Impact of AI on the SaaS Industry

I keep seeing the same pattern destroy SaaS companies: AI makes their customers insanely productive. Those customers need 80% fewer seats. Revenue falls off a cliff. The pricing model is literally eating itself. I've executed pricing transformations across 4 SaaS turnarounds. What worked 18 months ago now destroys value - every SaaS company is racing to embed AI, and it's breaking their revenue models. The automation paradox: AI makes customers wildly productive, so they need fewer seats. You just automated away your own revenue model. 85% of SaaS companies have abandoned pure per-seat pricing. The holdouts are learning why the hard way. Here's what actually works now: Track different data. Old way: Seats, tiers, revenue per account. New way: Token consumption, API calls, automated workflows. Found one enterprise using AI to replace 10 seats while consuming 100x the resources. Seat pricing misses this completely. Price outcomes, not access. Old way: ROI = human hours saved. New way: Automated resolutions, workflows completed. Saw $500/month AI running entire departments. Customer saves $2M annually. Your pricing is broken. Build hybrid models. Old way: Per-seat with usage tiers. New way: Base subscription + AI consumption. Example: $X base platform fee + $Y per 1,000 AI resolutions. Revenue jumps 3x. Churn drops. Value finally makes sense. Model the seat apocalypse. Old way: 20% churn assumptions. New way: Accounts dropping from 50 to 10 seats but 10x-ing AI usage. Price it right = 2x revenue. Miss it = -60%. Prove value first. Old way: Show features, hope they get it. New way: "Our AI resolves 1,000 tickets = 40 human hours." Now $2/resolution pricing clicks. Without proof, you're just taxing AI. CS becomes AI coaches. Script: "You're paying for 50 seats but AI handles 30 of those workflows. Let's optimize." Fewer seats, higher revenue. Trust wins. Real-time transparency. Token usage dashboards. Cost predictions. 80% alerts. Show exactly what AI costs vs human alternative. Black box pricing = dead company. Most SaaS companies still add 50% "AI premiums" to seat licenses. Meanwhile, Salesforce charges per conversation. Zendesk per ticket resolved. The leaders already moved. But the window's closing. Companies with consumption-based AI models report 38% higher growth. Foundation models commoditize by 2030. We have maybe 24 months. After that, it's a race to the bottom. The fundamentals from my 4 turnarounds still apply - but the game has changed. We used to price software that helped humans work. Now we're pricing software that replaces them. Get this transition wrong and you'll watch competitors eat your market share. Get it right and you own the next decade.

One of the biggest open questions with AI is its impact on software business models over time. What seems to be under-appreciated about AI is how it can enable significant TAM expansion for a large number of categories over time when software can deliver outcomes, and not just enable existing work. Right now the dominant business model in SaaS is a per seat model, which inevitably means that the total number of seats you can sell is limited to the number of employees in the organization that are relevant for your particular software. Legal software is roughly capped by the size of the legal team, audit software is capped by the size of the audit team, and so on. The implication of this is that the customer generally *already* has to have not only a need for your solution, but also the existing headcount in the organization to become users of your software. Incidentally, this is often why so many SaaS products tend to go after horizontal productivity categories, because this maximizes the number of potential users you have access to in an organization. AI flips this on its head, especially with the power of AI Agents, and you get a new form of “outcome-as-a-service”. When AI is actually doing the work within the software itself, you're no longer constrained by the number of employees inside the organization to use the actual software. The software is quite literally bringing along the work with it and delivering a particular business outcome. When you are no longer constrained by the size of a team or department to use your software, markets are no longer arbitrarily capped in size. In this new era, software that powers a legal workflow actually brings the equivalent of legal knowledge work along with it, and software for audits brings the equivalent of audit work with it. All of a sudden small businesses, under-resourced teams in large enterprises, and all new geographies begin to open to up as markets. AI will enable otherwise niche categories of software to become much larger, and already large categories of software to become even bigger. This transformation is similar to what we've seen in other markets where a new innovation has unlocked the size of a market well beyond its original demand. For instance, most investors and economists would have thought the size of Uber or Lyft's market was the size of the existing Taxi market, when in fact the market size was orders of magnitude larger once the shape of the product changed to make the offering easier to consume. We’ve seen this effect time and time again in areas like SaaS, cloud computing, a variety of mobile categories, and more. Clearly all new variables of monetization will need to emerge when you start to pay software vendors for outcomes as opposed to just the software itself. But inevitably, when you remove the existing dominant constraint of enterprise software, TAM expansion will follow.

