The New Outside Counsel Guideline: Measurable Efficiency
Corporate legal departments and insurance claims organizations have spent years trying to control litigation costs…
Corporate legal departments and insurance claims organizations have spent years trying to control litigation costs through rate negotiations, budgets, billing guidelines, and preferred panel programs. Those tools still matter, at least for now, but they do not solve the core problem.
As every buyer of legal services knows, a lower hourly rate may reduce the price of time, but it does not necessarily reduce the amount of time spent defending a case.
That distinction matters. Defense costs are driven not only by who is doing the work and what they charge, but also by how efficiently the work is performed. If lawyers and paralegals are repeatedly re-creating chronologies, re-reviewing medical records, manually extracting data from voluminous records, or starting from scratch every time a new report, deposition, expert review, or settlement evaluation is needed, the client is still paying for it.
The next generation of outside counsel guidelines should therefore focus on more than billing compliance. They should focus on measurable litigation efficiency.
This is especially true in high-volume or document-intensive defense litigation. Initial case assessments, medical record reviews, chronologies, deposition preparation, expert preparation, status reports, settlement evaluations, and trial preparation all require careful legal judgment. But the information management underneath those tasks is often repeatable and capable of being made more efficient with the use of artificial intelligence (AI) powered tools.
Can AI Efficiency Really Reduce Litigation Costs?
The plaintiff side of the legal services market is already using AI to improve their margins with products like EvenUp (demand letters), Anytime (litigation), and Eve (case intake).
In a market where AI itself is no longer novel, legal services buyers need to move beyond asking whether defense counsel is “using AI.” They should be asking whether the AI technologies being used on their matters are secure, consistent, defensible, and measurable. The goal is not to adopt AI for its own sake. The goal is to create a better operating model to improve the efficiency of defense litigation.
Billing guidelines have traditionally focused on what counsel may bill for, who may perform certain tasks, establishing rates, reporting formats, task coding, e-billing and bill review. That is useful, but incomplete. To drive efficiency, guidelines also need to address how case knowledge should be organized, how work product should be standardized, and measuring whether the workflow is actually reducing cost or cycle time. That is where AI powered workflows and structured data layers can make a big difference.
The New Standard: Workflow Efficiency, Not Just Billing Compliance
A modern outside counsel guideline should not simply tell counsel to avoid administrative work or use the lowest appropriate biller. It should define the expectations for how litigation work is performed.
For example, a legal department or claims organization may require panel counsel to use approved technology-enabled workflows for source-linked case summaries, matter chronologies, medical record review, deposition preparation, expert preparation, issue identification, and status reporting. These are not peripheral tasks. They are the foundation for how lawyers evaluate risk, prepare witnesses, advise clients, and resolve cases.
This is not about replacing defense counsel with AI. Lawyers still exercise judgment, develop strategy, assess credibility, evaluate exposure, negotiate, and try cases. But clients should not have to pay for work that can be done ten times faster with the right use of AI. The best outside counsel guidelines will increasingly define how the underlying case knowledge should be captured, structured and used throughout the life of the matter within an AI powered platform purpose built for defense litigation.
ROI Built into the Guideline
With efficiency as the goal, return on investment cannot be an afterthought. Legal services buyers should require a practical framework for measuring whether AI-enabled litigation workflows are creating value.
That measurement does not need to be overly complicated. It should be tied to real litigation activity: hours avoided on tasks like record review and chronology creation, faster turnaround from assignment to initial case assessment, reduced time preparing deposition or expert materials, time slashed on first drafts, faster onboarding when a matter changes hands, and more consistent reporting across firms and matters.
For claims organizations, ROI may also show up in faster reserve evaluation, earlier identification of settlement opportunities, and better communication between adjusters and defense counsel. For corporate legal departments, ROI may include better visibility across a litigation portfolio, more predictable outside counsel performance, and stronger evidence that legal operations initiatives are producing business value.
The point is that the use of AI should not be treated as a general productivity promise. It should be tied to specific litigation tasks, specific outputs, and specific measures of improvement. If the client cannot measure whether the workflow improved the defense of the case, then the technology conversation is incomplete.
Why the Buyer May Choose to Pay for the AI Solution
Many discussions about the use of AI by attorneys assume the law firm should select and pay for the tools its lawyers use. In some situations, that makes sense. Firms should invest in their own productivity, training, and internal operations.
But when an AI solution is being used on a client’s matters, particularly in litigation defense, there is a strong case for the legal services buyer to select and pay for the platform directly. That is especially true for corporate legal departments, insurance carriers, TPAs, and other organizations that manage litigation across multiple panel firms.
