Overview
AI models are now a production dependency for many businesses. This month, OpenAI highlighted this when it announced it will retire GPT 4o and other older ChatGPT models in its chat apps on the web and in the mobile app.
While this announcement applies to their applications, not the API, it is a strong reminder that you need practical plans in place to handle model deprecation and upgrades before you are forced to do so. This is because any change to a model you use can quietly break outputs across your AI systems and workflows, since prompts may no longer work as intended.
Not many companies run AI in production today. Even fewer are prepared to upgrade the model in an AI system or workflow.
The opportunity is simple. Treat model changes like any other business critical change, with an owner, a plan, and a standard review loop. Teams that do this will keep their speed while competitors get stuck in rework, brand mistakes, and last minute fire drills.
In AI, your competitive advantage comes from stable AI workflows you can trust. Being proactive and intentional about model changes lets you scale adoption faster than peers without increasing risk.
The Executive Take
What changed
AI providers are upgrading and deprecating models faster, and those changes can affect your workflows without your knowledge or approval
Even when APIs stay stable, model behavior can still drift when you upgrade versions in your own systems. This can change tone, accuracy, and output structure
AI security and governance are now operational, not theoretical. Model changes are one of the easiest ways to create accidental exposure or customer facing mistakes
Why it matters
Model changes create silent variance in companies’ outputs and pricing
Variance creates business risk, but it also creates measurable waste through rework, escalations, and loss of trust in AI outputs
If you cannot upgrade models predictably, you will struggle to scale AI beyond small pilots
Advantage
A Model Change Playbook lets you upgrade faster than competitors with fewer mistakes, which turns AI into a durable operating advantage
You build a prompt and workflow asset base that survives model shifts, so your team keeps compounding productivity instead of relearning every quarter
You gain negotiation leverage with vendors because you can switch models with less pain and clearer performance benchmarks
If you ignore it
Your teams will keep using AI anyway, but the quality will drift and you will not know until a customer or auditor finds it
You will get surprise “why did this change” incidents that waste leadership time and erode confidence in AI
You will fall behind competitors who can ship AI assisted workflows reliably and cheaply because they control unit economics and quality
What To Do This Week
Create a resource of your AI systems and workflows, along with what models they use for future reference
Take your most important AI system or workflow and create a plan to test a different model. Measure how it performs on:
Cost
Speed
Output (Objective: measurable performance)
Output (Subjective: tone, relevancy, and clarity)
Work through the initial steps of the Model Change Playbook below
Know what kind of model change you are dealing with
There are two main paths, and you have to treat them differently.
Path A: Model provider chat interfaces (ChatGPT, Gemini, Microsoft Copilot, and Claude chat)
This is when your LLM chat provider upgrades the model in its system. This is usually not a big deal because users are generally already using a newer model. However, it is important to understand that people are using it for real work right now, including customer emails, sales decks, board prep, HR templates, and policy drafts.
What is unique about this path:
Defaults can change without you deploying anything
Your employees can be on different versions at the same time
Existing conversations and custom GPTs can inherit new behavior automatically
You have less control over temperature, system instructions, version pinning, and logging
Path B: Model provider APIs inside your systems
This is when you have built an AI system or workflow to support your internal team or customers. Examples include your support bot, internal search, marketing content pipeline, analytics assistant, or agent workflows that call an API.
What is unique about this path:
You control when the model changes, if you designed the system correctly
You can pin versions, run A and B tests, and log outputs centrally
You can enforce structure, like JSON outputs and validation rules
You can tie cost per request and latency to business unit economics
APIs are slower to set up, but they are how you turn AI into a repeatable competitive advantage because you can measure and control it. This is the key area that you should to focus on when creating a model change playbook.
The Model Change Playbook Executives Actually Need
A Model Change Playbook is a lightweight plan to help you work through model upgrades or deprecations. It has 3 goals:
No surprises
Faster upgrades
Stable outputs that your business can trust
The Model Change Playbook
Step 1 - Pick the workflows that matter
Overview:
You only need to manage the workflows that can create real upside or real damage.
What to do:
Choose the top 10 AI Workflows or systems that touch customers, money, legal risk, regulated statements, or key decisions.
Assign a single owner of the Model Change Playbook (Ideally your existing AI Lead)
Goal to move on:
Top 10 list with a single owner
Step 2 - Document your AI systems and workflows
Overview:
If you don’t know what’s running, you can’t safely change models. This step creates visibility and prevents surprises.
What to do:
For each workflow/system, create a one-page “AI System Card”:
Purpose: what the system does and who uses it
Risk if wrong: what breaks (customer impact, legal/compliance, financial, security)
Model or models: exact model name and where it’s configured
Prompt location: repo/path or tool
Data sources: where inputs come from (databases, tickets, docs, APIs)
Output destination: where the result goes (customer, internal tool, downstream system)
Human review: where humans approve/override (if anywhere)
Failure plan: what happens if output is wrong (fallback, escalation, rollback)
Add two control basics for every workflow:
Logging: model version, prompt version, inputs/outputs (with privacy rules), latency, cost, errors
Rollback: a clear way to revert quickly (config switch or deploy toggle)
Goal to move on:
Every top workflow has an AI System Card, and basic logging + rollback exists (or a clear plan/date to add it)
Step 3 - Review models in use and pick where to start
Overview:
You want to switch models intentionally, not because a vendor forces it or a team changes something quietly.