Generative AI is going to change the SaaS pricing model - and that’s a good thing. For years, the "per-seat" model has been the go-to for SaaS companies, which tend to grow in tandem with the companies they serve. With the advent of AI-driven efficiency enhancements, however, the landscape of SaaS pricing is undergoing a seismic shift. The conventional wisdom of scaling alongside customer growth no longer holds true in a world where fewer personnel are needed to achieve higher efficiency levels. Consequently, the outdated per-seat model fails to meet the evolving needs of businesses focused on maximizing efficiency. This realization opens doors for founders to innovate their pricing strategies. No longer bound by the constraints of traditional models, entrepreneurs are embracing the freedom to experiment with new approaches that better align with the value they provide to customers. In this evolving landscape, it’s my opinion that value-based pricing will emerge as the North Star. By tethering pricing to tangible outcomes such as cost savings and customer satisfaction metrics (e.g. CSAT score for customer support interactions), businesses can establish a more equitable exchange of value with their clientele. This customer-centric approach fosters stronger partnerships and ensures that pricing reflects the true impact of the service provided. In essence, companies now have the ability to shift their pricing structure to whatever model makes it easiest for their customers to buy in. And with Generative AI, we have the means to make these solutions more creative and impactful than ever before. By prioritizing customer needs and business objectives, founders can differentiate themselves in a crowded market and solidify their position as industry leaders.

Is AI Quietly Rewriting the Buying Journey? It certainly seems that way. Something interesting is happening behind the scenes at OpenAI. They’re testing a checkout experience within ChatGPT that allows users to complete purchases directly in the chat, without needing to bounce to a website or app. It’s a consumer-first feature (for now). But if you’re in B2B, this is worth watching closely. Because we’re not just talking about better UX. This signals a shift toward AI becoming the primary interface between buyers and brands. And it will impact how discovery, evaluation, and purchase decisions happen across every category, including SaaS, eventually. What that means in practical terms: Your buyer might ask ChatGPT: “What’s the best solution for scaling revenue ops in a mid-market SaaS company?” Instead of sending a link, the assistant walks them through the shortlist, sets up a demo, and may even facilitate a purchase on the spot. Very interesting, right? The top-of-funnel isn’t a search bar anymore. It’s more of a conversation. So, if your content and brand aren't optimized for how LLMs retrieve and synthesize insight, you’re going to be harder to find. Also, attribution gets messier, too. When a buyer learns about you through an AI conversation, possibly sparked by a trusted voice or earned mention, how do you track that? This is an example where influence and infrastructure intersect. The B2B brands that win won't be the ones shouting loudest. They’ll be the ones who show up with clarity, relevance, and trust even when the buyer never visits their site. We’ve spent the last decade building strategies around what people search for. Will the next decade be shaped by how people ask? Link to the story in comments 👇

SaaS companies that create business applications have to adopt AI or face extinction. As I speak with business stakeholders about AI use cases, one thing is evident: They want less manual work. Digital transformation created a surplus of tools for business teams, a blend of user interfaces, standard workflows, and data. However, it also created more work for team members. The workflows became more complex as business requirements changed, creating more screens for workers to click through to do their jobs. Also, spreadsheets were required in the absence of workflows. Now, they want their time back. If this were Star Wars, Digital Transformation would be Episode IV: A New Hope. AI would be Episode V: The Empire Strikes Back. The conversations I'm having start with a business user saying, "Can't we get AI to..." If I were to translate, what they're saying is, "I no longer want to..." They're showing up to the conversation ready with what they want and don't want. I don't have to pull it out of them. This means SaaS companies need to find ways to simplify workflows and leave the complexity behind the scenes. Here are a few examples. ➢ When a business signs a contract with a new vendor, someone wants to say, "Please create a purchase order for ACME Inc. in the amount of $50,000." ➢ Tasks are assigned in a project meeting. The project manager wants to say, "Please create/update the project plan based on the meeting we just had." They want the outcome, not all the steps to make it happen. Of course, this requires much complexity. Metadata and knowledge graphs are needed to understand the requestor and the business's structure. Once that is realized and created, you tap into it to make this new functionality work. For SaaS companies, sales calls with potential customers must address AI strategy. Demos that show a person clicking through multiple screens or filling out spreadsheets to perform a task will be met with, "No thanks."

