Before committing resources to a proof-of-concept (POC), build a preliminary ROI case. If the potential return isn't substantial enough for the customer to reallocate budget or personnel, the deal is unlikely to close. This step prevents wasting both your and your customer's time on unwinnable evaluations.

In the AI space, the sales cycle is inverted. Motivated prospects often build a proof-of-concept integrating a vendor's product *before* speaking to a sales team. The first call is no longer for discovery but for validating the work they've already done and discussing specific deployment or security needs.

Frame initial customer conversations around seeking advice on their biggest AI automation needs. This lowers their guard, provides valuable feedback, and often leads them to sell themselves on your future solution, making pre-selling easier.

Instead of pitching a large, expensive AI project, FDEs should first sell a paid "audit." This initial engagement maps workflows and identifies high-ROI automation opportunities. It builds client trust, proves value upfront, and serves as a lower-risk entry point to a full implementation.

Many enterprises explore AI due to pressure, not strategy, a phenomenon called "AI tourism." To avoid wasting resources on these tire-kickers, Sierra requires paid proofs-of-concept. The payment, even if modest, serves as a powerful filter for serious buyers with a real intent to deploy.

Eliminate the "send me a proposal" stall by defining the next step as a valuable, paid engagement, like a diagnostic or workshop. By charging for this, you force the money conversation early, filter for serious buyers, and avoid creating free documentation that can be shopped around.

Many teams fail with AI because they try to force-fit the technology onto problems. The winning approach is to first identify a critical business challenge, define success metrics, and only then determine if AI is the appropriate solution. This avoids applying a solution in search of a problem and addresses crucial change management.

AI products are so flexible that proofs-of-concept (POCs) can drag on forever as customers explore endless possibilities. To close deals, sales teams must enforce discipline by defining strict success criteria and a non-negotiable end date upfront. This prevents the POC from becoming an indefinite research project.

To avoid wasting time on low-impact pilots, BackOps asks prospects to rate a potential use case's importance from 1 (irrelevant) to 10 (business-critical). They only proceed with use cases that score a 7 or higher, ensuring genuine business impact and stakeholder buy-in.