• Develop and maintain practical AI roadmaps for clients based on readiness, risk tolerance, and business value • Assess the current state of client AI capability and translate findings into clear, phased plans that stakeholders understand and can rally around • Define governance structures, decision frameworks, and success metrics appropriate to each tier of solution delivery • Stay actively current on the rapidly evolving AI landscape and translate industry developments into practical, actionable guidance for client environments • Advise clients on model selection, platform tradeoffs, and emerging capabilities across the LLM and GenAI ecosystem • Distinguish signal from noise in AI trends, helping clients invest in approaches with staying power rather than chasing the next headline • Use AI-native development workflows and agentic IDEs to rapidly prototype working solutions that make abstract concepts concrete • Demonstrate what is possible in working sessions with clients, reducing the gap between strategy and tangible output • Leverage prompt engineering and LLM-driven code generation to accelerate solution development • Help clients build a foundational understanding of agentic AI and what it can realistically do in their environment • Evaluate client use cases to assess whether agentic architectures are the right fit, or whether simpler approaches will deliver better outcomes • Guide clients through deployment decisions, including build vs. buy, orchestration tooling, and phasing of rollout • Serve as a knowledgeable translator between agentic AI capabilities and the practical, operational realities of the client's business • Evaluate and recommend data architecture patterns that protect PII and citizen data, particularly in public sector environments subject to regulatory oversight • Design guardrails, access controls, and governance policies that enable AI adoption without compromising security or public trust • Help clients distinguish between cloud-hosted AI solutions and on-premise or hybrid architectures based on their specific data residency and sovereignty requirements • Engage stakeholders at all levels, from executive sponsors to frontline staff, with approaches tailored to their concerns and readiness • Navigate organizational complexity and resistance with patience, credibility, and practical empathy, particularly in environments with significant fear around AI adoption • Lead workshops, demonstrations, and awareness sessions that build understanding and momentum without overwhelming or alienating participants • Coach client-side staff to build internal AI capability, reducing dependence on external support over time • Identify opportunities to create additional client value across engagements and contribute to Liberum's growing AI practice