Key Problems for AI Startups to Address

My biggest takeaway from Chip Huyen: 1. Most AI product problems aren’t AI problems. When companies think they have an AI performance issue, it’s usually a user experience problem, an organizational communication gap, or a data quality issue. One company thought their AI lead scoring system was broken, but the real issue was that the marketing team wasn’t asking the right questions to get useful data. 2. Your best performers benefit most from AI tools. In a controlled experiment, the highest-performing engineers got the biggest productivity boost from AI coding assistants, not the lowest performers. Senior engineers who already knew how to solve problems used AI to work even faster, while low performers often just copied and pasted code they didn’t understand. 3. How you prepare your data matters more than which database you choose. Companies see their biggest AI performance gains from better organizing and preparing their information—breaking content into the right size chunks, adding summaries, converting content into question-and-answer format—rather than agonizing over which technical infrastructure to use. 4. The biggest improvements to your AI product come from talking to users and understanding their feedback, not from adopting the latest models or staying glued to AI news. Many companies waste time debating which technology to use, when the real wins come from better user experience and data preparation. 5. Fine-tuning should be your last resort. Before investing in fine-tuning a model, try simpler solutions first: improve your prompts, add basic post-processing scripts, or fix your data pipeline. One company caught 90% of its model’s mistakes with a simple script. Fine-tuning creates ongoing maintenance headaches and should only be used when everything else has been maxed out. 6. You don’t need to be perfect to win. Many successful companies choose “good enough” over perfect when implementing AI systems. They calculate whether investing two engineers to improve accuracy from 80% to 85% is better than using those same engineers to launch an entirely new feature. Often, the new feature provides more value. 7. AI productivity is nearly impossible to measure. Companies invest heavily in AI coding tools but can’t clearly prove they work. When forced to choose between expensive AI subscriptions for their team or hiring one additional person, many managers choose the person, not necessarily because AI doesn’t help but because headcount feels more tangible. 8. Many people don’t know what to build despite having powerful tools. Even with AI tools that can build almost anything, many employees face an “idea crisis”—they simply don’t know what to create. The best approach: spend a week noticing what frustrates you in your daily work, then build small tools to solve those specific pain points.

Thought provoking and great conversation between Aravind Srinivas (Founder, Perplexity) and Ali Ghodsi (CEO, Databricks) today Perplexity Business Fellowship session sometime back offering deep insights into the practical realities and challenges of AI adoption in enterprises. TL;DR: 1. Reliability is crucial but challenging: Enterprises demand consistent, predictable results. Despite impressive model advancements, ensuring reliable outcomes at scale remains a significant hurdle. 2. Semantic ambiguity in enterprise Data: Ali pointed out that understanding enterprise data—often riddled with ambiguous terms (C meaning calcutta or california etc.)—is a substantial ongoing challenge, necessitating extensive human oversight to resolve. 3. Synthetic data & customized benchmarks: Given limited proprietary data, using synthetic data generation and custom benchmarks to enhance AI reliability is key. Yet, creating these benchmarks accurately remains complex and resource-intensive. 4. Strategic AI limitations: Ali expressed skepticism about AI’s current capability to automate high-level strategic tasks like CEO decision-making due to their complexity and nuanced human judgment required. 5. Incremental productivity, not fundamental transformation: AI significantly enhances productivity in straightforward tasks (HR, sales, finance) but struggles to transform complex, collaborative activities such as aligning product strategies and managing roadmap priorities. 6. Model fatigue and inference-time compute: Despite rapid model improvements, Ali highlighted the phenomenon of "model fatigue," where incremental model updates are becoming less impactful in perception, despite real underlying progress. 7. Human-centric coordination still essential: Even at Databricks, AI hasn’t yet addressed core challenges around human collaboration, politics, and organizational alignment. Human intuition, consensus-building, and negotiation remain central. Overall the key challenges for enterprises as highlighted by Ali are: - Quality and reliability of data - Evals- yardsticks where we can determine the system is working well. We still need best evals. - Extreme high quality data is a challenge (in that domain for that specific use case)- Synthetic data + evals are key. The path forward with AI is filled with potential—but clearly, it's still a journey with many practical challenges to navigate.

