https://davidohnstad.net/enterprise-ai-implementation-stalling-production-deployment/ AI & Machine Learning in Enterprise Software | Product Management Mon, 31 Aug 2026 16:37:05 +0000 hourly 1 https://wordpress.org/?v=7.1 https://davidohnstad.net/enterprise-ai-implementation-stalling-production-deployment/#comment-862 Mon, 31 Aug 2026 16:37:05 +0000 https://davidohnstad.net/?p=211#comment-862 […] Before any architecture discussion, before vendor evaluation, before the data science team touches the project—David Ohnstad recommends running what he calls the Pre-Deployment Alignment Framework: a four-stage process that forces stakeholders to commit to measurable outcomes before anyone writes code. This is not a kickoff meeting. This is a structured negotiation where product, engineering, and business leadership agree on three binding elements: the decision the AI will support, the threshold at which the output becomes specific, and the feedback mechanism that will surface failure within 30 days of launch. For more on this, see enterprise AI implementation challenges today. […]

]]> https://davidohnstad.net/enterprise-ai-implementation-stalling-production-deployment/#comment-854 Mon, 31 Aug 2026 16:19:10 +0000 https://davidohnstad.net/?p=211#comment-854 […] Let’s start with the uncomfortable truth: Google’s AI Overviews are eating informational content clicks for breakfast. For more on this, see why enterprise AI implementation stalls. […]

]]> https://davidohnstad.net/enterprise-ai-implementation-stalling-production-deployment/#comment-821 Mon, 24 Aug 2026 09:18:03 +0000 https://davidohnstad.net/?p=211#comment-821 […] This is where most AI teams go wrong. They optimize for launch, not for usage. They build the minimum viable model instead of the minimum viable product. The distinction matters. For more on this, see Why implementation efforts are stalling. […]

]]> https://davidohnstad.net/enterprise-ai-implementation-stalling-production-deployment/#comment-816 Mon, 24 Aug 2026 09:13:43 +0000 https://davidohnstad.net/?p=211#comment-816 […] David Ohnstad has watched this pattern repeat across data product deployments: teams automate before they standardize. The correct sequence is to document your process, measure it, improve it to the point where exceptions are genuinely rare, and only then consider automation. Most organizations skip directly to the automation step because it’s more exciting than the taxonomy audit. The result is an agent that amplifies every inconsistency in your existing workflow at machine speed. For more on this, see enterprise AI implementation challenges persist. […]

]]> https://davidohnstad.net/enterprise-ai-implementation-stalling-production-deployment/#comment-814 Mon, 24 Aug 2026 09:11:41 +0000 https://davidohnstad.net/?p=211#comment-814 […] The failure mode isn’t technical — it’s organizational. Most enterprise AI initiatives start with a CTO or VP who attended a conference, saw a demo, and came back convinced the company needs “more AI.” Engineering gets a mandate to explore machine learning capabilities. Product gets asked to identify use cases. And nobody stops to build the connective tissue between what’s technically possible and what would actually move a business metric. For more on this, see Why enterprise AI implementation stalls. […]

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