Back to the Boring: GenAI That Ships

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 be audited, outputs can be replayed, and you avoid the trap of an LLM taking unrepeatable actions that you can’t explain later.No hype. No digital revolution required. Just a grounded playbook for turning GenAI from a pilot factory into a value delivery engine, because there’s a long backlog of boring problems waiting to be solved.You’ll leave this talk with:- Real examples of “boring” GenAI use cases already running in production- A concrete way to structure GenAI systems so they stay understandable, auditable, and under control as they evolve- Practical techniques for making GenAI behaviour traceable and repeatable, instead of opaque and one-off- A stronger instinct for saying “no” to bad AI ideas, and focusing effort where GenAI can deliver value now

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