Surprisingly, this did not get much coverage. So, this analysis is very helpful. Everyone was busy talking about the US-China AI race, funding rounds, and model benchmarks while Stanford quietly dropped one of the most important warnings founders will read this year: Security is now the #1 blocker to scaling agentic AI. Not compute. Not cost. Not regulation. Security. Here are 5 things founders should pay attention to RIGHT NOW: 1️⃣ 62% of organizations said security and risk are the TOP barrier preventing agentic AI deployment. Not tied for first. Not close. It beat technical limitations by 24 percentage points. 2️⃣ AI incidents are clustering inside aggressive adopters. The percentage of companies with AI incidents stayed flat at 8%… BUT organizations reporting 3–5 incidents jumped from 30% to 50%. Translation? The companies moving fastest in AI are repeatedly getting burned. 🔥 3️⃣ AI incident response confidence is COLLAPSING. Organizations rating themselves “excellent” at handling AI incidents dropped from 28% to 18% in ONE YEAR. Meanwhile “needs improvement” jumped to 21%. That is terrifying considering how many companies are racing to deploy autonomous agents right now. 4️⃣ Cybersecurity AI solve rates went from 15% to 93% in 12 months. Read that again. 93%. 🤯 That means AI agents are rapidly becoming capable of autonomously performing sophisticated cybersecurity tasks. Offensive and defensive. And yet most startups are still giving agents broad access to CRMs, emails, cloud storage, Slack, code repos, customer records, and APIs with weak governance. 5️⃣ Only 13% of organizations integrated AI into their security strategy. This one honestly shocked me the most. Everyone wants AI productivity. Very few are redesigning security architectures for AI-native environments. Most startups right now are treating AI agents like productivity features. They should be treating them like privileged employees with superhuman speed, infinite curiosity, and access to your entire company. What founders need to do NOW: ✅ implement least-privilege access ✅ audit every agent workflow ✅ isolate credentials ✅ log EVERYTHING ✅ govern at the data layer, not just the app layer ✅ stop assuming model guardrails are enough Because the companies that survive the AI era will not be the ones with the coolest demos. They will be the ones whose agents did not accidentally become internal threat actors. #Kiteworks

When everyone can build anything with AI, the game shifts to these 5 harder problems. 1. What should exist? (Taste & judgment) AI can execute. It cannot decide what’s worth building. This comes from deep user understanding, pattern recognition, and intuition. 2. Who is it for? (ICP clarity) If you can’t clearly say who will use the product, who will pay and why they pay then your product is already dead. 3. Why will they buy from you? (Trust & positioning) In a world of clones, people don’t buy features. They buy trust built on social proof, brand value and authority. 4. How will it grow? (Distribution engine) The unfair advantage today is not code. It’s deep partnerships and network effects something which is difficult to replicate. 5. Why will it survive? (Moats & iteration speed) If someone can copy your product in a weekend, your moat is ecosystem and customer relationship. —- You need PMs more than ever as AI don’t own decision making yet. Right?

𝘉𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 𝘧𝘰𝘳 𝘦𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯 𝘱𝘳𝘦𝘴𝘦𝘯𝘵𝘴 𝘴𝘦𝘷𝘦𝘳𝘢𝘭 𝘬𝘦𝘺 𝘤𝘩𝘢𝘭𝘭𝘦𝘯𝘨𝘦𝘴 𝘴��𝘤𝘩 𝘢𝘴  1️⃣ Data quality and availability: AI agents require large amounts of high-quality, structured data to train and operate effectively. To automate customer service responses needs access to a comprehensive database of past customer interactions, including queries, responses, and outcomes. If this data is incomplete, inaccurate, or poorly structured, the AI's performance will suffer. 2️⃣ Integration with legacy systems: Many enterprises rely on older, complex systems that may not have modern APIs or easy data access points. An AI agent tasked with automating financial reconciliation might need to interact with an outdated ERP system, a modern cloud-based accounting software, and several custom-built internal tools. Ensuring seamless integration and data flow between these disparate systems can be challenging. 3️⃣ Handling exceptions and edge cases: While AI can be trained on common scenarios, business processes often involve numerous exceptions and special cases. Automating invoice processing might encounter invoices with non-standard formats, missing information, or requiring special approvals. The AI needs to be sophisticated enough to recognize these exceptions and either handle them appropriately or escalate to human intervention. 4️⃣ Explainability and transparency: For many business processes, especially those involving financial or legal decisions, it's crucial to understand how and why decisions are made. If an AI agent is approving or denying loan applications, the enterprise needs to be able to explain the reasoning behind each decision, both for regulatory compliance and customer satisfaction. 5️⃣ Continuous learning and adaptation: Business processes and rules change over time, and AI agents need to adapt accordingly. Supply chain optimization needs to continuously learn and adapt to changes in supplier relationships, global events affecting logistics, and shifting consumer demands. 6️⃣ Security and compliance: AI agents often handle sensitive business data and need to operate within strict regulatory frameworks. Automating healthcare billing needs to ensure full HIPAA compliance, protecting patient data while interacting with various healthcare providers and insurance systems. 7️⃣ Human-AI collaboration: Designing systems where humans and AI agents can effectively work together, especially for complex tasks, is challenging. In a customer service scenario, an AI agent might handle initial customer queries but need to smoothly hand off to a human agent for more complex issues, ensuring all context is properly transferred.

