First year credit burn reached 45 to 70 percent of the committed pool, and the unused balance rarely rolled over
The AELA is sold as one number, one renewal, one discount. Underneath it is a forecast bet: the buyer commits to a consumption pool before anyone knows the run rate, and the pool does not wait around for the demand to arrive.
Prepared by Redress Compliance · August 16, 2026 · Salesforce advisory. 25 to 35 Salesforce AI and Data Cloud engagements, 2024 to 2025.
Executive summary
Burn reached 45 to 70 percent of the committed pool in the first year, so between 30 and 55 percent of what was bought went unconsumed, and unused credits rarely roll over.
Salesforce modelled consumption 30 to 50 percent above actual first year use. The proposal forecast is the vendor's estimate of your demand, produced before either side has a run rate to work from.
Splitting the commit paid 18 to 30 percent less over the term. A smaller base plus a priced expansion option beats a single large commitment, because it prices the demand you can prove and defers the demand you cannot.
The headline discount hides the uplift. Annual uplift clauses inside the AELA ran 7 to 12 percent, and the renewal frequently resets to list, which is where a multi year discount narrative comes apart.
What the agreement actually is
The AELA is an enterprise commitment pooling AI and data consumption into one drawdown balance. It sits above the per product editions rather than replacing them, which is the detail that decides how the risk lands.
| Dimension | How Salesforce presents it | What it means for the buyer |
|---|---|---|
| Structure | One commit across Agentforce, Einstein, and Data Cloud | One forecast covering three products with different run rates |
| Pricing | Discounted credit rate against a committed pool | Discount is real, and it is priced against volume you must predict |
| Consumption | Metered drawdown as the products run | Unused credits rarely roll over |
| Uplift | Standard annual escalation | 7 to 12 percent, frequently resetting to list at renewal |
| Agentforce metric | Per conversation | Hard to forecast in year one, which is when the commit is set |
The pitch is simplicity and the mechanism is risk transfer. A single pooled commit removes the work of negotiating three consumption models separately, and in exchange the buyer accepts the forecast risk for all three at once. That trade can be worth making. It is only worth making at a volume you can defend, which is why the sizing question matters more than the credit rate, and why the credit rate is the part the conversation tends to focus on.
A discount on credits you do not burn is not a discount
Every AELA conversation opens on the rate per credit, because that is the number where a discount is visible and comparable. The engagement file says the rate is rarely where the money is. Across 25 to 35 Salesforce AI and Data Cloud engagements, first year credit burn landed between 45 and 70 percent of the committed pool. Between a third and a half of what was purchased went unconsumed, and because unused credits rarely roll over, that portion was bought at an effective rate of infinity.
The reason the gap is so consistent is that the forecast comes from the wrong side of the table. Salesforce modelled consumption 30 to 50 percent above what customers actually used in year one, and it does so in good faith: nobody has a run rate for a product category this new, so the model is built from adoption assumptions rather than from observed behaviour. Agentforce compounds the problem by pricing per conversation, a unit that is genuinely hard to forecast before deployment. The buyer is asked to commit to a volume at precisely the moment they have the least information about it, and the vendor incentive on that forecast points in one direction.
The move that worked is structural rather than rhetorical. Buyers who split the commitment into a smaller base plus a priced expansion option paid 18 to 30 percent less across the term. That works because it separates two different problems: the demand you can evidence today, which should be committed and discounted, and the demand you hope for, which should be priced now and bought later if it materialises. A single large commit merges them and charges you for both. The expansion option costs something to negotiate and it is almost always cheaper than the credits it replaces.
Two things then need holding in the paper. Independent benchmarking on the credit rate remains the single highest value pre signature lever, because the rate is unpublished and a buyer with no reference point cannot tell a real discount from a presented one. And the uplift needs the same attention as the rate: annual escalation inside the AELA ran 7 to 12 percent with a frequent renewal reset to list, which quietly undoes the multi year discount narrative that justified the commitment in the first place. The per conversation economics sit in the Agentforce cost benchmark, the credit mechanics in the AI credits model, and the wider library in the Salesforce practice.
- Percentile standing for your exact deal size and industry, from real closed transactions
- Scenario simulation before the call: base plus expansion priced against a single commit
- A negotiation playbook, talking points, and a two page executive brief on day one
Where the risk actually sits
- Over commitment is the most common loss, not a bad rate. Consumption is metered, unused credits rarely roll over, and the pool does not wait for demand to arrive.
- The forecast in the proposal is the vendor's, and it ran 30 to 50 percent above real first year use. Treat it as an opening position on volume, exactly as you would treat an opening position on price.
