AI Limitations in Business Process Automation

AI models like ChatGPT and Claude are powerful, but they aren’t perfect. They can sometimes produce inaccurate, biased, or misleading answers due to issues related to data quality, training methods, prompt handling, context management, and system deployment. These problems arise from the complex interaction between model design, user input, and infrastructure. Here are the main factors that explain why incorrect outputs occur: 1. Model Training Limitations AI relies on the data it is trained on. Gaps, outdated information, or insufficient coverage of niche topics lead to shallow reasoning, overfitting to common patterns, and poor handling of rare scenarios. 2. Bias & Hallucination Issues Models can reflect social biases or create “hallucinations,” which are confident but false details. This leads to made-up facts, skewed statistics, or misleading narratives. 3. External Integration & Tooling Issues When AI connects to APIs, tools, or data pipelines, miscommunication, outdated integrations, or parsing errors can result in incorrect outputs or failed workflows. 4. Prompt Engineering Mistakes Ambiguous, vague, or overloaded prompts confuse the model. Without clear, refined instructions, outputs may drift off-task or omit key details. 5. Context Window Constraints AI has a limited memory span. Long inputs can cause it to forget earlier details, compress context poorly, or misinterpret references, resulting in incomplete responses. 6. Lack of Domain Adaptation General-purpose models struggle in specialized fields. Without fine-tuning, they provide generic insights, misuse terminology, or overlook expert-level knowledge. 7. Infrastructure & Deployment Challenges Performance relies on reliable infrastructure. Problems with GPU allocation, latency, scaling, or compliance can lower accuracy and system stability. Wrong outputs don’t mean AI is "broken." They show the challenge of balancing data quality, engineering, context management, and infrastructure. Tackling these issues makes AI systems stronger, more dependable, and ready for businesses. #LLM

🚩 Up to 50% of #RPA projects fail (EY) 🚩 Generative AI suffers from pilotitis (endless AI experiments, zero implementation) 𝐃𝐈𝐓𝐂𝐇 𝐓𝐄𝐂𝐇𝐍𝐎𝐋𝐎𝐆𝐈𝐂𝐀𝐋 𝐍𝐎𝐒𝐓𝐀𝐋𝐆𝐈𝐀 𝐘𝐨𝐮𝐫 𝐑𝐏𝐀 𝐩𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐢𝐬 𝐧𝐨𝐭 𝐞𝐧𝐨𝐮𝐠𝐡 𝐟𝐨𝐫 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 In the race to adopt #GenerativeAI, too many enterprises are stumbling at the starting line, weighed down by the comfortable familiarity of their #RPA strategies. It's time to face an uncomfortable truth: 𝐲𝐨𝐮𝐫 𝐩𝐚𝐬𝐭 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐞𝐬 𝐦𝐢𝐠𝐡𝐭 𝐛𝐞 𝐲𝐨𝐮𝐫 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐨𝐛𝐬𝐭𝐚𝐜𝐥𝐞 𝐭𝐨 𝐀𝐈 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧. There is a difference: 1.    𝐑𝐎𝐈 𝐅𝐨𝐜𝐮𝐬 𝐈𝐬𝐧'𝐭 𝐄𝐧𝐨𝐮𝐠𝐡 AI's potential goes beyond traditional ROI metrics. How do you measure the value of a technology that can innovate, create, and yes, occasionally hallucinate? 2.    𝐇𝐢𝐝𝐝𝐞𝐧 𝐂𝐨𝐬𝐭𝐬 𝐖𝐢𝐥𝐥 𝐁𝐥𝐢𝐧𝐝𝐬𝐢𝐝𝐞 𝐘𝐨𝐮 Forget predictable RPA costs. AI's hidden expenses in change management, data preparation, and ongoing training will be a surprise and can be non-linear. 3.    𝐃𝐚𝐭𝐚 𝐑𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬 𝐈𝐬 𝐌𝐚𝐤𝐞-𝐨𝐫-𝐁𝐫𝐞𝐚𝐤 Unlike RPA's structured data needs, AI thrives on diverse, high-quality data. Many companies need complete data overhauls. Is your data truly AI-ready, or are you feeding a sophisticated hallucination machine? 4.    𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐨𝐬𝐭𝐬 𝐀𝐫𝐞 𝐚 𝐌𝐨𝐯𝐢𝐧𝐠 𝐓𝐚𝐫𝐠𝐞𝐭 AI's operational costs can wildly fluctuate. Can your budget handle this uncertainty, especially when you might be paying for both brilliant insights and complete fabrications? 5.    𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐈𝐬 𝐨𝐧 𝐀𝐧𝐨𝐭𝐡𝐞𝐫 𝐋𝐞𝐯𝐞𝐥 RPA handles structured, rule-based processes. AI tackles complex, unstructured problems requiring reasoning and creativity. Are your use cases truly leveraging AI's potential? 6.    𝐎𝐮𝐭𝐩𝐮𝐭𝐬 𝐜𝐚𝐧 𝐛𝐞 𝐔𝐧𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐚𝐛𝐥𝐞 RPA gives consistent outputs. AI can surprise you – sometimes brilliantly, sometimes disastrously. How will you manage this unpredictability in critical business processes? 7.    𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐌𝐢𝐧𝐞𝐟𝐢𝐞𝐥𝐝 𝐀𝐡𝐞𝐚𝐝 RPA had minimal ethical concerns. AI brings significant challenges in bias, privacy, and decision-making transparency. Is your ethical framework robust enough for AI? 8.    𝐒𝐤𝐢𝐥𝐥 𝐆𝐚𝐩 𝐈𝐬 𝐚𝐧 𝐀𝐛𝐲𝐬𝐬 AI requires skills far beyond RPA expertise – data science, machine learning, domain knowledge, and the crucial ability to distinguish AI fact from fiction. Where will you find this talent? 9.    𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞 𝐈𝐬 𝐒𝐡𝐢𝐟𝐭𝐢𝐧𝐠 Unlike RPA, AI faces increasing regulatory scrutiny. Are you prepared for the evolving legal and compliance challenges of AI deployment? Treating #AI like #intelligentautomation, in learning about it and in its implementation is a path devoid of success. It's time to rewrite the playbook and move beyond the comfort of 'automation COE leadership'. #AIleadership

