Intapp 官网博客介绍 Firm AI 在律所计时和账单场景的应用

How is AI changing what law firms need from their timekeeping technology?

KEN HOUSEMAN

Clients expect their law firms to use AI to complete work faster — and be charged for fewer hours as a result. But the time a lawyer spends on client work doesn’t necessarily reflect the value being delivered, so firms are now under pressure to look at their cost structures differently.

To protect margins, law firms need timekeeping technology that both captures billable hours and identifies which AI tools are being used where within each hour.

Tech built on Firm AI makes this level of insight possible. Firms can use this intelligence to better understand the relationship between AI usage, delivery costs, and client outcomes, so they can make more informed decisions about pricing and risk.

Because Firm AI makes it possible to have full visibility into matter economics, firms can also analyze and test pricing models. This allows them to shift confidently into alternative-fee arrangements and fixed-fee pricing without risking their margins.

Revenue leakage is a huge problem for law firms. What’s the most common cause, and how can Firm AI help them address it?

KEN HOUSEMAN

Most revenue leakage happens in the moments between doing the work and recording the work. For example, a partner might plan to do their timecard on a Friday, then put it off because they’re tired. By the time they get around to it, they have trouble recalling everything they did. So time is written off, invoices are rejected for inaccuracy, and collections are delayed, which results in client billing guideline violations and discounts.

While your rate may be $300 per hour, at the end you may only collect $225 of that.

But with Firm AI built specifically for legal, lawyers won’t even have to record time entries, because their firm will have already recorded their efforts.

Here’s the key: Firm AI understands outside-council guidelines (OCGs) and what’s expected of phase, task, and activity (PTA) coding. It will take each activity, group it logically, then pre-narrate and pre-tag it in a compliant, client-centric manner based on the firm’s unique rules. So all each lawyer has to do is review each entry and correct it if needed.

Time entry and billing involve highly sensitive data. What are the differences between how generic and purpose-built AI tools handle data privacy and ethical walls?

KEN HOUSEMAN

The best way for an AI model to build a quality billing narrative is to read the work product. Because of this, many generic tools only have two options: Read everything or read nothing. The firms that choose everything simply have to trust that sensitive data won’t be shared.

On the other hand, purpose-built Firm AI means law firms don’t have to compromise on governance and data security.

By default, they honor a firm’s access rules, security rules, and ethical walls, and firms can provide work-product access in a way that keeps them safe. For example, work-product reading can be enabled by practice group, restricted to emails only, or limited to the first 500 words of a document.

To protect themselves, firms should look beyond flashy demos when evaluating AI tools. They need to find out if the tools will be reading work product, where data is stored, and how that data is protected and shared.

How can law firms get the security they need from AI without sacrificing scalability?

KEN HOUSEMAN

With local models, any data you share stays on your machine, so it’s very secure. But it’s also very expensive. For firms looking to scale AI broadly, the cost can be difficult to sustain.

With general-purpose AI models, data is shared with an external large-language model (LLM). That makes it critical for firms to understand what information is being shared, whether the LLM retains it, and how it’s protected. Without the right safeguards, firms risk exposing sensitive client or firm information in ways that may not align with their security and governance requirements.

Then there is Firm AI, where each client has their own provisioned instance that only contains their information, and respects user access permissions. Data isn’t co-mingled or shared with other clients or systems. That gives firms the scalability of a general-purpose AI model while maintaining security and governance requirements.

Understanding how the different AI approaches impact security, cost, and scalability is key to choosing the right AI timekeeping and billing tools.

What’s the greatest untapped opportunity for Firm AI in timekeeping and billing?

KEN HOUSEMAN

Time capture and billing don’t drive value for firms on their own. They support a much bigger value chain that includes pricing, scoping, staffing, and collections.

So I think the greatest opportunity is connecting every part of this value chain, and defining how Firm AI can rebuild time and billing solutions in service of it.

For instance, how can Firm AI be used to reshape timekeeping tools so they enable more profitable pricing on the next matter? Or how can it be used to improve collection cycles, so we can then use collections data to create better time-entry rules?

The idea is to create a continuous feedback loop where each engagement improves the next one, making it more profitable, compliant, and automated. Firms could learn and improve in real time instead of discovering six months later that something went wrong. That’s where Firm AI will be transformational.

Watch our recent Intapp Celeste event  on demand to learn about Intapp’s vision for Firm AI and see how Celeste — the expert AI coworker for professional firms — brings it to life.

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