Sometimes the idea of a new tool is more energizing than actually putting that tool to work.
Maybe you know what I mean. Ever looked at a DIY project walkthrough online, jovially ran off to the hardware store to get everything you needed to get it done, and then returned home and felt suddenly quite deflated by the enormity of the task in front of you?
I sure have. (Sad shoutout to my still-dripping kitchen faucet.)
Sometimes, AI adoption looks a lot like this: a company or a firm hears a lot of eagerness for AI, buys a new tool, announces its availability internally, and then … watches it collect dust while everyone defaults back to the way things have always worked.
Sound familiar? If so, you're not alone. As it turns out, the gap between a promising AI investment and an impactful, functional AI-powered workflow isn't really about the technology itself. Typically, it's about how that technology gets embedded – or doesn't – into the day-to-day of real people’s real work.
The AI Visionaries who inform the insights that follow are proof of that. What they've found is that successful AI workflow implementation comes down to some surprisingly grounded fundamentals: start small, define what success looks like, ask clear questions, and invest in your people as much as (actually, probably even more than) your tools.
Start By Asking the Right Questions
Before you can build a workflow around AI, you have to know what you're trying to accomplish – which sounds obvious, but in practice, teams often skip this step in their enthusiasm to get started.
If you onboard AI internally and begin by telling colleagues “you can use it on anything!” you’re sure to set them up for a serious blank-page problem (or at least a little decision paralysis).
If, on the other hand, you do some homework before bringing in a tool, you can select the best tool with those requirements and mind and start by saying: “We’re bringing AI on board. Here’s one way to get started.”
AI Visionary Bobby Coppola, chief strategy officer at PLUSnxt, frames the getting-started stakes in competitive terms.
“You are missing out on a tactical advantage by not leveraging AI for investigations and litigation,” he says. “Early adopters of AI have a strategic advantage through information velocity and asymmetric early enlightenment, creating better outcomes in these contexts.”
These tasks are data-intensive and familiar for many firms, which make them prime opportunities for implementing legal AI. Launching with a solution to your team’s preexisting challenges will set you up for greater success, and guard against some of the potential risks of using AI less thoughtfully.
AI Visionary Angela O'Neal, the director at Nextra Solutions who works across complex litigation and investigative matters, puts it plainly: AI isn't a magic wand that compensates for vague or poorly formed prompts. “You do have to be careful – people know just enough to be dangerous,” Angela said. “Search logic matters. AI only works if you ask the right questions.”
So begin by asking your team the right questions: where do you feel AI can accelerate your work? What rote task is wrecking your week time and again?
Scope Things Out: Start Small, Build Evidence
Once you’ve gathered those inputs, plan to begin implementing AI with a proof of concept that produces real results. Measured restraint will earn you the trust required to expand.
“We start small, establish workflows, and set clear expectations and outcomes,” Andrew Milauskas, AI Visionary and chief operating officer at Page One, explains. “Some clients are naturally hesitant, particularly when the work is high-stakes. We find it to be very impactful when we can demonstrate the technology against human pre-coded data, allowing us to quantify the results. Once clients see the results and understand that AI supports human judgment, their confidence grows quickly.”
“A good way to test your AI is to ask something you already know the answer to,” Angela says.
That kind of validation step – testing the tool against a known answer – turns out to be a theme not just in getting started, but in validating AI investments overall. It's how teams build confidence in AI results and build confidence in their ability to use AI effectively.
Andrew Milauskas, chief operating officer at Page One, takes a similarly data-first approach to demonstrating AI's value: “AI delivers consistent, repeatable outcomes, while human coding often varies from reviewer to reviewer. Being able to show that proof, using real data rather than theory, has been incredibly powerful for building confidence in AI-driven workflows.”
Workflow Design Is Really People Design
Okay, big pause here. We’re getting really operational – and that’s a good thing. You need to operationalize thoughtfully to help AI implementations be successful as quickly as possible.
But ops aren’t the end-all or be-all of successful legal AI adoption. AI workflows run on software, sure – but they also run on people. Getting folks genuinely on board – not just compliant, but enthusiastic and curious and cautious! – requires investment in education and change management and conversation, too.
Angela, for her part, is emphatic on this point: “Education is critical. I don't want people showing up just because leadership told them to. I want champions – people who are genuinely bought in.”
She knows the friction involved. “Permissions, workflows, and change management are hard. We've been doing things one way for so long that the backlash might not feel worth it right now,” she acknowledges. “But the positives outweigh the negatives.”
The goal of building those champions is to free up the whole team for higher-value work – which is, ultimately, what good workflow design is for.
“AI allows case teams to focus on strategy instead of operational questions,” she continues. “I don't need people emailing me about searches – I need them looking at reviewed documents and telling me what matters.”
This reorientation – from operational to analytical – can help your firm take AI from a novelty into a genuine competitive asset.
Slow Down to Go Fast
Getting this human buy-in is what will really help move the needle on your firm’s AI strategy. Even when the value of AI is apparent, knowing where to start is its own challenge. As Andrew explains, education and hands-on experience create the most reliable bridge from uncertainty to confidence.
“While AI's potential is clear, many people are still uncertain about how to apply it in practice or where to start,” he says. “The best way to close that gap is through simple training, hands-on experience, and clear examples of how AI can assist with day-to-day tasks. Once people experience technology in action, their confidence will grow quickly.”
For those wondering how to evaluate AI providers as you build these workflows, Andrew offers a practical frame: "You don't have to be technical to hold AI providers accountable. The key is asking simple, practical questions about how the technology works, how results are validated, and what safeguards are in place if something goes wrong. Trust comes from providers being open about what their AI can and cannot do as well as their willingness to stand behind the results.”
Go beyond tech assessments and look to establish partnerships with your providers, because this technology is evolving quickly; you’ll want their help balancing innovation with reliability and the education you’ll need to keep up.
Making these up-front investments in evaluation, training, and validating will set you up to implement fit-for-purpose AI in optimal ways. In the long run, this approach will help you stay ahead of the curve as data science and its impacts on legal work continue to evolve.
The Work Worth Doing
AI's impact isn't evenly distributed across every type of task, and understanding where it delivers the most value is part of designing a workflow that actually sticks.
“AI delivers the greatest impact in areas where work is repetitive or consistency in decision-making is challenging,” Milauskas observes. “It can quickly identify patterns, surface critical information, and quality-check results, saving time and reducing mistakes. This enables people to allocate more time to problem-solving and higher-value work.”
That's the real promise of a well-designed AI workflow: not just efficiency, but capacity. Capacity for the kind of nuanced, strategic, human-driven work that no AI can replicate – and that legal professionals got into this field to do.
Bobby, reflecting on how his own perspective has evolved, captures the spirit well: “As a litigator, you see areas where technology can benefit clients.” Bringing that vision into action, he says, is part of what makes this moment in the legal profession so exciting.
“It takes collaboration and constant innovation. At PLUSnxt, we like to skate to where the puck is going, not where it currently is. We view ourselves as early adopters. I view myself as a synthesizer of the many great visions of our really smart people,” he reflects.
The drive to deliver for clients has always been at the center of legal work. AI, designed thoughtfully into daily practice, offers pretty powerful new ways of getting there in the face of modern legal data challenges.
Graphics for this article were created by Kael Rose.
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