What a typical engagement looks like, by vertical.

A mid-market manufacturing company (75 employees, ~$12M revenue) deployed 3 AI agents after their Dynamics 365 CE stabilization: Sales Intelligence (account prioritization), CSR Knowledge Assistant (case resolution), and Field Service Dispatch Optimization.

Results after 90 days: sales reps spent 40% less time on manual account research (2 hours/week down to 1.2 hours/week per rep) — equivalent to roughly one additional sales development role. CSRs closed 18% more cases per week using the knowledge assistant without new hiring. Field dispatch improved technician utilization by 12%, equivalent to one additional engineer's capacity.

Cost and payback: the engagement combined an initial assessment fee, base infrastructure, and a monthly primary-system fee. Efficiency gains covered the engagement cost within 9 months, with annual productivity gains exceeding annual costs by 3x by Month 12 — typical for manufacturing/B2B Dynamics shops with engaged implementation teams. Actual results for your team emerge during the Week 1–2 assessment.

A regional multifamily operator (42 properties, ~9,000 units across three states) deployed 3 AI agents after consolidating their resident-communications stack: a Resident Communications Assistant (maintenance request triage and FAQ deflection), a Leasing Intelligence agent (inbound lead scoring and follow-up sequencing), and a Back-Office Workflow agent (lease renewal reminders, AP coding assistance).

Results after 90 days: the resident contact center deflected 31% of routine maintenance calls to self-service without adding headcount. Leasing agents followed up with hot leads 3x faster (average first-response time dropped from 4.1 hours to 1.3 hours), lifting tour-to-lease conversion by 6 points. Back-office staff cut manual AP coding time by 25%, freeing roughly half an FTE for renewal-retention outreach.

Cost and payback: the engagement combined an initial assessment fee, base infrastructure, and a monthly primary-system fee. The lift in tour-to-lease conversion alone covered the engagement cost within the first two leasing cycles (~4 months); combined with maintenance-call deflection, annual productivity gains ran roughly 2.5x annual cost by Month 12.

A multi-unit fitness franchisee group (28 clubs across a regional territory) deployed 3 AI agents on top of their existing club-management and POS systems: a Member Retention agent (flagging at-risk members from check-in and billing patterns before they cancel), a Front Desk Sales Intelligence agent (prioritizing trial-to-member follow-up by likelihood to convert), and a Multi-Location Reporting agent (rolling up daily KPIs across all 28 clubs into one regional dashboard).

Results after 90 days: proactive retention outreach cut voluntary cancellations by 14%, worth roughly $9K/month in retained dues across the portfolio. Trial-to-member conversion improved 5 points as front-desk staff called the highest-propensity leads first instead of working the list in order. Regional managers cut weekly reporting prep from 6 hours to under 1, freeing that time for in-club coaching visits.

Cost and payback: the engagement combined an initial assessment fee, base infrastructure, and a monthly primary-system fee. Retained-dues value alone covered the engagement cost within 3 months; combined with the conversion lift, annual value ran roughly 4x annual cost by Month 12 — typical for franchise groups where member data already lives in one system and the gap is attention, not information.

A regional restaurant chain (54 locations, mixed corporate and franchise) deployed 3 AI agents to support their franchise development and store operations teams: a Guest Feedback agent (triaging reviews and complaint tickets across all locations by severity and store), a Franchise Lead Qualification agent (scoring inbound franchise-development inquiries against the chain's actual approval criteria before a human ever calls), and a Store Compliance agent (rolling up daily health-and-safety checklist completion into one exceptions-only dashboard for area managers).

Results after 90 days: the franchise development team spent 35% less time on unqualified calls, cutting average time-to-first-qualified-call from 11 days to 6. Guest complaint response time dropped from an average of 2.1 days to same-day for flagged high-severity issues. Area managers cut checklist review time by half by working an exceptions list instead of reading every store's submission.

Cost and payback: the engagement combined an initial assessment fee, base infrastructure, and a monthly primary-system fee. The reduction in unqualified franchise-development calls alone paid back the engagement within 6 months; store-level time savings pushed annual value to roughly 2.5x annual cost by Month 12.