🚨 𝐓𝐡𝐞 𝐁𝐢𝐠𝐠𝐞𝐬𝐭 𝐌𝐢𝐬𝐭𝐚𝐤𝐞𝐬 𝐒𝐭𝐚𝐫𝐭𝐮𝐩𝐬 𝐌𝐚𝐤𝐞 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 Over the past years, I’ve had the privilege of mentoring AI-driven startups and scaleups across 5 continents, guiding them from early-stage innovation to multimillion-dollar MRR success. As an advisor, and strategist—working closely with both high-growth companies and institutions—I’ve seen firsthand what makes AI initiatives succeed… and where they fail. Despite groundbreaking potential, too many AI projects collapse under their own weight—not because of technical limitations, but due to misalignment with business goals, data realities, and scalability. AI is not just about throwing machine learning/deep learning models at problems. The real challenge isn’t complexity—it’s alignment with business goals, data quality, and execution. 💡 85% of AI projects fail due to unrealistic expectations and misalignment with business needs (Gartner, 2024). So why do so many AI startups struggle? They focus on tech over strategy. 🔍 The 5 Biggest AI Mistakes Startups Make 🚫 Overestimating AI Capabilities - AI isn’t magic. Start small with realistic expectations and narrow use cases. 🚫 Neglecting Data Quality - AI is only as good as the data it learns from. Bad data = bad models. 🚫 Skipping Proof of Value (PoV) - Test small before scaling. A failed PoV is cheaper than a failed product. 🚫 Ignoring Domain Expertise - AI alone isn’t the answer—combine AI with industry knowledge. 🚫 Overcomplicating Solutions - Simple, explainable models often outperform complex black-box systems. ✅ How to Build AI That Delivers Real Value 1️⃣ Start with a Proof of Value – Validate the value of your product with real clients/prospects. 2️⃣ Prioritize Clean, Representative Data – Quality over quantity. 3️⃣ Involve Domain Experts Early – AI needs business context to work. 4️⃣ Focus on ROI & Scalability – If it doesn’t scale, it doesn’t matter. AI success is about strategic alignment, not cutting-edge complexity. #DrAI #ArtificialIntelligence #TechStrategy #StartupSuccess

Most startups are sleeping on AI—either treating it like a fancy cost-cutting tool or thinking they need to build the next OpenAI to win. Both are wrong. Here’s the reality: AI isn’t about efficiency. It’s about unlocking opportunities you couldn’t touch before. Google Cloud’s latest AI Perspectives for Startups 2025 drops some goldmine insights, and if you’re serious about building something that lasts, you need to pay attention. Let’s break it down. 1. AI Will Reshape the Startup Playbook Forget the hype. AI is not here to replace humans—it’s here to supercharge what we do. The smartest founders will use it to: ✅ Automate mundane tasks (customer service, admin work, data processing) ✅ Make software 10x more powerful with multimodal AI (text, images, video, voice) ✅ Build smarter, personalized solutions that feel less like software and more like a mind-reading assistant AI is going from a tool to an experience, and the startups that get this will dominate. 2. Time to Market is Everything Here’s the harsh truth: If you’re moving slow, you’re already behind. The AI landscape moves at hyperspeed, and you don’t have the luxury of spending 2 years perfecting your MVP. 💡 Ship fast. Test fast. Iterate even faster. The best AI startups? They launch half-baked products, see what sticks, and refine based on real user feedback. Speed > Perfection. Always. 3. If You’re Just Wrapping an LLM, You’re Dead in the Water Relying solely on an AI model like GPT-4 won’t cut it. Why? ➡️ Everyone has access to the same models. No moat. ➡️ Tech giants will keep dropping better, cheaper versions. No defensibility. What works? ✅ Proprietary data – The bigger & cleaner your dataset, the smarter your AI gets. ✅ Niche focus – Solve specific, high-value problems that generic AI can’t. ✅ AI-powered search – 80% of business data is unstructured. Whoever masters retrieval-augmented generation (RAG) wins big. 4. Pricing Models Are Changing Per-seat pricing? Dead. Startups need to align price with value. AI allows for usage-based and outcome-driven pricing. If your product makes businesses more money, charge based on results, not seats. 5. The Future is in AI Agents & Automation The endgame? AI running entire workflows on autopilot. We’re moving toward: ➡️ AI agents handling transactions, finances, and operations (Web3 will play a role here) ➡️ AI-powered search making enterprise data retrieval effortless ➡️ Personalized AI experiences (think Netflix recommendations, but for every aspect of life) Bottom Line: AI isn’t just a tool—it’s the biggest paradigm shift since the internet. The startups that embrace it now will shape the future. The ones that don’t? Well… they won’t be here in 5 years. Want to build an AI-first startup? Speed, strategy, and smart execution will set you apart. #AIStartups #TechInnovation #MachineLearning #ArtificialIntelligence #Entrepreneurship #FutureOfWork #Automation

