Why Companies Pour Money Into AI - And See Little Return
AI fails when implemented in fragmented systems; enterprise value requires orchestrated workflows, integrated data, and coordinated intelligent agents across unified operating models.
Alomana raised €4 million in seed funding to deploy Alo, an AI operating layer that automates enterprise workflows across data, documents, applications, and code without requiring extensive integration work.
Perplexity launches Computer for Enterprise, an AI orchestration service that automates business tasks across integrated cloud applications like Gmail, Slack, and Salesforce.
Architecting for Global Scale: Inside DoorDash's Unified, Composable Dasher Onboarding Platform
DoorDash rebuilt its Dasher onboarding system into a unified, modular workflow platform that accelerates global expansion by replacing fragmented legacy systems with configurable, step-based orchestration.
Agentic Workflows in Scala (Without the Buzzwords)
Durable, decision-driven systems require explicit state, clear decision points, and explicit workflow orchestration rather than opaque autonomous agent loops.
QCon SF: Database-Backed Workflow Orchestration Challenges Traditional Architecture
PostgreSQL can serve as the orchestration layer for workflows, using checkpoints and ACID transactions to provide exactly-once execution and simpler recovery.
Microsoft released the open-source Microsoft Agent Framework by merging Semantic Kernel and AutoGen to support workflow and agent orchestration with multiple model and protocol options.
Good AI innovation means focusing on workflow not cutting jobs
AI delivers the most value when integrated with redesigned workflows, orchestration, and human oversight rather than by focusing solely on headcount reduction.
AI Workflows For Marketing Campaigns: Automate, Optimize, And Scale Your Strategy
Marketing automation is not a hands-off autopilot; AI workflows depend on well-defined criteria and guidelines to function properly and deliver results.