Who Should Pay for Litigation Defense AI?
The defense side of litigation is facing a difficult question: who should pay for the AI tools that panel counsel uses on assigned matters?
At first glance, the answer may seem obvious. Law firms should pay for their own tools, just as they pay for laptops, research platforms, document management systems, and practice management software. If a firm wants to become more efficient, shouldn’t that be part of the firm’s own cost of doing business?
It depends.
AI is not just another back-office productivity tool. When used in litigation, it can shape how case facts are organized, how medical records are reviewed, how chronologies are built, how exposure is evaluated, how experts are prepared, how reports are generated, and how defense strategy is developed. Those are not merely internal law firm activities. They are core components of litigation defense.
That is why the better question is not simply who should pay for AI. The better question is who should control the AI-enabled workflow being used to defend the case.
For insurance carriers, corporate legal departments, TPAs and other high-volume legal services buyers, that question is becoming urgent.
The Defense Side Cannot Treat AI Adoption as Optional
A recent Claims and Litigation Management article discussing its latest Litigation Management Study described plaintiff-side AI adoption as one of the industry’s top concerns. More than 80% of claims executives described the plaintiff bar’s use of AI as moderately or significantly concerning. No executive said those developments were not a concern.
The concern is not just that plaintiff lawyers are using AI to draft documents faster. The larger concern is that plaintiff-side platforms may be used to profile defense negotiation behavior, build intelligence about carriers and defense strategies, and drive up indemnity, defense costs or both. In other words, AI can become a strategic weapon, not just an efficiency tool.
The same article also highlighted a mismatch on the defense side. Most executives had not yet agreed to pay for AI tools used or sought by defense counsel. Half believed firms should bear the cost, while the other half were still working through the issue. At the same time, many executives reported that defense attorneys had rarely or never approached them to discuss their firm’s AI use.
That combination should concern litigation leaders. If the plaintiff bar is moving quickly, and if defense firms are not consistently engaging clients about AI strategy, then waiting for organic law firm adoption may be too slow.
The defense side needs a more deliberate model.
Why “The Firm Should Pay” Is Only Partly Right
There are good reasons to expect law firms to invest in technology. Firms benefit from more efficient workflows for non-billable work. A firm-selected AI tool may be appropriate for marketing, administrative functions, tracking time, improved internal training or firm-wide knowledge management. Those uses are part of the firm’s operating model and should generally be treated as firm overhead.
But litigation-specific AI is different when it is applied directly to a litigation matter: discovery, testimony, experts, damages analysis, or reporting. At that point, the tool is not simply helping the firm operate more efficiently. It is improving how the lawsuit is defended.
That distinction also matters for data security, confidentiality, privilege, consistency, billing, and measurement. A defense firm may choose a tool that fits its internal preferences. A carrier or corporate legal department may need a tool that fits its litigation portfolio, data governance standards, panel counsel guidelines, reporting requirements, and long-term cost strategy.
Those are not always aligned.
Why the Legal Services Buyer May Be the Right Party to Pay
For legal services buyers managing litigation across multiple firms, there is a strong case for selecting and paying for the AI platform used on their matters that creates direct matter-level or portfolio-level value.
The first reason is control. If every panel firm chooses its own AI solution, the buyer may get inconsistent outputs, inconsistent security standards, inconsistent reporting, and inconsistent levels of adoption. One firm may use a secure, litigation-specific platform with strong safeguards. Another may use a general-purpose tool with different retention, access, or training policies. A third may avoid AI entirely. From the buyer’s perspective, that is not a strategy. It is fragmentation.
A buyer-selected platform allows the client to set standards. The client can decide how case materials are processed and retained, what outputs should be created, how summaries and chronologies are developed, what reports should look like, and how work product should be reused as the case develops. That creates consistency across firms and matters, which is especially valuable for insurance carriers and corporate legal departments managing large portfolios.
The second reason is data governance. Litigation files often contain privileged communications, protected health information, employment records, claims materials, expert work product, personally identifiable information, and confidential business records. The legal services buyer has a direct interest in deciding where that data goes, who can access it, whether it is retained, and whether it can be used to train a model. When the legal services buyer funds and controls the platform, it can put security, confidentiality, privilege, and approved-use requirements at the center of the workflow.
The third reason is measurement. If the buyer wants to know whether AI is reducing cost, shortening cycle time, improving reporting, or supporting better litigation outcomes, it needs consistent data. That is difficult if each firm is using different tools in different ways. A buyer-controlled platform makes it easier to compare adoption, turnaround time, work product quality, and hours saved across matters and firms.
A fourth reason is alignment. In an hourly billing model, law firms may have mixed incentives to adopt tools that reduce billable time. Many firms will use technology responsibly and pass efficiencies along to clients, but the economic structure does not always reward speed. When the legal services buyer funds the AI technology and builds expectations into outside counsel guidelines, the adoption decision is no longer left entirely to a model that may slow it down.
