¸½½üÉÏÃÅ

By and

AI is redefining software’s role, enabling products that perform end-to-end work. This shift brings new pricing models, value metrics and GTM tactics.

At , we’ve observed these four playbook stages that leading AI-first companies are using to scale.

Pricing and ROI: Selling value in a usage-based world

New pricing logic: AI products often use usage-based or hybrid pricing. That’s powerful, but less familiar. To succeed, teams must align pricing with outcomes and clearly articulate ROI.

Budget alignment: Unlike SaaS licenses or headcount, usage models need justification. For instance, Ìý1 charges per healthcare task automated — directly mapping cost to labor savings. This model resonates in labor-constrained industries.

Hybrid models for predictability: Blending tiered plans with usage minimums gives customers cost control while scaling affordably. For example: 10,000 credits at $500/month vs. 50,000 for $1,500. Lower unit costs reward growth.

Selling urgency: When pain isn’t acute, sellers should frame the cost of inaction. Ask:

• What’s the cost of staying manual?
• What happens if volume spikes?
• Can hiring solve this sustainably?

These questions qualify fit while creating urgency.

Discovery and qualification: Finding the right buyer

AI products require upfront investment, so qualifying buyers early is crucial.

Learn before pitching: Use discovery calls to understand how buyers currently tackle the problem. You can ask:

• What’s the current workflow?
• Who’s involved?
• Have you tried outsourcing or automation?

Position as labor alternative: Frame your product as a cost-effective way to avoid hiring. Ask:

• Is headcount or tooling the main constraint?
• Could this offset planned hiring?

Uncover real fit: Ask about competitors and hesitations around variable pricing. AI-first tools require real commitment — poor fit means wasted proof of concept and long sales cycles. Prioritize pain, urgency and organizational alignment.

Consultative selling: Guiding buyers through change

Once qualified, move from pitching to partnering.

Coach, don’t sell: Buyers often know the problem but lack a vision for solving it. Help them reimagine workflows and quantify the upside (speed, quality, reduced risk). Explain how your AI improves decisions — not just efficiency.

Build trust, not hype: Position your team as expert advisers. Highlight how competitors are adopting AI and frame your product as essential — not experimental. Focus on real problems, not futuristic features.

Co-create value: Buyers don’t want complexity. Understand their pain, then tailor a solution around it. When buyers feel heard and guided, they’re more willing to rethink their approach.

Proof through POCs: demonstrating real impact

A proof of concept isn’t just a technical validation — it’s also the key to proving value and earning trust.

Modern POCs = measurable outcomes: AI products tackle complex, variable tasks. POCs should reflect that — demonstrate consistent results across real scenarios, not just toy demos.

Structure for success: Successful teams scope tightly, set metrics early, and stay hands-on. Example:

• compares POC costs to hiring SDRs.
• Another AI platform focuses more on user enthusiasm and internal adoption than strict ROI, embedding in the workflow early.

Plan for conversion: Don’t wait until the POC ends to talk about the next steps. Begin commercial conversations midway, adjust pricing if needed, and ensure all stakeholders are aligned for expansion.

Final Word: Set the stage for long-term growth

AI adoption still feels experimental to many buyers. That’s why what happens after the sale matters just as much. Effective onboarding, early wins and long-term support are the foundation for retention and growth.

In our next piece, we’ll explore how AI-first companies succeed post-sale: from implementation playbooks to navigating internal resistance.

is general partner at , where she partners with founders from the earliest stages. She previously spent seven years as a partner at , backing early-stage companies including , , , and . Agarwal started her career at , founded two startups (Skedge.me and Roomidex), and invested at . She studied social studies at , where she rowed varsity crew, and earned her MBA from .

is an analyst at Defy, where he supports the full investment process, with a focus on sourcing and connecting with exceptional early-stage founders. He studied computer science and economics at . Before Defy, he was the first go-to-market hire at a high-growth startup, where he helped scale from early traction to repeatable enterprise sales.

Illustration: