How AI is changing B2B software pricing models

The most desired AI PM qualification in 2025 is shipping production-ready B2B agents. Here’s my 3-step playbook to go from idea to production: Step 1: Build a Scrappy Prototype - Forget complex front-ends. Start with no-code tools (n8n/ MSFT Co-pilot Studio) and focus on speed. - Describe your goal in English: Use tools like Microsoft Copilot Studio to build an agent by simply describing what you want it to do. - Use existing apps: Integrate with Mail or Slack for your front-end. Meet your users where they already are. Start with a one-pager: Your goal is a working prototype based on a simple requirements doc, not a 50-page PRD. Step 2: Evaluate Ruthlessly - Building is easy. Building a reliable agent is hard. This is where most people fail. - Acknowledge the limits: The tech for full human replacement isn't there yet. Reasoning is still hacked into models, and accuracy on hard benchmarks is low. The cost of stabilizing a reliable agent can be 10-100x the cost of the initial build. - Use the HHH Framework: Evaluate your agent on three simple questions: Is it Helpful? Is it Honest? Is it Harmless? Set Clear Launch Criteria: Work with experts to define what "good" looks like and set objective scores (e.g., "70% helpfulness") before you ship to a wider audience. Step 3: Iterate Relentlessly - Use your evaluation data to guide your roadmap. - Focus on Assisting, Not Replacing: The winning strategy is building tools that assist people and deliver tangible artifacts. Think of a tool like Loveable(now with cloud+AI support) that builds a functional website, not just code snippets. - Let the Data Guide You: Use the feedback and evaluation scores from your early users to set your next targets and features. This data loop is what turns a prototype into a scalable product. Very few AI PMs have actually done this, and you’ll immediately stand out if you do. I’ve seen it myself: This is the exact process that members of my cohort on @Maven have used to automate complex workflows and save their companies millions.

Many people think that writing effective prompts means they have to prompt “one and done”.  Yet, when it comes to writing a book or a report, they understand that the first draft is never the final draft.  Taking that mindset into prompting, you’ll treat each output like reviewing drafts. So, what makes a prompt effective, and how can AI help you write one? What makes a prompt effective is clarity, rich context, structure, making it specific to the role or perspective, as well as treating it iteratively. If you finding writing an effective prompt challenging, use AI to co-create clear, accurate, relevant, and actionable prompts with you.  For example, if your goal is to improve the customer experience, you may be tempted to write a short prompt like this: How can we improve the customer experience? While it’s a strong, open-ended prompt,  it’s so broad that AI might give you generic advice (like “be more customer-centric”), which is not helpful.  To make it sharper and tailored to your needs, you need to add context, pain points, and goals. So ask AI to ask you questions that will help you improve your prompt.  It will come back with guiding questions: 1. Define the Context - Who are your customers? (e.g., B2B clients, retail shoppers, SaaS users) - At what stage of the journey are you focusing? (onboarding, support, checkout, renewal, etc.) - What channels matter most? (in-person, phone, web, mobile app, email, etc.) 2. Clarify the Pain Points - What complaints or issues do customers raise most often? - Where are customers dropping off or disengaging? - Are you hearing specific frustrations from support tickets, surveys, or reviews? 3. Specify Outcomes You Want - Do you want to increase retention, boost satisfaction scores, Here’s the refined prompt based on my answers to the guiding questions: “How can we improve the customer experience for our SaaS users during the renewal stage, specifically in our mobile app? Our biggest pain points are: Customers citing weak integration capabilities that make implementation difficult and hurt workflow efficiency. Drop-offs after the free trial ends. Frequent mentions of integration issues in support tickets, surveys, and reviews. We want practical recommendations that will: - Increase free-to-paid conversion. - Improve retention among paying customers. - Strengthen customer confidence in integrations and workflow efficiency.” This version gives AI context (SaaS + mobile app + renewal stage), pain points (integrations + drop-offs), and clear goals (conversion + retention). The outputs you’ll get using this prompt will be closer to a roadmap than just generic advice.

