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A statement of work crossed our desk last year with a line in the scope section that read, in full: “Automate the contract intake process.” No volumes, no definition of intake, no statement of where the process started or stopped. It had been signed by both parties. That line is not unusual, and it is not really about contracts. It is about a category error that runs through most legal automation projects. “Intake” in that firm turned out to be seven separate waits: a business unit emailing a shared mailbox, someone opening the mailbox, someone deciding which reviewer it belonged to, the reviewer opening it, the reviewer asking for the missing counterparty details, the business unit answering, and the reviewer picking it up again. Almost none of the elapsed time was work. Nearly all of it was waiting for the next person to look. That distinction decides which legal workflows are worth automating first, and it is the organising idea of everything below. What intelligent automation means in legal work Intelligent automation is the combination of three things that used to be bought separately. Rule-based automation, usually called RPA (robotic process automation), moves data between systems and performs deterministic steps. Document intelligence reads a file and extracts structured fields from it. A model, increasingly a generative one, drafts or classifies or summarises. Orchestration decides which of these runs, in what order, and when a human is required. In legal work the useful framing is narrower than the vendor framing. Intelligent automation does not replace legal judgement, and any description of it that implies otherwise should be treated as a sales artefact. What it does is remove the steps between judgements: the routing, the chasing, the copying of the same eight fields into a third system, the assembling of the pack that a reviewer needs before the reviewer can think. Two acronyms recur below and are worth fixing now. ESI means electronically stored information, the term the discovery process uses for anything that lives in a system rather than on paper. GAI means generative artificial intelligence, which is the term the American Bar Association uses in its ethics guidance, so it is the term used here when discussing that guidance. Sort your use cases before you cost them The six workflows legal teams usually shortlist are not one category. They divide cleanly by what is actually holding them up, and the division changes both the business case and the risk posture. 1 Queue-bound work. The elapsed time is dominated by waiting for the next person to look at it. The work itself is short and largely uncontested. Automation here removes handoffs, and removing a handoff removes a wait, which is why the gain is large and shows up in days rather than minutes. Intake and triage, compliance evidence assembly, status communication to requesters and stakeholders, and matter reporting and billing preparation all sit here. 2 Judgement-bound work. The elapsed time is dominated by someone reading carefully. Automation here does not remove the reading; it changes what the reader is given. That can be a real gain, but it arrives with a verification obligation attached, and the verification is new work that did not exist before. Substantive contract review and discovery review sit here. The practical consequence is uncomfortable for pilot selection. Judgement-bound work is more interesting, demos better, and is what most vendors lead with. Queue-bound work is where the first measurable win lives. A programme that starts with contract review because it is impressive, rather than with intake because it is slow, tends to produce a well-received demonstration and no defensible number. The four queue-bound candidates Intake and triage. A request arrives, and something decides what it is, whether the mandatory information is present, which reviewer or queue it belongs to, and what the counterparty and value bands are. Where information is missing, the requester is asked automatically rather than after a reviewer notices. This is the single highest-yield legal automation in most organisations and it involves no legal judgement at all. Compliance tracking and evidence assembly. Obligations with dates attached, and the evidence that each was met, collected as the work happens rather than reconstructed before an audit. GDPR is explicit that this is the controller’s job rather than a nice-to-have: Article 5(2) states that the controller “shall be responsible for, and be able to demonstrate compliance with” the processing principles, and Article 30 sets out what the record of processing activities must contain and requires it to be made available to the supervisory authority on request. Stakeholder communication. The requester is told where their agreement is without anyone being asked. Most of the chase traffic in a legal inbox is not a request for work; it is a request for status, and status is a field, not a judgement. Matter reporting and billing preparation. Time, stage and volume data assembled into the shape the finance function and the client actually need, rather than reassembled by hand at month end. The two judgement-bound candidates 1 Contract review support. The system extracts the terms that matter (term length, renewal mechanics, liability cap, indemnity, governing law, termination triggers, data-protection clauses) and compares them against a playbook position. It does not approve anything. Its output is a reviewer’s starting position, which the reviewer then has to check, and the checking is real work. This distinction matters enough that it reappears in the model below. 2 Discovery support and legal holds. Custodian identification, hold notices, acknowledgement tracking and volume reduction before review. It is worth being accurate about what the reference model here actually says, because the sequence is often described as a pipeline and it is not one. EDRM (EDRM.NET) states plainly that its diagram “represents a conceptual view of the e-discovery process, not a literal, linear or waterfall model”, that “one may elect to carry out the steps in a different order than shown here”, and that the process is iterative: “one might repeat the same step numerous times, honing

