FLO

Jak se dělá AI v České spořitelně, Partners Bank nebo Mastercard? Lídři z praxe sdílí své reálné zkušenosti na Business Breakfast od FLO a Salesforce.

Is Data The Currency of the Future? Businesses in 2025 are battling rigid systems, inconsistent data, rising costs, and higher customer expectations than ever. The ones pulling ahead? They treat data as their most valuable asset. On November 6, FLO and Salesforce are hosting an exclusive executive session at Salesforce Tower Sydney – and you are invited. What to expect: 👉 A practical “Crawl / Walk / Run” roadmap for SMBs 👉 Insights on turning messy data into smarter decisions, efficiency, and loyalty 👉 A real-world success story with Leo Gagic, CEO at Access Mercantile Services 👉 Expert strategies from Malcolm Vu and Tim Maxwell at FLO And we’ll wrap with drinks nearby so the conversation continues. Seats are limited — register now to secure yours.

Is Data The Currency of the Future? Businesses in 2025 are battling rigid systems, inconsistent data, rising costs, and higher customer expectations than ever. The ones pulling ahead? They treat data as their most valuable asset. On November 6, FLO and Salesforce are hosting an exclusive executive session at Salesforce Tower Sydney – and you are invited. What to expect: ✅ A practical “Crawl / Walk / Run” roadmap for SMBs ✅ Insights on turning messy data into smarter decisions, efficiency, and loyalty ✅ A real-world success story with Leo Gagic, CEO at Access Mercantile Services ✅ Expert strategies from Malcolm Vu and Tim Maxwell at FLO And we’ll wrap with drinks nearby so the conversation continues. Seats are limited — register now to secure yours.

Most prompt engineering fails in production for the same reason: someone asked one large agent to do everything, from discovery workshops down to deployment code. In a desktop demo it looks fine. Under a real project pipeline it drifts, reaches for the wrong tool, and starts inventing. I have watched a single do-everything agent wire a flow to the wrong object because two fields shared a label, then write a passing test around its own mistake. It looked green the whole way. We build the other way. The delivery lifecycle is split into single-purpose agents, each with a narrow role and explicit input and output contracts. No agent can see or act outside its own step. That narrowness is what turns automation from an experiment into something you can rely on twice. A general-purpose prompt is a party trick. A network of specialised agents is an engineering tool. 👉 Explore FLO's AI-Native Delivery Framework here: https://lnkd.in/gtuj_8jh #PromptEngineering #AINativeDelivery #EnterpriseAI

I see the same thing across APAC. Leadership watches a polished AI demo in the boardroom, signs the cheque, and the delivery team goes back to the same manual workflow they were running a decade ago. That gap is where most software transformations stall. When a rollout falls behind, the problem is rarely the technology itself. It is the way the work gets delivered. Put an AI wrapper on a broken process and you don't buy speed, you automate the mistakes and add to the technical debt you'll pay down later. We rebuild the pipeline instead. In our AI-native delivery model, nine specialised agents take the mechanical work that eats project time: spec generation, ticket triage, test scaffolding, documentation upkeep. Each has one job and a defined data contract. That gives your engineers their attention back for system design, architecture, and the judgement calls you hired them for. A modern platform on a legacy playbook is still a legacy project. 👉 Explore the full framework here: https://lnkd.in/gtuj_8jh #AINativeDelivery #EnterpriseAI #Salesforce