What the 2026 Cloud Results Mean for Law Firm Performance in 2027

The Great Divide: What the 2026 Cloud Results Mean for Law Firm Performance in 2027

Sarah Spinosa: Today, we're joined by Elisabet Hardy, Chief Product Officer here at Elite, and Jeremy Menkes, Managing Director at Harbor. They're going to share their insights with you, so I'll kick it over to Elisabet to get started.

Elisabet Hardy: Thank you, everyone, for joining. I'm really thrilled to be doing this with my longtime legal industry expert and friend, Jeremy.

I think this may be our first webinar. Hopefully, it's not the last one we do together. I don't know what took us so long to get to this point, but we're really thrilled to see so many law firm leaders join us today for what we think is going to be a great session, with topics that are relevant both today and as we move into the future.

For those of you who have heard Jeremy or me speak, we do like to dabble a little bit in predicting what's around the corner. That's what we're going to do today, too.

We're going to focus on the mindset shift around how law firms are operating and what it looks like to move toward a best-in-class, enterprise view of how firms need to operate today and, more importantly, in the future.

There's a lot that's changed around us. Technology is shifting how we think about operating faster, operating more effectively and responsibly using new technology. At the same time, there are pressures coming not only from technology, but also from clients and the industry in general.

I don't think it's been a more exciting time to be in the legal industry than right now.

I don't think it's news to anyone that we're increasingly moving toward an enterprise operating model. It's becoming less about how each area or practice within the law firm wants to operate and more about how we can harness standardization and best practices to move faster through business operations with an enterprise-wide view, rather than running everything in bespoke ways across the law firm, locations and countries.

Clients of law firms are also becoming more sophisticated. I was speaking with a law firm CFO not too long ago who is getting pressure to talk to clients about how they're using AI and collaboration tools to deliver legal services.

That requires firms to think differently. How does that sophisticated client mindset—and the questions clients are asking—put pressure on operations? How do we need to think about business operations differently?

At the end of the day, we need to answer questions and serve our clients differently today and tomorrow than we did yesterday.

That also extends to how lawyers are practicing law and the tools they're using. We're moving heavily into a mixed workforce between humans and agents. How does that affect the way legal work product gets produced? How does that mean we need to think differently about capturing work activities? And how do we value those activities to preserve profitability around our matters and the law firm's P&L?
What does it mean to manage both people and agents? What will people do that's different from agents? And how do those two work together?

When we think about the operating model shifting, it starts with that enterprise view and how all the segments of that enterprise need to work from start to finish.

What Best-in-Class Operations Could Look Like

Elisabet Hardy: From a Harbor and Elite perspective, we're seeing what operating on a modern SaaS platform—with a holistic, enterprise mindset—can deliver.

We're measuring real value-based metrics and outcomes that customers are enjoying today, including significant improvement in lockup periods, or days outstanding, within the first 12-plus months of using new technology and operating more efficiently from a work-to-cash perspective.

We're seeing month-end close happen faster. That's important. No one likes drawing out month-end close to understand how you performed for the month and what you need to think about going into the next month. You don't want to be halfway through the next month with lagging indicators telling you what you potentially already needed to implement.

We're also seeing less rework when it comes to billing. Some of the results we're measuring with our cloud customers show close to 60% less rework. If there's one thing we all hate in this process, it's having to redo things over and over again.

How can we get invoices accepted by clients with less rework and move them through workflows faster? Having close to 60% less rework can provide significant efficiency gains for the firm.

And then there are write-offs, a very popular subject. How can we drive fewer write-offs? It really comes down to getting paid for the work in the manner in which it was performed and not getting a lot of challenges back on the work your legal team provided to clients.

We wanted to share some of these benchmarks because I think, in the future, we'll be talking about these as markers of what best-in-class looks like. What should you be looking at from a financial metrics perspective to say you have a best-in-class, modern cloud-based operation?

