As enterprise AI programmes accelerated, our client needed to build an AI architecture capability that could support large-scale production deployment rather than experimentation. However, the market for AI architects had become increasingly noisy, with many candidates claiming GenAI experience but lacking exposure to enterprise-scale implementation, governance, and cloud infrastructure design. The client required a detailed market assessment to determine whether the capability they sought genuinely existed at scale and where adjacent talent pools might offer stronger long-term value.
Savannah mapped 414 AI architecture candidates across 229 organisations, assessing each profile against six highly specific criteria including enterprise AI architecture leadership, GenAI and LLM implementation, AWS AI platform expertise, governance design, and modern data architecture capability. We focused heavily on candidates operating in complex enterprise environments across consulting, SaaS, financial services, and cloud platform businesses. The research also examined the prevalence of emerging skills such as RAG architectures, LLM orchestration, and AI governance frameworks.
Our research demonstrated that despite an apparently large market, genuinely qualified enterprise AI architects were extremely rare. Only one individual achieved a perfect score across all assessment categories, while fewer than 50 candidates demonstrated strong alignment overall. The analysis highlighted governance and AI security as the most constrained capability areas, helping the client refine expectations and broaden consideration toward adjacent architecture backgrounds. The resulting intelligence enabled the organisation to reshape hiring strategy, prioritise capability trade-offs, and benchmark its AI architecture ambitions against realistic market supply.client to refine both the role design and long-term AI leadership strategy.
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