The firm profile.
Let's specify. 38 attorneys. $30M revenue, 60% litigation, 40% mixed corporate and employment. iManage Work for DMS. iManage Time for time capture. Outlook on Microsoft 365. 16 paralegals, 4 firm administrators, 1 IT lead. No Innovation Partner. The managing partner has read everything you've read about AI, talked to three vendors, and has an open-but-skeptical posture.
The firm's biggest pain, by their own assessment in the diagnosis call, is unbilled partner time. Their last billing audit suggested $1.1M-$1.6M annually escapes capture. The managing partner believes it. The other partners have varying degrees of belief but agree there's a real number there.
This is the median diagnosis call we run with mid-market law firms. What we'd build, in this case, follows.
Build one: matter-aware time capture & billing reconstruction. Weeks 1-4.
The first build at this firm is straightforward to scope because the leakage is named, the dollar figure is real, and the data needed lives in systems the firm already has. Outlook calendar (where partner time happens), iManage activity logs (which documents got opened, edited, emailed, by which user), Microsoft Teams call history (who-talked-to-whom), and the firm's matter taxonomy in iManage.
The custom AI reads all of this overnight, by partner, drafts time entries with descriptions matter-mapped to the right billing categories, surfaces them to the partner in iManage Time the next morning. Partner reviews and signs in 5 minutes per day instead of the current 90 minutes per week. Captures the work the partner would have written off as too small to bother reconstructing.
Architecture: data ingestion runs in the firm's Azure tenant under NDA. Microsoft Graph for Outlook + Teams. iManage REST API for activity + matters + time. Permissions queried with the partner's actual credentials, not a service-account super-user. Time entries written to iManage Time as drafts; partner approves through the iManage interface they already use. Full architecture in the iManage AI Integration Playbook.
Numbers at handoff: based on the audit pattern across audited firms in this size band, recovery in year one runs 40-60% of the modeled $1.1M-$1.6M leakage. So $440K-$960K in newly-billed work. At the firm's blended realization, that's roughly 8-19x payback on the $90K-$140K engagement fee, in year one alone.
Build two: associate-hours research RAG over the matter archive. Months 3-5.
The second build, scoped at month two of the first, ships in months three through five. The leverage point is associate ramp.
This firm has 16 associates at varying tenure. Year-one and year-two associates spend 30-40% of their billable time on research that effectively reconstructs work the firm has already done in prior matters. The senior partner who could surface the relevant prior work in 30 seconds is rarely available; the associate spends three hours combing iManage and frequently misses the most relevant precedent.
The custom RAG layer queries iManage with the associate's actual permissions, returns the top 8-12 most relevant prior matters with the partner who handled each, the outcome, and the specific paragraphs that match. Associate cites and adapts; partner reviews; firm bills full hours instead of writing off ramp time.
Numbers at handoff: 6-12 months of effective ramp time per associate. At 16 associates and a blended associate billing rate, the recovery is structurally larger than the year-one capture but takes longer to materialize.
Build three: AI intake triage & conflict-clearance. Months 6-8.
The third build, by which point the firm has internalized the AI commissioning pattern. The leverage point is intake leakage.
At a 38-attorney firm with 60% litigation and 40% corporate/employment, inbound intake comes in 22-40 forms per week from web, phone, referral. Median time-to-first-touch from a prospect is currently 8-22 hours. The fast-responding firms (often the firm's actual competition for the better matters) are at under 5 minutes. The conversion gap is real.
The custom AI receives the intake (form, email, voicemail-transcribed-via-call-handler), runs conflict-clearance against iManage matter history, drafts the engagement letter from the firm's template, creates the iManage matter workspace with correct profile values. Partner reviews the package and signs the engagement letter; the firm captures the matter without manual assembly.
Numbers at handoff: 8-22% of qualified leads previously lost to slow intake, recaptured.
Where this ends up.
By month nine, the firm has three custom AI systems in production, running on iManage + Outlook + Teams. The systems are owned by the firm at handoff; maintenance is roughly $4,500/month across all three. Year one new revenue captured runs $1.2M-$2.4M against engagement spend of $250K-$370K plus $40K maintenance.
The firm's senior staff are AI-fluent. Two paralegals + one firm administrator have transitioned into something like an AI-systems-ownership role. The managing partner is no longer skeptical; they're scoping the fourth build, which is firm-knowledge ingestion ahead of two senior partner retirements.
This is the median trajectory at firms in this profile that ship the first build in 2026. It's not aspirational; it's the pattern at the firms that have done it.
What's different about your firm.
Probably nothing structurally. The variance comes from: practice mix (mostly litigation vs mostly corporate changes which workflows pay first), billing realization rates (changes the dollar figures), partner culture (changes adoption velocity), and existing tech stack (iManage vs NetDocuments vs Clio vs SharePoint changes the architecture but not the leverage points).
If your firm's profile differs materially from the hypothetical above (smaller, larger, different stack, different practice mix), the order of the builds may shift. The shape stays the same.