Recently I listened to a conversation with the CEO of Legora, an AI workspace used by lawyers. I expected the usual “AI makes professionals faster” story. What stood out was something more structural: law is beginning to reorganise around what is now cheap and reliable, and what remains scarce.
This is not a post about legal tech for its own sake. It is about what happens when a profession that runs on judgement, trust, and review meets systems that can read, search, compare, and draft at scale.
If you work in wealth management and advice, you will probably recognise the patterns.
The old (current) legal stack: lots of tools, little leverage
Legal technology has existed for decades, but it grew as a patchwork. The pre-LLM world was full of point tools: templating, translation, redlining, research tools, clause libraries, document management add-ons. They all touched text, but they did not change the economics of the work.
That fragmentation was a rational response to a hard constraint: legal work is messy, unstructured, and context-heavy.
A big share of legal tasks are “high risk, low differentiation”. Due diligence, clause extraction, precedent checks, research memos. They are expensive and time-consuming, and clients rarely love paying for them, but you cannot skip them.
Earlier ML approaches could sometimes work when language was consistent. They struggled when the same concept appeared in different wording across documents. The founder gives a simple example: you could train systems to detect a change-of-control clause when it looked like the examples, but they were poor at identifying the meaning when phrasing varied.
That is where real work lives.
What changed: models crossed a practical threshold
The shift was not that someone found better prompts. It was that models became usable on real legal language at a level that could support daily workflows.
Using earlier models (including BERT-era approaches) they handled Swedish poorly, then seeing GPT-3.5 open up entirely different behaviour.
Two details matter:
- The initial insight was concrete. A lawyer spent months summarising cases by hand, which is clearly computable if a system can read and reason.
- The first thing they built was not an “enterprise platform”. It began as a simple contract explainer, then quickly expanded once they realised the ceiling had moved.
This is usually how new operating models start: one narrow use case, then rapid expansion as soon as the underlying capability is real.
What they built: not a chatbot, a workspace
Legora’s shape is instructive because it is not a thin chat interface bolted onto documents. It aims to become the surface where work happens.
A web workspace
It began as chat over your own documents and files. It evolved into an agentic workflow experience, where you can ask for multi-step work such as: do research, convert it into firm-standard language, draft a memo, output a finished deliverable.
The meaningful change is not “answers”. It is “work completed”.
Old Tech Meets new Tech: Microsoft Word add-in
Lawyers live in Word. So the product goes to Word.
The founder compares this to Cursor for lawyers: the system can read the document, propose edits, and apply structured workflows, but inside Word’s UI constraints.
This matters because adoption follows workflow. If the tool requires people to work somewhere else, it competes with habit, precedent, and time pressure.
The real leap: turning review into supervision
The moment that stuck me the most was not chat. It was a workflow for doing large-scale review without doing large-scale labour.
They describe a due diligence workflow where you upload many documents, then run many questions across all of them in a grid. Each document is a row. Each question is a column. The system executes the cross-run and links answers back to citations.
If you have ever watched a diligence team grind through documents one-by-one, you immediately see what changes:
- The work shifts from manual scanning to exception handling
- You can ask more questions because the marginal cost drops
- Quality becomes easier to standardise because answers are tied back to sources
Legora’s point is that the hard part is not “prompting”. It is running this at scale while keeping citations correct, handling long documents, definitions, and cross-references.
This is a different class of product. It is workflow infrastructure, not a clever interface.
Encoding standards as playbooks
Another theme in the conversation is playbooks: collections of rules, preferred language, and fallbacks that can be applied consistently.
The described flow is simple: define what “good” looks like, define what to do when it is not met, then run that set of rules across a contract and mark it up.
Two implications are worth calling out:
- Consistency becomes a product outcome. A firm can encode standards once and apply them repeatedly.
- Expertise becomes distributable. The founder describes non-legal teams using playbooks to negotiate common agreements before involving legal.
When a profession can distribute its standards this way, the organisation changes. The bottleneck moves.
Trust, reliability, and auditability are the product
A recurring idea in the conversation is that the first requirement was not magic, it was trust.
They talk about early compliance constraints, particularly for Europe: data residency, no retention, no training on customer data, and controls around human review.
They also emphasise reliability and scalability as foundational. There is a line I keep thinking about: you get one chance when a professional logs in, and if the system fails, they may never come back.
In high-trust work, “mostly works” is not a feature. It is a dealbreaker.
The deeper pattern: when intelligence gets cheaper, work re-bundles
Zooming out, the legal story is a clean example of a broader shift.
When systems can read, compare, search, extract, draft, and synthesise at scale, several things tend to happen:
- Point tools lose relevance, because one underlying capability can serve many tasks
- The workflow re-bundles into fewer surfaces
- Humans move from producing every intermediate step to supervising outputs
- “Software” starts to blur into “service”, because the buyer cares about outcomes, not interfaces
The founder explicitly notes that as they go deeper into the legal stack, the boundary between software and service is blurring.
That is not unique to law.
Three common mistakes organisations make
When an industry hits this stage, most organisations make the same mistakes, even smart ones.
1) Buying tools instead of redesigning work
It is natural to add “AI” as another layer in the stack. That approach tends to increase complexity, not leverage.
2) Treating pilots as strategy
A pilot answers “can we do this?”. Strategy answers “which workflows must change because the unit economics and risk profile have changed?”.
3) Keeping workflows that should not survive
This is the quiet killer. Many workflows exist because intelligence was scarce and review was expensive. When those assumptions change, holding the old workflow constant prevents you from capturing the new leverage.
A semi-concrete view: before and after
I find it useful to understand this shift without pretending the final target state is already known.
Before
- Multiple systems of record
- Many tools for narrow tasks
- Manual review and sampling to manage risk
- People acting as glue between steps
- Quality enforced by senior review and institutional memory
After (directionally)
- Fewer “tools”, more workflow surfaces
- Continuous review across large corpuses, not periodic sampling
- Standards encoded once and applied repeatedly
- Humans supervising outputs, handling exceptions, owning accountability
- Reliability and provenance becoming central, not optional
What replaces today’s stack is still being worked out. Everyone is figuring it out in real time. But it will be different, because the constraints have changed.
The mirror, Wealth Management and Advice
If you run wealth management and advice inside a large institution, you already recognise the shape of your environment: a set of systems of record, a patchwork of tooling, manual review where risk demands it, and advisers and teams acting as connective tissue.
That is status quo. It is how complex, trust-bound work has always been delivered.
The legal story is simply an early, visible case of what happens when that environment meets systems that can compress analysis and drafting, and do it with traceability.
The more interesting question is not “will people still matter?”. It is “what do people do when the machine can do the middle of the workflow?”
Legora predicts lawyers will increasingly become reviewers and managers of agentic work, still responsible, still accountable, but spending more time supervising than producing.
That is a plausible future for advice-driven work more broadly.
Law is not special. It is just early.
Clinton Cunningham 