https://davidohnstad.net/ AI & Machine Learning in Enterprise Software | Product Management Wed, 09 Sep 2026 08:13:05 +0000 hourly 1 https://wordpress.org/?v=7.1 https://davidohnstad.net/googles-new-generative-ai-search/#comment-883 Wed, 09 Sep 2026 08:13:05 +0000 https://davidohnstad.net/googles-new-generative-ai-search/#comment-883 […] For more on this topic, see Google’s Generative AI Search Revolution: What Enterprise Software Companies Must Do Now. […]

]]> https://davidohnstad.net/why-enterprise-ai-projects-fail-after-poc/#comment-880 Mon, 07 Sep 2026 14:25:28 +0000 https://davidohnstad.net/?p=231#comment-880 […] connects directly to why enterprise AI projects fail after proof-of-concept success—the gap isn’t technical capability, it’s operational readiness. Teams prove the model […]

]]> https://davidohnstad.net/googles-new-generative-ai-search/#comment-879 Mon, 07 Sep 2026 14:24:59 +0000 https://davidohnstad.net/googles-new-generative-ai-search/#comment-879 […] For more on this topic, see Google’s Generative AI Search Revolution: What Enterprise Software Companies Must Do Now. […]

]]> https://davidohnstad.net/the-real-impact-of-emerging-tech-trends-on-everyday-problem-solving/#comment-873 Fri, 04 Sep 2026 09:03:18 +0000 https://davidohnstad.net/the-real-impact-of-emerging-tech-trends-on-everyday-problem-solving/#comment-873 […] As a result, individuals are now expected to operate across overlapping knowledge areas rather than within isolated expertise zones. For more on this, see AI and machine learning applications. […]

]]> https://davidohnstad.net/googles-new-generative-ai-search/#comment-872 Fri, 04 Sep 2026 09:01:40 +0000 https://davidohnstad.net/googles-new-generative-ai-search/#comment-872 […] Layer 2: Process Documentation Depth. Is your workflow documented at the decision-node level, not just the happy-path level? This is where most teams fail. They document what happens when everything goes right, but they haven’t mapped the 30+ edge cases where human judgment is currently required. An agent can’t replicate judgment—it can only follow rules. If your process relies on employees “knowing when something feels off,” that’s not a process an agent can execute. The test is simple: could a new hire follow your documentation and make the same decisions your senior team makes? If not, you haven’t documented the process—you’ve documented an outline. David Ohnstad ran this audit for a data validation workflow at Veeam and discovered that 40% of the “process” was tribal knowledge held by three engineers who had been there since launch. Automating that workflow would have meant losing the error-catching that happened when those engineers noticed anomalies that didn’t fit documented rules. The correct move was to extract that tribal knowledge into explicit validation rules first, test them for six months, and only then consider automation. For more on this, see Google’s generative AI search capabilities. […]

]]> https://davidohnstad.net/googles-new-generative-ai-search/#comment-866 Tue, 01 Sep 2026 11:10:16 +0000 https://davidohnstad.net/googles-new-generative-ai-search/#comment-866 […] For more on this topic, see Google’s Generative AI Search Revolution: What Enterprise Software Companies Must Do Now. […]

]]> https://davidohnstad.net/googles-new-generative-ai-search/#comment-865 Tue, 01 Sep 2026 11:03:52 +0000 https://davidohnstad.net/googles-new-generative-ai-search/#comment-865 […] For more on this topic, see Google’s Generative AI Search Revolution: What Enterprise Software Companies Must Do Now. […]

]]> https://davidohnstad.net/enterprise-ai-platform-replacement-myths/#comment-864 Tue, 01 Sep 2026 11:03:19 +0000 https://davidohnstad.net/?p=155#comment-864 […] David Ohnstad has seen this failure mode from both sides—as a Senior Data Product Manager shipping AI integrations at Veeam Software, and as the person debugging runaway agent behavior at 2 AM when cloud bills spike. The problem isn’t that AI agents don’t work. The problem is that most enterprise teams deploy them with assumptions borrowed from static automation playbooks—assumptions that break the moment an agent starts making decisions without human checkpoints. What follows are four myths that persist across enterprise AI implementations, why they survive despite mounting evidence, and what actually happens when you replace them with operational reality. For more on this, see Enterprise AI platform myths debunked. […]

]]> 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/googles-new-generative-ai-search/#comment-859 Mon, 31 Aug 2026 16:33:46 +0000 https://davidohnstad.net/googles-new-generative-ai-search/#comment-859 […] For more on this topic, see Google’s Generative AI Search Revolution: What Enterprise Software Companies Must Do Now. […]

]]>