New Value Models, New Playbooks: Tactics AI-first Companies Use to Scale

Why AI-First Products Require a New Approach to Pricing, Sales, and POCs

AI is transforming not only the workflows but the role of software within organizations altogether. As we’ve discussed in the past, AI has enabled new opportunities for distribution because AI-first products can provide utility much closer to that of human employees by executing work end to end. We’ve explored how this unlocks entirely new value and business models with the shift from fixed subscription fees to usage-based or hybrid pricing.

At Defy, we’ve noticed companies capitalizing on this trend adapt across four key stages, which we will dive into at depth through this article:

1. Pricing and ROI Strategy: With the shift in value proposition, strategically framing ROI and structuring pricing models aligns pricing with the real value delivered, allowing teams to unlock larger budgets.

2. Discovery and Qualification: Sellers must hone their sales fundamentals in order to identify and prioritize high-potential leads early due to the upfront investment required to deploy AI solutions effectively.

3. Consultative Selling: Top teams guide customers through the ambiguity, educate them on what “good” looks like, and help them reimagine how work gets done. This builds internal champions and long-term buy-in that go beyond the initial sale.

4. High-Leverage POCs: AI-first POCs must prove value in dynamic, high-stakes workflows. The strongest GTM teams scope POCs tightly, define success metrics upfront, and ensure hands-on engagement throughout.

Is there ROI? Selling Value In Variable Usage Pricing

The core question in any sales motion is: Is there clear ROI? This section explores three components of building a modern pricing narrative: aligning budgets, navigating unfamiliar spend structures, and mapping value.

Aligning budget: Traditional SaaS pricing and labor costs are familiar budget line items, but usage-based AI solutions introduce complexity. Crafting compelling ROI narratives, developing accurate usage forecasts, and securing broad stakeholder buy-in are essential. The case for AI is stronger in industries like healthcare or manufacturing, where labor shortages create operational bottlenecks, delayed services, or errors.

For example, Synthpop, a Defy portfolio company, streamlines healthcare workflows, automating up to 80% of administrative tasks. Synthpop’s value proposition resonates with leaders who are experiencing chronic staff shortages and high error rates due to employee turnover. Its outcome-based pricing—charging per completed task—aligns with how customers think about their existing costs, making the value proposition clear and compelling.

Navigating unfamiliar spend structures: New hybrid pricing models, combining tiered subscriptions or platform fees with usage minimums, can bridge the gap between familiarity and flexibility. Combining tiered subscriptions or platform fees with usage-based minimums offers predictability while accommodating upside with increased usage.

For instance, a plan offering 10,000 credits for $500/month and 50,000 credits for $1,500 reduces unit costs from $0.05 to $0.03, delivering better ROI as consumption scales. This approach mitigates concerns about ballooning costs while accommodating scalability.

Mapping value: For customers without acute pain, proactively highlight the compounding costs of inaction over the long term. Guide prospects with questions like:

• What’s the cost of continuing manual processes?

• What’s the impact of delaying solutions by 3-6 months? Is your current approach sustainable?

• How does your team handle volume spikes—overtime, backlogs, or hiring?

These questions double as fit assessments. Qualify out misaligned leads early to save time and effort. Focus on prospects where your solution addresses critical pain points and aligns with organizational priorities.

Discovery & Qualification: Uncovering Needs and Aligning on Value

The next step is to find the right buyer. This requires a deep understanding of their organization and decision-making processes. This section covers organization-specific discovery, positioning, and the significance of fit.

Organization-specific discovery:

Early discovery calls should focus on listening and learning. Rather than dominating the conversation with a product pitch, ask targeted questions to uncover challenges, workflows, and priorities. A good starting point is how they’ve tried to solve the problems your product addresses:

• How are you handling this challenge today?

• Which tools or teams are involved?

• What solutions have you tried, such as automation or outsourcing?

Positioning and perspective: If prospects have previously hired staff or outsourced tasks, they’re likely already framing the problem in terms of labor costs. This makes it easier to position your product as a labor-saving alternative. If they haven’t, probe their openness to new approaches, particularly usage-based pricing:

• Is headcount or tooling the bigger constraint?

• Do you have a budget for improving this workflow, or would this offset planned hiring?

• What’s your ideal outcome—greater efficiency or offloading tasks from your team?

• How would your team benefit if you could scale results without increasing headcount?

Additionally, inquire about alternatives they’re considering: “What other options are you evaluating—additional hires, competing tools, or outsourced services?” These questions not only reveal competitors but also the prospect’s value evaluation model. Some may hesitate to adopt variable pricing or question whether AI can truly replace labor. Addressing these concerns early—well before pricing discussions—builds trust and aligns expectations.

Frame your product as a scalable, cost-effective alternative to labor, making variable pricing feel logical and your solution as a strategic lever for growth.

Significance of fit: In AI-driven sales cycles, poor qualification is costly due to the intensive resources required for integration and running successful POCs. Though always essential to a great sales process, the opportunity cost of poor qualification is now much higher. While variable pricing introduces new challenges and requires adapting tactics, it reinforces the importance of strong sales fundamentals.

Consultative Sales: Guiding Buyers to the Future of Work

Once a buyer is qualified, the goal is to partner, not pitch. This section covers coaching through change and building long-term trust.