This is where we are today. These are real numbers and real value we're providing to clients currently.

Moving AI From Experimentation to Accountability

Elisabet Hardy: We promised this would be an engaging and fast-paced preparation and prediction for next year and beyond. Jeremy, they've heard enough of my voice by now, so I'm going to let you chime in on how the conversation is shifting from AI being experimental.

I'd like to note that we made it 10 minutes without mentioning the word AI, but here we are.
We're using AI and experimenting with AI. How do we move that toward accountability? And the big question: Is it really returning the investment we expected?

Jeremy, you're out in the field talking to a lot of law firms. What should firms be thinking about? What are you hearing? What's top of mind, and how are people approaching these accountability measures?

Jeremy Menkes: When I think about accountability and AI, I think about it in two different ways.
The first is the accountability of AI offerings to be accurate and reliable.

Three years ago, if I was having a conversation with a partner at a firm about their billing process, they would talk about how long it takes to review their proformas every month, how they have to work late, come home with a stack of paper or bring their laptop to their kids' soccer game. I'm sure everybody on the call has heard that every single month.

But when I would challenge them and say, "Okay, we can completely change your process so you don't need to review your proformas at all," they always reacted negatively: "No, no. I need to be the one to review my proformas. It can't be left to anyone else."

If we take an honest look at the process, it never made sense to have your most expensive, revenue-generating people handling all these administrative activities. But they felt they had to do it themselves.

The first concept when I think about AI accountability is trusting AI to do its job—having AI create or suggest time-entry narratives that won't get flagged in e-billing and edit those that will.

There will certainly still be a human element, but accountability means reducing that human involvement.

The second way I think about accountability is how firms are starting to use AI.

I have a conversation every week with a CFO who says, "What should we be doing? We're getting a lot of pressure."

A lot of firms are starting to dip their toes in the water or make really small bets, but they're not accountable for actually having AI improve their business operations.

Firm leadership is giving mandates and setting expectations that AI has to be used, but there's not really a clear plan or direction as to what or how.

If an initiative doesn't move the needle forward, it doesn't really matter if it's successful or not. That's not what accountability looks like.

As we think about moving from AI experimentation to accountability, it's about making sure you have a clear plan and success measures—moving from experiments to proper implementation.

Elisabet Hardy: I agree. I think we've entered the era of responsible AI, or what I've heard described as "AI with a human touch."

How do we inspect it? How do we know it's right? It's easy to throw AI at everything, but how do we get it to do the right things, make the right suggestions and perform correctly within a workflow?
What's the right mix between AI operating autonomously and humans still being involved? Are there levers and guardrails that you see around that?
Jeremy Menkes: The legal industry is extremely risk-averse, so there's very much a trust-but-verify approach.

I'm always amazed by the pace at which technology is moving. It's incredible where things are going.
Firms need to become more comfortable with it, and the only way to become more comfortable with it is by working with it regularly.

AI Is Also a Data Story

Elisabet Hardy: That leads us to the next area. The AI conversation quickly became a data story.
Do we have a data strategy? Do we know where all the data sits so an AI agent, for example, can access it? How detailed is that data?

If the data is too aggregated, it's not going to be useful for AI. If it sits in too many fragmented systems and too many places, that's difficult as well.

I wonder whether we've become distracted by AI as the shiny object and aren't focusing enough on understanding the firm's data strategy.

Do we understand the cleanliness of the data, which has been a longtime issue for anyone who's been in this industry? Do we understand how real-time that data needs to be? An agentic workflow is going to need access to real-time data or it can't move—or it's going to make mistakes.

Jeremy Menkes: Absolutely. Firms have become distracted by AI and aren't really focused on the bigger problem, or on having a formal strategy or plan. That speaks to those little bets being made in operations.

We hear stories about partners who are vibe coding or doing things on their own over the weekend. They develop an unrealistic expectation that it must be really easy because they can do it themselves. "I can take my proforma and throw it into Copilot, so surely you guys can do something."
The reality is exactly what you said: AI needs data, and it needs accurate data to provide meaningful outcomes.