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.

Why most AI tools fail after the initial "wow" moment I was recently having lunch with a senior law firm partner who said something that stopped me cold: "It takes longer to prompt my legal AI tools to redline a contract than to just do it myself." This explains why VCs are worried about "vibe revenue" vs "recurring revenue" in AI. People sign up for AI tools due to FOMO, try them for a few months, then churn because the tools require too much manual context-feeding to be useful. The problem isn't the AI - it's that most tools are just "ChatGPT for X" without understanding how professionals actually work. Real example: Slide generation tools like Gemini or Copilot can create great presentations, but only if you write 500-1000 words of context in your prompt. At that point, a PowerPoint expert might as well build it themselves. The solution? Contextual AI agents that already know your working patterns - your email history, decision precedents, team dynamics, and industry nuances. Today you feed context to AI. Tomorrow, AI will already know your context. The companies that crack this "contextual layer" problem won't just build better AI tools - they'll make AI invisible infrastructure, like electricity or the internet today. Until then, context remains king. Full thoughts in my latest City AM column: https://lnkd.in/edH-MpJX What's your experience with AI tools requiring too much setup? 👇 #AI #ArtificialIntelligence #AIAgents #MachineLearning #TechStartups #VentureCapital #AIAdoption #DigitalTransformation #FutureOfWork #TechTrends #AIProducts #StartupLife #ProductManagement #AIStrategy #BusinessIntelligence #Innovation #TechLeadership #AIRevolution #Automation #SaaS #EnterpriseTech #AITools #TechInvesting #AIEntrepreneurship #BusinessStrategy

