SaaS Without AI: Is Your App Someone Else's Feature?

🔮 What If SaaS Became Autonomous? The Next Frontier: Self-Managing, Self-Evolving Software We’ve seen SaaS evolve from cloud-hosted tools to intelligent, user-centric platforms. But what comes after smart SaaS? 👉 Autonomous SaaS — platforms that manage, optimize, and evolve themselves without human intervention. Sounds far-fetched? Maybe. But not for long. --- 🤖 What Autonomous SaaS Might Look Like: 1. Self-Healing: Bugs get diagnosed and fixed automatically using telemetry + LLM agents. 2. Self-Scaling: Infra expands/shrinks based on real-time usage and cost optimization goals. 3. Self-Evolving UX: UI adapts based on user frustration patterns, not just analytics. 4. Autonomous Feature Decisions: Roadmap is shaped by data signals, not just stakeholder opinions. 5. Auto-Support: LLM agents resolve 95% of tickets — before users even raise them. --- 🔬 Why Now? The convergence is real: 1. LLM Agents (AutoGen, CrewAI, LangGraph) for intelligent decision-making 2. AI-powered observability + AIOps for infra and anomaly handling 3. GenAI copilots for both devs and users 4.Modular backends + composable architecture Together, they’re forming the Autonomous SaaS Stack — where each layer learns, optimizes, and collaborates. --- 🧠 Autonomous SaaS = SaaS That Builds & Improves Itself At Uplatz SaaS Studio, we’re reimagining software as something that doesn’t just serve, but thinks, adapts, and evolves — just like a human team would. We’re not talking about a marginal AI add-on. We’re talking about the complete redefinition of what SaaS is. --- 💥 From MVP to MSP — Minimum Self-Sufficient Product Tomorrow’s SaaS startups won’t just ask: “What does the user need?” They’ll ask: “How can the product learn what the user needs before they ask?” --- 🔁 The Endgame? SaaS as an autonomous partner — not a passive tool. Built once. Improved forever. Evolving on its own. Are you building for this future? 👇 Drop your thoughts. What part of SaaS do you think will become autonomous first? --- 📩 Want to Explore the Autonomous SaaS Future Together? Whether you're a founder, builder, investor, or just wildly curious about what's next in software — we’d love to connect. 👉 Drop us a message or collaboration request at: 📧 support@uplatz.com 🌐 https://uplatz.com/saas --- Let’s build the future of SaaS -- where products evolve, think, and grow with you. #UplatzSaaSStudio #BuildWithUs #FutureOfSoftware #LLMAgents #AIOps #AutoDev #UplatzSaaSStudio #SaaSRevolution #ComposableAI #MVPtoMSP #SelfHealingSystems #PlatformInnovation

Most AI startups start with SaaS because it's easy. You can make products using tech that's off the shelf and quickly sell to customers. But in the enterprise, this is a HUGE mistake. What I find is that a lot of enterprises don't like SaaS, especially if it involves an AI system that's accessing very sensitive information. What's unique about MemVerge is that instead of SaaS, everything we do is customer installed software. The reason we made that decision consciously is that we sell to large enterprise clients, and when you sell into large enterprises, SaaS is actually a barrier to entry. Enterprises don't want SaaS, because you're dealing with sensitive information that needs to stay in-house. There's this catch-22 -- the more quality information your AI system can access, the more useful it is. But most of the time when you start to have a real production use case that's high value, you're dealing with sensitive information: - Proprietary source code - Customer information - Health records What we're doing is letting users run everything locally - whether it's in their own virtual private cloud in AWS, on their own servers, on prem, or in a data center that they control. The problem with SaaS models is that the utility only goes so far. When you get to breakthrough production use cases, that's exactly when you're dealing with the information enterprises absolutely will not put in someone else's system. In today's age of AI especially, it's a really important lesson.

🚀 SaaS is dead. Long live Agentic SaaS. We're witnessing the most significant shift in business software since the cloud revolution. Traditional SaaS tools that wait for human commands are being replaced by autonomous agents that independently pursue business objectives. What's changing: → Organizations are shifting from fixed to variable costs, replacing permanent staff with scalable AI solutions → Pricing models are evolving from simple per-user subscriptions to outcome-based agreements → Companies are developing flatter hierarchies as AI handles operational tasks → Teams are focusing on strategy while agents execute routine work The real game-changer? We're moving toward multi-agent ecosystems where specialized AI agents collaborate, negotiate resources, and solve complex problems without human intervention. But here's the challenge: This isn't just about technology - it's about fundamental organizational transformation. Companies that resist this evolution may find themselves at a growing disadvantage as competitors leverage autonomous capabilities to reduce costs and accelerate operations. The question isn't whether this shift will happen, but how quickly your organization will adapt to capitalize on it. Are you ready for the agentic future? What's your take on AI agents taking over routine business operations? #AgenticAI #SaaS #DigitalTransformation #ArtificialIntelligence #BusinessStrategy #FutureOfWork #Innovation Read the full analysis: https://lnkd.in/gDVBZD4P

