One-size-fits-all fits no one
Your tools should fit your practice, not the other way around.
Talking to Lawyers about AI
On 4 March, I held an event at SMU. More than 30 Personal Injury and Property Damage (PIPD) lawyers showed up to learn more about what AI can do for their practice. My two realizations:
First, there is real interest in what AI can do for legal work today.
Second, there is still a big knowledge gap: many firms do not yet realise how much useful work AI can already do for them.
What I learnt from the event
One of the clearest lessons was that even within PIPD, firms can have very different needs.
PD-heavy practices often face a high-volume, lower-margin operational problem. Each matter may pay less, teams are understandably more price-sensitive. For these firms, their goal is to manage deadlines and make sure matters do not stagnate because of admin burden or handoff issues.
For firms like that, the immediate value may come from tighter operational workflows rather than from the most sophisticated features.
PI-heavy practices can look quite different. They often need much more powerful tooling around medical expenses and income documentation.
In one customer matter, there were around 600 physiotherapy invoices spanning six years, and our AI had no issue compiling everything into a clean Excel sheet in 15 minutes. That is a very different problem from simply keeping a high volume of smaller matters moving.
These are not minor differences. They lead to different priorities, different buying decisions, and ultimately different software needs. As a software maker, it is our job to cater to these different needs.
Modular options are possible
This is why I do not think one-size-fits-all product makes sense in the age of AI.
In some cases, the right answer is a more scoped-down version of the product at a lower price point, focused on the operational issues that matter most. In other cases, the right answer is a fuller deployment across multiple workflows.
That also means pricing should not be one-size-fits-all. Different firms have different levels of volume, different levels of complexity, and different appetites for rollout.
A firm using a more scoped-down workflow should not be forced into the same pricing structure as a firm using heavier drafting and compilation features at scale.
This matters especially for AI software because, unlike traditional software, it can carry meaningful operating costs. Volume matters. Complexity matters. The right commercial model should reflect that.
One consistent piece of feedback from current clients is that they value how responsive we are in customising the software to their needs. That includes workflow changes, scoping decisions, and firm-specific templates that preserve not just house style, but the secret sauce that makes their practice their own.
Put differently, we are putting the service back into Software-as-a-Service.
In some cases, the right answer is bespoke software built around a specific firm’s needs. If firms have different bottlenecks, they should not be forced into the same product shape.
This is not theoretical
Since the SMU event, we have signed multiple customers.
Those customers have different profiles and are using the software in different ways.
One is a PD-heavy firm using a more limited feature set focused on the operational issues that matter most.
Another is a firm using multiple workflows and asking for more advanced features, including conflict searches, interest calculations for special damages versus general damages, and other higher-complexity functionality.
That matters because it shows this is not only for firms with the largest budgets or the most standardised operations.
We have also built bespoke software for some customers where that made more sense than trying to force a standard package onto a non-standard workflow.
Even within PIPD software, firms want to customise the AI’s outputs to preserve their house style and their secret sauce. Our software supports that too.
Why generic and imported tools often miss the mark
If there is already this much variation even within PIPD, it should not be surprising that generic AI tools and legal tech built for other markets often miss the mark.
Tools like ChatGPT and Gemini can be useful, but they are generic by design. Overseas legal tech products may be impressive, but they are often built around different legal workflows, different client expectations, and different market realities.
Neither is the same thing as software built for a specific area of practice, in a specific jurisdiction, with the actual workflow of a Singapore firm in mind.
If you are evaluating AI for your firm, do not assume the only options are:
a generic AI tool like ChatGPT or Gemini
an overseas legal tech product that was not built for your practice or the Singapore market
a large, expensive all-or-nothing software rollout
doing nothing and waiting
A better way is possible.
In my experience, the best way to build legal software is incrementally. Get a useful system into the hands of users, see where it helps, collect feedback, and iterate from there.
That is usually far better than trying to design a large, expensive all-or-nothing rollout upfront.
The better question is not “should we buy AI?” It is: where is the operational pain in our practice, and what is the smallest useful system, built for our actual workflow, that would move the needle?
If you are thinking about that question for your own firm, feel free to contact me at warren@northbridgelab.com.