"Qualifying AI projects: A framework for success"

👔 Your Next Teammate Might Be an AI Agent Imagine a system that doesn’t just follow instructions it understands goals, plans proactively, adapts in real time, and collaborates like a human. This isn’t sci-fi. It’s the rise of Agentic AI and AWS just released a guide every executive should bookmark. ▶️ What is Agentic AI? It’s the next evolution of AI autonomous systems that: • Set and prioritize goals • Coordinate actions across tools and departments • Make decisions with minimal human input • Improve with each task through memory and feedback loops ✅ Why It Matters Now: Companies like Amazon, Rocket Mortgage, and Genentech are already building agentic AI into logistics, HR, and finance. These aren’t beta tests, they’re business accelerators. 🔍 How It’s Different from Traditional AI: • Not just automating tasks AI agents own processes • Shift from workflow automation to strategic execution • They function like junior teammates, not just tools 📊 From the AWS Executive Guide: • Agents boost cost efficiency by streamlining redundant tasks • They increase speed of innovation by removing operational friction • They scale intelligence across teams by sharing context and learning 🔧 Start Small, Scale Smart You don’t need a full AI lab to begin. Try this: • ✅ Pick one department (e.g., Customer Support) • ✅ Map one goal (e.g., reduce response time by 30%) • ✅ Assign one agent to prototype and iterate 💡First step: Download the AWS Agentic AI Executive Guide (It’s practical, not just hype.) 🔄 Remember: Tools don’t transform companies. Cultures do. 🤖 Is your organization ready to manage not just adopt AI agents? #AgenticAI #ExecutiveStrategy #AWS #AIFuture #DigitalLeadership #AIAgents #WorkforceTransformation #NextGenAI

Adding AI to Your Product? Here’s What Your CTO Should Know AI is no longer a futuristic add-on — it's a competitive edge. But adding it to your product isn’t just about plugging in a smart algorithm. It's a strategic move that requires deep technical insight and clear business alignment. And that’s where your CTO becomes essential. Whether you’re launching a new AI-powered feature or enhancing existing functionality, your tech leadership needs to make the right calls early — to avoid delays, data issues, and architectural roadblocks later. Here’s what your CTO should be thinking about before you build: 🧠 Problem fit first: Does AI actually solve the user problem better than rule-based logic or automation? 🔍 Data readiness: Do you have the right data, and is it clean, structured, and accessible? 🛠️ Tech stack compatibility: Can your current system integrate AI models or services without major rework? 🔄 Build vs. buy: Should you develop models in-house or use existing platforms like OpenAI, Hugging Face, or AWS AI? 📈 Scalability & cost: How will performance and cloud costs scale as usage grows? 🔐 Security & ethics: How will you handle data privacy, bias, and transparency? An AI initiative without clear answers to these questions can become expensive fast — and miss the mark with users. ✅ At Hristov Development, we help startups and growth-stage companies build smarter, faster, and more confidently with AI. From concept to deployment, we guide your team through a clear and scalable path to intelligent features. 💡 Thinking about adding AI to your product? Let's talk hristovdevelopment.com #AIProductDevelopment #CTOInsights #SoftwareStrategy #HristovDevelopment

"Find the 10x opportunities, not the 10% ones." Good observation from Matthias Patzak, Enterprise Strategist at Amazon Web Services (AWS), about commercial #GenAI adoption strategies. For a CEO with a business school-rooted mindset it's probably always tempting to use any new technology (including #GenAI) asap for immediate automation & #productivity increases: the "economising" impulse. ▶️ "Automate workflow xyz in HR and save so-and-so many FTE with that". The economising approach unfortunately misses out on the systemic level: work out how things should really work. Not: trying to refactor the technical implementation with a shiny new #AI #UI but otherwise leaving all workflows in place - meaning, changing nothing but the surface. Well, you might get your 10%... Grounding a #GenAI PoC on a systemic question will nearly always result in different answers and approaches. Namely those relating to 10x thinking. In-place process productivity improvement targets will hardly be able to deliver that initially. I can only surmise that it's because of this that some initial #GenAI adoption projects are perceived as "failed" - as currently reported across the range -, wenn "economising" productivity improvement projects meet ambitious PoC implementation efforts. It's so much easier to "re-invent the telephone book with #AI" (introduce new tech into old workflows), than to touch workflows themselves (against expectable organizational resistance). "You get what you want". Just don't blame #technology for it, when the root cause is actually #organizational. That's indeed what the #CTO should discuss with the #CEO over a cup of coffee. Traditional Business School thinking is not always the best approach when technology #disruption (sic) actually requires a look behind the systemic curtain. The (yet undiscovered) potential of agentic #AI might justify it. https://lnkd.in/eW36GxC9

I have often seen companies spend months perfecting there models during training, deploy them into production - but without AI observability in place.👀 Once live, focus shifts away from improvement. I have experienced this firsthand. I was introduced to a client who was not happy with the low accuracy of their demand forecasting model. On the surface, the model looked fine — but the results weren’t adding up. We introduced basic observability(Drift, data quality, Explainability) and quickly spotted a huge drift for a key SKU–Customer combination. Digging deeper, we found the root cause: a backend pipeline issue that was exaggerating sales. So, the model wasn’t the problem at all — it was being trained on wrong data. Once the backend pipeline was fixed, accuracy shot up and the client was happy. That’s the power of AI observability. It’s not just about dashboards or metrics — it’s about finding hidden issues, fixing them fast, and making AI trustworthy at scale. "If you’re not measuring, you’re not improving." ✅ #AIObservability #MLOPS #LLMOPS #ResponsibleAI

