With 78% of organizations now using AI in at least one business function, the question isn't whether to adopt AI workflows, it's where to start. Here's my framework for identifying the highest-impact automation opportunities.
The Adoption Gap
McKinsey's data shows rapid AI adoption, but most organizations are still in the experimentation phase. They've tried ChatGPT for emails or dabbled in image generation. Real workflow automation, where AI handles multi-step processes, makes decisions, and orchestrates across systems, remains underutilized.
My Framework: Start with Friction
The best candidates for AI workflow automation are processes with high friction: repetitive tasks, manual data entry, approval bottlenecks, and multi-system handoffs. Map your team's daily activities. Identify the tasks that drain time without requiring creative judgment. Those are your starting points.
Building vs. Buying
You don't always need a custom solution. Tools like Zapier, Make, and n8n handle many automation scenarios. But when workflows require domain-specific logic, custom AI agents, or deep system integration, a custom build delivers better results. The key is knowing which problems warrant which approach.