Is SaaS making way for Agentic Platforms? Agentic Platforms are the convergence of SaaS, software, and cloud infrastructure, built around adaptive, AI-powered systems delivering measurable, autonomous outcomes that are reshaping how businesses operate.  Here's what the transformation looks like: 🤖AI Agents as the New Interface - Tasks that required UIs and human intervention are now delegated directly to intelligent agents. 🎯 Centralized Intelligence - Agentic data hubs that think, learn, and act are replacing different SaaS subscriptions. ⚡ Dynamic vs. Static Workflows - Pre-programmed processes are giving way to dynamic and learning-based workflow systems that adapt to changing business needs. 🔧 Composable Architecture - Monolithic applications are being replaced with modular, mix-and-match services, for businesses to build exactly what they need, when they need it. 💰 Token-Based Value Creation Pricing models are shifting from subscriptions to outcomes—powered by autonomous agents delivering measurable business value. The implications: Entire consumption models, and business architectures will need to be reimagined from the ground up. As Satya Nadella, CEO of Microsoft, in recent interview, envisioned a future where AI agents, rather than users switching between different SaaS applications, will handle complex tasks and workflows. Is the traditional SaaS model becoming obsolete? Daniel Newman's insights in Forbes perfectly capture this shift. Read the full analysis from Daniel Newman: https://lnkd.in/gQiWrQmC What are you seeing in your industry? #AgenticAI #FutureOfSaaS #DigitalTransformation #EnterpriseAI #TechLeadership #Innovation

As enterprise technology evolves, artificial intelligence (AI) agents are fast becoming the new linchpin of digital operations. But alongside their promise of productivity and automation, these digital workers are quietly upending one of the most fundamental assumptions in enterprise IT: how software is priced. #AIAgents #ArtificialIntelligence #ChiefInformationOfficers #CIOs #EnterpriseIT #SaaS #SaaSPricing

“…Generative and agentic AI are disrupting software as a service (SaaS) by automating tasks and replicating workflows. SaaS leaders can manage the risks by identifying where AI can enhance their offerings and where it might replace them. To stay ahead, they must own the data, lead on standards, and price for outcomes, not log-ons, in an AI-first world. With the right playbook that includes deep AI integration, strong data moats, and leadership on standards, incumbents can shape, not just survive, the next wave of SaaS…“

It’s the end of SaaS as we know it … and yeah, I feel fine. (Shoutout to R.E.M. and anyone who remembers floppy disks.) At our last CloudZero retreat, one of our investors shared a story about vibe coding an entire SaaS app over half a bottle of wine. He’s not an engineer. It was a mic drop moment that made one thing clear: the ground under SaaS is shifting. The way we build, buy, and value software is being rewritten in real time by AI. But this isn’t the end. It’s the beginning of a new era, one where the best companies will thrive by leaning into what truly sets them apart: domain expertise, data, and deep business integration. I wrote about what’s coming, why margins are moving, and how companies can stay relevant (and profitable) in the age of AI. Give it a read: https://lnkd.in/eQDMD7E7 #SaaS #AI #FinOps #CloudZero #CloudCostOptimization