One and a half minutes. That is what generated summaries take off the after-call work on a single voice contact in the model further down this page. It sounds too small to chase. Across a sixty-seat operation handling 358,800 contacts a year it comes to 8,970 hours, which is just under six full-time equivalents. Six people’s worth of time, and none of it is money yet. It becomes money only if somebody decides in advance what those six people will do instead, and then makes sure it happens. That decision is usually nobody’s job. It is the reason contact centre automation programmes report savings finance cannot find, and it is the thread running through everything below. So this is not a tour of features. It is the work ordered by where the money actually is, the four places it gets stuck, and the three numbers that decide whether any of it pays. Start where the time goes, not where the demo starts Vendor demonstrations open with the conversational interface, because that is the part that looks like the future. Cost sits somewhere less impressive: in the minutes after the customer has hung up, and in the manual steps between systems that nobody designed and everybody works around. That mismatch is the first decision, and getting it backwards is expensive. Front-end automation touches every contact and carries reputational risk on each one. Back-end automation touches the same volume with almost none, because the customer has already gone. If you are going to be wrong somewhere, be wrong where the customer cannot see it. One caution before picking a target, and it is the reason to map the work before shortlisting tools. Occasionally the workflow that scores highest on automatability is holding up something that is not operational at all: a compliance record, an audit trail, a judgment call that someone downstream depends on being made by a person. Automating it improves every figure on the dashboard and quietly removes the thing the business was relying on. No dashboard can show you that trade, which is why somebody has to go looking for it while the build is still cheap to cancel Map one high-volume workflow end to end before anything else: the trigger, the systems it crosses, the decisions a human makes and why, the exception paths, and the steps where being wrong causes harm. Teams that do this honestly all report the same finding. The pain is concentrated in the glue between systems, not in the systems themselves. The four seams where contact centre work gets stuck Work does not get lost inside systems. It gets lost passing between them, and between the people and software either side of a handoff. There are four such seams in a contact centre. Each one has automation worth doing, a way that automation flatters itself, and something specific you can sample to find out which is happening. Seam one: intake, and the handoff out of it Customers like self-service when it resolves the thing. They hate it when it holds them. Virtual agents handle status checks, policy questions and basic troubleshooting well; knowledge search works when it matches the customer’s own words rather than the taxonomy your content team built. The stakes at this seam are rising on a documented timeline. Gartner’s published guidance on customer service artificial intelligence (AI) states that by 2028, at least 70% of customers will use a conversational AI interface to start their customer service journey. Read that as a claim about the front door, not about resolution. If most journeys will begin in a conversational interface, the quality of the exit from that interface becomes the thing that determines customer experience. Which means the escalation rules are the design, not an afterthought. Define the triggers explicitly: the customer asks for a person, confidence is low, the same step fails twice, or the intent carries risk such as a billing dispute, a legal request, or anything medical. When escalation fires, the agent should receive what the customer already tried, what the bot already told them, and what data was already captured. Most failures blamed on the bot are failures of what did not survive the handoff How this seam flatters itself: containment. A contact that resolves nothing but produces a callback tomorrow is not contained, it is postponed, and postponement and resolution look identical on a containment dashboard. Read containment only ever alongside repeat contact rate inside 72 hours. What to sample: fifty escalated contacts, checking whether the agent could see the bot transcript without asking the customer to start again. Seam two: the routing decision Routing is not only which agent answers. It is a sequence of decisions about channel, queue, priority, skill match and what happens next. Automation earns its place in intent detection, where even coarse classification stops “I want to change my delivery address” landing in general support; in skill matching; in priority rules for regulated complaints, account security and time-critical delivery problems; and in load balancing, so a queue spike does not need a supervisor watching it. How this seam flatters itself: routing accuracy. Aggressive skill-based routing raises first contact resolution on the contacts it classifies correctly while generating transfers on everything it does not, and the two effects can cancel out while both dashboards look busy. Pair routing accuracy with transfer rate and read them as one number. What to sample: every transferred contact for one week, sorted by the intent the classifier assigned. The misroutes cluster, and the clusters are fixable. Seam three: the agent’s desktop Most leaders talk about deflection. Several of the largest wins come from supporting the person instead: customer context, recent history, the relevant policy line, the next best action, a draft reply. Less searching, fewer mistakes, more consistency between agents. One rule outranks the feature list. Assistance has to appear inside the application the agent already has open. A second window is a tax, and agents will route around a tax. How this seam flatters itself: average handle time (AHT). Time falls

