Sidu Ponnappa + realfast blog agent
May 29, 2025
Every Agentforce business case I've reviewed this month makes the same costly mistake.
CIOs are budgeting for AI success, but they're missing the hidden cost of AI effort.
Let me be clear: Flex Credits pricing is revolutionary. Moving from $2 per conversation to $0.10 per action finally aligns AI costs with actual value delivered. It's the transparent, usage-based model the industry needed.
But here's the thing: revolutionary pricing requires revolutionary planning.
The pattern is predictable.
Finance approves deployments based on 85% cost reduction projections. Six months later, budget surprises emerge.
The culprit? A fundamental misunderstanding of how consumption-based AI pricing works in production.
Here's what everyone gets wrong: they assume AI failures are free.
The logic seems sound. If AI can't resolve a ticket, it escalates to a human. Zero credits consumed, right?
Wrong.
Our analysis of early Flex Credits deployments revealed the truth: tickets where AI tries hardest are your most expensive interactions.
The universal estimation error
Organizations build budget models like this:
The theory:
Successful deflections: 70% × 2 actions × $0.10 = $0.14 per ticket
Failed deflections: 30% × 0 actions × $0.10 = $0.00 per ticket
Human handling: 30% × $7.00 = $2.10 per ticket
Total: $2.24 per ticket (68% savings)
Finance approves. Implementation begins. Everyone celebrates.
The reality:
Quick wins: 60% × 1.5 actions × $0.10 = $0.09 per ticket
Complex successes: 15% × 4 actions × $0.10 = $0.60 per ticket
Expensive attempts: 25% × (10 actions × $0.10 + $7 human effort) = $2.00 per ticket
That 25% of interactions? They consume ~75% of your Flex Credits budget.
The AI isn't failing cheaply. It's failing expensively.
The AI effort paradox
The better your AI gets at attempting complex resolutions, the more expensive your failures become.
Excellence creates a hidden cost penalty.
Watch what happens when sophisticated AI meets a complex query:
Search knowledge base (1 action)
Analyze customer history (1 action)
Attempt workflow automation (2 actions)
Try alternative resolution path (2 actions)
Make API calls for context (2 actions)
Execute diagnostic workflows (2 actions)
Escalate after exhausting options (1 action)
Total: 11 actions = $1.10
Each step represents AI working exactly as designed. Thorough. Methodical. Customer-focused.
But when it ultimately fails? You pay twice:
$1.10 for the AI attempt
$7.00 for the human resolution
A simpler AI that escalates quickly? Same outcome at $0.20 instead of $1.10.
Your most sophisticated behavior generates your highest costs when it fails.
Why everyone misses this
Human intuition about costs is broken when it comes to AI.
Human agents: Time spent = salaried cost (feels "wasted" but doesn't increase expense)
AI agents: Actions spent = discrete costs (each attempt adds measurable expense)
Pilots make it worse. At 50 tickets/day, that 12-action failure looks like noise. At 5,000 tickets/day, those failures become 25% of interactions and 70% of costs.
Result: Systematic underestimation that only appears at production scale.
The quick-complex-expensive estimation framework
Forget "success vs. escalation." Think three economic behaviors:
Quick wins (60-70%)
1-2 actions
$0.10-$0.20 per resolution
Exactly what everyone projects
Complex successes (10-15%)
3-5 actions
$0.30-$0.50 per resolution
Still excellent ROI
Expensive attempts (20-30%)
6-12 actions before escalation
$0.60-$1.20 in credits + $7 human
8x more expensive than successful deflections
Traditional thinking: Maximize deflection rates.
New reality: Optimize the ratio of successful actions to total actions – your "AI Efficiency Score."
Implementing the framework: The realfast.ai approach
At realfast.ai, we've updated our entire implementation methodology around the Quick-Complex-Expensive Estimation Framework. Here's how we help clients optimize for AI economics, not just AI performance:
The 6-Action Rule Stop expensive thrashing. No meaningful progress after 6 actions? Escalate.
The Effort Dashboard Track actions-per-interaction by query type. Spot expensive patterns before they scale.
The Success Ratio Metric Successful actions ÷ total actions = real-time efficiency that correlates with cost.
The Cost Ceiling Strategy Hard limits per interaction type. No query consumes more than predetermined credits.
Key insight: Sometimes accepting slightly lower deflection rates delivers dramatically better cost discipline.
The strategic implications
This isn't just Agentforce. Every platform is moving to consumption pricing:
OpenAI's API costs
Microsoft's Copilot Studio actions
Google's Vertex AI pricing
Master this transition or face:
Budget surprises that undermine AI confidence
Deployments that improve experience but miss cost targets
Executive skepticism about future AI investment
Winners will achieve:
Accurate budgets that survive production reality
Sustainable optimization balancing performance and economics
Strategic cost management enabling aggressive AI adoption
Competitive advantage through lower total ownership costs
The New Rules of AI Economics
Consumption pricing changes everything:
Effort becomes visible and measurable Balance thoroughness with efficiency.
Failure patterns are cost patterns Where AI consumes actions unsuccessfully matters as much as where it succeeds.
Success metrics must include economics Deflection rates matter. So does action efficiency.
AI behavior design is financial strategy How you train agents directly impacts cost structure.
Looking Forward
Agentforce Flex Credits is a genuine advancement – when implemented thoughtfully.
The transparency and usage alignment solve real problems. But success requires sophisticated understanding.
Traditional planning: Face $2M budget surprises
Quick-Complex-Expensive Estimation Framework: Achieve 65-75% cost reduction with high predictability
The technology delivers regardless. The difference is understanding how capabilities translate to costs under consumption pricing.
The Budget Question
That costly mistake? It's real.
It's the gap between projected and actual costs when using traditional models versus the Quick-Complex-Expensive Estimation Framework.
For a typical customer service deployment, it's the difference between celebrating 70% cost savings and explaining 30% budget overruns.
More than budget variance, it determines whether AI transformation builds confidence or creates skepticism.
Most importantly: It's entirely avoidable.
In consumption-based AI, your most expensive interactions aren't the ones requiring human help – they're the ones where AI tries hardest to avoid requiring human help.
Understand this paradox. Plan for it. Optimize around it.
That's how you achieve sustainable AI transformation.
Ready to implement Agentforce with realistic cost planning? If you're considering Agentforce for your organization, realfast.ai offers free estimation workshops that help you build accurate budgets using the Quick-Complex-Expensive Estimation Framework. Our AI-native approach ensures your business case survives contact with production reality.
Sidu Ponnappa is co-founder of realfast.ai, the first AI-native Salesforce implementation partner. His team specializes in data-driven optimization strategies that maximize both AI performance and economic efficiency across enterprise deployments.
