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10 Aug 2026

Leading AI-Enabled Agile Organizations - August 10

Leading AI-Enabled Agile Organizations - August 10

Leading AI-Enabled Organizations is a practical leadership event designed to help non-technical and technical leaders guide their organizations through AI adoption with clarity, discipline, and measurable business value.

Participants will learn how to understand AI without hype, build an AI strategy tied to real business outcomes, lead people through AI-driven change, establish responsible governance, and measure the impact of AI initiatives. The session emphasizes that AI is not just a technology initiative—it is an enterprise capability involving strategy, people, process, risk, and accountability.

Attendees will leave with actionable frameworks for selecting AI pilots, defining ownership, communicating change, creating acceptable-use policies, and tracking ROI. The central message is that AI amplifies human judgment; it does not replace leadership, ethics, or accountability.

Author: Rod Claar
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2 Sep 2026

Leading AI-Enabled Agile Orginizations - September 2, 2026

Leading AI-Enabled Agile Orginizations - September 2, 2026

Leading AI-Enabled Organizations is a practical leadership event designed to help non-technical and technical leaders guide their organizations through AI adoption with clarity, discipline, and measurable business value.

Participants will learn how to understand AI without hype, build an AI strategy tied to real business outcomes, lead people through AI-driven change, establish responsible governance, and measure the impact of AI initiatives. The session emphasizes that AI is not just a technology initiative—it is an enterprise capability involving strategy, people, process, risk, and accountability.

Attendees will leave with actionable frameworks for selecting AI pilots, defining ownership, communicating change, creating acceptable-use policies, and tracking ROI. The central message is that AI amplifies human judgment; it does not replace leadership, ethics, or accountability.

Author: Rod Claar
0 Comments
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Executive Summary

The 6-Step Framework for Selecting the Right AI Model

Strategic premise

Model selection is a capital allocation decision — not a technical experiment.

In 2026, organizations do not suffer from lack of AI options. They suffer from poor allocation of AI capability relative to business goals, cost structure, and risk exposure.

The competitive advantage is not access to models. It is disciplined model selection and lifecycle management.

Why this matters

Poor model decisions result in:

  • 3–12 months of rework
  • Escalating cloud spend
  • Latency failures in production
  • Compliance and governance exposure
  • Erosion of executive trust

Disciplined selection leads to:

  • Faster product iteration
  • Controlled operating margins
  • Sustainable AI scaling
  • Reduced architectural churn

The 6-step model selection framework

  1. Define the business outcome. Start with a measurable KPI—select 1 primary metric plus guardrails (cost, latency, safety).
  2. Classify the task type. Match the problem to the right family (classic ML, RAG, lightweight LLM, multimodal, etc.).
  3. Map constraints. Identify non-negotiables (latency, cost ceilings, privacy/residency, interpretability, licensing, capability).
  4. Build a trade-off matrix. Score shortlisted models on quality, speed, cost, privacy, operability, and compliance using weighted criteria.
  5. Run the cheap evaluation loop. Baseline → rapid prototype → offline eval → small A/B (1–5% traffic) to generate signal before scaling.
  6. Decide and plan for change. Architect for adaptability; monitor KPI/cost/drift; re-evaluate every 6–12 months.

Executive insight

AI success does not come from selecting the most powerful model. It comes from clear KPI alignment, accurate task classification, explicit constraint discipline, evidence-based evaluation, and continuous lifecycle governance.

Model capability is abundant. Strategic allocation is rare.

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