AI Readi is an AI adoption and transformation platform built to close the information gap that causes most AI initiatives to fail. Here's the gap: 88% of organizations use AI in some form, but only 7% have scaled it. 62% are stuck in pilots. 80% of projects never deliver measurable impact. The technology works fine; the problem is a disconnect between what the business wants to achieve, what AI can deliver, and whether the organization can actually operate it. The knowledge to close that disconnect exists inside the company, scattered across people, processes, and context. Nobody holds the full picture. Consultants interview a handful of executives and leave with a static report. Vendors demo their own stack. Integrators build what you can articulate from a narrow set of stakeholders. All three centralize decisions from a fraction of the available knowledge. AI Readi does something different: it captures ground-level insight from the people who actually do the work, then connects that insight to strategic goals and operational reality. **How it works.** Each person follows a structured path through the platform. They set their context (industry, function, role, AI maturity), define measurable objectives through OKR frameworks, browse AI use cases filtered to their situation, evaluate feasibility through contributor assessments or AI-powered simulations, and map how AI would affect their workflows and value chains. As more people participate, the organization builds a complete picture of its opportunities, capabilities, and readiness. That knowledge compounds over time; the platform becomes institutional memory for AI transformation. **Two evaluation modes.** Simulation mode uses AI to model outcomes without collecting organizational data first, so teams can learn before they commit resources. Real evaluation mode crowdsources insight from contributors across the organization, producing assessments grounded in operational reality rather than leadership assumptions. **Three products cover the full lifecycle:** - **Accelerate** provides a curated knowledge base of AI use cases filtered by industry, function, and maturity level. Teams browse contextually relevant opportunities and crowdsource feasibility evaluations from the people who'd actually work with the technology. Strategic clarity in weeks instead of the 3-6 months a consulting engagement takes. - **Decide** turns distributed organizational knowledge into investment decisions. It maps how initiatives ripple through existing workflows, catches dependencies before they get expensive, and grounds resource planning in what the organization can actually deliver. - **Deliver** (planned) engineers value chains so AI fits into how work gets done, then tracks outcomes against the strategic goals set from day one. **What's live today.** Five of eight planned phases are in production: context setup, strategic objectives, use case selection, use case evaluation, and value chain impact analysis - including problem discovery. The knowledge base includes AI use cases with pre-computed evaluations covering value metrics, complexity, risk, capability requirements, and resource estimates. A capability taxonomy maps 200+ dependency relationships for prerequisite analysis. And value chain analysis uses AI for process discovery and workflow simulation across five phases. **Who it's for.** SMEs that have domain expertise but lack the resources for strategic AI guidance. Mid-market organizations with different maturity levels across functions. Enterprises that can't see the full picture despite having AI expertise in-house. The platform works across industries: pharma, retail, financial services, manufacturing, healthcare, and technology. **The science behind it.** AI Readi is built on complexity theory, collective intelligence research, and information theory. The platform applies conditions from collective intelligence research (diverse viewpoints, independent assessment, structured aggregation) to surface knowledge that's already distributed across the organization. Organizations using these principles see a 67% success rate on AI initiatives, compared to the 20% industry baseline.