How Law Firms Will Win in 2026: Part 1

We’ve been publishing our “How Law Firms Will Win” analysis for six years now, and there’s a reason we keep coming back to it. Accuracy matters in this business, and we’ve been consistently right about where legal marketing is headed. From predicting the shift toward data-driven attribution in 2021 to calling out AI’s impact on content strategy in 2023, our annual series has become a roadmap for firms that want to stay ahead rather than play catch-up. But 2026 presents challenges unlike anything we’ve seen before: private equity consolidation is destroying agency relationships, AI model capabilities are advancing every quarter, and the gap between firms with analytical frameworks and those without is widening at an unprecedented pace.

At Market My Market, we don’t write these analyses to chase trends or manufacture urgency. We write them because we’re in the trenches every day, analyzing tens of thousands of data points, building custom systems, and watching what actually works versus what just sounds impressive in a sales pitch. This year’s series will cut through the noise and give you the verifiable insights you need to make strategic decisions, starting with the problems nobody else wants to talk about.

Our Track Record

If you’re new to this series, we’ve been analyzing legal marketing trends since 2021. Our previous editions have consistently identified shifts before they became industry consensus:

Each year, we focus on what’s actually changing in the market rather than recycling the same advice you’ll find everywhere else. For accountability’s sake, we encourage you to review our past predictions and see how they’ve held up. This year’s analysis addresses three critical issues that will separate winning firms from those left scrambling to catch up.

The Private Equity Problem Nobody Wants to Talk About

Private equity consolidation in legal marketing has been quietly destroying agency relationships over the past year. I’m watching founders I’ve worked with for over a decade, experienced operators in their late 40s and 50s who’ve built legitimate agencies, take acquisition offers. I’m not there yet myself, but I understand the appeal of an exit after years of grinding. But here’s what really happens when private equity acquires agencies in professional services: you don’t really know what the outcome is going to be.

It absolutely depends on the leadership, the long-term goals, and whether they’re trying to roll this in to get more value out of something that they will then package and sell further along. Any acquisition of this nature is not from the goodness of their heart or for the love of the industry. It’s an effort to make more money. The outcome varies by acquisition, but the underlying motives always change.

What I’m seeing right now is a pattern where exclusivity agreements that once protected client relationships suddenly become flexible. Experienced account teams who knew your firm and understood your market get reassigned to new business development because that’s where PE firms drive value. Resources redirect from client retention to acquisition because growth metrics matter more than relationship quality in the boardroom. The people who built genuine relationships with your firm are replaced by account managers who are managing 50 clients instead of 15, reading from scripts instead of understanding your market dynamics, and optimizing for their own performance metrics rather than your results.

This isn’t speculation. I’m having these conversations with law firm CMOs and managing partners every week. They’re watching response times slow down, strategic recommendations become generic, and the institutional knowledge that made the relationship valuable evaporate as experienced team members leave or get reassigned. The agencies still produce work, but the quality and strategic insight decline because the incentive structure has fundamentally changed. When an agency is optimizing for PE-driven growth targets rather than client retention, you feel it in every interaction.

The AI Model Gap That Nobody’s Fixing

A lot of people have all these systems, tools, and proprietary backend SaaS or dashboards that were built a few years ago. Compare that to today. I mean, Claude Opus 4.6 just released. There are web application companies like Replit and Lovable that are doing incredible things that would take months, multiple devs, and hundreds of thousands of dollars. People are literally vibe coding their way to victory every single day if they’re patient and thorough enough.

There’s a lot of complacency that happens because people get what they were looking for quickly. And what they’re looking for, frankly, isn’t particularly impressive, certainly not by the standards my team and I would hold. I can’t even quantify what the inferiority would be between a 2023 model and current technology. A tenth of the effectiveness? A hundredth? Some companies, agencies, or practices can be slow-moving ships, and there tends to be a good enough mentality because, so what if productivity is already increased by 50%?

Some people just have different mindsets of what they’re trying to accomplish. If they get there, that’s that, and it could be that way for a long period of time. But every new model release opens up the potential for better products and better efficiencies by 50% or more. You just have to get used to it. You have to get used to the fact that you’re going to be reimagining things potentially every three months, and if it’s more than every nine months, you’re falling behind because that’s where we’re at right now.

Understanding how to leverage AI where it matters most in your workflow isn’t about adopting every new tool. It’s about recognizing when your existing systems have become obsolete and having the operational maturity to rebuild them before you fall too far behind.

