Dan M

Every organisation complains about too many meetings. Few recognise that the real problem isn't the meetings themselves, but the invisible coordination tax they represent, and what it reveals about structural dysfunction.

Enterprise AI adoption creates new organisational boundaries faster than companies can perceive them. We examine why deploying AI without structural visibility accelerates failure modes rather than resolving them.

The prevailing assumption is that autonomous AI agents are the future of enterprise automation. We argue that auditable, deterministic pipelines with human validation produce better outcomes, and explain why the distinction matters.

We examined 23 enterprise AI initiatives from proof of concept to production deployment. The correlation between PoC success metrics and production value delivery was effectively zero. Here's what actually predicts whether an AI initiative will deliver.

AI systems are trained on historical data and deployed into a changing world. Without feedback loops that connect production performance to retraining decisions, models degrade silently. We found that 73% of enterprise AI systems had no systematic mechanism for learning from their own production errors.

The build-vs-buy debate frames AI as a technology procurement decision. The actual decision is about organisational capability: what do you need to understand deeply enough to control, and what can you safely treat as a commodity?

Every enterprise we studied had an AI ethics framework. None had successfully translated it into production-level decision-making. The gap isn't cynicism, it's structural. Ethics frameworks and production systems operate on different planes.

We studied nine organisations that invested $5M+ in data platform builds. Two became meaningfully more data-driven. The difference wasn't the platform. It was whether the organisation changed how it made decisions.

As organisations accumulate data, it develops gravitational pull, attracting applications, processes, and decisions toward it. We examined how data gravity constrains AI strategy and creates structural inertia that no technology migration can overcome alone.

Every major consultancy offers an 'AI Readiness Assessment.' We compared the dimensions they measure against the factors that actually predicted AI deployment success across 18 organisations. The overlap was less than 30%.

Enterprise AI teams obsess over model accuracy, F1 scores, and inference latency. But the model is typically 15-20% of the value chain. The other 80% (integration, workflow design, feedback loops, trust) is where deployments succeed or fail.

Most enterprise AI strategies are structured around vendor capabilities rather than organisational problems. We examined 11 AI strategy documents and found that 9 defined their approach by the technology they'd purchased rather than the outcomes they needed.