Assess & Align

We conduct a structured diagnostic of your organization across four dimensions: data, technology, operations, and leadership readiness. Using a calibrated maturity framework, we evaluate what is ready to build on and what needs to be addressed first. The result is a diagnosis tailored to your specific operations — not a generic scorecard.

• Discovery & Stakeholder Interviews: Structured conversations with key leaders across operations, technology, and finance to understand strategic priorities, pain points, and existing AI efforts.
• Data Audit: Assess the quality, accessibility, and structure of your organization's data; surface gaps that need to be addressed before AI can be deployed reliably.
• Tech Stack Review: Map existing systems (CRM, ERP, internal tools, databases) to understand what AI will need to connect to and where integration complexity lives.
• Operations Audit: Identify manual, repetitive, and error-prone workflows where AI could realistically affect business outcomes.
• Leadership Readiness Review: Evaluate decision-making capacity, executive sponsorship, and change readiness across the leadership team.
• Maturity Scoring: Synthesize findings into a calibrated AI readiness score across dimensions, with clear identification of what is ready and what is not.

• AI Readiness Report: Full assessment of maturity across data, technology, operations, and leadership, with findings and recommendations.
• Gap & Blocker Summary: Clear identification of what must be addressed before building can begin.
• Foundation Recommendations: What to fix, in what order, and what each gap will require to close.
• Executive Presentation: Board-ready summary of findings and recommended next steps.

• Know exactly what has to be true before committing budget to a build
• Avoid the costly mistake of building on a foundation that isn't ready
• Walk into leadership conversations with an evidence-based view of your AI readiness

We build a comprehensive AI strategy that connects your business priorities to specific, sequenced AI investments — with the financial case to back each one. The roadmap doesn't just tell you what to build; it tells you what to build first, why, what it will cost, what it will return, and what needs to be true before you start. Designed to be presented to a board and to survive scrutiny.

• Business Alignment: Define the strategic goals that AI investments must serve; ensure the roadmap is anchored to business outcomes, not technology for its own sake.
• Current State Assessment: Evaluate existing analytics capabilities, AI experiments, and organizational readiness to build on what is already in place.
• Use Case Identification & Prioritization: Identify the full universe of AI opportunities and rank them by impact, feasibility, and speed to value; surface the trade-offs about what to pursue first.
• Business Case Development: For top-priority initiatives, build the financial case: estimated cost, expected ROI, and timeframe; the financial detail required to approve budget.
• Sequencing & Dependencies: Map the order of initiatives and identify what must be true before each one can succeed — data readiness, system access, team capacity.
• Governance & Accountability Framing: Define who owns AI decisions inside the organization, how success gets measured, and how teams get brought along.

• AI Strategy & Roadmap Document: Comprehensive, time-phased plan for AI investment over 12–36 months.
• Use Case Prioritization Matrix: Scored and ranked list of AI opportunities with rationale and business case.
• 30-60-90 Day Action Plan: Immediate next steps to initiate momentum before the full build begins.
• Executive Presentation: Board-ready summary designed for leadership alignment and budget approval.
• Governance Framework Outline: High-level design for AI decision-making, accountability, and performance tracking.

• Enter board and budget conversations with a defensible, data-backed AI investment plan
• Concentrate resources on the two or three initiatives most likely to deliver measurable returns
• Know what to build, in what order, and why — before any build begins

We take your highest-priority AI initiative and build a working prototype in days or weeks, not months. The goal is not a polished product — it is a working demonstration that surfaces real-world complications, generates unfiltered feedback from the people who will use it, and produces a clear, evidence-based recommendation on whether to proceed to a full build, and what that build would actually require.

• Use Case Selection: Identify the single highest-priority initiative from the strategy roadmap to prototype first.
• Rapid Prototype Build: Construct a working, lightweight version of the AI solution using appropriate tools; built to demonstrate the concept and surface real obstacles, before significant budget is committed.
• Stakeholder Demonstration: Present the prototype to the people who need to approve the full build; generate firsthand reactions and surface objections before significant budget is committed.
• Testing & Feedback: Evaluate the prototype against real use cases and gather structured feedback on functionality, usability, and business fit.
• Build/No-Build Recommendation: Synthesize what was learned into a clear recommendation: proceed, redesign, or stop — and what a full build would actually require in scope, cost, and time.

• Working Prototype: A functional demonstration of the AI concept built against your actual data and systems.
• Build Recommendation Memo: A concise, evidence-based assessment of whether to proceed, what was learned, and what full implementation would require.
• Pilot Implementation Plan: If proceeding, an action plan with scope, roles, risks, and timeline for the full build.

• Validate your highest-stakes AI bet with a fraction of the cost and risk of a full build
• Build stakeholder alignment around a working prototype — not a slide deck
• Make a go/no-go decision based on evidence, not assumptions