Your data needs to be uniform across the organization. For years, we've talked about aligning data and master data, but it's been much more of a conversation than an action.

Now firms are starting to see the impact of not having accurate and aligned data across the organization.

Who are your clients? Who are your people? What kind of work are you doing? There needs to be one answer to those questions.

Disparate systems with disparate data cause AI confusion.

More firms are moving toward an ecosystem model with shared data, but there still needs to be data alignment across the systems you have throughout the organization. That's where your data lake comes into play.
Newer data lake technology is really built for the cloud, not on-premises. Repatriating data, or bringing data back from the cloud to an on-premises environment, is technically much more difficult, and there's much less you can do with it using AI.

Elisabet Hardy: I don't think anyone disagrees with that. The question is whether you actually have the time and space to step back and say, "Let's look at this holistically. Let's have a strategy in mind."
From a Harbor perspective, you're working with a lot of clients to put that in place, which then helps platforms like Elite come in and say, "Great, now we have a good place to start."

Let's think about this from start to finish. If you're thinking about work-to-cash, we're not going to dip into a bunch of disparate systems. We're going to try to move through processes quickly because that will generate clean data and give you the speed you need to get cash back into the firm—which is the name of the game.

Is a Performance Divide Emerging?

Elisabet Hardy: One of the most provocative statistics in the 2026 Cloud Results Report is what we're seeing as a true performance gap from a data perspective: only 14% of firms can answer basic profitability questions in near real time.

About half of the people we surveyed also said that fragmented systems are a barrier to AI success.
Jeremy, are we starting to see a divide between firms that can operate in real time and firms that can't?

Jeremy Menkes: I would say the report is clearly telling us that we are.
The people joining us today probably aren't surprised to hear that firms can't answer basic profitability questions in real time.

Historically, law firms focused on the quality of their work and their client relationships. You could certainly make the argument that that's where their energy should have been spent, but they didn't necessarily focus on running the law firm like a business.

We've come an incredibly long way in a short amount of time, but there are still a lot of legacy practices.
As you mentioned earlier, law firm clients are becoming increasingly sophisticated. They're looking for ways to pay law firms less. People on this call deal with it every day, whether that's through outside counsel guidelines, challenges to rates or clients bringing more work in-house within their corporate legal departments.

Without real-time information, you're fighting with one arm tied behind your back.

If you don't understand the implications of a discount, it's difficult to know whether you can agree to it. If the information isn't readily available, the decision takes longer, which means it can take longer to accept new work into the firm.

Not having real-time access to information is starting to hinder firms. This is where we're seeing that inflection point between those who can and those who cannot.

The Importance of Unified Financial Data

Elisabet Hardy: That leads to the next imperative as we go into 2027: understanding and having a unified view of financial data.

Do you have a clear view into everything that surrounds your financial records? One of the things we've started talking much more about is whether you understand your matter economics. That's going to be critical.

This webinar isn't necessarily about pricing models going forward, but perhaps that's a topic for our next webinar.

This is where it becomes really important to think about the financial record and whether you have a unified view. If you don't, it's going to be very difficult to get to some of the things we just discussed.

Jeremy Menkes: That's exactly right.

The firms with unified data will be the first to meaningfully leverage AI. I would take it one step further: firms that have unified financial data in the cloud will really be the first to meaningfully operationalize AI.

As we've discussed, AI and cloud just work better together.

Elisabet Hardy: When we think about what's available today, part of what we do together from an Elite and Harbor perspective is walk clients through what is available on a SaaS platform.

How do we unify your financial records and data? How do we embed AI to help you move through processes with as few errors and as quickly as possible? And how do you start to get insight into real-time data?

Real-time data isn't a new concept. We've always chased what "real time" actually means. Is it really real time, or close or near to real time?