In the race to cut costs, many teams choose the cheapest option. But here's the truth: You don’t pay for experience you invest in it. An experienced professional might charge more upfront, but they bring: - Fewer mistakes - Faster execution - Strategic foresight - The ability to pivot when things go sideways Meanwhile, inexperience often costs more in the long run: - Delays - Rework - Missed opportunities - Burned bridges Whether you're hiring a developer, designer, marketer, or consultant the cheapest option isn’t always the most affordable. I've learned this firsthand in game development, where a single misstep in balance, onboarding, or visual clarity can ripple across the entire player experience. Paying for experience means paying for precision, insight, and speed. Cheap work costs more when it has to be redone. Experienced work costs more because it gets done right. Let’s stop asking “How much does it cost?” and start asking “What’s the cost of getting it wrong?”

"How should you monetize AI?" NEW data from Kyle Poyar for you PMMs: Kyle jus published a new essay "Tremont | Growth Unhinged State of B2B Monetization report" with data from more than 240 software companies. Six takeaways for you: "1. Seats & flat-rate pricing are increasingly under threat. The rise of AI, and AI agents in particular, means value gets decoupled from customers needing to buy more seats. And the costs of delivering AI means software companies need a mechanism to protect themselves if & when AI usage spikes. 2. As AI continues to gain traction, hybrid has become the ‘it’ pricing model. Seats and flat-rate pricing are being replaced by hybrid pricing, i.e. combinations of subscriptions and usage. Hybrid pricing is up from 27% to 41% adoption (!) in the last 12 months. Software and AI are becoming inseparable. More than half of survey respondents said they include AI capabilities as part of their core software product offering. 3. There are a seemingly infinite number of ways to structure hybrid pricing. Choose wisely (or support multiple models). Software founders used to tell me their pricing was inspired by Salesforce or maybe Slack. Now they tell me they were inspired by Clay. Clay's hybrid model has multiple routes to expand customers while keeping pricing (relatively) simple: more features (subscription packages) and more usage (credits). 4. Outcome-based pricing is seen as the 'holy grail'. It's still out of reach for 95% of the market. A mere 5% said their pricing model is outcome-based right now. But 25% said they expect to have outcome-based pricing by 2028. 5. The shift toward pricing transparency seemed inevitable. This isn’t playing out. My two cents: many software companies, especially early-stage and AI companies, don't have pricing totally figured out. And as soon as they publish pricing, it gets way harder to adjust. 6. Pricing models keep evolving. Most of the market is unprepared to keep up. Be careful to avoid pricing ‘no-man’s land’ -- this happens between $5-20M ARR when ownership often falls through the cracks." -- 👋 P.S. What's your take PMMs? Make sure you give @yle a follow and check out his full essay. Great read, thanks for sharing Kyle! 

I’ve noticed the same trend — as numerous AI companies rely on Nue to manage their pricing models, the frequency of pricing changes is accelerating, and the pricing and billing systems must be highly adaptive to allow these changes to take place.

#AI is forcing every B2B software company to rethink how they price. I just read a great piece by BCG — “Rethinking B2B Software Pricing in the Era of AI” — and it really hit home. #AI isn’t just a new feature to add on top of your product. It’s changing how customers perceive value — and that means our pricing models have to evolve too. Here are a few takeaways that stood out to me 👇 1️⃣ Don’t just add AI and charge more. If the feature doesn’t create real, visible value for customers, it probably belongs in the core offering — not as a paid add-on. But if it does deliver measurable outcomes, show that value first before monetizing it. 2️⃣ Think beyond seats and licenses. Usage-based or outcome-based models make more sense in an AI world — but they come with risks. If you move too fast, you could compress your revenue. It’s about finding the right balance. 3️⃣ Data will drive pricing decisions. You can’t price around value if you can’t measure it. Telemetry, usage tracking, and forecasting are becoming essential to link AI usage to real customer outcomes. 4️⃣ Hybrid models are the future. Most companies won’t switch overnight to fully outcome-based pricing. Mixing subscription, usage, and value components gives flexibility while learning what works. 5️⃣ Work with your customers, not just for them. Defining “value” or “outcome” can’t happen in isolation. It needs open conversations, pilot projects, and transparent ways to measure success. 🎯 My takeaway: #AI isn’t just changing what we sell — it’s changing how we charge for it. The companies that get this right will not only grow faster but also build deeper trust with their customers. Curious to know — how are you or your company thinking about AI pricing models right now? source: https://lnkd.in/e4HTXgGF #AI #B2B #SaaS #PricingStrategy #Innovation #ProductManagement

