David Navalho

Everyone’s chasing the Unicorn: agentic workflows, autonomous everything, “AI-first” transformations, and flashy demos. Meanwhile, the reality reported by MIT is clear: about 95% of GenAI pilots fail to deliver. But the issue isn’t with the models, it’s with the problems we choose to address. This talk focuses on the 5% that succeed. Not by taking on larger, shinier projects, but by targeting the boring work that exists in every enterprise: repeatable workflows, exceptions, approvals, reconciliations, handoffs, and “someone needs to write a report and decide what to do next.” These are the places where small improvements compound quickly, and where GenAI can create real productivity gains now. I’ll show a delivery pattern where GenAI is embedded into a set of small, focused microservices, coordinated through Kafka as the system-of-record for workflow state and decisions. Kafka provides a shared backbone for service-to-service communication, with schemas and contracts enforcing structured data, and with traceability treated as a first-class design goal. That traceability is what turns GenAI from an unpredictable demo into a repeatable system: every step is observable, decisions can…