That's become much more important because of modern technology and what it needs.

Then we can use that data to provide suggestions and recommendations about what you can do next to prevent a mistake or delay farther down the operational workflow.

That's what's exciting about today's technology.

As technology continues to move quickly, this brings us back to having an enterprise platform and mindset. How do we get to speed? How do we get to best-in-class?

Part of that is being willing to change some of the ways we're operating today in order to gain efficiencies and become more effective. That includes shifting repetitive tasks toward managing by exception.

There's no reason today's technology shouldn't allow you to log in in the morning and be welcomed by an agentic screen that tells you the top things you need to worry about today.
If you're a billing director, for example, the application could tell you how many proformas are in your inbox but recommend that you start with a particular one because that client tends to be delinquent in its payment patterns. The faster you can get that bill out, the better.

It's about getting smarter about how you're working and shifting toward exception management rather than clicking buttons and treating everything with equal urgency throughout the day.

We're also seeing clients increasingly ask law firms for transparency about how they're being billed and how work is being performed.

We know lawyers are using agents and other AI tools. What does that mean? Where and how are those tools being used? How do clients participate in those gains or efficiencies?

That brings us into an interesting conversation about capturing the right information and thinking about work capture or activity capture rather than only looking at time entered by humans in the way we always have.

From the Elite and Harbor perspective, we take a consultative approach: Here's how you operate today. Here's how we think you need to operate in the future. What does that realistically mean?

Otherwise, it's just software for software's sake. There's also change management and best practices to consider.

From Time Capture to Value Capture

Elisabet Hardy: As we move into the last few imperatives for 2027, the concept of value capture—not necessarily just time capture—is very interesting.

I don't think we've seen the last of this or what it means for firms. It's a real challenge today, and we've had several conversations over the last few months with firms trying to think through it.

Jeremy, what's your point of view? Where is it today? What can firms realistically start to do, and where do you think things are going? I assume it's going to become more complex to track how work is being done.

Jeremy Menkes: I think you should personally trademark the term "value capture," because I absolutely love it. I believe that's where we're going.

We've talked about law firms typically being risk-averse. For the second-oldest industry in the world, it's incredible to me how quickly a lot of the front office has adopted AI.

Law firm clients now expect firms to use AI to produce faster results.

For years, we've talked about predictions around the death of the billable hour. This isn't necessarily another call for the death of the billable hour. But clients are going to expect work to happen faster, and in a billable-hour model, faster means cheaper.

Every firm has access to similar AI tools. That's not really the differentiator. The differentiator is your work product, your precedent and your client context.

Some of that resides in people's heads, but a lot of it lives in your own data, especially your non-public data.

I think it's reasonable to expect that we'll move more toward a value-based billing approach, focused less on the hours involved and more on outcomes.

From the administrative side, firms will need to adjust to this value-based approach to billing. That starts with capturing time saved using AI.

This is another conversation I have with firms every week: "We're being asked how much time is saved using AI. How do we start to represent this to the firm and to our clients?" That's the first step.
The second step is: How do we deal with pricing?

Initially, my guess is that we'll start by looking backward. How long would this have taken in a traditional billable-hour model? How much time would it have taken, and how does that compare with the new approach?

To do that requires accurate, classified data. What type of project was this? How long did it take? How comparable is my new work to it?

Naturally, that will evolve and change as we start to break free from using time to define the actual value to the client.

Elisabet Hardy: Another place I think this will go as we look into 2027 is having a process or controls around taking in a matter and determining upfront which areas or activities will use AI to deliver the matter.

Today, I think decisions about how we're going to use AI happen more during the stream of work. In the future, we'll be more thoughtful upfront about where and how we deploy AI to deliver the outcome the client wants or is hiring us to achieve.

That shifts how we think about the matter lifecycle.

We're still in the beginning stages. Like any new technology, you have to experiment. You have to learn how you want to implement it and where the use cases are most beneficial in delivering the efficiencies you're looking for.

