{"stages":[{"id":"stabilizer","cards":[{"pain_point_id":"STAGE-STB-01","hook":"Token costs outpace returns","explain":"As AI usage scales, token and cloud costs rise without a matching increase in business value.","image":"images/stage-cards/tiny-magiq-token-costs-outpace-returns.webp","pain_text":"Our AI usage is growing, but token costs are increasing faster than the returns we see.","cdrops_category":"ops_efficiency"},{"pain_point_id":"STAGE-STB-02","hook":"Risk slows rollout","explain":"Security, privacy, compliance, and approval concerns delay AI adoption.","image":"images/stage-cards/tiny-magiq-risk-slows-rollout.webp","pain_text":"Security, privacy, and compliance approvals are slowing down every AI rollout.","cdrops_category":"risk_compliance"},{"pain_point_id":"STAGE-STB-03","hook":"AI needs ongoing control","explain":"Monitor, improve, and govern AI systems after launch.","image":"images/stage-cards/tiny-magiq-ai-needs-ongoing-control.webp","pain_text":"AI systems need monitoring, improvement, and ownership after launch.","cdrops_category":"risk_compliance"}]},{"id":"amplifier","cards":[{"pain_point_id":"STAGE-AMP-01","hook":"Hallucinations weaken trust","explain":"AI hallucinations produce inaccurate or fabricated answers that are difficult to verify and trust.","image":"images/stage-cards/tiny-magiq-hallucinations-weaken-trust.webp","pain_text":"Our teams hesitate to rely on AI because hallucinations make its answers difficult to verify and act on.","cdrops_category":"product_superiority"},{"pain_point_id":"STAGE-AMP-02","hook":"AI misses business context","explain":"Company knowledge is spread across tools, documents, systems, and people.","image":"images/stage-cards/tiny-magiq-ai-misses-business-context.webp","pain_text":"Our AI keeps missing our business context because our knowledge is scattered across tools, documents, and people.","cdrops_category":"data_mgmt"},{"pain_point_id":"STAGE-AMP-03","hook":"Hard to scale across teams","explain":"What works for one team doesn't easily carry to other departments or business units.","image":"images/stage-cards/tiny-magiq-hard-to-scale-across-teams.webp","pain_text":"What works for one team does not carry across our other departments or business units.","cdrops_category":"scalability"}]},{"id":"catalyst","cards":[{"pain_point_id":"STAGE-CAT-01","hook":"Demos fail in production","explain":"AI that works well in development often struggles with real-world data, users, and production conditions, preventing pilots from becoming business outcomes.","image":"images/stage-cards/tiny-magiq-demos-fail-in-production.webp","pain_text":"Our AI demos work in development but struggle to deliver reliable outcomes under real-world production conditions.","cdrops_category":"product_superiority"},{"pain_point_id":"STAGE-CAT-02","hook":"Too many AI ideas","explain":"Everyone wants to use AI, but it's unclear which ideas are worth pursuing first.","image":"images/stage-cards/tiny-magiq-too-many-ai-ideas.webp","pain_text":"We have too many AI ideas and no way to tell which ones are worth doing first.","cdrops_category":"ops_efficiency"},{"pain_point_id":"STAGE-CAT-03","hook":"No clear starting point","explain":"Leaders want to move on AI, but teams are unsure where to begin.","image":"images/stage-cards/tiny-magiq-no-clear-starting-point.webp","pain_text":"We want to move on AI but our teams do not know where to begin.","cdrops_category":"ops_efficiency"}]}]}