Stop duct-taping “AI features” onto a broken SaaS stack. Here’s the uncomfortable truth: Most SaaS companies don’t have an AI strategy. They have an AI widget. They slap on a chatbot here, an AI helper there, and wonder why: 🔴 Product sprints still slip 🔴 Customer feedback still dies in Notion graveyards 🔴 Resource allocation still stalls in Slack threads 🔴 Data stays fragmented and stale, the minute it’s pulled The real shift isn’t about sprinkling “AI” on top. It’s about re-architecting how your company works, beneath the surface. 💡 Your SaaS business doesn’t need another shiny AI app. You need an AI Operating System. What does that look like? ✅ Orchestrates workflows end-to-end: dev sprints, roadmaps, releases ✅ Automates task planning so your team updates fewer Jira tickets and ships more ✅ Turns messy customer feedback into prioritized roadmaps, automatically ✅ Aligns goals & OKRs across teams, tracking progress live not lost in slide decks ✅ Connects every tool in your stack so your insights don’t die in dashboards, they fuel execution Why does it matter? Because duct-taping another AI chatbot onto your SaaS won’t make your teams move faster, but building on an AI-native OS will. That’s why we built Kroolo. Not as a bolt-on tool, but as your AI-native Operating System: 🚀 AI Projects | AI Task Planner | AI Workflow Builder 📊 Smart Dashboards | Automated Workspaces | Seamless Integrations #AIOperatingSystem #AgenticAI #SaaS #SaaSFounders #ProductOps #AIWorkflows #KrooloAI #FutureOfWork #NoMoreSilos #AIProductivity #OperationalExcellence #SaaSLeadership

How do you present on a topic that you’re just figuring out yourself? That’s the reality of anyone trying to talk about AI these days. We’re all wandering in the dark with no flashlight. I had to think about that in preparing my presentation for the GenAI Week conference last week. And I decided that rather than presenting best practices, I’d share worst ones - that I’m hoping to avoid. I made a list of 10+ mistakes we're trying to not make as we remake Gainsight into an AI-First business: 1. Not Working Backward From the Client: While the attractiveness of Cursor’s $500M in ARR in just a few quarters is good motivation, it’s not the most powerful one. We keep trying to remind ourselves that the real impact of AI-First and Agentic business models is that we can finally deliver on the promise of SaaS to our customers. 2. Making Excuses for Not Getting Started: There are so many reasons - margins, rapidly changing tech, etc. - to sit on our hands. We're trying to not let those cause us to freeze. 3. Not Leading From the Front: I’m 100% convinced that if we're not learning to work in an AI-First manner as leaders, our company will never make the turn. 4. Using Existing Talent Best Practices: If we let conventional salary bands or organizational inertia block us from bringing in experts, we won’t truly transform our company. 5. Porting Existing Roles to Agentic: If we allow our thinking to get lazy and say “we are just making an AI CSM” we’re missing the point. Agentic models are about automating tasks and reinventing work - not just automating roles as-is. 6. Assuming SaaS Speed: In SaaS, we have all acclimated to a release schedule that feels fast compared to on-premise software but glacial versus the pace of AI. 7. Making Long-Term Architecture Decisions: It’s tempting to spend time deliberating the perfect LLM or architecture - but more prudent to just start building. 8. Charging for What’s Expected: 2 years ago, we made a very unconventional (at the time) decision within Gainsight - that our AI functionality inside our CS product should be available at no additional cost. Basic AI features are just expected now. 9. Making 3 Year Financial Plans: What’s our 3 year agentic revenue? How does it affect long-term profit? No one knows. But the prize is so big that it’s worth going for it. 10. Not Bringing Clients On the Journey: While many of us are obsessed with the latest in MCP, MOE or A2A, most customers are just trying to do their day jobs. The more we can educate clients on the steps to get to the new world, the faster we’ll all get there. Big thanks to Jay Allardyce for inviting me to speak! Here’s to learning more mistakes in the months and years to come.

Every other video on my YouTube feed claims AI will wipe out all SaaS by 2027. Am I the only one who thinks that’s a bit dramatic? I use AI daily. I even built a full product with an AI builder. It’s incredible in so many ways, but here are some limitations I deal with daily: 1. It constantly misses context. I constantly have to tell it to reference important context in files otherwise it might go off and do one thing that breaks another part of the software. 2. Integrations are doable, but difficult 3. There’s no standardization. The benefit of entire SaaS ecosystems is the standardization of data which makes #2 much easier 4. Security - It’s deleted full companies I’ve had to manually restore AI is a game-changer, for sure. But I think we need more grounded takes on its impact. What am I missing?