I think we're going to see a lot of changes here as we move into next year and beyond.

Jeremy Menkes: Absolutely. We're going to move from a more reactive to a more proactive model as firms figure out what it's like to work with a combined human-and-agent workforce.

There will be an evolution. Right now, we're still in a reactive mode where we're figuring it out later.

Cloud Readiness and the AI Timeline
Elisabet Hardy: That brings us to timelines and how to accelerate.

The report includes metrics about how cloud migration timelines have accelerated while simultaneously making firms ready for what's next.

That's important because we've been in this mindset that it's going to take years and years to get there. What we've seen in reality is that we can compress timelines quite a bit.

Jeremy, what's your point of view on timelines? Are they accelerating?

Jeremy Menkes: This has been one of the common themes of today's discussion: cloud and AI go hand in glove.

In order to have a full AI strategy for your finance operations or elsewhere, it really requires being in the cloud. That could mean leveraging native functionality within 3E or connecting to other AI providers firms are working with.

Things are moving very quickly. There's a lot of prognostication in the market and in the world, and nobody really knows what's coming next. But I feel very comfortable saying that change will continue to come, and it will continue to come rapidly.

Whatever the new advances are, firms in the cloud will be better positioned to take advantage of them.
Cloud migration timelines are going to start accelerating as firms recognize that this is also their AI timeline.

How Work Could Become More Autonomous

Elisabet Hardy: The last point is how work itself will change.

The notion of work running autonomously can mean a lot of different things. But as we look into the future, workflows that previously required an end user to actively push buttons to get to the next step will start to run more on their own.

If you think about it, having people push buttons simply to keep workflows moving isn't necessarily a good use of their time.

For business professionals involved in running the law firm, the focus is going to shift toward higher-value work: work that's more complex and requires more analysis and judgment.

We can make machines run autonomously to a point. We still need the right intervention from people to make good decisions, review things and make the difficult calls.

That's where the potential of new technology, a SaaS platform and AI embedded into the work comes into focus.

In the years ahead, I think we'll increasingly talk about what work can be done autonomously and where we need highly skilled people because more judgment is involved.

Jeremy Menkes: Sometimes I think about the evolution of what we thought AI would be.

Probably 15 years ago, we thought AI would do all our grunt work and we'd sit on the beach composing music. Then two years ago, we thought, "Oh no, we're going to do all the grunt work and AI is going to sit on the beach composing music."

We're certainly landing somewhere in the middle.

The focus does move toward higher-value work and automating workflow steps that really shouldn't involve a human.

It's also about getting ahead of issues. Problem clients who pay late, or partners who don't want to handle administrative work beyond dealing with their clients, can become the focus.

I think it means a better partnership between the front office and back office to serve as trusted advisors in running the business.

It's about spending less time on mechanics and administration and more time on the business of law to make firms more successful.

I think a lot of innovation will be driven from the business-of-law side. There are a lot of new tools being used in the practice of law, but I think real innovation will also be driven from the business side because that's what will revolutionize how the firm operates.

It's going to look very different from a workforce perspective and very different in terms of what you spend your time doing.

What Is the Cost of Waiting?

Elisabet Hardy: The danger is that if you stay doing the same thing you're doing today, what's the cost? How much do you fall behind?

Those are hard questions to answer.

When we look forward, we'll know more about how long people waited and whether waiting was a good thing or not.

Jeremy, when you think about the cost of staying where you are and where that compounds, what are the top things you help firms weigh when they're asking: Should I migrate to SaaS? Should I do this project right now? Is now the right time, or is it a year or two years from now?

Jeremy Menkes: Sometimes when firms say to me, "We're not ready for cloud," or, "Cloud is not ready for us," my reaction is: Good. Let's start right now, because it's going to take us time to get there.
By the time you're there, you'll be ready for it and it will be ready for you.

