Tools that compound.

Six-dimension AI assessment that determines whether your initiative requires better execution of what you already do — or requires you to become a fundamentally different organization. The result is saved and recalibrates every downstream tool. Most initiatives fail because they are resourced as one when they are actually the other.

A structured assessment across ten domains of transformation readiness — covering culture, leadership, funding model, workforce and talent, technology, measurement, and strategy. Identifies which domain is the most dangerous gap for your specific initiative and tells you which toolkit tool to open next based on where you actually are.

Maps your organization across five capability domains: Leadership & Organizational Maturity, Execution Discipline, AI-Enabled Insight, Strategic Alignment, and Measurement & Feedback Loops. Each domain scores on a 1–5 maturity scale. The AI identifies which two or three gaps are highest priority, how they compound each other, and produces a recommended ADAPT learning path sequence.

Measures how much transformation capacity your organization has left — across all concurrent initiatives, not just the one you are assessing. Makes the critical distinction between saturation (requires load reduction and sequencing) and resistance (requires trust-building and reframing), because they look identical on the surface but require opposite interventions.

Measures how quickly and reliably your organization converts experience into changed practice — because every ADAPT tool produces insights, but most organizations are not structured to act on them. Scores capture rate, integration rate, and propagation rate. Identifies which tool outputs are most at risk of being acknowledged and then ignored.

A structured workspace for designing, running, and debriefing the 30-day experiments that ADAPT puts after every learning module. The AI guides five pre-experiment questions covering the assumption being tested, what success looks like, the smallest runnable version, who needs to be involved, and what you predict. After the Quest runs, a structured debrief captures what happened and how to design the next one differently.

Maps the full stakeholder landscape for a specific transformation initiative — senior sponsors (genuine versus nominal), external stakeholders whose behavior constrains the transformation, internal veto players who do not appear on any change management plan, and the specific interests each stakeholder needs to see served to remain supportive. Produces a tailored engagement strategy and communication calendar.

A full structured workshop integrating four change frameworks — Switch, Bridges Transition Model, ADKAR, and the Kübler-Ross Change Curve — into a single coherent change plan. Unlike standalone change tools, this workshop pre-populates from your Transformation Readiness Diagnostic, Stakeholder Mapper, Saturation Diagnostic, Narrative Builder, and Incentive Audit results, producing a change plan anchored to your actual diagnostic data rather than generic templates.

A 70-question Westrum-based culture diagnostic measuring your organization across seven dimensions — Information Flow & Safety, Cooperation & Bridging, Failure & Learning, Leadership & Trust, Adaptability & Resilience, Mission Clarity, and AI Readiness. Loads your Change vs. Transformation result and recalibrates — the same culture gap scores differently depending on whether your initiative is change or transformation.

A recurring 15-question pulse instrument administered monthly to a leader's direct reports. Measures five behavioral dimensions — psychological safety architecture, adaptive vs. technical problem response, authority-without-control, feedback loop behavior, and stress regulation. After three months, produces a Leadership Signal Report showing behavioral trend by dimension. Results go to the leader only, not to HR or their manager.

Guides Agile practitioners through a five-why analysis of a specific persistent retrospective impediment. The AI conducts a structured sequence, pushing back on surface answers and asking for organizational evidence at each level. Produces a Root Cause Classification — structural, process, incentive, or governance — with a Resolution Tool Recommendation and a Presentation Brief to take to the right leader.

A 35-question team-level instrument that measures psychological safety not as a feeling but as architecture: the structural conditions that determine whether safety is real or performed. Measures five architectural dimensions: error signal transparency, dissent pathway clarity, authority gradient management, learning loop speed, and recovery cycle design. Produces the specific structural gap most likely to be suppressing learning right now.

Assesses team performance across five structural dimensions — Clarity of Mandate & Role, Decision-Making Quality, Conflict Resolution Patterns, Interdependency Management, and Learning Behaviors. Unlike individual capability assessments (Leadership Behavioral Signal Monitor) or organizational culture surveys (Culture Survey), this tool targets the team-level structural conditions where most transformation effort disappears. Produces the single structural change most likely to improve effectiveness in 30 days.

