Firm AI and the future of Big Law

LAURA SAKLAD

The traditional law firm model links revenue growth to headcount growth. You win more work, you hire more associates and staff, and leverage expands. AI disrupts this model in ways that are both a profitability opportunity and a business model question.

On the profitability side, the near-term gains are real: better realization and fewer write-offs with AI-assisted billing and outside-council guidelines (OCG) compliance tracking, and faster time-to-bill because friction in the prebilling process is reduced.

The harder part is what happens to leverage when AI absorbs meaningful work that associates currently perform. If first-year document reviews or due-diligence work is compressed significantly by AI, a firm can take on more work with the same headcount or use fewer people for the same volume.

Firms that reconfigure their business model — taking on more complex, higher-value work while letting agentic AI coworkers handle commodity tasks — will capture the upside. Firms that try to protect the traditional model will find clients doing the pushing.

The more consequential shift is in the fee model itself. The hourly billing model has been under pressure from clients for years, and AI accelerates that pressure. When work that once took a team of associates 200 hours can be completed in a fraction of that time with AI assistance, billing by the hour becomes increasingly difficult to defend.

This is where Firm AI becomes strategically important. To price work on value rather than hours, firms need to understand their actual cost of delivery – including not only the cost of attorney time, but also the cost of professional staff time, AI and other technology, infrastructure, and risk and compliance overhead. Very few firms have that visibility today.

Because Firm AI is built on a connected data foundation, it can integrate time capture, matter management, billing, and technology cost data. This means firms can begin to model what specific types of work cost to deliver and price it accordingly. That’s the foundation for moving to alternative fee arrangements that clients want – and that firms can sustain profitably.

In the end, the firms that will thrive with alternative-fee models are those that understand their cost structure deeply enough to price on value while protecting margin and meeting client demands. That requires data, discipline, and a willingness to have a business-model conversation that most law firms find uncomfortable. The firms that start that conversation now, while they still have time to shape it, will be in a far stronger position than those who wait until clients force the issue.

LAURA SAKLAD

The honest answer is that this is different for different roles — and the profession needs to have a candid conversation about it rather than defaulting to the comfortable assumption that AI will uniformly augment everything.

For partners and senior lawyers, the core of what they do — judgment, relationships, accountability, and the counsel that clients pay premium rates for — is largely resilient. Firm AI gives them better information faster, surfaces risks they might miss, and reduces the time they spend on work that doesn’t require their expertise. That’s a genuine augmentation story. 

The question for associates and junior lawyers is more complex. If AI compresses the time required for document-intensive work, the career path that has historically served as the training ground for the profession changes. Firms are already rethinking how junior lawyers develop judgment and expertise in an AI-assisted environment — and the ones that figure this out thoughtfully will have a recruiting and development advantage. 

For professional staff in business development, pricing, finance, and legal operations, Firm AI is largely additive. BD professionals who work with AI-generated relationship intelligence and pipeline analysis are more effective. Pricing analysts with better data on matter economics make better decisions, and finance teams with AI-assisted billing workflows move faster and with fewer errors. These efficiency gains allow firms to scale without increasing their staffing costs.

LAURA SAKLAD

The greatest opportunity lies in closing the gap between what firms know and what they act on. Big Law firms hold extraordinary institutional intelligence — decades of matter history, relationship signals, billing patterns, expertise maps — but very little of it informs day-to-day decisions in a structured, systematic way. A partner preparing for a client call still has to manually piece together what the firm knows about that client, which matters are active, and who else in the firm has relevant relationships. That process is slow and inevitably incomplete.

Firm AI changes this by making the firm’s collective intelligence available at the moment of action. The most immediate opportunities are in three areas, the first being client development. Firm AI delivers signals that prompt partners when to reach out, surface client needs that they can address, and identify who else in the firm should be involved in the conversation.

The second area is risk and intake, where Firm AI helps partners evaluate new matters faster by providing them with more complete information about the client, their risk profile, and the nature of the work. And the third is billing and realization, where Firm AI surfaces write-off patterns and client billing requirements early, so firms can address potential issues before they impact revenue.

These are much more than marginal improvements; they compound across a 2,000-lawyer firm in ways that move the financial model. 

LAURA SAKLAD

The two-tier problem is fundamentally an information and habit problem. The partners driving the majority of new business – who we call Activators – don’t just work harder at business development. They also have better information, better judgment about when to reach out, and systematic BD habits. For most of their colleagues, however, BD activities are erratic and feel like a chore. 

Firm AI democratizes the Activator advantage with Activator playbooks that surface the right signals at the right moments. For instance, the playbooks will generate notifications when a client announces an acquisition, a key contact changes roles, a matter is nearing close, or a cross-sell opportunity emerges. This turns reactive partners into proactive ones without requiring them to build new habits from scratch. 

The research we conducted with DCM Insights, published in Harvard Business Review, found that Activators generate up to 32% more revenue than their peers. At scale, even moving 20% of a firm’s partners meaningfully toward Activator behaviors is a material financial event. 

laura saklad

Pursuing AI initiatives in isolation from data quality can undermine the very outcomes firms are trying to achieve. I’ve had conversations with firm leaders who are excited about deploying AI agents to accelerate business development, only to find that their firm’s underlying relationship data is incomplete or inconsistently maintained.

Layering AI on top of poor-quality data doesn’t generate intelligence — it generates confident-sounding bad outputs, which is arguably worse than no outputs at all.

The challenge is that Big Law’s data has accumulated over decades in silos: time and billing systems, CRMs, document and matter management systems, and email. Much of it is unstructured, inconsistently entered, and disconnected.

The issues I see most often are inconsistent matter classification, relationship data that lives in individual partners’ heads, and client and matter data that’s accurate enough for billing but not rich enough to support AI-generated intelligence.

The upside is that AI-ready data is more achievable than firms often assume. It starts with an honest inventory where firm leaders ask, “Where is our client and matter data, how complete is it, and who owns it?” From there, firms should prioritize the data assets AI needs most — whether that’s client relationships, matter history, or billing and realization patterns. Then, they should assign clear ownership and define standards for maintaining the data. This can’t be a one-time cleanup; it needs to become an integral part of how the firm operates.

Data quality is especially important as firms move from fragmented AI tools to Firm AI. Unlike siloed AI tools, Firm AI draws on proprietary client, deal, relationship, and engagement data across a law firm’s core systems to deliver actionable insights and execute agentic workflows. The more complete and accurate that data becomes, the more meaningful value Firm AI can provide.

LAURA SAKLAD

AI doesn’t create new compliance obligations. Big Law firms have been managing confidentiality, conflicts, attorney-client privilege, data residency, and OCGs for decades. But AI dramatically amplifies the consequences of existing gaps. If an AI tool lacks proper governance, a policy gap or access-control failure can propagate across thousands of interactions before anyone notices.

The risks of not getting AI governance right are significant. For example, AI tools that surface matter or client information across information barriers create substantial conflict exposure. And with OCGs increasingly addressing AI use explicitly, firms whose AI deployments fail to comply with terms can incur significant financial and reputational damage.

The firms that are deploying AI safely and effectively do three things consistently. First, they create a governance structure with clear ownership – they have a CAIO or technology committee with partner-level engagement. Second, they define rules for what AI can do independently and where human review is required. And third, they invest in infrastructure that enforces all their rules, ethical walls, and access policies across every AI tool.

Our partnerships with AI providers including Harvey and Legora make it possible for firms to enforce their compliance requirements within the tools their lawyers already use daily. 

AI will drive fundamental changes in how law firms manage and drive their business. The firms that scale it compliantly and effectively will have a tremendous advantage that’s hard to replicate.