𝗪𝗵𝘆 𝗔𝗜 𝗜𝘀𝗻’𝘁 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴: 𝗧𝗵𝗲 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 is today’s 𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗼𝗯𝘀𝗲𝘀𝘀𝗶𝗼𝗻. Yet despite $35-$40B invested in GenAI tools and $44B raised by startups in 2025, MIT’s 𝗚𝗲𝗻𝗔𝗜 𝗗𝗶𝘃𝗶𝗱𝗲 report shows 𝟵𝟱% 𝗼𝗳 𝗽𝗶𝗹𝗼𝘁𝘀 𝗳𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗼𝗻𝗹𝘆 𝟱% 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗿𝗲𝗮𝗹 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻. The issue isn’t technology, but a “learning gap”: companies can’t weave AI into workflows, processes, and culture. 𝟭. 𝗧𝗵𝗲 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗜𝘀𝘀𝘂𝗲 𝗶𝘀 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹, 𝗻𝗼𝘁 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 The real barrier to AI adoption isn’t data or algorithms, it is the culture. AI disrupts decisions, power structures, and roles. Projects rarely fail from weak models or messy data; they fail because organizations resist change. When initiatives stall, executives blame accuracy, integration, or data quality, valid issues, but often just smokescreens. 𝟮. 𝗧𝗵𝗲 𝗕𝘂𝗱𝗴𝗲𝘁 𝗙𝗶𝗿𝗲𝗵𝗼𝘀𝗲: 𝗥𝗮𝗻𝗱𝗼𝗺 𝗦𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆  Companies chase flashy demos like chatbots instead of focusing on repeatable, high-ROI tasks. By skipping basics, business cases, ROI definitions, and success metrics, executives prioritize what looks impressive over what delivers real value, leaving bigger, faster gains untapped. 𝟯. 𝗧𝗵𝗲 𝗕𝘂𝘆 𝘃𝘀. 𝗕𝘂𝗶𝗹𝗱 𝗧𝗿𝗮𝗽 Enterprises waste millions either betting on hyperscalers to “solve AI” or insisting on building everything in-house. Both fail: real workflows span systems and can’t be vibe-coded or fixed with a big check. The winning model is hybrid, external experts to accelerate and de-risk, internal teams to ensure fit. Don’t outsource your brain, but don’t amputate your arms. 𝟰. 𝗣𝗼𝗼𝗿 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: 𝗪𝗵𝗲𝗿𝗲 𝗚𝗼𝗼𝗱 𝗜𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝘀 𝗗𝗶𝗲 Enterprises get swept up in AI mania, flashy dashboards, or pilots that never scale. Shadow AI usage, fueled by weekend ChatGPT experiments, creates the illusion of progress while deepening the chaos. Without a disciplined approach, projects stall in the messy middle, becoming costly theater rather than true enterprise transformation. 𝗧𝗵𝗲 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝗳𝗼𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝙎𝙩𝙖𝙧𝙩 𝙨𝙢𝙖𝙡𝙡: Automate with clear, measurable outcomes. 𝙋𝙧𝙞𝙤𝙧𝙞𝙩𝙞𝙯𝙚 𝙞𝙣𝙩𝙚𝙜𝙧𝙖𝙩𝙞𝙤𝙣: Fit AI into workflows. 𝘼𝙘𝙠𝙣𝙤𝙬𝙡𝙚𝙙𝙜𝙚 𝙞𝙣𝙚𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚: Partner with experts. 𝙐𝙥𝙨𝙠𝙞𝙡𝙡 𝙖𝙣𝙙 𝙢𝙖𝙣𝙖𝙜𝙚 𝙘𝙝𝙖𝙣𝙜𝙚: Ready people and culture. 𝙎𝙚𝙩 𝙚𝙭𝙥𝙚𝙘𝙩𝙖𝙩𝙞𝙤𝙣𝙨: Distinguish pilots from scaled transformation. MIT’s finding that 95% of AI projects fail isn’t about AI, it is about execution. AI works; enterprises don’t. Winners won’t be those with the biggest budgets, but those willing to change workflows, culture, and habits. Less spectacle, more substance. #AI #GenerativeAI #DigitalTransformation #BusinessStrategy #FutureOfWork

Most enterprise AI projects fail to deliver business value. New MIT research found 95% of GenAI initiatives show zero measurable ROI despite $30-40 billion in investment. The pattern is consistent: - Impressive demos get built quickly - Pilots launch with excitement - But accuracy collapses in real workflows The issue isn't infrastructure or compute. It's that current systems must be grounded in knowledge from both public data and private company data, with enhanced reasoning capabilities and robust fact-checking for complex workflows. Organizations that succeed: - Partner with vendors rather than build internally (2x success rate) - Demand workflow integration over flashy features - Measure business outcomes, not model benchmarks At You.com we see this repeatedly. Companies come to us after failed experiments with generic tools because they need accuracy they can trust, research-grade citations, and systems that can really integrate. Here's what actually works: → End-to-end solutions that integrate deeply into workflows → Composable building blocks that developers can combine We do both. We're powering nearly a billion queries monthly through our APIs, delivering more accurate answers and reliable results for LLMs and agents. For enterprises that need complete solutions, we build custom AI systems that learn from their data and processes. Interesting that the research also shows real ROI often comes from back-office automation (replacing BPO contracts, cutting agency spend, automating document processing), not the flashy front-office use cases getting all the attention. Building a demo is straightforward, but success goes to systems that can learn and evolve.