Choosing the right model is a product, economic, technical, and risk decision. This accelerator tests whether teams have the selection criteria, evaluation evidence, cost visibility, and operating discipline to pick models that fit.
Mind the Gap!
Too many teams choose the model that looks strongest in isolation, then pay for the cost, latency, control, and quality trade-offs later. Without selection discipline, model choices become expensive defaults.
- Are we selecting LLMs based on solution fit, or defaulting to the most expensive ones?
- Where could weak selection criteria create cost, latency, quality, or risk issues?
- Do we have the discipline to continually optimize LLM choices as solution needs change?
Build the Model-Selection Discipline GenAI Scale Demands
We help leaders build a stronger model-selection discipline, clarify the trade-offs that matter most, and choose models that better fit the experience, economics, and risk they need to manage.
- Identify key stakeholders
- Explore what “good” looks like
- Explore Real-World Use Cases
- Review Key Competencies
- Assess Your Readiness
- Add Comments for Context
- Define Group Readiness
- Identify Mis-Alignment
- Capture Group Themes
Plan
- Understand High-Impact Gaps
- Explore Gap Closure Options
- Prioritize For Impact & Effort
- Define Key Steps
- Align on Ownership
- Define Target Timeline
- Committed Target
- Stretch Goals
- Controls
- Execute your plan
- Mitigate Risks
- Validate Your Impact
- Identify Stakeholders
- Communicate Changes
- Action Feedback
- Re-baseline Readiness
- Select Next Gaps
- Update your readiness plan
Outcomes you can expect
See where evaluation gaps are weakening fit, economics, and control.
Align on the trade-offs that should drive smarter model choices.
Prioritize the improvements that most strengthen fit, economics, and control.
Build a stronger model-selection discipline for more confident GenAI decisions.
Improve the odds that model choices strengthen customer experience, margins, and scale.
Frequently Asked Questions
- Who is this GenAI Model Selection readiness accelerator for?
AI, product, engineering, platform, risk, finance, and procurement leaders choosing production models. - When should we assess our GenAI Model Selection readiness?
Assess before model decisions create cost, quality, latency, or risk trade-offs. - How is this different from a standard model bake-off?
It builds decision discipline beyond one-off model comparisons.
- What exactly gets assessed in GenAI Model Selection readiness?
We review evaluation criteria, test sets, benchmarks, cost, latency, risk, and deployment fit. - What inputs and artifacts should we bring into the accelerator?
Bring model candidates, evaluation results, prompts, use cases, constraints, and decision records. - What will we receive at the end of the accelerator?
You get a model-selection readiness view, priority gaps, and a decision framework.
- How long does the accelerator take?
Plan on roughly 12 weeks, from diagnosis through prioritized gap closure. - How do the three phases work in practice?
Diagnose model-selection gaps, align trade-offs, then close the highest-leverage issues. - How hands-on is the 12-week period?
Hands-on enough to turn model trade-offs into stronger decisions and next steps.
- Which teams should participate?
Include AI, product, engineering, platform, risk, security, procurement, and finance owners. - How much time should leaders and working teams expect to commit?
Sponsors join key decisions; working teams support diagnostics, reviews, and action planning. - How will the right teams work together during the accelerator?
Teams align on model trade-offs, approval criteria, evidence, and ownership.
- What changes when GenAI Model Selection readiness improves?
Model choices become clearer, defensible, and better matched to product goals. - How quickly can we act on the findings?
Immediately. The accelerator prioritizes gaps leaders can act on right away. - What should we do after the readiness assessment is complete?
Prioritize model-selection fixes, decision criteria, and evaluation evidence.