The pricing work coming across our desk is at a point where candidates to support are needed. Exciting opportunities like AI companies trying to figure out outcomes-based models, SaaS scale-ups rebuilding their packaging architecture, post-Series C companies that have outgrown their original pricing - the live conversations are good ones. I'm looking for a few senior independents to plug in. You'd work with me and the Northlane team (www.northlane.partners). Real pricing strategy, real client decisions, not staff-aug, you're still autonomous as a contractor. What I'm looking for: top-tier consulting background, with pricing or monetisation experience, preferably in software/tech, plus ideally operating experience e.g. at VC/PE value creation teams, product teams, pricing teams. If you're thinking about going or already are independent and want a way into better-fit projects and more consistency of deal flow, this is that. email me [email protected] with your resume and/or linkedin! #ProjectOpportunities #ProjectOpportunity

I’ve been thinking about how pricing is evolving in the context of AI agents, and I’d really value your perspective. Historically, pricing has often been a bottleneck in the sales process—but over time, we’ve built systems (CPQ, billing, usage-based models) that make pricing more structured and scalable. With AI agents, it feels like we’re facing a new version of that challenge—not in pricing execution, but in defining and measuring value. While costs (tokens, infra, etc.) are relatively transparent, the value created by agents is much harder to quantify consistently. We’re currently exploring ways to decode this as part of our product journey. I’d love your thoughts on a few open questions: 1. Defining value: What key parameters or signals should be captured to quantify the value created by an AI agent (e.g., time saved, revenue impact, quality improvements)? 2. Data accessibility: In your experience, how willing are customers to share internal metrics required to measure value? What has worked (or failed) in getting this data? 3. Human-in-the-loop (HITL): How should we think about attributing value when humans are partially involved in the workflow? This seems like one of the hardest aspects to standardize. 4. Customer success criteria: How do you recommend aligning pricing with what “success” actually means to the customer, given that this can vary widely? 5. Product vs. sales responsibility: Should value measurement and tracking be embedded within the agent/product itself, or handled externally in the sales/pricing layer (e.g., CPQ, analytics tools)? If you’ve seen strong frameworks, patterns, or even failed approaches in this space, I’d be very interested to learn from them. Thanks in advance for your time—I’d really appreciate any guidance you can share.

Just watched a webinar "The hidden cost of hybrid pricing" (by Nue), where the panelists discuss challenges with unpredictable revenue due to credit-based pricing. Here's what I understand is the key takeaway from the hour-long discussion. Credit usage is unpredictable = revenue might fluctuate and becomes unpredictable too. Recommendation: 1) Use credit to cover cost for features that aren't unique to the SaaS platform. Example: credits for LLM usage. 2) Apply value-based pricing (subscriptions or some other fee) for unique value your platform provides. Example: workflows, actions and other outcomes the customer cares about Are you structuring your pricing this way as well (credits for cost, non-credit for value), or do you apply credits to everything?

I've been thinking about pricing in the context of AI-driven quoting which leads to highly dynamic pricing. Let's say we get really good at coming up with prices that maximize conversion (AI + sales). But what happens once the customer commits and we start charging on a regular basis? That's where I see a gap. In most setups I've seen, we lose the context behind the price very early on: - why is this customer paying $X instead of $Y? - was it an AI suggestion, a discount, or a manual override? - what inputs influenced the price point? I'm thinking about this problem from the perspective of plan migrations, where we often don't know how to handle certain (small) cohorts with non-standard billing setup. In your experience, is it a common practice to link pricing decisions (CRM / quoting + context) to the actual billing objects (subscriptions, customer records, etc.) in a structured and automated way?