SIMA Flow™ serves as the execution engine, tying SIMA Core™ models to SIMA Kit™ resourced through two cycles that run simultaneously: the macro Core Cycle (Assess Capabilities → Set Groundwork → Execute) for organizational advancement, and the project-level FLAI Cycle for execution within it.
SIMA Flow™ operates through two complementary cycles — one for organizational alignment, one for disciplined, evidence-based learning
Evaluate maturity across five perspectives
Prepare organization for execution using SIMA Kit™
Run FLAI cycles as disciplined organizational learning
Define the specific operational condition to study — narrow enough that two observers would agree on it
Introduce one deliberate, constrained intervention, treated as an experiment — not a rollout
Observe whether behavior actually shifted, through controls tied to that behavior — not whether activity happened
Decide from what was learned: extend it, target what's still unstable, or reframe the constraint
The macro flow for evaluating, preparing, and executing AI initiatives with structured learning loops
The diagnostic entry point that contextualizes readiness across the five Perspectives: Strategy, Governance, Data, People, and Technology. This is the phase SIMA-Probe is built to support.
Prepare the organization for execution using SIMA Kit™ tools and templates aligned to current maturity levels and perspectives.
Run FLAI as disciplined organizational learning, not project delivery — each cycle targets whichever perspective is currently the constraint, with what the cycle studies shifting as maturity increases: clarity first, then consistency, then adaptability.
A structured method for examining how AI operates inside the organization and whether that operation produces reliable outcomes over time. Improvement here is an organizational learning problem, not a delivery effort — something implemented, rolled out, and closed. FLAI treats each cycle as a controlled study, not a project plan.
PDIA is the established empirical cycle for defined execution work — it assumes the organization already knows what outcome it's pursuing. AI maturity improvement, especially at the ambiguous early levels, often doesn't meet that assumption: the organization has to learn its way to the answer. Renaming the verbs isn't cosmetic. Plan assumes a known objective; Frame acknowledges the objective must be discovered through careful constraint definition. Do assumes execution toward a known outcome; Learn treats the intervention as an experiment. Inspect evaluates conformance to a standard; Assess observes whether behavior actually shifted. Adapt optimizes a working system; Improve may require reframing the constraint entirely.
Connecting capability assessment with project execution through structured learning loops
SIMA-Flow is the cycle architecture. SIMA-Kit is the operational resource layer it draws from. Flow determines when and how guidance gets applied; Kit ensures that guidance exists at every Maturity Level.
Assessment ensures AI projects align with organizational maturity, avoiding both underperformance and overreach
Each project becomes both an application and generator of learning, building institutional knowledge
Groundwork phase ensures preparation efforts are tailored to maturity levels and perspectives
Transform your AI initiatives from high-risk bets into structured, repeatable capability-building exercises