AWS
“ UNLIKE CLOUD ADOPTION WHICH
FOLLOWED A LINEAR PATH DRIVEN BY IT, AI TOUCHES EVERY PART OF THE BUSINESS. ORGANISATIONS ARE OFTEN AT MULTIPLE
ADOPTION STAGES SIMULTANEOUSLY”
AWS trust it, don’ t understand it, or resist it, the solution fails. AI-literate organisations see 2x better adoption.
2.5 AI stages of adoption( AISA) – how to assess your readiness
The AI Stages of Adoption framework maps customer maturity using Investment vs. Value Realisation axes. Unlike cloud adoption which followed a linear path driven by IT, AI touches every part of the business. Organisations are often at multiple adoption stages simultaneously – marketing might be at Optimising while finance hasn’ t started yet.
Investment in this context is multidimensional: financial investment( compute, licenses), people investment( training, hiring), data investment( cleaning, governance), process investment( redesigning workflows) and time investment( calendar time and person-hours).
2.5.1 Observing The board is asking questions about AI at every meeting. The CEO is seeing competitors make splashy AI announcements. Everyone is feeling the pressure, but they haven’ t actually started using AI yet. These customers don’ t need complex solutions – they need education and inspiration. Think about offering workshops to build understanding, executive briefing sessions to show possibilities and“ art-of-the-possible” presentations.
2.5.2 Experimenting Customers have pockets of AI activity. Marketing is playing with ChatGPT, developers are trying Amazon Bedrock and someone is running a computer vision pilot. This is where“ shadow AI” emerges, with business units independently experimenting with tools to solve immediate business challenges. Typical projects focus on quick wins: chatbots for customer service; image recognition for product quality control; predictive analytics for sales forecasting.
Key characteristics: ad-hoc initiatives driven by individual departments; no formal AI strategy or governance; limited
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