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Agentic AI—autonomous software “agents” that can observe, decide, and act within business processes—offers a new frontier for efficiency and innovation. It’s therefore tempting to replicate existing workflows with the power of agents to automate human tasks.
Automating a flawed process however only accelerates its weaknesses. If steps are redundant or unclear, automation will amplify errors and complexity rather than eliminate them. A common mistake is to drop an autonomous agent into an inefficient workflow and hope for miracle gains.
To truly maximize flow and value delivery, agentic AI workflows must be designed and deployed with the same process improvement principles that have long streamlined human workflows. Forward-thinking organizations are finding that Process Analysis, Value Stream Mapping and Lean thinking (eliminating waste, optimizing flow, and pursuing continuous improvement) are not old-fashioned in the AI era—they are critical success factors.
Leading teams apply Lean’s value focus up front. They start by clearly defining the expected value outcome or problem to solve. They then design the workflow with a clear understanding of agentic AI capabilities. They not only streamline the process to be AI-ready, but actively build on these capabilities to drive efficiency, scalability, and strategic alignment.
Value Stream Mapping offers a big-picture, end-to-end view of a workflow, which is especially valuable when designing processes that include autonomous or semi-autonomous AI agents. In the context of AI agents, this visibility is critical. It answers questions like: Where are the delays that an always-on agent could eliminate? Which repetitive tasks are consuming skilled workers’ time? Which process variations cause errors that AI might prevent? By mapping a process end-to-end and gathering data (through interviews, observations, or process mining), you build a comprehensive picture of the current state. This analysis often uncovers surprising facts.
Business process analysis and task decomposition are essential next steps before deploying any advanced automation. They ensure you’re automating the right things in the right way. Business Process Analysis is the practice of examining your organization’s workflows, step by step, to understand how things really get done. It’s a bit like doing an X-ray of your operations – revealing every activity, decision point, handoff, and potential pain point in a process. The goal is to identify inefficiencies, bottlenecks, and improvement opportunities. In the context of automation, BPA helps you answer critical questions upfront:
- Is this process working as well as it should? (Are there delays, errors, or rework loops? Any “broken” steps?)
- Which parts of this process are the best candidates for automation? (E.g. repetitive tasks or routine decisions that don’t need human creativity.)
- How would automating this process align with our business goals? (Will it save significant time or cost, improve accuracy, or enhance customer satisfaction?)
Just as Lean emphasizes flow—the smooth, end-to-end passage of value to the customer—agentic AI systems must be integrated into well-orchestrated workflows. If an AI agent is forced to work in a fragmented, stop-and-go process, its impact will stall. Lean process optimization lays the groundwork for AI agents to excel. That can mean standardizing inputs, removing handoff delays, and clarifying decision points so the AI can operate with minimal friction.
The same focus on waste avoidance should apply to the choices on where and how to apply AI computation to avoid unnecessary ecological impact casuse by agentic AI automation.
Designing agentic AI solutions through a Lean lens means marrying cutting-edge autonomy with time-tested operational discipline. Business and technology leaders who apply Lean principles — from waste elimination and value focus to workflow streamlining and continuous improvement — are uncovering new ways to maximize the flow of value delivered by AI. They avoid the trap of automating disorganized operations and instead build AI agents into disciplined, high-quality processes that are stable, measurable, and always improving.