AI isn’t just accelerating software development – it’s rewriting the economics of how companies scale
AI isn’t just accelerating software development — it’s rewriting the economics of how companies scale.
In a recent TechCrunch interview, Microsoft CoreAI Corporate VP Amanda Silver described agentic AI as a shift comparable to the rise of cloud computing (Russell Brandom, Feb 11, 2026).
From where I sit in the Microsoft Dynamics and NetSuite talent ecosystem, that comparison is accurate — and if anything, understated.
We are witnessing a pretty big shift in how companies operate.
Multi-step agents are now capable of the following key actions:
• Maintaining and modernizing enterprise codebases
• Diagnosing and resolving production incidents
• Compressing operational workflows that once required specialized teams
• Enabling companies to scale capability faster than headcount
This is producing a new type of organization. Organizations built with leaner teams, higher output per employee, and faster execution cycles.
Despite all of the excitement around AI, there is still a great deal of confusion with many business leaders.
AI adoption is not constrained by technology; it is constrained by organizational clarity and talent bandwidth and capacity.
Across ERP and CRM environments, the most common failure pattern isn’t model performance — it’s a lack of definition around:
→ the business objective being automated
→ the workflow being redesigned
→ the data and governance structure supporting the system
That gap is precisely why specialized enterprise talent is becoming more valuable, not less.
At DynamicsFocus, we operate exclusively at the intersection of ERP platforms, enterprise transformation, and AI-enabled operating models.
Through our FocusFramework™ methodology, we see firsthand that AI compresses execution work, while simultaneously increasing the premium on elite solution architects, practice builders, and transformation leaders who can operationalize it at scale.
In other words: AI reduces labor intensity, but it raises the bar for capability.
If you’re in the process of building or scaling a Microsoft Dynamics or NetSuite capability and rethinking what your team should look like in an AI-enabled operating model, I’m always open to exchanging ideas with founders, PE operating partners, and practice leaders navigating this shift.
In a recent TechCrunch interview, Microsoft CoreAI Corporate VP Amanda Silver described agentic AI as a shift comparable to the rise of cloud computing (Russell Brandom, Feb 11, 2026).
From where I sit in the Microsoft Dynamics and NetSuite talent ecosystem, that comparison is accurate — and if anything, understated.
We are witnessing a pretty big shift in how companies operate.
Multi-step agents are now capable of the following key actions:
• Maintaining and modernizing enterprise codebases
• Diagnosing and resolving production incidents
• Compressing operational workflows that once required specialized teams
• Enabling companies to scale capability faster than headcount
This is producing a new type of organization. Organizations built with leaner teams, higher output per employee, and faster execution cycles.
Despite all of the excitement around AI, there is still a great deal of confusion with many business leaders.
AI adoption is not constrained by technology; it is constrained by organizational clarity and talent bandwidth and capacity.
Across ERP and CRM environments, the most common failure pattern isn’t model performance — it’s a lack of definition around:
→ the business objective being automated
→ the workflow being redesigned
→ the data and governance structure supporting the system
That gap is precisely why specialized enterprise talent is becoming more valuable, not less.
At DynamicsFocus, we operate exclusively at the intersection of ERP platforms, enterprise transformation, and AI-enabled operating models.
Through our FocusFramework™ methodology, we see firsthand that AI compresses execution work, while simultaneously increasing the premium on elite solution architects, practice builders, and transformation leaders who can operationalize it at scale.
In other words: AI reduces labor intensity, but it raises the bar for capability.
If you’re in the process of building or scaling a Microsoft Dynamics or NetSuite capability and rethinking what your team should look like in an AI-enabled operating model, I’m always open to exchanging ideas with founders, PE operating partners, and practice leaders navigating this shift.
(Interview: Amanda Silver, Microsoft CoreAI — TechCrunch, Feb 11, 2026)
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