in their growth, they become more creative with tech innovation rather than resistant to it.

Mindful Legal Leader

As someone who has navigated multiple tech disruptions while maintaining a contemplative practice, I’ve learned that sustainable change happens when we work with our human psychology rather than against it.

The legal profession’s relationship with AI ultimately will be determined not by the sophistication of the algorithms but by the wisdom with which we guide human beings through this transition.

Leaders who combine tech insight with genuine care for human flourishing will build the most innovative, resilient, and successful e-discovery teams and legal organizations of the AI age. During my yearlong meditation facilitation training at UCLA, one of the teachers said something that has stayed with me: “The opposite of uncertainty is possibility.”

This possibility means there is potential for people to become more fulfilled and do their best work—not just for more efficient legal services.

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

Ruth C. Hauswirth is special counsel and head of e-discovery at Cooley.

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