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23 Jun 2026

Leading AI-Enabled Agile Orginizations - September 2, 2026

Leading AI-Enabled Agile Orginizations - September 2, 2026

Author: Rod Claar  /  Categories: AI Leaders  / 

Event date: 9/2/2026 1:00 PM - 4:00 PM Export event

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Leading AI-Enabled Organizations is a practical leadership session for executives, managers, product leaders, Scrum leaders, and organizational decision-makers who need to understand how to lead effectively in the AI-first era.

This event is not a technical deep dive or coding workshop. Instead, it focuses on the leadership capabilities required to evaluate AI opportunities, build responsible strategies, guide teams through change, manage risk, and measure business impact.

Participants will learn how to:

  • Explain AI concepts clearly without technical jargon
  • Build an AI strategy tied to real business outcomes
  • Identify high-value pilot opportunities
  • Lead people through AI-driven organizational change
  • Apply governance, ethics, and risk-management practices
  • Measure AI impact using meaningful business metrics
  • Sustain innovation through repeatable leadership practices

The session emphasizes practical decision-making, human-in-the-loop accountability, AI governance, organizational readiness, and measurable outcomes. Attendees will leave with concrete frameworks they can apply immediately, including approaches for AI strategy, adoption planning, acceptable-use policy development, and impact measurement.

This course is designed for leaders who want to move beyond AI hype and start building real organizational capability with clarity, confidence, and responsibility.

 
 
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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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