Start with a real process, define the roles and controls, and run AI tasks and agents inside an accountable workflow.
An AI agent can read documents and call APIs, but without process context it doesn't know in what order to act, where a human decision is required, which exceptions are allowed and what result counts as correct. That's why many AI pilots stay demos and never reach operational work.
Prioritize by volume, cost of errors, cycle time and data availability.
Collect employee knowledge and reconstruct actual execution from systems.
Identify decisions, manual checks, information lookup, document preparation and communication where AI can help.
Separate the actions of people, existing systems, automation and AI agents.
Fix rules, exceptions, approvals, KPIs, risks and ways to observe the new process.
Deploy the TO-BE inside BP1, assign human and AI steps, run a pilot and compare against the original KPIs.
BP1 doesn't end AI transformation with a recommendation or a diagram. The approved model becomes an executable workflow where actions are split across people, AI tasks and AI agents, and results feed back into Process Mining and improvement.
Track the share and depth of AI-enabled work across the company, processes and roles. Use transparent progress views and responsible gamification to accelerate adoption — without rewarding AI activity for its own sake.
The limits stay explicit: which actions an AI agent performs autonomously, where human approval is required, which integrations are available and how errors are controlled.