We write about what we see in the field — what works, what fails, and how business leaders can think clearly about AI implementation.
Healthcare AI pilots consistently fail to reach production—not because the models don't work, but because organizations underestimate the integration, workflow redesign, and clinician trust-building required. Learn why Boston Children's Hospital succeeded where most health systems stall, and what the successful 20% of AI deployments do differently.
AI StrategyEnterprise software vendors promise agentic AI will automate workflows and accelerate delivery, but most organizations will fail—not because the technology doesn't work, but because they're building agents into structures designed for human bottlenecks. This guide examines what actually changes when you deploy AI that acts rather than suggests, the hidden costs vendors omit from proposals, and why organizations taking the redesign path see 50–70% reductions in cycle time while those on the automation path struggle to move the needle.
ImplementationEnterprise AI projects fail not at the proof-of-concept stage, but when organizations try to scale into production. This analysis examines why Cisco and OpenAI's engineering partnership highlights a common pattern: early wins create excitement, but the integration valley—data debt, workflow disruption, and maintenance overhead—derails most initiatives. Learn what the 20% of successful implementations share and the critical questions to ask before scaling any AI pilot.
Enterprise AI adoption stories follow a predictable arc in vendor case studies: executive sponsor, pilot success, organization-wide rollout, transformed culture. What these narratives leave out is the invoice…
ImplementationAdventHealth'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.