All you need is a brain. For decades, Software-as-a-Service (SaaS) has been the backbone of modern business. But what if I told you its reign is rapidly ending? We're not just talking about AI enhancing SaaS; we're talking about AI cannibalizing it. The future isn't about subscribing to countless apps; it's about a single, intelligent "brain" that learns your data and takes autonomous action. The autonomous "brain" is here. Imagine software that doesn't wait for your clicks but proactively executes complex tasks, learns continuously, and adapts in real-time. That's the power of autonomous AI agents, powered by advanced LLMs. They're not just tools; they're self-driving systems that perceive, plan, and act independently to achieve your goals. Why SaaS Can't Compete: Traditional SaaS is rigid, generic, and creates data silos. You're locked into fixed features, slow updates, and often pay per user for underutilized licenses. AI agents shatter this model: Autonomy: They handle multi-step workflows, from lead generation to inventory management, without constant human input. Klarna's AI assistant, for example, handled 2.3 million conversations in a month, performing the work of 700 human agents. Personalization: Unlike template-based SaaS, AI agents deeply understand context and adapt to your exact needs, crafting bespoke solutions. Continuous Learning: They get smarter with every interaction, refining their performance without waiting for vendor updates. Cost Efficiency: By automating repetitive tasks and dynamically allocating resources, AI agents drastically reduce labor costs and software sprawl. The Great Cannibalization: AI is already replacing core SaaS functionalities across industries: Customer Support: AI chatbots and voice agents are handling Tier 1 support, matching human satisfaction. Sales & Marketing: AI identifies and qualifies leads, drafts personalized outreach, and logs results, cutting manual prospecting time. Development: Tools like GitHub Copilot and Cursor generate code, debug, and write tests, accelerating development cycles. Supply Chain: AI optimizes inventory, forecasts demand, and detects risks, as seen with Shopify's acquisition of an AI-driven fulfillment platform. The Economic Shift: The predictable seat-based pricing of SaaS is giving way to outcome-based or usage-based models. AI-native competitors, with leaner structures, are offering superior solutions at lower prices, squeezing traditional SaaS margins. The value is shifting from owning the  platform to owning the data and the agent orchestration layer. The Inevitable Future: SaaS companies integrating AI are often just delaying the inevitable. The true competitive advantage will lie in proprietary data and the intelligent agents built upon it. The future demands a radical rethinking: less about static applications, more about dynamic, autonomous "brains" that drive outcomes. Are you ready for a world where "all you need is a brain"? #AI

#PDMthinkingnote 🚀 Micro SaaS in the Age of AI I recently watched Greg Isenberg’s video “Making $$$ with MicroSaaS”. In it, he breaks down the Micro SaaS concept in detail — advantages, real-world success stories, the Growth Flywheel, user journey, funnel metrics, and more. It’s broad yet easy to digest, and I recommend it to anyone interested in this space. I believe Micro SaaS will become an increasingly accessible path for more people to try. With AI tools lowering the barrier to product development more than ever before, the barrier to competition is also dropping. In this environment, the ability to identify real user pain points becomes even more critical. 🧠 As I search for an idea worth pursuing, I will focus on these three steps: 1. Observe and uncover real problems Closely study the daily routines and workflows of a specific audience, and look for unmet needs — often hidden in their small, recurring complaints. 2. Ask the right questions and assess the market Don’t jump straight to a solution. First ask: “Why hasn’t anyone solved this yet?” and “If it were solved, would they pay for it?” 3. Validate the problem’s reality Before going all in, use interviews or an MVP to quickly test demand strength and willingness to pay. AI can speed up development and iteration — but it can’t decide what’s worth solving or what you want to solve. That’s why I believe the success of Micro SaaS will rely more than ever on the accuracy of problem selection and deep user understanding. 💬 Would you like to try building a Micro SaaS? Or, what’s something in your daily life that frustrates you enough that you wish it could be solved right now? ----------------------------------------------------------------------------- 🚀最近看了 Greg Isenberg 的影片“Making $$$ with MicroSaaS”，
他在影片中詳細拆解了 Micro SaaS 的概念，包括優勢、實際成功案例、使用者旅程與漏斗指標等等，內容廣泛且易於理解，推薦給對這領域有興趣的人。 Micro SaaS 將會是一個越來越多人嘗試的方向，AI 工具的普及讓產品開發的門檻比過去低得多，但這同時意味著競爭門檻也在下降。
 在這種環境下，我認為「找到使用者痛點」的能力變得更加重要。 🧠還在嘗試找到想投入的idea，我會嘗試下面這三件事： 1. 觀察並發現真實問題 深入觀察特定族群的日常與流程，找到未被滿足的需求，發現那些隱藏在人們日常中的小抱怨。 2. 問對問題及考量市場 不急著跳到解法，先用不同角度拆解痛點，問清楚「為什麼現在沒有人解決」與「如果有解法，他們會付錢嗎？」 3. 驗證問題的真實性 在一頭栽下去之前，快速用訪問、MVP 驗證需求強度與付費意願。 AI 或許可以幫我們加快開發與迭代，但我們要去選擇我們想要解決什麼問題以及什麼值得解決。
因此，Micro SaaS 的成功，將更依賴在問題選擇上的準確度與對用戶的深入理解。 💬你想嘗試打造一個 Micro SaaS 嗎？
或者，你生活中有哪些讓你覺得煩到想立刻解決的事情？

Legacy platforms face growing pressure as #AI-native tools deliver adaptive, personalised, and faster #SaaS solutions to demanding #enterprises, Rupesh Thakkar and Badri Narayanan Gopalakrishnan, PhD. #generativeai #chatgpt https://lnkd.in/gARh9ZqR