RETURN Founder Angelina Bazzi: The AI Problem Starts Before the Prompt

Angelina Bazzi, founder of RETURN Strategy & Systems, argues that a growing weakness in AI support begins before a user types a prompt: many people know what they want to accomplish but cannot yet identify what they need. In a new article, Bazzi describes RETURN, an adaptive approach she says she developed and tested across personal, business and operational settings, which pairs AI-supported tools with human judgment to help people turn an unclear starting point into a practical direction.

The Gap Before the Question

Bazzi points to everyday examples of the problem. A business owner who asks for more customers may need help with visibility, pricing, funding, customer follow-up or capacity. A team asking for a better schedule may in fact be waiting on missing parts or an unresolved approval. A person who says they no longer feel like themselves may not know where to begin. In each case, she says, understanding enough of the situation to choose a useful response is part of the work.

Her experience across business, healthcare operations and personal reflection, Bazzi says, showed her that people are often asked to choose a service, category or solution before they understand which would help. RETURN is designed to let the person start with what they know while the support adapts to the context, including what remains uncertain and where another person, resource or system needs to become involved.

"Helping them understand their options and identify a usable route can be more valuable than giving an answer they cannot act on."

— Angelina Bazzi, Founder, RETURN Strategy & Systems

Where the Approach Applies

Bazzi describes applications across several areas, which she says illustrate the concept rather than individual customer results. They include business direction, such as distinguishing launch planning from funding readiness or operations; personal support for people who struggle to explain what they are experiencing; and navigation of services or external systems. Operational uses include shared trackers and workflows for teams whose work is scattered across messages and spreadsheets, as well as scheduling that connects availability, staffing, requests and approvals.

She also cites more technical settings, among them coordinating build readiness and test resources for engines, scheduling and troubleshooting around diagnostic imaging, and guided technical support in plain language. On scheduling, Bazzi credits Amber Saad of Dearborn, Michigan, with introducing a scheduling concept intended for municipalities, which RETURN's methodology extends to other industries. For specialized field work, she points to RAW Systems, which she also founded. She adds that the broader concept could support construction, manufacturing, field-service work orders, grant applications and workforce coordination, with each application configured and evaluated for its environment.

Boundaries and Availability

Bazzi stresses that useful AI support needs limits. It should preserve uncertainty when information is incomplete, recognize when a human decision or specialist is needed, and stop once the person has enough clarity to act. She notes that healthcare requires clinical expertise, engineering requires technical authority, and legal and financial matters require their own professionals, and that clinical interpretation of diagnostic images remains with qualified professionals.

RETURN Strategy & Systems offers assessments, strategy, workflow design, configurable tracking and scheduling projects, systems development, management and implementation support, shaped around each situation. Inquiries can be sent to info@rawsystems.net.