Builds a transformation measurement framework from first principles — behavioral leading indicators, structural leading indicators, and cultural leading indicators — rather than vanity metrics or activity counts. The three-layer framework distinguishes compliance evidence (people following the process) from capability evidence (people applying the thinking in novel situations) from outcome evidence (the transformation producing business value). Includes a six-month test that would convince a skeptic.

Builds the change narrative for your specific transformation — not a communication plan, but the four-part architecture of a narrative that creates commitment rather than compliance: current reality, the challenge, direction, and invitation. The AI challenges vague drafts and tests each component against four narrative failure modes. Output feeds the ADAPT Change Management Workshop and the Stakeholder Engagement Mapper.

The foundation for every other product tool. Guides a leadership team from an application-and-function view to a customer-outcome view — producing a three-level taxonomy of Portfolio → Product Groups → Products, each with lifecycle stage, product type, and team ownership. The AI proposes an initial taxonomy from natural language inputs and flags misclassifications: projects disguised as products, technology labeled as the product rather than the vehicle.

Two-phase tool. Phase one creates the transformation-level North Star — why your organization is moving from project to product, in language anyone can understand. Phase two creates a product-level North Star for each product in your taxonomy. The AI actively challenges vague drafts, asking what is specific to your customers and your context. North Stars are saved and seed the OKR Designer.

Per-product OKRs anchored to the product North Star and lifecycle stage — because an Explore-stage product needs learning metrics, not revenue metrics. The AI flags output-based objectives and proposes outcome rewrites. Checks internal consistency across products and flags OKR sets that will force teams to compete for the same resources.

Takes a team through the complete OKR design process — integrating OKRs with KPI design and flow thinking simultaneously. Six structured questions covering time horizon, capability vs. activity metrics, the behaviors the OKR needs to change, and who needs to agree for it to stick. Output: Objectives with Key Results, a KPI set distinguishing capability KPIs from activity metrics, the failure mode for each OKR, and a 90-day review protocol.

Now/Next/Later roadmaps built from your OKRs — not from feature wishlists. The AI checks for two failure modes: roadmaps that are too certain (no learning checkpoints in far-out quarters) and roadmaps that are feature-centric rather than outcome-centric. Forces prioritization discipline by asking which items would be cut if capacity dropped 30%.

Maps your end-to-end workflow from the trigger that starts work to the outcome the customer receives. Calculates the efficiency ratio — value-add time versus total elapsed time. The AI identifies the single highest-leverage handoff to eliminate and flags which handoffs will survive the operating model redesign because they are entrenched in governance or compliance. Output feeds the Operating Model Designer and the AI Workflow Redesign Lab.

Designs the operating model for each product in your taxonomy: team structure, business engagement model, planning cadence, and measurement system. The AI compares your design against documented failure modes — teams organized around functions, partial allocation, PMO-controlled prioritization. Loads Culture Survey results and Change vs. Transformation context to flag culture gaps that will limit the model before you deploy it.

For each product team being formed, maps current-state workforce against future-state roles. Produces a skills gap analysis, a role transition plan, and a development roadmap. For each gap, the AI recommends build, buy, or borrow — and specifically warns against the buy-everything approach that creates dependency and fails to build internal capability. Connects to ADAPT learning paths for the build option.

A living document generated from all product tool outputs — taxonomy, North Stars, OKRs, roadmaps, operating models, and workforce plans — with an AI-written executive summary that synthesizes the full picture. Regenerates as inputs are updated. The executive summary gives a C-level sponsor a clear view of where the transformation stands, what the risks are, and what decisions need to be made.

Separates personal AI readiness from organizational AI readiness. McKinsey research shows organizational readiness accounts for 48% of the difference between leaders getting real value from AI and those not — personal readiness accounts for 25%. Shows which side you are investing in, which side is the actual bottleneck, and produces a 90-day Organizational Readiness Sprint targeting the three changes most likely to move the needle.

