Four AI employee case studies with published numbers: biopharma procurement, pharma rollout, freight settlement and multi-entity billing.
Buyers ask for proof before they hand a job to an AI employee. This page collects four Zamp deployments in one place. Each one comes from a full case study with its own numbers, and each customer stays anonymous. The summaries below cover what the job was, what changed, what the AI employee does, and what stays with people.
Industry: Global biopharma enterprise.
Job: Reviewing purchase requests (PRs) against procurement rules before they become purchase orders.
Before: Reviewing a PR means checking the amount and currency, whether a cited quote has expired, whether the purchase falls under an existing multi-year contract, and which tax rules apply. Done by hand at volume, the case study calls this slow and inconsistent.
What the AI employee does: It checks each request against the company's procurement rules and supporting documents, then gives a human reviewer its findings and a recommendation. It writes its full reasoning for each decision inside SAP Ariba, so a reviewer can read why a request was approved or flagged.
After: The AI employee went live in June 2026 and handled 205 requests in its first week. It recommended a decision on 36% of them and sent 64% to manual review. After the team restructured how the agent works through rules and exceptions, its decision rate rose to 85% and manual review fell to 15%. Weekly volume grew to 2,712 PRs by week seven, and cost per PR fell from $11.57 to $1.21, a 90% reduction.
What stays with people: Anything the agent is not confident about goes to manual review instead of being guessed at. That is what let it run on live requests from day one.
Read the full case study: https://www.zamp.ai/blogs/ai-employee-biopharma-procurement-case-study
Industry: Pharmaceuticals.
Job: A digital workforce across clinical, manufacturing, regulatory, commercial and IT operations, under full auditability and 100% compliance with regulated-process requirements.
Before: Most of the underlying processes were undocumented, needed deep pharmaceutical knowledge, and depended on custom legacy systems. QA reviewers checked every batch record by hand, which was a hard bottleneck on release cycles. Vendor onboarding re-collected the same information on every new engagement.
What the AI employees do: In manufacturing, one runs a first-pass review of each executed batch record against the company's GMP procedures. Clean batches go straight to QA for confirmation, and flagged batches arrive with a structured escalation report. In clinical operations, discipline-based personas answer ICH guideline questions in the team's chat tool, and an ontology agent builds a controlled vocabulary from scattered PDFs. In commercial operations, one handles the step between an approved draft and a published page. In regulatory operations, one runs data access requests end to end through ServiceNow. In IT operations, one handles vendor onboarding as a one-time knowledge capture.
After: The deployment started with batch record review in manufacturing and grew to hundreds of digital workers deployed or in progress, with an estimated impact in the eight figures. Vendor onboarding cycle time went from weeks to hours.
What stays with people: QA still confirms clean batches, and reviewers start from a documented finding instead of a blank record. Stakeholders in vendor onboarding are pulled in only for genuinely new, process-specific questions.
Read the full case study: https://www.zamp.ai/blogs/enterprise-wide-ai-employee-rollout-pharma-case-study
Industry: Freight.
Job: Settling freight orders in vendor transportation portals that expose no APIs.
Before: The company books thousands of freight orders every month. Each settlement meant logging into a vendor's portal, retrieving load details, comparing them with internal records, flagging discrepancies, and accepting or rejecting the order. With more than 50 vendors and no APIs, this was one analyst per portal, at more than 50,000 cases a month, and backlogs kept growing.
What the AI employee does: It picks up each order, finds the vendor's standard operating procedure, logs into the portal through browser automation and follows the SOP step by step. Every portal interaction is recorded as a timestamped session video, and the load data goes straight into internal systems.
After: The deployment runs at more than 95% browser-agent accuracy across more than 50 vendor systems without a single API integration. Settlement turnaround dropped 40%, and the team reclaimed more than 20,000 human hours a year.
What stays with people: Threshold mismatches, missing information and deviations from a vendor's SOP are escalated with full context and the recording attached, so the operations team steps in only on cases that need a judgment call.
Read the full case study: https://www.zamp.ai/blogs/ai-employee-freight-order-management-case-study
Industry: Retail Marketplace
Job: Monthly billing across thousands of statements of work and five billing types: time-and-materials, fixed price, milestone, volumetric and resale.
Before: The billing team requested data from delivery teams, validated it against contract terms, prepared invoice packages and routed them for approval by hand, across multiple entities, currencies and jurisdictions. An error at any stage cascaded into a delayed invoice or a disputed charge. The case study says the bottleneck was coordination, not calculation.
What the AI employee does: It ingests contracts, statements of work, purchase orders and rate cards, then sets up billing schedules and triggers each cycle. It identifies which statements of work are due, sends collection requests to the delivery contacts who hold the data, validates submissions and follows up until everything is in. It then parses the files into line items, applies the contract rate cards and prepares invoice packages with an audit trail.
After: The deployment runs at more than 70% browser-agent accuracy on the portal-dependent parts of the workflow, with 100% compliance to SOPs and SLAs across the cycle. Billing turnaround dropped 50%, and the company reclaimed more than 150,000 human hours a year.
What stays with people: Invoice packages are routed for internal approval before they are submitted to the billing system, and exceptions are flagged with specific detail for people to resolve.
Read the full case study: https://www.zamp.ai/blogs/ai-employee-billing-operations-case-study
Industry | Job | Result | Detailed Blog |
|---|---|---|---|
Global biopharma | Purchase request review | Cost per request $11.57 to $1.21 (90% lower) | |
Pharmaceuticals | Five-function rollout | Hundreds of digital workers, impact estimated in the eight figures | |
Freight | Settlement across 50+ vendor portals | Over 95% accuracy, 40% faster, 20,000+ hours reclaimed a year | |
Not named | Multi-entity billing | 50% faster, 150,000+ hours reclaimed a year |
Four patterns repeat across the case studies.
Two of the deployments also worked where there was no clean integration surface. The freight agent works through more than 50 vendor portals with no APIs, and the billing agent covers the portal-dependent parts of its workflow.
For a wider list of jobs, see AI Employee Examples: 25 Back-Office Jobs Enterprises Delegate. For a definition, see What Is an AI Employee?.