A vertical SaaS platform serving operations teams in a service industry (around 40,000 seats across its customer base) embedded 3 AI agents directly into its product: an In-Product Support Assistant (answering configuration and workflow questions inside the app instead of routing to a ticket), an Account Health agent (surfacing expansion and churn-risk signals to customer success from usage data the product already had), and an Onboarding Acceleration agent (walking new admin users through first-setup instead of a scripted implementation call).

Results after 90 days: the support team deflected 27% of tickets that would previously have required a human response, without adding support headcount as the customer base grew. Customer success caught 22% more at-risk accounts before renewal conversations, up from reactive flagging. Time to first value for new customers dropped from an average of 12 days to 7.

Cost and payback: this engagement was structured as a multi-quarter build partnership rather than a flat assessment fee — see the embedded-AI vignette on our Work page for the engagement shape. Support-ticket deflection alone offset the cost of one support hire within the first two quarters; combined with the churn-risk catch rate, the AI layer paid for itself within the first year and became a retention lever the product team now sells on.

A mid-size commercial general contractor (12 active jobsites, ~$85M annual volume) deployed 3 AI agents on top of their project-management and accounting systems: an RFI/Submittal Triage agent (routing requests for information to the right project engineer and flagging ones at risk of missing the response-time clause), a Bid Intelligence agent (surfacing comparable past-project cost data when estimators built new bids), and a Field Safety Reporting agent (rolling up jobsite incident and near-miss reports into one dashboard instead of site-by-site binders).

Results after 90 days: RFI response time inside contractual windows improved from 71% to 94% on-time, reducing schedule-risk exposure. Estimators cut bid-preparation time by 20% by starting from AI-surfaced comparables instead of searching past job files manually. Safety leadership got same-day visibility into incidents across all 12 sites instead of a weekly rollup, catching two repeat near-miss patterns before they became recordable incidents.

Cost and payback: the engagement combined an initial assessment fee, base infrastructure, and a monthly primary-system fee. The reduction in RFI-related schedule risk and bid-prep time together covered the engagement cost within roughly 8 months — typical for GCs with project data already centralized in one PM system.

A lower-middle-market private equity firm (six active portfolio companies, small deal team) deployed 3 AI agents to support both deal sourcing and portfolio operations: a Deal Screening agent (triaging inbound deal flow against the firm's investment thesis before a partner spends time on it), a Portfolio KPI Rollup agent (pulling monthly financials and operating KPIs from each portco's own systems into one standardized view for operating partners), and a Due Diligence Research agent (drafting first-pass market and competitor research for live deals as a starting point, not a final memo).

Results after 90 days: the deal team cut time spent on initial screening of inbound opportunities by 45%, letting partners focus review time on deals that actually matched thesis. Portfolio KPI rollups that used to take an analyst 2 days each month across six companies now take half a day. First-pass diligence research cut the time to a workable first draft from roughly 3 days to 1.

Cost and payback: the engagement combined an initial assessment fee, base infrastructure, and a monthly primary-system fee. Analyst time reclaimed from screening and KPI rollups alone covered the engagement cost within 5 months; freed partner time on higher-thesis-fit deals is the harder-to-measure but larger long-term return.

A K-12-focused group purchasing organization (GPO) representing 140 member school districts deployed 3 AI agents to support its procurement and member-services teams: a Contract Compliance agent (flagging member spend that fell outside negotiated GPO contracts before it became a lost-rebate problem), a Member Support agent (answering routine district procurement questions and drafting first-pass RFP language against the GPO's existing contract library), and a Vendor Performance Rollup agent (aggregating vendor pricing, delivery, and rebate performance across all 140 districts into one dashboard for contract renegotiation).

Results after 90 days: off-contract spend flagged for member outreach rose 3x as compliance monitoring moved from spot-checks to continuous, recovering an estimated $180K in missed rebates across the member base in the first quarter alone. Member support response time on routine procurement questions dropped from an average of 1.5 days to same-day. Contract renegotiation prep time for the vendor performance team fell by 30%.

Cost and payback: the engagement combined an initial assessment fee, base infrastructure, and a monthly primary-system fee. Recovered rebates from the first quarter alone exceeded the full Year 1 engagement cost — the clearest payback case of the group, because GPO economics turn small percentage recoveries into large absolute dollars at member-base scale.