When the buyer controls the solution, the buyer can decide what is best for its matters rather than relying on whatever tool a particular firm has chosen. This gives the buyer a common operating standard across panel counsel, including preferred formats for summaries, chronologies, deposition materials, expert preparation, and reporting. The result is not merely firm-level efficiency. It is portfolio-level consistency.
Buyer control also matters for data security and confidentiality. Litigation and claims files often contain privileged communications, protected health information, employment records, claims materials, expert work product, personally identifiable information, and confidential business records. The client has a direct interest in deciding where that data goes, how it is processed, whether it is retained, who can access it, and whether it can be used to train language models.
A buyer-funded solution allows the buyer to apply its own security review, contractual requirements, confidentiality standards, and approved-use policies before the tool is used on its matters. That is much harder to manage if each panel firm independently selects its own AI tools and applies its own governance standards.
There is another potential benefit: buyer-funded use of AI can make alternative fee arrangements more attractive. Many legal departments and claims organizations want more predictable pricing, including fixed-fee or other AFA models, but firms are often reluctant to accept that risk when the amount of work is uncertain. When the legal services buyer and panel counsel have a better understanding of the work required and the efficiencies created by AI, it becomes easier to move appropriate categories of work away from open-ended hourly billing and toward more predictable pricing models.
Finally, buyer control reduces the risk of hidden or inconsistent AI use. Outside counsel may already be experimenting with AI tools, but not all tools are created equal and not all firms will apply the same standards. Even well-regarded firms have been sanctioned for filing pleadings with hallucinated case law and citations. A guideline that specifies approved tools and workflows gives firms a safe, client-approved path for using AI while protecting the client’s data, expectations, and litigation strategy.
In that sense, buyer-funded AI is not simply a technology expense. It is a governance mechanism, a cost-control strategy, and a foundation for more predictable litigation management.
What to Add to Outside Counsel Guidelines
Legal services buyers do not need to rewrite their entire outside counsel guidelines to begin addressing these issues. They can start by adding focused language in a few key areas.
First, guidelines should state whether counsel may use AI or other litigation technology on client matters, what approvals are required, and whether the client has designated a preferred or required platform. Second, they should address data handling, access controls, retention, confidentiality, privilege protection, model training restrictions, and the treatment of sensitive records.
Third, they should define when counsel is expected to use approved workflows, such as record review, summaries, chronologies, deposition preparation, expert preparation, or reporting. Fourth, they should clarify the expected work product standards, such as whether summaries and timelines should be source-linked, reusable, and formatted consistently across matters.
Finally, guidelines should address billing entries and ROI. If approved technology is expected to reduce time spent on tasks, the billing entry should reflect that expectation. The client should also identify which metrics will be tracked for AI-enabled workflows such as hours saved, turnaround time, and cycle time. Likewise, metrics on the client side could include improvements in reporting timeliness and consistency.
The goal is not to create a compliance trap for panel counsel. The goal is to align the client, the firm, and the technology around a common definition of efficiency.
The Best Panel Firms Should Welcome This Shift
Some firms may view client-directed AI requirements as a threat. The better firms will see them as an opportunity.
A well-designed technology workflow helps defense counsel deliver higher-value work. For example, it can reduce time spent on extensive document review and initial drafts, allowing lawyers to focus more effort on strategy, evaluation, negotiation, witness preparation, and trial readiness.
It can also make the lawyer’s work more visible. When counsel can show that a case summary is source-linked, that a chronology was built from the actual record, that deposition preparation was completed faster, or that reporting was delivered earlier, the client has a clearer view of value delivered.
That matters because legal services buyers are not simply looking for the cheapest lawyer. They are looking for the right combination of judgment, responsiveness, efficiency, predictability, and control. AI-enabled workflows, when properly governed, can help strong firms prove that value.
The Future of Litigation Management with AI
The legal market has moved beyond the stage where saying “we use AI” is enough. The more important question is: What does the technology actually improve in the delivery of legal services?
For corporate legal departments and insurance claims organizations, the answer should be measurable improvement in the defense of litigation: lower costs, faster cycle times, better reporting, more consistent work product, stronger data controls, less duplication, earlier insight into risk, and greater pricing predictability.
That requires a new kind of outside counsel guideline. One that does not stop at rates. One that does not treat AI as a vague innovation initiative. One that defines how legal work should be performed, how sensitive data should be protected, and how results should be measured.
The next frontier in outside counsel management is better litigation workflow with a structured AI-powered data layer, giving panel counsel the tools to deliver it, and measuring to what extent the promised value is being delivered.
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.