We don't know what's coming, but we feel very confident in saying that delaying is not going to be to your advantage.

It's reverse logic to say, "I'm not ready, so I'm going to wait."

If you're not ready, let's go right now, because that's what will actually get you ready for that future state and allow you to take advantage of advanced technologies.

Looking at this graph, I would say it's probably not going to be linear. It's going to be exponential. When the inflection point happens, that curve is going to move upward very quickly.

Audience Q&A: Building a Data Strategy

Elisabet Hardy: Hopefully what we've shared today has been insightful, both in terms of what we're seeing in the survey and report from peer firms around the globe and how we think about shaping it from an advisory and technology perspective.

We have a couple of questions from the audience.

When you think about data strategy, Jeremy, how does Harbor typically approach a project like that?

Jeremy Menkes: There are multiple lenses to it.

When you're thinking about data strategy, it's about where your data lives and who owns what data.
It still starts somewhat with a master-data conversation around the authoritative source, the system of record and defining those things.

Then it's about which system owns what and how the systems speak to each other.

There's also the technology component of moving into a Fabric- or Azure-based environment so you can start to interrogate that data using newer technologies.

Where it gets really interesting to me is when you start bringing in practice data.

There are a bunch of finance people on the call, and we think finance is the only thing that matters in the world. But when you start to leverage your practice data, that's information unique to you, and it becomes really powerful.

You can start answering questions before they're asked.

From a finance lens, that could be around pricing: How much is this work going to cost?

Or it could be: What is going to be the outcome of this case?

Based on the information you already have and all of your data, you can start to make some of those predictions.

Audience Q&A: Where to Start With AI in Work-to-Cash

Elisabet Hardy: Another question is about AI and getting very specific about where Harbor and Elite are seeing AI leveraged within work-to-cash.
Where do you see the most gain? Where should firms start?

Jeremy Menkes: I would start by alleviating the amount of time your partners or fee earners have to spend in the process.

Where are the real tangible gains?

It's suggesting time-entry narratives and providing recommendations during proforma review.

Whether that's using native technology within 3E or connecting with other solutions the firm is already using, that's where I would recommend starting: reduce the pain for arguably your most important and certainly your most expensive resources, and speed up the process there.

Audience Q&A: How Long Does Cloud Migration Take?

Elisabet Hardy: One last question before we wrap up: What's the average time it takes for a typical 3E or Elite firm that's on-premises to migrate to the cloud?

On average, what we're seeing today from 3E on-premises customers is about a six- to eight-month migration time.

That will vary somewhat depending on the size of the firm, how many locations it has and how it wants to approach the project.

We have really large firms that have gone through migration in no more than six months. We also have firms that take a little longer—up to 10 or 12 months.

But that's a dramatic shift from where we were a few years ago, when projects were taking longer.
The reason we're seeing migration timelines accelerate is because of the tools and technology we have. We can work with customers to get through this much more quickly because, at the end of the day, no one wants to stay in a migration project for a really long time.

I always say to customers and firms we talk to that getting to the cloud and getting to the SaaS platform is just the beginning of the journey.

When you get there, you can start leveraging the capabilities and continue to improve day in and day out because cloud is not a fixed destination.

Unlike on-premises, where you may not do upgrades very often because they take a lot of time and consume internal resources, with cloud you can take advantage of continuous innovation based on the release cadence of new capabilities.

That's the beauty of it. Neither Harbor nor Elite wants firms to stay in migration projects for a long time. We want you to get to that next point in the journey and transform your firm's business operations.

I urge you all to download the report, and thank you to those who submitted questions.

Hopefully you found today's session engaging and took away some thoughts you can leverage. Both Jeremy and I are happy to speak with you as you move forward and have questions.

Thank you very much for taking the time to join us, and thank you, Jeremy, for doing this in partnership with Elite.

Jeremy Menkes: Absolutely. Thank you to Elite, and thank you to everybody who joined.