Key takeaways

• Practice AI tools are raising the bar for strategic client service, but law firm BD capacity has not grown to match.
• Today’s legal marketing tech stack sits in siloes with the cross-data analysis layer missing
• By 2030, expect one common data layer with shared AI agents feeding a 1:1 view for attorneys and a 1:many view for MBD teams.
• Build vs buy is not all or nothing and should not be considered one or the other. Rather, buy core products from a few strong vendors, and build where your firm's data creates a unique POV.
• Bring MBD into technology buying decisions early, not after Finance has decided.
• Start with outcomes and strategies, then workflows, then data, then AI.

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Why law firm BD teams face a capacity problem

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Harvey and Legora are big names in the legal tech space and represent how practice AI tools are increasingly pushing law firms toward a future built on strategic client relationships and proactive engagement. AI has painted a future where MBD teams are exepcted to deliver personalized and strategic support across every attorney in their firm. Despite this, the capacity to do this has not scaled accordingly. In fact, Calibrate Strategies’ 2024 study put the average BD to attorney ratio near 1:25, roughly 30 minutes per attorney per week. In a recent CMO roundtable, where Sumathi presented the same topic, after reading out this statistic, hands shot up claiming it was more like 1:100.

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So the real question being asked of CMBDOs isn’t which tool they should buy but how the AI stack can evolve to support this capacity problem.

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Why is choosing legal AI tools so confusing right now?

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AI completely shook the snow globe, as we’ve coined it at Kaitongo, meaning that every vendor is pitching a new AI solution or layer. You can barely scroll a minute on LinkedIn without hearing talk of actionable signals, AI agents, and yet, no one agrees on what exactly those terms mean or how the pieces come together. SO buyers have every right to feel confused about the landscape and take the first step. And guess what? Vendors are feeling it too: a product can feel legacy in as little as six months. Sumathi was candid that summer 2026 felt like navigating a snowstorm from the vendor side.

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What is the 2030 inevitability?

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The 2030 Inevitability prediction stems from the idea that by 2030 the snow will have settled. AI will continue to evolve, but firms and MBD teams will be further along and set up for the new “normal.” The returns from reassessing technology will diminish, and every vendor will have defined its spot in the stack. That said, getting there required a roadmap.

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What is wrong with today's law firm marketing tech stack?

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The issue with today’s law firm marketing tech stack is that most firms are running a CRM for clients, an ERM for relationships, company and market data, experience data, and workflow tools for content. Phew.

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Each keeps its data in its own silo, and none of them talk to each other (as they should). The cross-data analysis layer is missing, so BD teams are left to stitch it together by hand. And not to mention, we have AI sprawl to worry about too. A separate AI tool on each silo repeats the problem instead of solving it.

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What will the 2030 MBD tech stack look like?

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Now we’re getting to the meat of the conversation. What will the 2030 MBD tech stack really look like? The stack will run on common data infrastructure that brings internal firm data and external market data together. On top of that will sit an AI layer: data cleansing, tagging, taxonomy alignment, cross-data analysis and customizable practice agents.

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The layer powers two separate views: a 1:1 view for each attorney, and then MBD gets a 1:many view. The output is work triggers grounded in firm context and not just another feed of alerts.

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Where is your firm on the path to AI maturity?

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We blew through a lot, and some of you might still be feeling a bit lost. You’re not alone. To help alleviate some of that pressure, we’ve designed an AI maturity assessment with clear next steps that you might take to build your 2030 stack. We framed it in five stages.

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1. Just the beginning. Light, scattered AI use, mostly drafting emails or content.
2. One off usage. Individuals experiment without leadership direction or shared workflows.
3. Random acts of purchase. Tools bought for specific uses, with data left disconnected and siloed.
4. The waiting game. Leadership sets a roadmap, and IT builds data foundations while BD waits.
5. The full shebang. Connected data and AI workflows, with the focus on adoption and impact.

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You can get a link to the assessment at the bottom of the blog.

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Should law firms build or buy AI? The panel's answer

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The panel landed on and around the same conclusion when it came to answering this question. The strongest approach is almost always going to be building a strong partnership with a few hand-selected vendors, buying their core product, and making sure it connects to whatever else you decide to build or buy.

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Celine working directly within a firm took a fairly clear stance on optimizing for value. She raised a good point that the firm should prioritize deriving value from the data that it already has, and then moved into the  adoption piece and the importance of figuring out the best way to get lawyers to act on it. She was also clear that her team doesn’t believe that it makes sense to build something when a great tool already exists. Instead, she argued to build AI functionality where it creates a real opportunity, and ask what the firm’s own data says about its unique POV.  

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Jason was quick to name a blind spot in most buying processes. That being, finance teams are the ones often leading technology purchases, and MBD joins the process after the decision has been finalized. The issue here is that the business use cases, specific problems, and workflows MBD knows best never really get to shape the choice. And so, his big takeaway is that before you decide to build or buy, ask who needs to be in the room and what they need to see in order to say yes.

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Ultimately, building carries a real cost: data, models, maintenance, expertise, governance and ongoing spend. Start by prioritizing which workflows come first, who owns them and what budget they need.

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How law firms can use data to differentiate thought leadership

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Another point touched on at the conference started with Celine’s opinion on thought leadership and its being a key way in which a firm can differentiate in the market. Sumathi built on it, saying that sector reports show what other firms have already published on a topic and the whitespace is where your firm’s unique point of view lives, and that is where the next thought leadership idea should come from.

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Frequently asked questions

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Who spoke on the LMA Canada 2026 build vs buy panel?

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Sumathi Pundit, Founder and CEO of Kaitongo, presented the session with Celine Gilmore, Chief Marketing Officer at Davies Ward Phillips & Vineberg, and Jason Kennedy, Founder and CEO of Choate Frazier.

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What is the 2030 MBD tech stack?

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It is the marketing and business development technology stack we expect law firms to settle on by 2030: one common data layer of internal and external data, an AI layer of shared agents, and two views on top. Attorneys get a personalized 1:1 view. MBD teams get a 1:many view across key accounts, sectors and practices.

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Should law firms build or buy AI for business development?

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Most firms should do both. Buy core products from a few excellent vendors and make sure they connect. Build AI functionality where the firm's own data supports a unique point of view. Building means owning data, models, maintenance, expertise, governance and ongoing cost.

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What is cross-data analysis in legal marketing?

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It is analysis that connects client, relationship, experience, market and content data instead of reading each system on its own. Most law firm tech stacks lack this layer today, so BD teams do it by hand.

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How do law firms get attorneys to adopt AI tools?

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Answer "what's in it for me?" first. Adoption follows when the tool solves the attorney's own problem, and when people, process and technology change together.

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Link to request assessment: https://www.kaitongo.com/ai-maturity-assessment-page