"Your AI systems are too simple to scale" a founder said about my B2B automation stack He spent 20 minutes explaining how I needed: • Enterprise architecture • 15+ tool integrations • Dedicated team • $50k implementation budget Meanwhile, my "too simple" 3-tool stack just processed 1,700 founders at Istanbul Slush'D. Let me break down what "too complex to fail" really means: 𝗧𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗲𝗿'𝘀 "𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲" 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: → 8 different platforms → 3 full-time engineers → Weekly maintenance windows → $12k/month burn rate Result: Still debugging while competitors ship 𝗠𝘆 "𝗧𝗼𝗼 𝗦𝗶𝗺𝗽𝗹𝗲" 𝗦𝘁𝗮𝗰𝗸: → AI agent for automation → CRM for data → Analytics for insights Result: $127k generated for clients last quarter Here's what actually happened: While that founder was pitching complexity, I was building the entire ticketing system for Istanbul Slush'D. CRM, website, auto-qualification - everything. 1,700 attendees. 4 founders trusting me. The "simple" system handled it flawlessly. Same stack powered: • B2B founder's $30k in 14 days • Agency replacement in 17 minutes • 987 comments on ONE post • 43 calls booked automatically in ONE week The founder's response when I showed him? "But what about edge cases?" Brother, 1,700 founders at a major event IS the edge case. Complex systems don't scale. They break. Simple systems that work? Multiply. I've never needed more than 3 tools. Never had a system fail. Complexity is where good ideas go to die. What complex system are you maintaining that 3 tools could replace? P.S. That founder? Still "validating his tech stack." Meanwhile, my clients are too busy counting revenue to count tools.

Free trials don't work for B2B AI products. Here's what does: The traditional SaaS playbook: → 14-day free trial → Hope they see value → Convert to paid The problem with AI products: Value isn't obvious in 14 days. Especially for back-office automation where staff won't self-report time savings to their boss. The better approach: 3-month paid trial with money-back guarantee, for a B2B product! Here's the framework: Month 1: Track everything → Measure exact hours saved per task → Document every manual process eliminated → Calculate dollar value (hours × wage rate) Month 2: Report results → Show owner: "We saved 120 hours = $2,040" → Compare to monthly cost → Highlight potential headcount reduction Month 3: Decision point → If savings < cost: Full refund → If savings > cost: Customer keeps it (obviously) Why this works better than free trials: Commitment: Upfront payment creates skin in the game Staff actually implement it properly Proof: Measured results > vague "efficiency gains" Owner sees actual dollar impact Trust: Money-back guarantee removes risk But payment creates seriousness Real example: At SkipTheDishes, we launched digital advertising with this model. Not free trials. Paid with performance guarantee. Result: 70-80% retention Because we could prove the ROI. For AI products specifically: Your AI might save 200 hours/month. But if staff won't tell their boss... The owner never knows the value. The solution: You track it. You report it. You prove it. Then price at 60-70% of savings. The framework: 1. Charge upfront ($3,000/month, not $500) 2. Track exact time/dollar savings 3. Report monthly to decision-maker 4. Offer refund if savings < cost 5. Retain 80-90% because math doesn't lie Free trials hope customers see value. Paid guarantees prove customers get value. Big difference.

