The Cost of Choosing the Wrong AI Model in Agentforce
When it comes to AI delivery, most people focus on the prompt and overlook the engine behind it. But if you have ever wondered why your Agentforce response is slow, inaccurate, or just off, chances are the model choice is the issue.
At We Lead Out, we have learned that model selection is not just a technical detail. It is a delivery decision. And choosing the wrong one can quietly cost you real time and money.
The real problem
We have seen it happen more than once. A team drops GPT 4o into every use case, assuming it will outperform everything else. What follows is a mix of slow responses, inconsistent results, and growing costs.
Just because it is new or powerful does not mean it is right for the job.
How we approach model selection
Here is how we think about model choice in Agentforce, and how we help clients avoid the trap of using one model for everything.
WLO Model Selection Framework; Choosing the right AI model for the job
| Model | Strength | Weakness | Best For |
|---|---|---|---|
| GPT 3.5 | Fast and affordable | Struggles with nuance or logic | FAQs, keyword routing, basic lookups |
| GPT 4 | Strong reasoning | Slower and more expensive | Classification, decision trees, escalation triggers |
| GPT 4o | Handles vision, fast responses, mixed input | Still evolving and sometimes inconsistent | Generative replies, smart forms, mixed content |
| Claude (if available) | Excellent for long-form content and summaries | Can be overly cautious and indirect | Email summarisation, tone-sensitive tasks |
The key is not just asking what a model can do, but what it should do for the task at hand.
A real delivery example
While developing our Agentforce approach internally, we ran multiple proof-of-concepts to explore how different models handled automation in our sales review and pre-sales workflows. Initially, we tested GPT 4o to summarise opportunity notes, classify deal stages, and support internal handovers. It made sense in theory, but the responses were inconsistent and the processing time was higher than expected.
We then switched the prompt to GPT 4, refined the logic, and added tighter constraints. The result? Accuracy improved by over 30 percent, and response time dropped significantly. For more straightforward flows like FAQs and internal process lookups, GPT 3.5 handled the job with ease and saved on cost.
These internal tests have shaped the way we approach AI delivery and reinforced that choosing the right model is just as important as writing a good prompt.
What you can do right now
Review where each model is being used and ask if it matches the task
Consider cost, speed, and risk in your model choices
Do not default to the newest or most powerful model for everything
Build model selection into your delivery process
Reach out if you’d like help choosing the right model, crafting prompts, or anything similar.
Follow We Lead Out on LinkedIn to keep learning with us.