Three-layer survey run simultaneously across individual contributors, managers, and senior leaders. Produces the Adoption-License Gap — the delta between seats purchased and seats generating genuine workflow change, in dollar terms. Classifies your adoption bottleneck as friction, trust, workflow-design, or culture. Produces a prioritized 30-day action list with the single highest-leverage intervention per bottleneck type.

Three-session workshop tool. Session one maps a current workflow and calculates the Workflow Friction Score — value-add time versus total elapsed time. Session two identifies three AI intervention points that eliminate handoffs rather than accelerate them, checked against five documented failure modes. Session three produces a 30-day pilot design with a specific success metric, named owner, cost ceiling, and rollback plan written before the pilot starts.

Six-session tool producing a complete AI Governance Playbook. Covers: Authority Map (who can authorize an AI agent to act autonomously), Data Access Matrix (what data agents can read or write), Cost Governance Protocol (per-agent spending ceilings and shutdown triggers), Incident Response Design, Shadow IT Audit Protocol, and a 90-day Governance Adoption Plan. Downloadable as a formatted playbook.

Forces the business case to be defined before any AI investment is approved. Requires a named workflow, a specific performer, and a measurable output. Builds a conservative value model including the Checking Tax — the ongoing cost of reviewing AI output that most organizations omit from their projections. Requires an accountability architecture: who owns the metric, what proves it worked at 30/60/90 days, and the decision rule for stopping if evidence does not appear.

A structured canvas for designing any AI agent deployment before a developer builds it — tool-agnostic, works for Claude, GPT, Copilot Studio, or any custom agent. Seven questions covering trigger definition, authorization matrix (what the agent can and cannot do, and under what conditions), authority transfer design at three points (Human→Agent, Agent→System, System→Human), cost ceiling, failure mode mapping, rollback protocol, and governance architecture. Output is an Agent Design Brief with governance built in from the start.

Maps every role or task category where AI has absorbed work that was previously building junior staff judgment. Calculates the Bench Development Gap — the expected timeline until your organization faces a shortage of people with the judgment that the absorbed work was developing. Produces deliberate development redesign patterns and a succession risk assessment for decisions that require years of accumulated experience.

Measures the hidden cost of AI adoption most organizations are not tracking. Botsitting — the average 6.4 hours per week per moderate AI user spent feeding context, supervising outputs, debugging errors, and cleaning up results. Botshitting — offloading understanding, judgment, and responsibility to AI output without verification. Produces a Botsitting Burden Score per team and a Botshitting Risk Profile by role.

Most AI measurement creates the wrong behaviors. Goodhart's Law applied to AI: when a measure becomes a target, it ceases to be a good measure. The audit examines your current metrics against the five-metric gap between transformative and non-transformative organizations and identifies which behaviors each metric is actually incentivizing. Produces a Metrics Integrity Score and flags tokenmaxxing, approval-rate gaming, and completion-rate inflation patterns.

The judgment paradox: AI makes oversight more important while eliminating the disfluency cues that used to trigger it. Polished AI output gets processed faster and questioned less — regardless of accuracy. This assessment measures whether your organization has built the judgment infrastructure to compensate: explicit quality bars, structured review practices, when-not-to-use-AI norms, and the Centaur framework. Produces a Judgment Infrastructure Score per function.

Shared ownership is not ownership. Maps who is accountable for each of six functional AI responsibility zones: Strategic Direction, Operational Process, Technical Foundation, Compliance & Risk, Customer-Facing Trust, and Last-Mile Employee Use. Every blank zone is an AI risk. Produces an Ownership Gap Summary identifying unowned zones, single-person concentration risks, and shared-ownership zones with no documented tie-breaker for decisions.

BCG, Gartner, Bain, McKinsey, and NIST have each published a framework that fixes a different AI strategy problem — pilot chaos, adoption stalls, governance gaps, budget misallocation. Nine diagnostic questions identify which two or three frameworks apply to your specific situation and produce a Framework Application Brief showing how to use them together.

