At Array, AI is already being pushed beyond document review and into the full litigation lifecycle. That’s not yet the market standard—but it’s where the market is heading.
The premise is straightforward, if uncomfortable: AI does not create order. It depends on it.
That runs against how much of legal tech is still approaching generative AI in 2026—introducing it first, then trying to retrofit structure around it. The results are increasingly familiar: strong demos, contained pilots and uneven performance when it matters most.
What’s taking hold instead is a quieter shift. Less about what AI can do, more about where—and when—it actually works.
At Array, where Cory Flynn stepped in as Chief Technology Officer in 2025, the focus has been on making AI operational under real litigation conditions—embedded into workflows, governed and defensible. Not as a layer on top, but as part of the system itself.
“If AI results can’t be clearly explained to a court or regulator and tied to a defensible process, it’s not ready.”
That distinction is starting to separate signal from noise. E-discovery has long been a proving ground for applied AI. TAR, clustering and predictive workflows have been standard for years. What’s changed is not just the sophistication of the tools, but the expectation placed on them. AI is now expected to surface insight earlier—summarizing, connecting and shaping case strategy before document review is complete.
That shift is real. But it only holds under the right conditions.
Modern matters are no longer driven by email and file shares alone. Short-form messaging, collaboration platforms and dynamic data structures now sit at the center of discovery. They are fragmented, constantly evolving and often incompatible with traditional document review tools. Legal teams are spending as much time making data usable as they are analyzing it.
In that environment, AI doesn’t fail because it lacks capability. It fails because it lacks structure.
This is where most efforts stall. Pilots succeed in controlled environments—clean data, narrow scope, limited stakes. Production introduces everything those pilots avoid: messy inputs, shifting parameters and the requirement that outputs be explainable and defensible.
“Production environments aren’t clean. You’re dealing with messy data, evolving scope and real defensibility requirements.”
The gap isn’t technical. It’s operational.
The teams beginning to close it are not starting with AI. They are starting with the system—defining workflows, normalizing data and embedding governance so outputs can be validated, traced and defended. In that context, AI becomes reliable. Outside of it, it remains provisional.
That shift reframes value.
The early returns—faster document review, lower cost—are real, but secondary. The more consequential change is upstream: understanding data sooner, identifying risk earlier and shaping strategy with greater clarity at the outset of a matter. Speed follows. Clarity leads.
It also sharpens the human role. As linear document review declines, expertise does not. It concentrates. Workflow design, validation and defensibility become more critical, not less. AI outputs cannot stand on their own. They must be interrogated, contextualized and ultimately owned.
By 2026, the industry is no longer asking whether AI works. It’s asking where it works—and under what conditions.
The answer is becoming clearer.
Structure first. Then AI.