We write about what we see in the field — what works, what fails, and how business leaders can think clearly about AI implementation.
AdventHealth's deployment of ChatGPT for clinical documentation signals that healthcare AI has moved from experimental to operational. But for mid-market health systems, the real challenges aren't the AI models—they're EHR integration costs, physician adoption curves that stall at 50%, and compliance overhead that stretches ROI timelines to 18 months. This analysis breaks down what the headline numbers miss and offers a decision framework for healthcare IT leaders weighing AI investments against tighter margins.
AI OperationsDatabricks integrates GPT-5.5 into enterprise agent workflows, but benchmark performance tells only part of the story. For mid-market IT leaders evaluating agentic AI, the real costs hide in integration engineering, guardrail development, monitoring infrastructure, and months of prompt iteration—typically 3-5x the quoted licensing fees. Learn which use cases deliver ROI, what questions to ask before committing, and why high-volume, low-variance processes offer the fastest payback on agent automation investments.
ImplementationEnterprise AI coding tools fail in deployment more often than in demos. The gap between proof-of-concept and production stems from infrastructure, governance, and workflow challenges—not model capabilities. Learn why hybrid and on-premise deployments cost 2-4x more than cloud alternatives, why productivity gains typically run 10-20% rather than the 30-55% vendors promise, and what the successful 20% of deployments do differently to achieve real ROI from AI coding assistants.
AI StrategyEnterprise AI has a delivery problem, not a technology problem. The gap between vendor demos and production reality is measured in months, millions, and executive patience. This analysis explores why Silicon Valley's consumer-tech thinking breaks in enterprise environments, what the vendor pitch leaves out, and how mid-market companies can avoid the common pitfalls that kill AI projects before they deliver value.
AI StrategyBCU's Agentforce deployment highlights what vendor case studies leave out: realistic ROI timelines for AI agents in financial services. Most mid-market credit unions reach payback in 14–18 months, not 90 days. This analysis breaks down the three-phase value model, where integration costs actually live, and what separates organizations that hit positive ROI faster from those that stall.
ImplementationMost enterprise AI projects fail not because the model underperforms, but because organizations cannot absorb what the technology produces. Learn why 70-80% of AI initiatives stall during deployment and what the successful minority does differently—from integration budgeting to governance frameworks and adoption strategies that actually drive business value.
AI StrategyVoice AI for customer service fails not because the technology doesn't work, but because organizations underestimate the gap between prototype and production. This framework helps operations leaders and IT executives evaluate three approaches—platform-first, build-on-foundation, or agent-assist—based on actual costs, implementation timelines, and organizational readiness rather than vendor capabilities alone.
AI StrategySingular Bank built an internal AI assistant using ChatGPT and Codex that saves bankers 60 to 90 minutes daily on meeting prep, portfolio analysis, and follow-up tasks. The key to their success: targeting three specific workflows instead of launching an enterprise-wide transformation. Learn why most AI banking projects fail, what the real costs look like, and how mid-market financial services firms can assess whether building an internal AI assistant makes sense for their organization.
AI StrategyFinance leaders evaluating AI partnerships like OpenAI-PwC should understand what these collaborations actually deliver versus what they promise. This analysis breaks down where finance AI projects fail, the true cost structure beyond vendor proposals, and what successful organizations do differently—from mapping exception-handling workflows to negotiating exit rights before signing contracts.
AI StrategyMicrosoft reports Copilot adoption surging across enterprise, but activation metrics hide a critical gap. Most AI rollouts stall because organizations measure feature clicks instead of workflow value. Learn why fewer than 20% of licensed users achieve meaningful adoption, the three patterns that break AI deployments, and what successful organizations do differently to turn AI features into actual competitive advantage.