Coaching through change: The goal of initial discovery is to confirm the buyer’s pain aligns with the value of your solution as well as their intent and readiness to adopt. Once you’ve established a strong fit, the next step is to demonstrate how your product addresses their challenges. Many buyers recognize inefficiencies but lack a clear vision for solutions. This gap offers AI-first go-to-market (GTM) teams an opportunity to act as consultative partners, guiding buyers to reimagine their processes. Quantify benefits like faster time-to-market, reduced risks, or improved customer satisfaction, building excitement about your product’s potential.

Buyers often struggle to quantify the “value of intelligence” that AI solutions deliver. Your role is to clarify how your product enhances not just efficiency and speed but also decision quality, adaptability, and innovation. Connect these benefits to specific outcomes that matter to your prospect, such as faster time-to-market, reduced operational risks, or improved customer satisfaction. Where possible, quantify benefits—like percentage improvements in processing times or cost reductions—while early discussions can focus on building excitement about the product’s potential, even if precise metrics come later.

Building long-term trust: Position your team as thought leaders, not just vendors, to build trust and influence. Buyers seek partners who understand their challenges and anticipate industry trends. Frame your product as a complete outcome, not a set of features. Share insights about how competitors are adopting similar solutions or market pressures to highlight the inevitability of transformation. Position your solution as the foundation for staying ahead, ensuring buyers see you as a guide to future-proofing their workflows.

However, buyers rarely invest in innovation for its own sake. They buy to solve pressing problems or improve prioritized workflows, even if they haven’t defined the ideal solution. Avoid presenting your product as a complex system they didn’t request. Instead, listen to their pain points, explore their goals, and co-create a vision for improvement. When buyers feel understood rather than sold to, they’re more open to rethinking processes and adopting innovative solutions.

In the AI-first landscape, where benefits can feel abstract and outcomes less predictable, consultative guidance is critical. By focusing on the buyer’s needs and positioning your product as a strategic enabler, you shift the conversation from transactional to transformative. This approach not only builds trust but also gives your GTM team a competitive edge in a rapidly evolving market.

Proving Value through POCs: Demonstrating Impact

With buyer buy-in secured, the focus shifts to proving value. This section explores rethinking proof-of-concepts (POCs) for the AI-first era, structuring them for success, and turning proof into progress.

Rethinking POCs for the AI-first era: POCs are no longer mere technical validations; they are critical opportunities to demonstrate your solution delivers efficiency, accuracy, and transformative value across complex, dynamic challenges.

Unlike traditional SaaS, which automates repetitive tasks, AI-first products tackle intricate problems with flexible, intelligent, and adaptive capabilities. These solutions are often expected to achieve high-level goals, requiring robust evidence to demonstrate consistent performance across varied scenarios. While traditional SaaS POCs may need only a few proof points, AI-first POCs demand robust evidence—through concrete examples and measurable outcomes—to validate their value.

Invest resources upfront to guide customers through integration and to train users on the product ensures they understand how to maximize value, fostering confidence and long-term commitment.

Structuring for success: Through our conversations with operators at leading AI-first companies, it is clear that there’s no one-size-fits-all POC approach, but tailored strategies yield results. Conversations with AI-first leaders reveal diverse, context-specific approaches.

For instance, Eric, Head of Sales at Origami, shared their ROI-driven strategy. Origami’s AI sales agents accelerate traditional sales development representative (SDR) research and prospecting workflows. Their POCs are tightly scoped with clear, quantifiable metrics set during discovery, comparing costs to alternatives like hiring or competing tools. By defining goals and decision paths for conversion, expansion, or scaling, Origami sets up the POC to prove measurable value at a significantly lower cost. This metrics-focused process aligns with variable pricing models, front-loading complexity to drive seamless conversions once value is demonstrated.

Conversely, Tejas Sethian, a sales lead at a rapidly growing Series B AI platform for financial analysts, shared a different strategy. Their platform enhances junior bankers workflows by integrating AI. Customers know AI should increase productivity but do not know where it would be best utilized. Since customers often lack this clarity, their POCs emphasize user enthusiasm and cultural adoption over rigid ROI metrics, embedding the product in the organization’s culture to foster advocacy and long-term commitment. While use cases and plans matter, the focus is on creating excitement and value that analysts champion.

Turning proof into progress: The goal is a seamless transition to full adoption or a contract. Structure POCs with clear timelines, success metrics, and agreed-upon next steps from the outset. Maintain transparent communication to gauge conversion likelihood of converting, assess satisfaction, and address concerns early, avoiding surprises at the POC’s conclusion.

For variable pricing models, where costs and therefore pricing may be uncertain initially, collaborate with customers to refine terms as the POC progresses and value becomes evident. Initiate commercial discussions before the POC ends, assuming positive progress, to maintain momentum and align expectations.

A well-executed POC not only proves the product’s worth but also accelerates time-to-value post-conversion, as customers are already familiar with its benefits and implementation. By prioritizing clarity, collaboration, and proactive communication, you build a compelling case for adoption, paving the way for a successful partnership.

As AI-first pricing and go-to-market (GTM) tactics evolve, post-sale strategies warrant deeper exploration. For many customers, AI adoption feels experimental — a short-term pilot rather than a permanent solution. This makes effective implementation, retention, and expansion essential to build a durable business.

In our next post we’ll explore how AI-first companies build trust after the sale, focusing on: onboarding playbooks, measuring early wins to navigating organizational inertia and earning the right to expand. In a landscape where customers are still defining what “good” looks like, vendors who guide them through ambiguity while delivering measurable results will secure long-term success.

At Defy, we’re actively looking to meet founders innovating GTM, pricing, or post-sales strategies for the AI-first world. If that’s you we’d love to hear from you!