Senior leadership funding case
Fund the factory. Reduce the run cost.
The Website Factory is a product team model for delivering governed websites repeatedly. The funding case is simple: keep the accountable internal leadership roles, retain external build capacity, and replace three recurring specialist roles with AI-enabled controls.
How the product team runs.
The operating model keeps business and technical accountability internal. External spend is focused where it creates build throughput, while AI takes over repeatable checks, release orchestration, and quality gates.
AI-enabled product team
AI token funding is estimated at $1.8K per year for each developer plus the internal Tech Lead: 4 users x $1.8K = $7.2K per year.
Six external roles at $60,000 per year.
Three developers plus $7,200 AI token funding.
Savings from lower product-team run cost only. Website value generated by the internal AI-powered factory comes on top.
What AI replaces.
The roles of repeatable tasks where custom AI skills can do the job.
Security by Design.
We build with ITPF embedded into code by using the AI skills.
QA becomes continuous verification.
Accessibility, responsive behavior, SEO basics, broken paths, and production-readiness checks move from manual late-stage review into an AI Playwright-cli skill.
Release patterns become templates.
Hosting, environments, deployment steps, smoke tests, rollback notes, and operational handoff become codified patterns instead of bespoke setup for every site.
12-month expected return.
At a planned throughput of four websites per month, the factory can deliver 48 websites in a calendar year. At $30,000 saved per website, throughput generates $1.44 million in annual value before considering the separate product-team cost reduction above.
Calendar-year net benefit
$1.253M
Modeled throughput savings after funding the $187.2K annual AI-enabled product team run rate.
Cumulative value generated.
Annual run-rate thresholdThe model adds $120K in website savings each month. Annual run rate is recovered during month 02; the projected $172.8K structural cost reduction is not added into this ROI calculation.
Test a lower-volume scenario.
Use the slider to explore a conservative first-year ramp-up from one to twelve completed websites, using the same editable savings-per- website assumption above.
At seven websites, the $210,000 delivery saving covers the $187,200 AI-enabled annual run rate and leaves $22,800 in net annual benefit.
Break-even timeline
The full AI-enabled annual run rate is covered by the seventh website. The $7,200 AI token funding itself is recovered during the first website because each site saves $30,000.
Governance process flows.
The factory will ship with documented flows that make routing, delivery, and support decisions visible. These flows explain when a request needs extra governance, how approved work moves through the product team, and how live websites stay maintained.
Intake routing decision tree.
A decision tree based on the intake form will determine whether a request must navigate the DISD process or can move directly into the Product Team backlog.
- Uses scope, risk, CMS, privacy, domain, and complexity signals from intake.
- Creates a clear handoff path before delivery work begins.
Product team delivery workflow.
Once intake is approved, the Product Team follows the factory stages from prototype through release: make the experience visible, harden production quality, provision launch, and release with evidence.
- Covers stages 02 to 05 of the landing-page factory model.
- Defines review gates, backlog movement, and release evidence.
BAU support and maintenance model.
A support flow will define how live websites are maintained by the Product Team after launch, including content changes, defects, monitoring, small enhancements, and escalation paths.
- Separates planned BAU changes from new project demand.
- Connects support activity back to backlog, ownership, and service expectations.