💡Challenges in Designing AI Systems With new AI tools launching almost daily, one thing is becoming clear: many of them are poorly designed. Despite impressive capabilities, they are often not aligned with real user problems, and, as a result, lack clarity and trust. This makes them hard to adopt or scale. Tia Clement created a nice diagram that maps the key challenges of designing AI systems across each stage of the Double Diamond process (https://lnkd.in/dizTq7Vy). The Double Diamond framework is know for its ability to help teams move from exploring the right problem to delivering the right solution. What makes this framework especially useful for AI products is that it doesn't just highlight the challenges — it aligns them with the actual phases of the design process. This makes it much easier to understand what issues to anticipate, what questions to ask, and what capabilities and constraints to plan for when building AI systems. 🔷 Discover (Explore the Problem) Designers are trying to understand the context and user needs, but AI introduces unique challenges: ✔ Unclear boundaries of AI capabilities: It's hard to define what AI can and cannot do. ✔ Data dependency: Whether something is technically feasible depends heavily on data availability and its quality. ✔ Lack of purposeful AI use: Teams often struggle to define why AI is needed in a product in the first place. ✔ Difficulty sketching divergent AI solutions: Traditional ideation tools don't translate well to speculative AI behaviors. 🔷 Define (Narrow Down the Problem) This phase focuses on synthesizing findings into a clear problem statement or design brief: ✔ Fast prototyping is hard: It's difficult to simulate or quickly prototype AI behaviors because it requires building a robust system. ✔ Unclear outcomes: Predicting the potential consequences of deploying AI systems is also challenging. 🔷 Develop (Explore Possible Solutions) In this phase, ideas are generated and tested: ✔ Fuzzy, open-ended interaction design: AI doesn't always follow fixed rules, which complicates UX. ✔ Explainability: It's hard to communicate the outcome generated by AI (what AI is doing and why). ✔ Communicating evolution: Users struggle to understand how AI systems change or improve over time. 🔷 Deliver (Narrow Down to the Final Solution) AI system is refined and launched but with unique concerns: ✔ Unpredictability: AI behavior can be unexpected or inconsistent, making testing and release risky. ✔ Creepiness / Uncanny Valley: Users may feel discomfort when AI systems seem too unnatural. ✔ Accountability: It's unclear who is responsible when AI makes mistakes—designers, developers, or AI system? #AI #design #UX #uxdesign

92% of users abandon AI tools within 90 days. I studied 20+ AI companies who solved this. Here's their secret sauce 👇 Introducing ANCHOR - a framework for sticky AI products (and how to avoid the "AI tourist" problem): 1️⃣ 𝗔lign Expectations Problem: Users quit when AI outputs disappoint Solution: -> Over-communicate limitations upfront -> Show exactly how to handle quirky outputs E.g.: Boardy 2️⃣ 𝗡urture Users Problem: Users struggle to extract full value Solution: -> Drop success stories directly in the user journey -> Place AI assists at friction points -> Leverage power users to create community templates E.g.: Descript, Icon, CrewAI 3️⃣ 𝗖alibrate Cognitive Load Problem: Complex setup kills early adoption Solution: -> Focus your UX on ONE key "wow" feature -> Use automation to accelerate the setup process E.g. Gamma, OpusClip, Typeform's Formless 4️⃣ 𝗛ook Into Daily Workflows Problem: Even great tools get forgotten Solution: -> Integrate into Slack/Email/Chrome/CRMs where work happens -> Use notifications and emails to DO WORK for the user, not just remind E.g. Creator Match 🧩, Gong, The Geniverse 5️⃣ 𝗢ptimize Pricing Problem: Users hesitate to commit before seeing value Solution: -> Extend free usage until the "aha" moment -> Match pricing to usage (pay-per-output) E.g. Clay, Relevance AI, Synthesia 6️⃣ 𝗥oot Through Personalization Problem: Generic tools are easy to abandon Solution: -> Allow deep customization to each user -> Make switching costs real through user investment E.g. Artisan, ChatGPT Pro, Character.AI Bottom line: Most AI products don't fail because of bad AI. They fail because they forget they're asking humans to change their behavior. Questions for you: - Which of these problems hits closest to home? - What's the cleverest example of any of these you've seen? Tag a founder who needs to see this 👇. And let me know in the comments if you want a deeper dive into these case studies. -- Hi, if we just met, I'm Lauren "🤖" Vriens. I obsess about AI products so you don't have to. Hit the follow button to stay up to speed on what the best and the brightest are doing with AI.