How AI is changing SaaS pricing and efficiency

The 'AI vs. SaaS' question isn't just theoretical; it's a strategic challenge for every business. This post provides a practical guide on navigating the evolving landscape. We'll identify which categories of SaaS are most vulnerable to direct AI disruption (e.g., simple automation, basic data entry) and which are thriving by deeply embedding AI (e.g., complex analytics, specialized industry solutions). Learn what questions to ask your current SaaS providers, how to evaluate new AI-powered tools, and strategies for future-proofing your technology stack. Get actionable insights to ensure your business not only survives but thrives in the AI era.

Let’s be real: the next big SaaS wave isn’t AI. 🤯 It’s consolidation. Founders are tired tired. 😵💫 → too many logins → too many invoices → too many duct-taped tools that don’t share data More tools ≠ more growth. More tools = more chaos. What brands actually want? → fewer platforms → one customer view → data that’s clean, connected, and actually usable That’s why I’m into what clearer.io is working on. It’s not just “another tool in the stack.” It’s rethinking how reviews, loyalty, and discovery should live together. No more stitching together insights from 10 dashboards and 3 “maybe this is right?” spreadsheets. Clearer’s not just riding the SaaS wave — they’re shaping the next one. 🌊

Build vs. Buy: An AI Workflow Yes, you can build it in-house. But should you? That depends. With the rise of GenAI, building your own AI workflows, document parsers, custom agents, and automation layers has never felt easier. And for many teams, it’s tempting to dive right in. But building is only the start. What follows is long-term ownership: maintenance, scaling, integration, model drift, API changes, and user requests. And these are the things that slow teams down. Gartner says 80% of AI initiatives stall post-deployment, not because they can't be built, but because they’re harder to sustain than expected. “In software, 90% of the cost is maintenance. In AI, it’s even higher.”: Martin Casado, a16z Before committing six months and six engineers to building your own stack, pause and think it through.

AI 101: Automating the Everyday Work That Slows You Down Every business runs on systems—QuickBooks, CRMs, SaaS tools that organize everything beautifully. But here’s the catch: someone still has to manually do the work. A bill comes in. It needs to be read, categorized, and entered. Someone spends hours doing it. With AI, that changes. We connect your systems so they can talk to each other—and let AI handle the data entry, processing, and organization automatically. That means less busywork, fewer errors, and more time focused on what actually grows your business. 🚀 Discover how AI can take care of the work behind the work → [latoai.com]

In finance, the real test of AI isn’t speed. It’s scope and trust. Most tools deliver one but not the other: broad but unreliable, or reliable but narrow. AI SaaS pushed automation past rule-based code and unlocked new workflows. But it still doesn't cover your full range of financial processes. Now a new category is emerging - built to combine both. We call it Large Rule Models. 👉 Swipe for strengths, limits, and real finance examples. ▪️ Bottom line: AI in finance only works when scope and trust come together.

The ROI Pressure Problem 58% of SaaS leaders say ROI pressure is the #1 blocker in their AI rollout — not accuracy, not compliance, ROI proof. What’s interesting? Most are measuring the wrong thing. Trust and adoption precede ROI. Without user confidence, your AI feature is just a science project. I recently summarized findings from a survey of SaaS product leaders on this — patterns, blind spots, and what helps adoption actually stick. -> Read the breakdown: https://lnkd.in/djVsphvu

At Fortune Brainstorm Tech with Allie Garfinkle, I spoke about a key reality -- AI is not SaaS, and the market has been slow to recognize that. Implementing AI effectively requires flexible platforms and scalable infrastructure, but to be successful every AI deployment has to be hyper-tailored to an individual company's specific workflow. And so almost every AI software company now has a significant services component, just as all major enterprise software companies have for the last 20 years (though many people ignore this fact) The best AI companies will have both: a durable software platform and a services engine that delivers real outcomes. That duality is the new paradigm: building scalable tech across industries while staying close enough to the problem to deliver impact. It’s not easy. But this is the new standard that will determine success in enterprise AI.

As an Operating Partner at Bloom Equity Partners, I work alongside founders and leadership teams of mission-critical enterprise software and tech-enabled services companies. My focus is on talent, organizational design, and M&A integration - unlocking growth by putting the right people in the right seats and building structures that let them thrive. This week’s Bi-Weekly Bloom highlights Bain’s latest Technology Report, which makes one thing clear: AI isn’t just enhancing software - it’s redefining it. AI agents are already bypassing user interfaces and going straight to the APIs, challenging per-seat pricing, human-driven workflows, and traditional org structures. For investors and operators, the implications are huge: 🔹 Data moats become the strongest defense against commoditization. 🔹 Outcome-based pricing will upend business models and require new financial and product thinking. 🔹 Talent is the ultimate leverage - technology may rewrite workflows, but it’s people who adapt, innovate, and capture the upside. At Bloom, we invest in B2B software and tech-enabled services companies with sticky customers, defensible data, and room to grow - but our real work begins after the deal closes. Whether integrating a carve-out or scaling a platform through acquisitions, our job is to unlock potential through strategic talent placements and thoughtful organizational design. The trajectory of a company is set not just by the market it serves, but by the leaders it empowers. The AI revolution only raises the stakes. The companies that win won’t just deploy AI - they’ll build teams capable of reinventing the business around it. You can read the full Bi-Weekly Bloom and Bain AI deep dive in the post below! #PrivateEquity #EnterpriseSoftware #TechEnabledServices #AI #Talent #OperatingPartner #BloomEquity

It’s important to understand how to address AI needs by splitting requirements into two categories: first, there are workflows that don’t require customization and aren’t unique to your organization. For these, your SaaS vendor will likely incorporate AI features, and that’s usually the most efficient way for you to capture value from AI out-of-the-box. The second category covers use cases that are categorically unique to your company—these are often what set large enterprises apart. The key takeaway is that you have to work backward from these unique workflows and core business processes, and let those requirements drive your architectural decisions for the AI stack. This approach is fundamentally different depending on whether your company’s workflows rely mostly on structured or unstructured data, but the guiding principle is always to start with the use cases and work backward. Once you have clarity on these needs, you can break them down into best practices and modular components that allow you to scale. Ultimately, how these elements come together—for different user roles and client types—is still being explored and refined across the industry.

We studied 126 deployments across Fortune 500 and high-growth SaaS teams — and found what the top 5 % did differently. Most AI pilots fail by Month 3. The demo is usally impressive. But the moment integration starts, performance drops. Why? Because context architecture is missing. Teams use multiple tools, but Models run in silos. Yet only 5 % of pilots actually scaled 👇 1. Strategic Pain over Generic Signals They focused on board-level initiatives instead of surface-level intent data. → Predicted buying at 2-3× higher accuracy. 2. Persona Psychology Drives Velocity They customized messages for how each decision-maker thinks, not just who they are. → Reduced deal cycles by 25 % and “no decision” losses by 50 %. 3. Embedded Proof Creates Momentum They used quantified evidence in every buyer interaction. → POC-to-Win rate jumped from 30 % to 70 % (+133 %). --- The difference between pilots that fail and programs that scale isn’t better models or automated workflows. It’s connected context, continuous memory, and embedded proof across every conversation. Want your AI pilot to land in the top 5%? 👉 Build intelligence that remembers, reasons, and orchestrates. We have written a detailed newsletter "The Real Reason Enterprise AI Adoption is Failing"on this. Link in comments below.