Three people can read “the customer needs a better price” and arrive at three different proposals. The seller sees a discount. Procurement wants certainty about next year’s spend. The customer’s finance team may have a spending ceiling that no proposed uplift can meet. Until those positions are reconciled, a polished recommendation can answer the wrong question.
AI can help organise this evidence and compare possible offers, provided the workflow gives it access to the relevant records and requires people to check its interpretation. The commercial value lies in exposing the choice: what the customer needs, what the supplier would give up and which conditions make the exchange acceptable.
Establish what the customer has actually said
Consider a subscription renewing at S$100,000 a year. The opportunity notes say “budget pressure”. A procurement email says the customer could consider a two-year agreement if the annual price stays predictable. Neither statement confirms that the customer can spend more than S$100,000.
An AI-generated brief should preserve that distinction. “Open to a longer commitment” is supported by the email. “Able to accept a 5% increase” is an assumption requiring confirmation. The missing answer matters enough to obtain before presenting a preferred offer to the customer.
Source quality also involves applicability. A signed order establishes the existing price and scope; an executed amendment may alter them. A current commercial policy sets the permitted renewal structures. A seller’s account note describes their understanding of the conversation. An old exception approval records a decision under particular conditions. These documents carry different authority, even when they contain similar language.
For the comparison, the reviewer should be able to open the relevant provision or record, identify its version and see why it applies to this customer, entity and renewal. Finding a document that mentions the right percentage is only the beginning of that check.
Price the exchange on a consistent basis
Assume, for this illustration, that current guidance permits a 7% uplift for a one-year renewal. A multi-year renewal permits a 5% increase applied once, with that annual price held flat throughout the commitment. Scope remains unchanged, and there are no usage charges, services fees or cancellation rights in these options.
The one-year proposal is S$107,000. A two-year proposal is S$105,000 each year, or S$210,000 in recurring TCV. Three years at the same annual price produces S$315,000 in recurring TCV.
The larger total does not establish a better deal. Three years extends the supplier’s price protection obligation, while procurement has only mentioned two. The comparison should show that additional exposure alongside the additional commitment. It should also distinguish the recurring annual amount used for ARR from contract value, billing dates and cash collection. S$210,000 of TCV does not mean S$210,000 will be paid upfront.
Use checkable calculations for these amounts. The AI’s prose can explain the results, but the pricing model should retain the inputs, formulas and exclusions. If implementation costs or expected usage affect the recommendation and are unavailable, identify the gap. An invented cost estimate can make an otherwise accurate price comparison misleading.
Write a recommendation that can survive a challenge
If the customer confirms it can accept S$105,000 annually and values predictability, the two-year offer has a credible rationale. It exchanges a lower initial uplift and two years of price certainty for the period of commitment the customer is willing to make. The recommendation should identify the remaining approvals and the specific proposal they cover.
If S$100,000 is a hard annual ceiling, all three illustrated options fail the budget test. Deal desk then has a different decision: assess reduced scope, or seek an exception for a flat renewal with a documented reason. Rewording the two-year offer as “budget friendly” would hide the unresolved constraint.
The whole package needs the same scrutiny. Quarterly invoicing changes collection timing. Free implementation adds an obligation outside the subscription price. A customer right to terminate after twelve months changes the value of a two-year commitment. Each may have an owner in finance, legal or delivery whose answer affects the commercial recommendation.
A paragraph explaining why an offer is attractive is not evidence that it is permitted. NIST’s Generative AI Profile identifies the risk of confidently incorrect output, including reasoning that appears to justify an erroneous answer. For this workflow, reviewers should check the underlying records and calculations as well as the explanation.
Change the facts before trusting the answer
A practical evaluation starts with cases that force a different response. Replace the assumed spending flexibility with a confirmed cap. Add an early termination request. Substitute an expired policy for the current guidance. Ask whether the recommendation changes, identifies the conflict or pauses for a named decision.
These checks test whether the workflow respects the commercial conditions, rather than whether it can produce a convincing approval brief. Reviewers should also look for omitted concessions and citations that open the right document but support the wrong claim.
After a decision, preserve its scope. Record the approved quote version, assumptions, conditions and authority. Delegated authority determines whether a person or system may prepare an output, update a record or release an offer. A recommendation cannot silently become permission for all three.
The final customer proposal should retain the exchange the reviewer accepted. If a later negotiation removes the commitment that justified the price, the case needs another decision. That is the point at which commercial AI proves its value: it makes the changed bargain visible while there is still time to respond.