Choose Models That Fit the Product, Economics, and Risk
Choosing the right model isn’t just a technical decision. It shapes customer experience, economics, control, and risk as GenAI scales.
Mind the Gap!
Too many teams choose the model that looks strongest in isolation, then pay for the cost, latency, and control trade-offs later.
Key GenAI Model Selection Questions
- Are we choosing models based on the trade-offs that matter most, or defaulting to what looks strongest in isolation?
- Where are our evaluation gaps making it harder to balance performance, cost, latency, risk, and control with discipline?
- Do we have the discipline to choose models that fit the product, economics, and operating reality we actually need to support?
The Bottom-Line
Poor model selection drives up cost and complexity while product fit suffers.
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.
Launch Pad
Assess Your Readiness
Weeks 1–2
Align the team
- Identify key stakeholders
- Explore what “good” looks like
- Explore Real-World Use Cases
Assess current state
- Review Key Competencies
- Assess Your Readiness
- Add Comments for Context
Define readiness gaps
- Define Group Readiness
- Identify Mis-Alignment
- Capture Group Themes
Mission Control & Lift-Off
Build Your
Plan
Plan
Weeks 3–4
Prioritize the gaps
- Understand High-Impact Gaps
- Explore Gap Closure Options
- Prioritize For Impact & Effort
Build the roadmap
- Define Key Steps
- Align on Ownership
- Define Target Timeline
Define success measures
- Committed Target
- Stretch Goals
- Controls
Accelerate
Accelerate Your Momentum
Weeks 5–12
Execute priority moves
- Execute your plan
- Mitigate Risks
- Validate Your Impact
Drive adoption & change
- Identify Stakeholders
- Communicate Changes
- Action Feedback
Review impact & what's next
- Re-baseline Readiness
- Select Next Gaps
- Update your readiness plan
Outcomes you can expect
Clarity
See where evaluation gaps are weakening fit, economics, and control.
Alignment
Align on the trade-offs that should drive smarter model choices.
Focus
Prioritize the improvements that most strengthen fit, economics, and control.
Readiness
Build a stronger model-selection discipline for more confident GenAI decisions.
Impact
Improve the odds that model choices strengthen customer experience, margins, and scale.
Model choice quietly shapes
margins, control, and
customer experience.
margins, control, and
customer experience.
Frequently Asked Questions
1. Overview & Fit
2. Scope & Deliverables
3. Process & Timing
4. Participants & Ways of Working
5. Outcomes & Next Steps
- Who is this Model Evaluation & Selection readiness accelerator for?
It’s best suited to product leaders, AI leads, engineering leaders, platform owners, procurement and risk stakeholders, and teams responsible for how model decisions affect product performance, safety, and economics. It’s especially useful when model choice feels consequential but the evaluation process isn’t yet disciplined enough to support confident decisions. - When should we assess our Model Evaluation & Selection readiness?
Run it before weak evaluation methods lock the organization into model choices that are hard to trust or expensive to reverse. Teams often use this accelerator when they’re comparing multiple models, revisiting an existing vendor decision, or trying to balance quality, cost, latency, and safety more deliberately. - How is this different from simply running a bake-off between models?
A bake-off can compare outputs in the moment, but it doesn’t always strengthen the underlying decision discipline. This accelerator assesses whether your criteria, benchmarks, testing methods, governance, and decision practices are strong enough to support better model choices over time.
- What exactly gets assessed in Model Evaluation & Selection readiness?
The review focuses on how models are compared, which criteria matter, how trade-offs are tested, how risk and safety are evaluated, and how selection decisions are governed. It also identifies where the current approach is too narrow, inconsistent, or weak to support product-scale GenAI decisions. - What inputs and artifacts should we bring into the accelerator?
Bring evaluation criteria, benchmark results, vendor comparisons, latency and cost data, safety testing materials, architecture constraints, and any documentation describing how model decisions are made today. These inputs help reveal whether the organization is selecting models with enough rigor and cross-functional alignment. - What will we receive at the end of the accelerator?
At the end, you’ll have a current-state readiness view, prioritized evaluation and selection gaps, and a practical action plan to strengthen model choice over time. The goal is to leave with clearer criteria, better governance, and more confidence in how models are selected and revisited.
- How long does the accelerator take?
The accelerator is structured across an initial diagnosis and read-out period followed by a guided acceleration period that can extend through roughly 12 weeks. That gives teams enough time to assess current evaluation discipline, align on priorities, and begin improving the most important gaps. - How do the three phases work in practice?
The first phase identifies the evaluation and selection gaps, the second prioritizes and plans how to close them, and the third supports execution and refreshes readiness. This sequence helps leaders move from ad hoc comparison to a stronger decision framework for model choice. - How hands-on is the 12-week period?
It’s hands-on enough to improve real evaluation practices without turning into a large-scale model research program. Most organizations use the period to sharpen benchmarks, clarify trade-off criteria, improve governance, and make model decisions easier to trust.
- Which teams should participate?
Product, AI, engineering, platform, procurement, security, and risk stakeholders should participate, along with anyone responsible for vendor choice, model governance, or the performance economics of GenAI. The right mix depends on who shapes the trade-offs behind model selection today. - How much time should leaders and working teams expect to commit?
Leaders usually join the kick-off, review sessions, and prioritization decisions, while working teams contribute benchmarks, artifacts, and evaluation detail. The work stays manageable because it’s anchored in real model choices and the practical trade-offs teams are already facing. - How will the right teams work together during the accelerator?
The accelerator creates a structured cross-functional process for comparing models, clarifying trade-offs, and improving how selection decisions are made. That helps the organization treat model choice as a product and business decision, not just a technical preference.
- What changes when Model Evaluation & Selection readiness improves?
The payoff is more confidence that model decisions reflect the right trade-offs across quality, latency, cost, safety, and reliability. It becomes easier to choose models that fit the product and to revisit those choices as needs evolve. - How quickly can we act on the findings?
Most teams can act on the findings quickly because the work usually surfaces practical gaps in benchmarks, selection criteria, governance, and testing methods. Early actions often make model decisions more transparent and easier to defend within the next quarter. - What should we do after the readiness assessment is complete?
Use the findings to strengthen the model evaluation discipline, assign clear owners, and embed better selection practices into how GenAI products are planned and governed. The strongest teams revisit readiness as use cases expand, vendors change, and operating constraints evolve.
Choose Models That Fit