Why Law Firms Need to Rethink Structure Before Scaling AI
I was listening to the Thoughtworks Technology Podcast recently. The focus was on how AI is changing the way engineering organisations are structured. The conversation wasn’t about lawyers, yet the challenges they raised sounded very familiar.
The easy first step
In tech, copilots and code assistants have been the obvious entry point. They slot into existing workflows and deliver a quick boost. Law firms are in the same spot with drafting tools, contract summaries, and research helpers. They’re safe, accessible, and easy to justify.
The mistake is treating them as the endgame. A few licences, a short training session, and a neat story for the website doesn’t equal transformation. If adoption stalls here, AI risks becoming a junior associate’s tool, not a firm-wide capability.
Bottlenecks and better paths
Engineering has already learned the danger of optimising a single step. Faster code generation means nothing if testing or deployment can’t keep up. The same problem appears in legal work.
AI might churn out due diligence reports in record time, but if partner review is still the bottleneck, clients won’t see matters move faster. Discovery tools can surface evidence quickly, yet without enough reviewers the queue just shifts further down the process. Even legal research can hit a dead end if insights aren’t captured and re-used.
The lesson is to think about flow across the whole matter. Instead of asking “where can AI speed us up?” the better question is “where does work get stuck, and can AI unblock it?”
Measuring outcomes that matter
There’s comfort in measuring adoption, like how many prompts were entered or hours logged in a tool. It looks like progress but tells you nothing about value.
The outcomes that actually matter are the ones a client can feel:
- Quicker turnaround on advice
- Fewer write-offs
- Clearer reporting
- Outputs that hold up under scrutiny
If AI isn’t moving the needle on those then it risks being written off as hype.
Enablement that empowers
Left unchecked, every practice group will run its own pilots. That creates duplication, uneven risk controls, and siloed learning. Tech companies solve this with enabling teams, not to centralise every decision, but to help others adopt effectively.
Law firms should follow that model, but with a twist. Centralising too much slows everything down. The smarter approach is enablement that equips lawyers to be better risk assessors themselves.
That means giving them practical ways to judge AI use in the moment:
- What’s the consequence if this output is wrong?
- How quickly would we know?
- Is the risk here client embarrassment or regulatory exposure?
Enablement isn’t about taking responsibility away from teams. It’s about arming them with the awareness and frameworks to make their own safe calls. That’s how you keep pace with technology that shifts faster than any central team can keep up with.
Boundaries and stewardship
Not all outputs are equal. A quick draft for an internal discussion can be disposable. Precedents, contracts, or regulatory filings demand careful review and long-term care. Client data certainly shouldn’t be piped into every shiny tool. Legal terms carry different meanings across jurisdictions so if you merge them carelessly and you create risk.
Drawing sensible boundaries and stewarding long-lived assets is what prevents today’s productivity gains turning into massive headaches further down the line.
Continuous learning
AI won’t stand still long enough for a one-off rollout. Adoption has to be treated as an ongoing practice, which you can do by:
- Rotating which practice areas pilot new tools
- Publishing short internal “AI case notes” after each experiment
- Retiring tools that don’t deliver measurable value
Engineering teams use tech radars and community forums to manage this cycle. Law firms need their own versions which should be simple, lightweight mechanisms that spread lessons quickly and stop everyone repeating the same mistakes.
Human judgement and intent
AI changes scale. It clears grunt work and opens new options, but it doesn’t change intent.
In software, the goal is to represent business logic in code. In law, the goal is still to deliver legal services to help clients understand risk, make decisions, and act with confidence. Generative AI doesn’t change that intent. It just changes how it’s expressed: less manual drafting, more interpretation and stewardship of AI outputs.
Judgement remains central. Lawyers still have to decide whether advice is defensible, interpret nuance across contexts, and provide ethical guidance. That part doesn’t move.
AI adoption in law isn’t about sprinkling tools onto old structures. It’s about rethinking how teams are organised, how knowledge flows, and how lawyers are empowered to handle risk in real time.
Engineering has already shown that chasing tools in isolation leads to wasted effort. Law firms can do better by treating AI as an organisational design challenge but while never losing sight of the real intent: delivering legal services that clients can trust.