Why Your Attribution System Is a Black Box

Getting a complete picture of your funnel requires several specific steps. But we need to start with where most firms are today. We still have law firms to this day that do the complete “I don’t know where I heard about us” approach, using Excel spreadsheets or even a notepad to be able to give some form of attribution that’ll get entered into who knows what, might not even be a CRM.

Then, we started to get a lot more sophistication in the last several years. Platforms like CallRail came along. In dental practices, it might be Mango or Weave. There are legal-specific intake centers and platforms that do their own tagging and attribution using some assemblance of AI to be able to tackle a level of control. But a lot of the backend and decision-making for this is just the AI. It’s a black box of exactly what the criteria is.

Take CallRail, with its Premium Intelligence, for example. For all we know, they’re connecting to the cheapest form of language model that exists, and it’s not able to necessarily parse through and make determinations, especially on a phone call, because that requires a lot of text and tokens to pass through. There are too many criteria and variables for the AI to reliably determine whether a call is qualified, what type of case it represents, or which procedure is being discussed. And you don’t really have the control you need without having to manually go through and QA or double-check, which defeats the whole purpose of the conversation.

Understanding the complex reality of marketing attribution means recognizing that most platforms give you simplified answers to incredibly complex questions. If you really want to take this as far as it can go, you’re going to have to have some customization beyond the confines of the platform. Depending on your scale, that’s where customization is going to come into play.

You should work with someone who knows how to build out custom AI workflows and potentially a bit of proprietary web applications that you’re specifically looking at. You’re not always going to want to long-term rely on the dashboard that’s been presented to you because the customization is never going to be spot-on to what you want. I know it seems like a daunting task to create your own thing. I’ve seen people try and fail and spend so much money doing it, but I guarantee we are in a place where it’s more accessible than ever before.

The GEO Question: Too Early or Too Late?

It’s always funny when you see small numbers people are celebrating. I know there was a verified conversation I had with some members of my team about a recent study showing that people are seeing AI citations up 550%. It’s like, oh my God, we have to react, that’s the next place we have to be. But that’s also understanding that maybe people are getting 10 or 20 visitors in the past year, and now they’re getting 100.

Some firms are celebrating 400% increases in AI citations, but when you dig into the data, they went from 5 citations to 25. That’s not meaningful traffic. The question is when to pull the trigger, timing matters. But overreacting doesn’t help. We saw this same pattern three years ago with AI. People overreacted and began using ChatGPT 1.0, 2.0 for everything. The result was an avalanche of low-quality content, terrible insights, and worthless consulting, all driven by fear and hype. Google spent months purging it, ultimately removing millions of pages permanently.

Do I think it’s time to overreact right now? Probably not, because this is about the mindset of understanding the signals and being prepared to do these things. We can’t overreact to everything and just chase the next best thing because what we’re hearing about is from marketers. And marketers, hopefully not such as myself, are always going to sound the bell for the next thing because they use sensationalism and alarm to be able to sell their services and make things look extremely presentable.

If you’re wondering whether you really need a ChatGPT ranking strategy right now, the answer depends entirely on whether you have the infrastructure to measure what’s actually working versus what’s just generating noise.

What We’re Actually Measuring in LLM Rankings

What we’re doing is thorough, meaningful analysis of what’s happening in generative AI and LLM rankings. We’re understanding what’s happening there to be able to get in front of it. In my previous article about measuring LLM rankings, I made it clear: there’s no algorithmic ranking system like traditional SEO. There’s just a presence. Sometimes the result is there, sometimes it’s not.

Right now, we’re spending a ridiculous amount of time analyzing who’s showing up the most often and why they’re showing up. Is it because of how long they’ve been in business? Is it because of their settlements? Is it because of reviews? Are the reviews coming from Google Business Profile or from Yelp? The only way to win this game is to work with data, verifiable data, not sensationalism and not just gut instinct.

This is the same approach I’ve been taking for years. It’s very analytical and proven by data. We look at trends, and we’re going to see if it matters where you’re located and if you have certain components of your website that are verifiable. Why are there instances where YouTube gets pulled in? Are things like Reddit responses or positions more prevalent directly in search, or do they show up in AI overview?

I would encourage you to look out for that because we’re compiling literally tens of thousands of scans for both search results relative to AI Overview relative to ChatGPT responses and seeing what correlations exist. You can’t work with a small data set here. You can’t do a couple searches and feel like, hey, this person showed up here, they’re dominating. There’s too much at stake. If you can’t see thousands of data points that are verified over a period of time, then there’s nothing to really act from, and there’s nothing verifiable or credible about the sources that are otherwise self-serving in these instances.