- Agentforce prices per conversation, which makes year one demand genuinely unpredictable, and year one is when the commit is set.
- The renewal reset to list is the clause that undoes the discount, so the multi year narrative needs the renewal floor written down rather than described.
- Three products, one pool, three different adoption curves. Data Cloud, Einstein, and Agentforce do not ramp together, and a single pooled forecast hides which one is actually consuming.
- Benchmark the credit rate independently before signature, because the rate is unpublished and without a reference point a presented discount cannot be evaluated.
What the AI agreements showed, 2024 to 2025
Across roughly 25 to 35 Salesforce AI and Data Cloud engagements advised on:
Share of the committed credit pool actually consumed in year one, with the unused balance rarely rolling over.
What buyers paid less over the term by taking a smaller base commitment plus a priced expansion option.
The consumption volume Salesforce modelled in the proposal ran 30 to 50 percent above what the customer actually used in the first year, and annual uplift clauses inside the agreement ran 7 to 12 percent, well above the headline discount narrative.
Salesforce introduced enterprise AI agreements as generative features moved from add ons to platform. The AELA is the commercial vehicle for that shift, and the shift is real. What it does not change is that a pooled commitment is a forecast bet, and the buyer is the one holding it.
Watch the briefing · 4:06Negotiating Agentforce and Data Cloud: The Credit EconomicsHow the pooled credit commitment prices, and where the forecast risk actually lands.
Your first five moves
- Size the first commit to provable demand, not to the vendor forecast, and treat the proposal volume as an opening position on quantity.
- Split the commitment into a base plus a priced expansion option, which paid 18 to 30 percent less across the term in the engagements benchmarked.
- Model the three products separately even though the pool is single, because Data Cloud, Einstein, and Agentforce ramp on different curves.
- Benchmark the credit rate independently before signature, since it is unpublished and a discount cannot be evaluated without a reference point.
- Write down the uplift cap and the renewal floor, because 7 to 12 percent escalation and a reset to list undo the discount that justified the commit. The Salesforce practice sizes the commit with you.
Frequently asked questions
What is the Salesforce AELA?
The AI Enterprise License Agreement, an enterprise commitment that pools AI and data consumption across Agentforce, Einstein, and Data Cloud into one drawdown balance. It sits above the per product editions rather than replacing them, and it is sold as one number, one renewal, one discount.
How much of the committed pool actually gets used?
Between 45 and 70 percent in the first year across the engagements tracked. That leaves 30 to 55 percent unconsumed, and because unused credits rarely roll over, that portion is bought and never delivered.
Is the Salesforce consumption forecast reliable?
It ran 30 to 50 percent above actual first year use. That is not necessarily bad faith, since nobody has a run rate for a product category this new, but the forecast comes from the party that benefits when it is high. Treat it as an opening position on volume.
What is the single biggest saving available?
Splitting the commitment. Buyers who took a smaller base commit plus a priced expansion option paid 18 to 30 percent less over the term, because it separates demand you can evidence from demand you hope for and only charges you for the first.
Do unused credits roll over?
Rarely. That is what turns an over commitment into a permanent loss rather than a timing difference, and it is why over commitment rather than the credit rate is the most common way money is lost on an AELA.
Why is Agentforce hard to forecast?
It is priced per conversation, and conversation volume in year one depends on adoption patterns nobody can observe before deployment. The commit is set at exactly the moment the buyer has least information about the metric that drives it.
What uplift sits inside the agreement?
Annual escalation of 7 to 12 percent, well above what the headline discount narrative implies, with a frequent renewal reset to list. Both need writing down, because together they can undo the multi year discount that justified the commitment.
Should we benchmark the credit rate?
Yes, and before signature. The rate is unpublished, so a buyer with no independent reference cannot distinguish a real discount from a presented one. It is the single highest value pre signature lever in the file.
Is the AELA a bad deal?
No, it is a risk transfer that can be worth making. One pooled commit removes the work of negotiating three consumption models separately. It is only worth making at a volume you can defend, which is why sizing matters more than the rate.
Should the three products be modelled together?
The pool is single but the products are not. Data Cloud, Einstein, and Agentforce ramp on different curves, and a single pooled forecast hides which one is actually consuming. Model them separately even when you commit them together.
Negotiating Agentforce and Data Cloud: The Credit Economy
Three currencies, three discount curves: Flex Credits, conversations, and Data Cloud credits. Vendor forecasts run 30 to 50 percent high and buyers burn 45 to 70 percent of commits. Sizing from telemetry, capping the 7 to 12 percent uplift, and keeping the AI severable.
