Built by a Technical Product Leader who spent six years watching AI adoption stall.

At AWS, we had a front-row seat to how organizations of every size approached technology adoption. Financial services firms, healthcare systems, defense agencies, SaaS companies from ten people to ten thousand. The pattern was always the same: tooling was never the bottleneck. Process was.

Teams would purchase the best-in-class AI models, build impressive proof-of-concepts, demo them to leadership — and then stall. Not because the technology didn't work, but because no one had defined how it fit into the existing development lifecycle. There was no handoff protocol. No requirements structure that accounted for AI-generated code. No way for a TPM to maintain visibility across twenty concurrent AI-augmented workstreams.

Forty thousand customer engagements a year — and the failure mode was almost never technical. It was structural. The organizations that succeeded were the ones that had someone, usually a technical product leader, who manually built the connective tissue between their AI tools and their delivery process. That connective tissue is what we eventually turned into AIDLC.

Before AWS, the work was in healthcare data modernization — building and shipping systems that processed sensitive patient data across state and federal boundaries. The kind of work where a bad handoff doesn't just slow a sprint; it triggers a compliance incident. That environment taught us something that most AI-first teams learn too late: speed without structure creates liability.

At AWS, the scale changed but the lesson held. The teams that shipped reliably with AI were the ones that had an explicit methodology — not just good engineers and powerful tools, but a defined sequence of phases, artifacts, and decision points that every role could follow. The methodology didn't come from a textbook. It came from watching what actually worked across thousands of real engagements.

The question was simple: why do some AI-augmented teams deliver consistently while others stall after the first sprint?

The answer, after hundreds of engagements, was always the same. The teams that delivered had an implicit structure — a way of breaking down requirements, validating AI output, and coordinating across roles — that they had built organically over time. The teams that stalled were trying to use AI tools inside a process designed for a world where humans wrote every line of code.

AIDLC makes the implicit explicit. It defines seven phases — from Problem Definition through Monitoring — with clear artifacts, role assignments, and AI integration points at each step. It is not a philosophy or a set of principles. It is a concrete, repeatable process that a team can adopt in a week and run in production the week after.

SimplyGoose delivers AIDLC in two forms. The first is AIDLC Workspace — a software product that installs the full lifecycle directly into your GitHub repositories. Markdown templates, phase gates, dashboards, and automation that enforce the methodology without requiring your team to learn a new tool. It lives in your repo, versioned alongside your code.

The second is AIDLC Consulting — hands-on implementation for teams that want expert guidance adapting the framework to their specific stack, team structure, and delivery cadence. Workshops, 90-day engagements, and certification programs for organizations that want to build internal AIDLC capability.

Both paths lead to the same outcome: a team that ships reliably with AI, at scale, without the coordination debt that slows everyone else down.