Alternative Perspectives: The “Wait and See” Approach

There’s a camp in legal marketing that advocates for the “wait and see” approach with new technology. Let others be the guinea pigs. Let the dust settle. See what actually works before committing resources. And honestly? I see the logic there. Not every firm has the bandwidth or budget to experiment with every new platform or model update that comes along.

Some practitioners argue that traditional SEO still delivers consistent results, so why chase AI citations that might not convert to actual clients? There’s merit to that position, especially when you’re looking at firms that have established referral networks and strong local presence. Why fix what isn’t broken?

But here’s where I land on this: the gap between early adopters and late adopters is widening faster than ever before. When Google rolled out major algorithm updates in the past, you might have had 6-12 months to adapt. Now? Three months feels like an eternity in AI development cycles. The firms that are building systematic approaches to measure and optimize for generative AI aren’t just getting ahead. They’re creating infrastructure that compounds over time.

The question isn’t whether to adopt new technology. It’s whether you have the analytical framework to know WHEN to adopt it and HOW to measure whether it’s actually working. That’s the difference between strategic early adoption and reckless trend-chasing.

What This Actually Means for Your Firm

The through-line in all of this is control and visibility. When PE firms acquire your agency, you lose control over who manages your account and what their priorities are. When you rely on black-box AI attribution systems, you lose visibility into what’s actually driving revenue. When you stick with 2023-era AI models, you’re operating at a fraction of current capability without even knowing it.

And when you chase sensational claims about AI citations without understanding the actual data behind them, you’re making decisions based on fear and FOMO rather than verified performance metrics. The solution isn’t to avoid new technology or stick with legacy systems out of comfort. It’s to build your own analytical capability.

That means either developing in-house expertise or working with partners who show you the actual data, explain their methodology, and admit when something is too early to measure reliably. It means asking harder questions of your vendors. What model is your AI tool running on? When was it last updated? Can you show me the actual citation numbers and not just the percentage increase? What does your attribution system actually tag as qualified, and can I customize those criteria?

Because at the end of the day, the firms that thrive aren’t those with the most sophisticated tech stack or the flashiest dashboards. They’re the ones who understand what they’re measuring, why it matters, and whether it’s actually connected to revenue. Everything else is just noise.

Partner with Market My Market for Data-Driven Digital Marketing

At Market My Market, we don’t chase trends or rely on sensationalism to drive results. Our approach is built on thorough analysis, verifiable data, and systematic frameworks that adapt as technology evolves. We understand that professional services firms need partners who can cut through the noise and deliver measurable outcomes, not just impressive dashboards or vague promises. Our team stays ahead of the curve by constantly reimagining our systems and tools, ensuring you benefit from the latest capabilities without the risk of reckless experimentation.

Whether you’re navigating AI citations, attribution systems, or the shifting landscape of digital marketing, we provide the analytical expertise and transparency you need to make informed decisions. We’ll show you the actual data, explain our methodology, and build customized solutions that connect directly to your revenue goals. If you’re ready to work with a team that prioritizes verifiable results over hype, contact our office to discuss how we can help your firm thrive.

Frequently Asked Questions

How does private equity acquisition change legal marketing agencies?

When PE acquires agencies, operational priorities shift immediately. Exclusivity agreements become flexible, experienced account teams get reassigned to new business development, and resources redirect toward growth metrics rather than client retention. The fundamental motive changes from relationship quality to revenue generation.

Why are 2023 AI models obsolete compared to current technology?

The gap between 2023 models like GPT-3.5 and current models like GPT-4o or Claude Opus is categorical, not incremental. Current models deliver 10x to 100x better effectiveness. Tools and dashboards built on older models operate at a fraction of current capability, yet firms are paying current prices for obsolete technology.

What’s wrong with call tracking attribution systems like CallRail?

Most attribution platforms use AI to tag calls as qualified or unqualified, but it’s a black box. You don’t control the criteria for what makes a lead qualified, you can’t see what language model they’re using, and you can’t customize the system without manual QA that defeats the purpose of automation.

Is it too early to invest in Generative Engine Optimization (GEO)?

It’s about having the analytical framework to know when to adopt, not chasing every trend. Most firms celebrating AI citation increases went from 5 to 25 citations, which isn’t meaningful traffic. The key is building systematic measurement with thousands of data points over time, not reacting to sensational claims.

How can law firms measure LLM rankings effectively?

There’s no such thing as LLM rankings like traditional algorithms. It’s about presence, which varies by query. Effective measurement requires compiling tens of thousands of scans across search results, AI Overview, and ChatGPT responses to identify correlations. You can’t work with small data sets when there’s this much at stake.