AI optimism, monetization jitters as buyers demand proof and transparent pricing

Cautious optimism for AI as software leaders weigh strategy

Software leaders see growth ahead, yet many still question how to turn AI into real revenue. Buyers are ready to spend, but they want proof, not promises. Pricing, adoption, and trust now decide who wins.

"Software leaders are walking a tightrope: Confidence in continued growth is colliding with anxiety over fast-moving AI disruption. This is pushing executive teams to balance bold innovation with measured risk," said Ruben de Lange, partner at Simon-Kucher.

Source: Simon-Kucher

Executive optimism meets AI reality

88% of software executives expect revenue to rise or hold steady in 2025. Yet many are unsure their AI offer truly fits current market needs.

Headcount stays flat for nearly half of firms, but roles are shifting. Inside sales is shrinking, while field roles and AI-leveraged positions are gaining ground.

AI features are shipping, but monetization lags

76% of companies have launched AI features, but those features contribute under 10% of revenue. Pricing and packaging are the choke points.

45% of vendors run two or more pricing motions for AI. Common tactics: bundle AI into premium tiers to drive upgrades, or sell as add-ons. Data monetization is rising too-83% already monetize or plan to. Top uses: benchmarking (68%) and customer profiling (47%); only 27% use customer data to train models.

Buyers are ready, but they want proof

Software spend is projected to grow 9% over two years, fueled by digital transformation and expansion. 74% plan to implement AI, and 54% already use third-party AI tools.

Their concerns are practical: usability, compliance, and clear ROI. Purchasing is now cross-functional for 56% of buyers, which raises the bar on security, integration, and business outcomes. The top complaint across roles is opaque or rigid pricing.

What this means for executives

• Anchor price to value. Tie AI fees to a clear value metric (time saved, cases resolved, leads qualified). Quote payback in months, not years.
• Make pricing easy to buy. Offer transparent tiers, add-ons, and a usage-based path. Remove hidden fees. Publish examples and calculators.
• Productize proof. Ship pilots with success plans, weekly ROI tracking, and pre-built integrations. Turn outcomes into case stories fast.
• Package AI where it matters. Prioritize customer success use cases: automated support, triage, and personalization. Price to outcomes, not features.
• Monetize data responsibly. Lead with benchmarking and insights. Gain explicit consent, segment by data sensitivity, and align to an AI risk framework such as the NIST AI RMF.
• Re-balance GTM. Invest in field teams, solution engineers, and sales enablement that can handle cross-functional deals and security reviews.
• Preempt buyer objections. Ship compliance packs, model cards, and security one-pagers. Provide a CFO-ready ROI sheet and a 2-page integration guide.
• Guarantee outcomes. Set adoption SLAs (time-to-value, usage targets) and link a portion of fees to achieved milestones.
• Run focused experiments. A/B test AI pricing models per segment. Set price floors, guardrails, and a monthly review cadence.

Key metrics to manage

• AI adoption rate by cohort (30/60/90 days)
• Payback period and net dollar retention for AI users vs. baseline
• Attach rate of AI add-ons and premium plans
• Proof-to-purchase conversion from pilots
• Share of revenue from AI features (target a step-up each quarter)

Strategic questions for your next leadership meeting

• What single value metric best reflects how customers benefit from our AI-and do we price to it?
• Which segments see immediate ROI from customer success use cases, and how do we package for them?
• Where does pricing confuse buyers today, and what can we remove this quarter?
• Do we have the proof points (benchmarks, case stories, security docs) buyers need to sign off cross-functionally?
• What is the minimum set of AI features that deserve a paid tier versus being included to drive adoption?

"What's holding vendors back isn't a lack of innovation - it's a lack of commercial clarity and sales excellence," said Lisa Neumeier, Partner at Simon-Kucher. "Turning AI into real revenue now hinges on demonstrating concrete customer value and anchoring price to it, but also proactively addressing adoption concerns."

Next steps

• Audit your AI value narrative, pricing pages, and sales assets against buyer concerns and cross-functional approval needs.
• Stand up a 90-day AI monetization sprint: one segment, one use case, one value metric, two packaging tests, weekly ROI updates.
• If upskilling your team will accelerate execution, explore role-based options at Complete AI Training.