OpenAI just dropped their AI agent builder, and the narrative is that automation platforms like Zapier and n8n are finished... But here's what nobody is telling you: this isn't the threat everyone thinks it is, it's the biggest opportunity of the year. The panic stems from a fundamental misunderstanding: n8n and similar platforms aren't just AI agent builders, they're comprehensive business process automation tools designed for complex workflows. OpenAI's agent builder is perfectly positioned for casual users and small businesses automating basic tasks: simple customer support agents, email responses, or straightforward workflows. This will dramatically lower the barrier to entry and accelerate market expansion. ---> Here is what happens next: Businesses will start experimenting and asking questions like "What else can I automate?" and "How do I connect this to my CRM?" They'll build internal proof-of-concept projects that will inevitably hit a wall. We see this all the time with our clients. Do-it-yourself agents work while you are on the "happy path" but fall apart when the unexpected happens (and success is business is all about handling the unexpected). Leadership quickly realizes these amateur builds aren't scalable, reliable, or production-ready. ---> This creates clear demand for professionals who can architect robust systems. We've seen this pattern before. WordPress democratized web creation and the market for professional developers exploded. Shopify democratized e-commerce and specialized agencies thrived. The opportunity comes from real limitations inherent in the agent builder: + Locked into OpenAI's models only + Cloud-based with no self-hosting + Designed for simplicity, not production-level complexity + Missing crucial enterprise features such as auditability, granular controls, and advanced debugging ---> So, if you're in this space, here is my advice: + Become an expert in the operations and bottlenecks of one or two specific industries + Train yourself to find root causes before proposing solutions + Frame proposals around tangible business value: "This will reduce your support costs by $20,000/month" or "This will increase conversions by 40%" Democratization doesn't eliminate experts. It creates more demand for them. Go and capture some of that demand. +++ 387labs partners with visionary companies to shape the future of decision-making through the power of data and AI.

Reports of successful SMB and AI integrations Here is a catalog of documented reports and case studies showcasing successful integrations of artificial intelligence (AI) in small and medium-sized businesses (SMBs), organized by source and focus area. Each example demonstrates measurable business outcomes and key insights from implementation experiences. Retail and Consumer Goods Local Bakery and Retailers (Sinjun AI, 2025) – A bakery used AI-powered forecasting to predict demand for specific pastries, reducing waste and increasing sales. Retail SMBs also adopted AI for inventory management and customer service automation, leading to improved profits and satisfaction.[1] SodaPup (U.S. Chamber of Commerce, 2024) – Colorado-based pet products company applied AI to automate product descriptions and customer service, enhancing efficiency without expanding staff.[2] CarGari (U.S. Chamber of Commerce, 2024) – Peer-to-peer car rental startup used AI to operate 24/7 with minimal staffing, competing effectively with larger rental networks.[2] Marketing and Sales Vendasta (Rapid Architect, 2025) – SMB-focused digital marketing platform deployed AI for content optimization and customer targeting. Results included a 35% increase in email open rates, 25% boost in conversions, and 20% rise in revenue.[3] Antematter (Rapid Architect, 2025) – A mid-sized e-commerce company improved profit margins by 30% through AI marketing automation and predictive analytics, reducing costs by 40%.[3] ReMarkable (Salesforce, 2024) – Norwegian digital tablet firm integrated Salesforce AI Agentforce to automate customer support, scaling service capacity while maintaining personalization quality.[4] Operations and Productivity BE A GOOD PERSON (U.S. Chamber of Commerce, 2024) – A lifestyle apparel brand leveraged AI tools from Shopify and QuickBooks to sustain growth without increasing workforce size, optimizing logistics and content management.[2] Microsoft’s SMB portfolio (Microsoft Cloud Blog, 2025) – Over 1,000 SMBs worldwide reported scaling operational efficiency with AI tools in logistics, HR, and service automation.[5] AI Maturity Findings (UseAIforAccountants, 2025) – SMBs at advanced AI maturity stages reported 3.2x higher revenue growth, 2.8x cost efficiency, and 2.1x improved customer satisfaction compared to early adopters.[6] Insights and Trends Salesforce Global SMB AI Survey (2024) – 91% of SMBs with AI observed boosted revenue and efficiency, with 80% predicting AI will be a long-term game changer for their sectors.[4] Techaisle 2025 SMB & Midmarket AI Trends – Noted accelerated adoption and ROI as structured implementation strategies became more common among SMBs.[7] SMB Group 2025 Report – Found that 78% of SMBs were using AI in some form, with significant clustering around marketing, automation, and predictive analytics.[8]