I had so much fun chatting with AI experts - super interesting to learn from their different perspectives - founder, operator, and investor. Thanks for keeping it real, spicy, and inspiring! Barr Yaron (Investor at Amplify), Michelle Kwon (Head of Operations and Partnerships at Runway), Keith Peiris (CEO and Co-Founder of Tome), and Caitlyn Vaughn (CEO and Founder of Ascend AI). To quote one of our panelists, Michelle, "To add to the AI hype flames, what’s really exciting is that this is the worst version of Gen AI we’ll ever see." AI tech is evolving at lightning speed - and SaaS businesses need to stay on top of it. Not because of the hype, but because AI development has reached a point where there’s no doubt it will dramatically change how we work. Like Barr said, "Every company should be thinking, what in my business is threatened by AI, and what is my strategy?" Some quick learnings from the discussion in case you missed it: 1️⃣ AI or not, you need PMF and a GTM strategy to support it. Finding the right audience for your AI tool is crucial. Like any SaaS business, understanding who will benefit the most from your product ensures its success and impact. Gen AI is cool tech! But, that alone isn’t enough to build a sustainable business. 2️⃣ Native AI users are probably not your buyers, but they should influence how you build. Gen Z preferences and behaviors are shaping the way AI is used and consumed. They’re the ones using it for research, outlines, and presentations… (while we’re still Googling and making PowerPoints!) Like Keith said, "AI reminds me of 2016 when the older folks were on Facebook and couldn’t understand the Snapchat thing." 😂  That being said, if you're building an AI product, you can't only build for those native users. You need to consider the people who need your product to win business. 3️⃣ When it comes to maximizing productivity, AI is your copilot. AI isn’t magic (at least not yet). Like any new tool, gen AI poses a learning curve for users. Train your team to use AI to work better and faster - not to blindly follow the recommendations it provides. It’s up to the humans in the driver’s seat to make strategic decisions based on AI driven insights. So, no - AI isn’t getting rid of human jobs, but it does make the market more competitive. Like Caitlyn said, "There's certainly going to be a shift in the market, there's no longer a need for a company to have 500 SDRs, but you still need your top 30 SDRs that are able to produce 100X more information." Let's keep the conversation going! Do you agree with the points above? How’s your team using AI to optimize their workflows?

🚨A serial entrepreneur-turned-investor, Mayfield Fund’s Navin Chaddha has backed 18 companies that have gone public, including Lyft and developer-tools company HashiCorp. He joins us for VC Wednesdays.🚨 ✒️ What were your biggest lessons from being an entrepreneur that you still apply as an investor? It's all about the people. You need to make sure that everyone you hire is aligned on your mission, vision and values. It's a team sport. Secondly, company-building is a marathon, not a sprint. There's no overnight success. It takes time, and you have to pace yourself accordingly. Third: It’s very important to be customer-first, and your solution needs to be a must-have, not a nice-to-have. Sell painkillers, not vitamins. Fourth — and this was my issue as a first-time founder — focus. You can’t be the jack of all trades. Finally, you need to be agile, because dinosaurs don't survive. ✒️ What’s your biggest focus area right now? I’m focused on how AI, or what we’re calling the ‘cognition era,’ can elevate us. You had IaaS for infrastructure, PaaS for developers and SaaS for business. Now one layer up, the AI revolution is ‘cognition as a service.’ We’re interested in companies solving for the 5 As: how AI helps humans automate tasks; how it accelerates our productivity; how it amplifies creativity; how it augments our capabilities; and finally, how it helps us become superhumans. ✒️ What’s a recent example that fits this thesis? We invested in a company called Sema4.ai, which provides a platform — middleware and tooling that sits on the data layer — on which businesses can build solutions to create ‘digital twins.’ It can be applied to industries from travel to banking. For example, a bank can use it to create a fraud twin for a fraud manager, helping them detect, troubleshoot and remediate the issue. ✒️ Won’t this impact jobs? Some jobs may be impacted, but it'll create net-new jobs. The areas we’re backing actually have shortages. DevOps, SecOps and nursing all have shortages of tens of thousands of people — where digital twins can step in. You need humans in the loop to manage them. From a product management perspective, humans will think about how the product needs to be priced and positioned. We'll get to do things we’re good at, rather than mundane day-to-day tasks. We’re smart, and we must remember who’s the jockey and who’s the horse — the horse is AI. ✒️ What areas within AI are overhyped? The most overhyped area is going after semiconductors and competing with NVIDIA. It’s hard because of their market share and scale — the lead they have on the technology, control and distribution. They have a lock on the market with the CUDA software platform to build AI apps, they’ve pre-bought manufacturing capabilities at TSMC and they have a backlog of demand from the cloud providers. The same is true with LLMs. For me, the opportunity is in the data layer, middleware and tooling. #VCWednesdays #vc #venturecapital #startups #TechonLinkedIn