What to do:
Build a simple Model Inventory from the AI System Cards:
Model name, version/date (if applicable), provider, cost tier, latency, and any known issues/call outs
For each model, check:
Age: how long it’s been in production
Deprecation risk: any vendor notices or likely end-of-life risk
Newer options: newer/cheaper/faster models that might work
Choose the first system to test using these rules:
High volume or high cost workflows (big savings potential), or
Deprecation risk workflows (avoid forced migration), or
High-risk workflows only if you already have strong evaluation + rollback
Choose the candidate model and write down the reason:
“Cheaper,” “faster,” “better quality,” or “current model being deprecated”
Goal to move on:
You have (1) a model inventory and (2) one chosen workflow + one candidate model + a clear reason for testing.
Step 4 - Decide how you will evaluate output
Overview:
If you can’t measure “better,” you’ll argue forever. Define success before you run tests.
What do to:
Pick the evaluation method for that workflow (use one or combine):
Metrics: accuracy, completion rate, defect rate, format correctness
LLM-as-judge: rubric-based scoring (with examples and guardrails)
Human review: a trained reviewer makes pass/fail calls
Define a simple rubric (keep it short and specific)
Set decision thresholds:
What “must not get worse” (e.g., compliance and format)
What “must improve” (e.g., accuracy or cost)
Build the test set:
Use real inputs from production (sanitized if needed)
Include edge cases and failure cases
Make it big enough to be meaningful (start with 50–200 examples; expand for high-risk)
Goal to move on:
You have a written rubric + decision thresholds + a test set ready to run.
Step 5 - Run both models on the same data
Overview:
Side-by-side testing removes guesswork and isolates the effect of the model change.
What do to:
Freeze variables so the comparison is fair:
Same prompt (or clearly versioned prompt changes)
Same inputs
Same tools/retrieval settings (if used)
Same output format requirements
Run:
Baseline: current model
Candidate: new model
Capture for each run:
Outputs, errors, latency, and cost
Any parsing/format failures
Any safety/compliance flags
Goal to move on:
You have paired outputs for baseline vs candidate, with cost and latency captured.
Step 6 - Evaluate results (quality + cost + speed)
Overview:
This is where you turn outputs into a decision, not a debate.
What to do:
Score outputs using the rubric from Step 4:
Use the same reviewers/judge method across both models
Spot-check disagreements if using LLM-as-judge
Summarize results clearly:
Pass rate, defect types, major regressions, major improvements
Compare operational impact:
Cost: cost per run and expected monthly cost at current volume
Latency: p50/p95 latency and worst-case behavior
Reliability: error rates/timeouts
Identify “deal-breakers”:
Any compliance failures
Any format failures that break downstream systems
Any new failure mode that is unacceptable
Goal to move on:
You have a one-page results summary: quality outcomes + cost/latency comparison + key risks
Step 7 - Make the switch decision and document why
Overview:
Create a clean record of why a model changed, who approved it, and what to do if it fails.
What to do:
Make a clear decision:
Switch now, do not switch, or switch with conditions
If you do switch to the new model, you will likely need to make small prompt tweaks
Write a short decision record (1 page):
What workflow, what model change, and why
Results summary (quality + cost + latency)
Known risks and mitigations
Required controls (human review, guardrails, monitoring)
Rollback plan and owner
If switching, define the release approach:
Start with internal traffic or low-risk segment
Canary rollout (small % first), then expand
Monitoring: defects, escalations, cost spikes, latency spikes
Clear rollback trigger (“if X happens, roll back immediately”)
Goal to move on:
A signed-off decision record exists, with a rollout + rollback plan and an accountable owner
Step 8 - Return to Step 3 until complete
Overview:
This turns model change into a repeatable operating process, not a one-time project.
What to do:
Move to the next workflow in priority order
Reuse what you built:
System Cards, evaluation rubric patterns, test set templates, and release checklists
Track progress in a simple dashboard:
Workflows completed, next up, blocked items, upcoming deprecations
Goal to move on:
All top workflows have gone through the loop (or have a documented reason they haven’t)
Step 9 - Stay ahead of deprecations with a cadence
Overview:
You want upgrades on your timeline, not forced migrations.
What to do:
Set a fixed review cadence:
Monthly: check cost, latency, and quality incidents for top workflows
Quarterly: re-run evaluations for the highest impact workflows
Track vendor changes:
Deprecations, pricing changes, new model releases, policy changes
Keep “next candidate” models ready:
For each critical workflow, maintain at least one candidate model and a current test set
Set internal deadlines:
If a vendor deprecates a model, set an internal migration deadline well before the forced date
Goal to move on:
No forced migrations, no surprise outages, and model upgrades happen predictably
Talking Points:
We treat AI model changes like production changes because output drift can create brand risk and rework
Our advantage comes from stable AI workflows we can trust, not from chasing new models
We will move fast by standardizing prompts, examples, and review rules for the few workflows that matter most
We use chat tools for speed and APIs for scalable advantage, because APIs give us control, logging, and rollback options
Glossary:
Inference: When an AI model generates an output in real time for a user request
Regression test: A repeatable set of tests you rerun after a change to make sure key workflows still work as expected
Canary release: A rollout method where a small percentage of usage moves to the new version first to reduce risk
Prompt package: A reusable set of instructions including task, constraints, format, and examples designed to reduce output drift
Output drift: When the same prompt produces meaningfully different results after a model or setting change
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Have a question or want to connect? Email me at treypezzetti@gmail.com