The AFA Opportunity: AI Can Make Predictable Pricing More Practical
The payment question is also connected to a larger issue in defense litigation: the industry’s continued reliance on hourly billing.
Many carriers and corporate legal departments want more predictable pricing, including fixed-fee arrangements, phased fees, success-based components, or other alternative fee arrangements. Defense firms often express openness to those models in theory, but they may resist them in practice because litigation work can be unpredictable. A case that looks routine at assignment can become document-heavy, expert-intensive, or strategically complex.
AI-enabled workflows do not eliminate that uncertainty but can reduce it. If a buyer and its panel counsel use a shared platform to organize records, build chronologies, summarize testimony, prepare deposition materials, and standardize reporting, the parties can better understand what work is required, how complexity evolved, and where efficiencies are available.
That creates a path toward more predictable pricing for defined categories of work. For example, a carrier may be able to set fixed or phased fees for early case assessment, medical record chronology, deposition preparation packages, expert review preparation, or periodic status reporting when the underlying workflow is standardized and supported by approved technology.
This does not mean every matter should move to a fixed fee. Complex litigation still requires judgment, flexibility, and room for the unexpected. But buyer-funded AI can make AFAs more realistic because it gives both sides a more stable operating model. It reduces the unknowns around certain repeatable tasks and makes it easier to price those tasks with confidence.
That benefits both sides. The buyer gets more predictability and a solid basis for measuring ROI. The firm has clearer expectations, better tools, and a more defensible way to price work without simply absorbing unlimited risk.
When Firms Should Pay and When Buyers Should Pay
The answer does not have to be all or nothing. A practical framework separates AI costs into two categories.
Firms should pay for tools that support general firm operations. This includes internal productivity tools, administrative automation, training tools, marketing tools, and systems that improve the firm’s own business but are not required by a specific client for a specific litigation workflow.
The buyer should consider paying for tools that create portfolio-level value. This starts with platforms that ensure data governance, confidentiality, performance tracking, and other analytics and extend to litigation workflows including source-linked chronologies, summaries, case reporting, deposition preparation and analysis, and more. When the legal services buyer wants consistency and control across firms, buyer funding makes sense.
This framework avoids the false choice between “the firm always pays” and “the client always pays.” It asks what value the tool is creating, who benefits from that value, and who needs to control the workflow.
What Buyers Should Require Before Paying
If a corporate legal department or insurance carrier pays for an AI platform used by panel counsel, it should not simply write a check and hope for efficiency. Buyer funding should come with clear expectations: defined use cases, security and confidentiality review, and clarity about how outputs will be reviewed by counsel. If a tool reduces time spent on legal work (e.g., record review, summaries, drafting, or deposition preparation), the buyer should expect that reduction to show up in budgets, invoices, or fixed-fee proposals.
ROI should be measured from the beginning. Useful metrics include hours saved, usage by firms and timekeepers, and tracking by line of business. In the longer term, ROI could extend to faster cycle times, improved reporting, fewer invoice disputes, and the potential to move defined work into predictable fee arrangements.
What Defense Firms Should Do Now
Defense firms should not wait for clients to raise (or force) the issue. The firms that will be best positioned are the ones that can have transparent conversation with clients about AI use.
That means telling clients what tools are being used and what benefits accrue to them from their use. It also means being prepared to explain what data is being processed, how confidentiality is protected, whether client data is used for model training, how lawyers review outputs, and how the workflow affects billing. It also means being willing to propose new pricing models where technology makes the work more predictable. (So-called “AI native” law firms are already doing this.)
Firms should view this as an opportunity rather than a threat. Clients are not merely trying to reduce fees. They are trying to manage rising litigation volume, rising defense costs, rising indemnity costs, increases in represented claims, policy limit demands, staffing constraints, and a plaintiff bar that has become increasingly technologically sophisticated.
A firm that helps clients respond to those pressures will be more valuable, not less.
Conclusion
The debate over who should pay for defense AI is really a debate over control, alignment, and value. What the litigation defense business needs right now are leaders in this space. They cannot allow plaintiff-side technology adoption to accelerate while defense-side adoption depends on scattered firm-by-firm decisions, unclear billing treatment, and inconsistent governance.
The better path is for litigation leaders to decide what workflows they want, what tools meet their standards, how those tools should be paid for, and how the results will be measured.
AI will not solve the defense industry’s cost and complexity challenges by itself. But if the right party controls the right workflow for the right reasons, it can become part of a more effective litigation defense model.
Schedule a demo to see how esumry helps defense teams turn case documents into structured, accessible litigation knowledge that supports trusted, faster, consistent pretrial work.
About the Author
James Chapman is a co-founder of esumry and a defense litigator. He writes about the intersection of AI, litigation strategy, and legal operations.
Using esumry, privilege is protected with ZDR (zero data retention), and case analysis is fast, strategic, and secure. Create timelines, tag testimony, assess credibility, and get ahead of how the other side will use the record—before they do.