Identifies and sequences the managers and senior practitioners who are the actual AI adoption activation mechanism. Maps who in this layer has the influence, credibility, and motivation to carry AI adoption forward to their teams — and who is the unintentional blocker. Produces a three-phase activation sequencing plan with specific conversations to have and specific roles to engage first.

Identifies where your evaluation system rewards the behaviors your AI adoption requires people to stop doing — rewarding hours worked when AI makes work faster, rewarding volume when AI makes volume trivial, measuring prompt counts when you want workflow change. Produces a Misalignment Report with each conflict named, which roles are affected, and the specific system changes that must precede the behavioral changes.

For product teams embedding AI into what they build. Maps AI integration opportunities across a product or product area using the BCG Deploy / Reshape / Invent framework and the Gartner Opportunity Radar — classifying each opportunity by the level of product change required. Produces a prioritized AI roadmap brief that feeds directly into the Product Roadmap Builder, with a 10-20-70 note on every opportunity (70% of the value requires people and process redesign, not just the technology).

For teams redesigning how people and AI agents work together. Produces a Human-Agent Collaboration Map — classifying every task in a workflow as human-only (T1), human-led with AI assist (T2), AI-executed with human review (T3), AI-autonomous with exception escalation (T4), or AI-fully-autonomous (T5). For every AI-executed task, defines the three authority transfer points and produces a weekly review cadence including a Checking Tax estimate — the hours per week the team will spend reviewing AI work.

Identifies which of Donald Sull's five Active Inertia traps are operating in your organization — Strategic Frames, Processes, Resources, Relationships, and Values. The trap that made you successful becomes the obstacle to what's next. This diagnostic names it before it derails the transformation.

Visual assessment of transformation readiness across key organizational dimensions. Produces a map showing your strongest assets and highest-risk gaps as a visible picture of where the transformation has organizational support and where it will encounter the most resistance.

Scenario-based decision simulator that surfaces leadership patterns under pressure. Each scenario presents a realistic organizational situation. Your responses reveal which Active Inertia traps are operating in your own leadership behavior — before they become visible to the people you lead.

Evaluates whether your transformation portfolio is coherent — or a collection of initiatives that will compete for the same limited organizational change capacity. Most organizations are running 3–5x more change than their culture can absorb simultaneously. This diagnostic makes that visible before the initiatives start undermining each other.

Hands-on OKR design and evaluation environment. Guides practitioners through writing, stress-testing, and refining OKRs against the 12 characteristics of good metrics — checking for output disguised as outcome, lagging indicators disguised as leading ones, and objectives that measure activity rather than impact.

A quick-format version of the Change vs. Transformation Assessment — a rapid directional read on whether an initiative is change or transformation. Good for a first-cut classification before running the full Cluster 1 sequence, or for use in a workshop or facilitated session where speed matters.

A structured strategic orientation tool that maps an organization across four compass points — where you are coming from, where you are trying to go, what is pulling you off course, and what would need to be true for the strategy to succeed. Surfaces the underlying strategic assumptions and tests whether they hold.

A facilitated workshop tool that guides a leadership team through the five design decisions that determine whether an operating model can absorb disruption or breaks under it — decision rights, resource allocation mechanisms, information flows, accountability structures, and team boundaries.

A rapid culture typology assessment that places your organization across four culture archetypes and shows the gap between the culture you have and the culture your strategy requires. Identifies which cultural features are your strongest assets and which are the most likely friction points for the change you are trying to make.

A public-access structured workshop tool for planning a specific change initiative — covering stakeholder mapping, resistance anticipation, communication architecture, and milestone design. For the full ADAPT-integrated version that pre-populates from your diagnostic results, see the ADAPT® Change Management Workshop in Cluster 1.

Reads your accumulated diagnostic data across all five clusters and the Bridge Tools, then synthesizes what the complete picture means — detecting cross-cluster tensions (where signals from different dimensions contradict each other) and alignments (where signals reinforce and create leverage). Produces an overall transformation posture (advancing / stalling / regressing / emergent), a dominant cross-cluster pattern, strategic priorities grounded in your actual data, and an executive brief. The view that only becomes possible when all five clusters are read together.