For the person applying, a disability decision is rent, heating, medication, and stability. For the firm representing them, it is a file that has to be moved through a federal process that does not reward speed and does not pay for effort. Those two facts sit on top of each other, and the second one is where the operational problem lives. The economics are fixed at the top A representative’s fee in an SSD case is set by statute, not by the market. Under $404.1730 of SSA’s regulations, where SSA makes a favourable determination and past-due benefits result, it pays the representative the smaller of twenty-five percent of total past-due benefits or the fee it sets, less an assessment capped at 6.3 percent. The dollar ceiling on that calculation rose to $9,200 effective 30 November 2024, announced in SSA’s Federal Register notice of 10 May 2024. There is a detail here that most commentary has not caught up with. That 2024 notice also committed SSA to publishing an annual notice from January 2026 explaining whether it was raising the cap or holding it. SSA rescinded that commitment in a partial rescission published 6 May 2025, retaining the $9,200 figure but stating it will now publish only when it increases the cap, on the grounds that an annual notice regardless of outcome imposes administrative burden the Act does not require. Read that as an operator rather than a lawyer. The ceiling is fixed, the twenty-five percent rule means the cap binds only once past-due benefits reach $36,800, and there is no longer a scheduled annual signal that the ceiling might move. A firm planning capacity cannot assume the revenue line rises on a timetable. It rises when caseload rises, which means the only reliable lever on margin is cost per case. That is the whole reason automation belongs in this conversation, and it is a narrower reason than most vendors offer. This is not about doing law better. It is about the administrative work stacked around the law, which the fee schedule never priced separately and which grows in direct proportion to volume. The wait is getting longer, and not smoothly SSA publishes combined disability processing time by fiscal year, and the trend is unambiguous. Its combined disability processing time series records 110 days in FY2016, rising to 120 by FY2019, then 165 in FY2021, 217 in FY2023, and a peak of 231 in FY2024. FY2025 came in at 226. The first nine months of FY2026 average 192. The honest reading of the recent monthly data is more interesting than the one usually offered. Within FY2026 the monthly figures move from 204 in October down to 184 by May, then back up to 185 in June. December to January also rose, from 193 to 194. The direction over the year is downward, but it is not a steady decline, and anyone describing it as one has not opened the monthly file. That distinction matters operationally: a firm sizing its pipeline against a smooth improvement curve will get its staffing wrong in the months where the line turns. One caveat belongs in the reading rather than in a footnote. SSA’s own denominator language defines this as processing time for cases with a decision in the period, which means it describes cases that finished, not cases still waiting. Improvement in the series is consistent with faster closure of a favourable mix as well as with genuine throughput gains. Where the administrative load actually sits Three places, and they are not the ones a partner usually names first. 1 Medical evidence handling. A case file accumulates records from every provider a claimant has seen, arriving as scans, faxes, portal downloads, and mail, in no consistent format. Someone opens each one, identifies what it is, extracts the dates and provider and findings that matter, and files it against the right case. In a firm running six thousand cases a year, that is the single largest consumer of non-attorney time. 2 Government portal work. SSA’s systems for representatives are functional and they are not built for scaled operations. Retrieving status, submitting evidence, and pulling hearing information all require session-based access, and the authentication model assumes a person at a keyboard rather than a queue of work. The constraint compounds: a firm cannot parallelise portal work by adding staff the way it can parallelise document review, because the bottleneck is the session, not the reviewer. 3 The technical denial category. Some claims fail for reasons that have nothing to do with medical severity, and SSA’s disability determination process is explicit that the field office screens non-medical requirements before the file reaches Disability Determination Services at all. Those are the cheapest failures to prevent and the most expensive to discover late. There is also a fast lane, and it is smaller than firms assume. SSA’s Quick Disability Determination model flags cases likely to be allowed on medical evidence alone, and its fast-track receipts data shows 180,116 of 2,155,574 national DDS electronic receipts in FY2024 were fast-tracked, which is 8.40 percent. That is up from 4.20 percent in FY2010, so the mechanism is real and growing. It is also unevenly distributed: 5.80 percent in the District of Columbia and 6.10 percent in Louisiana against 11.40 percent in Alaska and 12.90 percent in New Hampshire. A firm with a national caseload cannot plan on a single fast-lane rate, because its effective rate is a function of where its claimants live. Why scripted automation keeps failing here Most firms that have tried automation have tried rules-based robotic process automation, and most have been disappointed. The reason is structural rather than a matter of poor implementation. Scripted automation assumes that the thing arriving looks the way it looked last time. It works from screen positions, field labels, and fixed document layouts. SSD document flow violates that assumption constantly, because the format is set by whichever provider generated the record, not by the firm receiving it. A script built against one hospital’s