Forward Deployed AI Workflow
Prove AI ROI fast by automating one repetitive workflow, then expand company-wide
The Forward Deployed AI Workflow gives AI companies a structured land-and-expand playbook for enterprise accounts. The team audits the client's current AI inference spend to establish a cost baseline, then narrows to one high-frequency, repetitive workflow where ROI is fast and easy to measure. A tight pilot proves value in two to four weeks. The measurable ROI story arms an internal champion to unlock budget for expansion. This approach replaces the common mistake of pitching a full AI transformation—which delays the first win and erodes confidence before trust is established—with a disciplined beachhead strategy that compounds into a full deployment over time.
- Start with one workflow, not ten—a fast measurable win builds more trust than a comprehensive proposal
- Lead with the client's current cost as the opening argument, not your technology
- A measurable first win gives the internal champion the data they need to justify expansion budget
- Expansion follows trust—earn it with a tight successful pilot before broadening scope
- Audit the client's current AI inference spendAsk the client for their current monthly spend on frontier model APIs broken out by vendor. This number anchors the entire ROI conversation and identifies which workflow categories are consuming the most budget.Pro tipFrame the ask as a mutual audit: 'Let's look at what you're spending today so we can show you exactly where the savings come from.'
- Map the top repetitive manual workflowsWork with the client to list five to ten workflows that are currently manual, repetitive, and executed 20 or more times per day. Focus on workflows where the output is well-defined and easy to measure.Pro tipAsk operations managers rather than executives—they know which tasks are consuming their teams' time at the ground level.
- Select the highest-impact starting workflowChoose the single workflow with the highest combination of frequency, clearly measurable output, and obvious pain—ideally one where a specific person spends most of their workday on a single repeatable task.Pro tipThe best first workflow is one where a named individual runs the same task 30–50 times per day. This makes the before-and-after ROI story impossible to dispute.WarningResist pressure from the client to expand scope at this stage. A focused pilot outperforms a broad one every time.
- Document the current baseline metricsRecord exactly how long the workflow takes per instance, what it costs in labor or API spend, and how many times it runs per day. This is your control group for proving ROI after deployment.WarningWithout a documented baseline you cannot prove ROI later—even a dramatic improvement becomes an unverifiable anecdote that will not unlock expansion budget.
- Deploy a targeted pilot on the single workflowBuild and deploy the AI solution for only the selected workflow. Keep scope intentionally narrow to move fast and minimize the failure surface area during the pilot period.Pro tipSet a hard two-to-four week pilot timeline at the start. A deadline forces prioritization and prevents scope creep that delays the win.
- Measure, document, and package the ROI storyAfter two to four weeks, compare cost-per-instance and time-per-instance against the baseline. Package the findings into a one-page summary for the internal champion to share with their leadership.Pro tipTranslate savings into annualized figures—a $3,000 per month saving reads more compellingly as '$36,000 per year in recovered operational capacity.'
- Expand to adjacent workflows using the proven ROIUse the first-workflow ROI story to unlock budget approval for the next workflow. Repeat the audit-select-baseline-pilot cycle for each subsequent workflow until the deployment is company-wide.Pro tipAfter three successful workflows, propose a formal expansion agreement rather than continuing workflow by workflow—the pattern is established and trust is earned.
Corgi's operations team member Evelyn manually processed 40 insurance quote requests per day. Each required reading a startup's submitted data, determining required coverage, and inputting results into a pricing algorithm—consuming her entire workday. Aragon deployed a focused AI agent on this single workflow, trained on Corgi's coverage logic and historical quotes. The agent replaced the manual process while reducing inference costs to roughly one-third of Corgi's prior frontier model spend. The measurable ROI from this one workflow opened the door to broader Aragon deployment across Corgi's full operations.
Extracted from This Week in Startups (E2278), from Josh Cerot's (Aragon CEO) description of Aragon's forward deployed enterprise go-to-market motion and the Corgi Insurance implementation case.