10 most common AI transformation asks we get from enterprises right now (in order of frequency): 1) AI Diagnostic / ROI Study - Identify opportunities for AI, estimate ROI by use case, prioritize investments, and create an implementation plan. 2) Agentic Workflow Automation - Build agents, automations, and integrations that execute recurring business processes. 3) Architecture & SDLC Assessment - review codebase and architecture, identify technical debt and risks, evaluate engineers AI workflow fluency and create a modernization plan. 4) Security Testing & Patching - Test AI applications and agents for vulnerabilities, data leakage, prompt injection, and misuse. 5) Security Assessment - Identify security, privacy, and compliance risks and recommend how to address them. 6) Code Modernization - Legacy tech stacks create drag that decreases speed/accuracy for agents 7) Data Engineering - Build data pipelines, integrations, migrations, search systems, and knowledge systems. 8) MCP Gateway - Provide controlled access to models, tools, and data through authentication, routing, logging, and usage controls. 9) Model fine-tuning - Post-training an LLM on a smaller, targeted dataset so it gets better at verticalized task or domain. 10) Citizen SDLC - Give non-technical employees a secure, governed process for turning AI prototypes into approved production applications. Comment below with the use case # you're most interested in (i.e. #6# = code modernization), and I'll DM you specific insights from work we've done on it.