🚨𝗠𝗜𝗧 𝗥𝗲𝗽𝗼𝗿𝘁: 95% of generative #AI pilots are failing....That’s the headline from #MIT’s latest research. But here’s the truth: AI is not failing:) execution is. When applied with #focus, #alignment and #speed, AI delivers measurable business outcomes. Yet enterprises are investing billions into #GenAI, and most initiatives stall without delivering measurable #ROI. Why? 1. Flawed integration into existing workflows 2. Scattered pilots without business alignment 3. Overhyped expectations with underwhelming execution. Too many projects chase hype instead of solving real problems. At Prodapt, we’ve taken a different path purposeful AI initiatives that our customers trust - because they deliver measurable outcomes, not just headlines. As our #CEO Manish Vyas recently said: This is about building with purpose - not for headlines, but for impact. We’re seeing real impact by: :} Targeting high-value use cases :} Embedding AI seamlessly into enterprise workflows :} Delivering rapid outcomes - often an MVP in just weeks, not years. The lesson? The winners in this space won’t be the ones experimenting the most, but those executing with #clarity and #discipline. Do you want to see how we’re turning failed pilots into miracles for our clients? You might be surprised at what purposeful AI can achieve. Let’s create something in that 5% together..! #AI #Innovation #Technology #PurposefulAI #ServiceNow #Salesforce #Google #NVIDIA #Snowflake #Amazon #Prodapt #Synapt Jagan N Sumit Grover Varun Raja Varun Ravichandran Venkatachalam R. Beeshma Choudary Aniket Hinge Raghav Paramesh Vasanth Ilango Bobby Singh Sowmiyanarayanan Srinivasan Kannan Piedy Eniya M Vinith Raj Sabeshan (Sabs) Catherine Miller Rithika Sundararajan Aravindan E Shristi Sudarshan Sruthi Ravishankar Yash Gupta Lakshara Kempraj Ryan Connolly Gopinath Ganeshan Santhana Prabhu Muruganantham Muhammad Anas Abhishek Prakash Sriram Karanam

🎯 AI Market Evolution: Strategic Shifts Across Technology, Talent, and Operations The artificial intelligence landscape is experiencing profound structural changes that extend far beyond technological advancement into strategic business positioning. OpenAI's Grove fellowship program demonstrates sophisticated talent pipeline management, targeting pre-idea technical builders through equity-free engagement. This approach creates valuable early-stage relationships with potential collaborators while reducing barriers to participation, effectively positioning OpenAI within emerging entrepreneurial ecosystems before competitive alternatives establish market presence. Amazon's implementation of AI-generated review summaries represents fundamental infrastructure evolution in e-commerce customer experience. These AI-mediated product information systems require immediate adaptation of traditional optimization strategies, as sellers must now consider how artificial intelligence interprets and synthesizes customer feedback for prominent display on product pages. This shift creates new competitive dynamics in digital marketplace positioning. Microsoft's achievement of $500 million in annual savings through customer service AI automation illustrates the substantial operational efficiency gains available through strategic implementation. The focus on smaller customer segments while maintaining human interaction for high-value accounts demonstrates sophisticated tiered service approaches that balance cost optimization with relationship management. Structural changes are evident in xAI's workforce restructuring, eliminating one-third of data annotation roles while expanding specialist tutoring capabilities. This transition from generalist to specialized AI applications reflects broader market maturation patterns across the industry. China's SpikingBrain 1.0 development achieves comparable performance with minimal training requirements using domestic hardware, suggesting potential shifts in global AI supply chain dependencies. Meanwhile, Harvard's PDGrapher system demonstrates 35 percent performance improvements with 25-fold speed increases in drug discovery applications, indicating acceleration potential in therapeutic development timelines. My critical take: These developments reveal an AI market transitioning toward operational specialization and geographic diversification, creating both efficiency opportunities and supply chain vulnerability challenges. Organizations must reassess technology adoption strategies while navigating increased competitive complexity in talent acquisition and market positioning. #OpenAI #Amazon #Microsoft #xAI #SpikingBrain #PDGrapher #AITransformation

💰 MLOps is Not a Cost Center — It’s a Growth Enabler Many organizations hesitate to invest in MLOps because they see it as “extra engineering overhead.” But the reality is the opposite: ⚡ Without MLOps: • Models stay stuck in notebooks. • Experiments never reach production. • ROI from AI projects stays at 0%. ⚡ With MLOps: • Time-to-market for ML solutions shrinks dramatically. • AI projects scale beyond PoCs into production-grade systems. • Models adapt faster to new data → better business outcomes. 📊 The business impact: MLOps isn’t about building fancier pipelines. It’s about turning AI into measurable value—from customer personalization to fraud detection to operational efficiency. 💡 Takeaway: MLOps isn’t “nice to have.” It’s the bridge between AI investment and AI impact. 👉 Question: Does your organization measure the ROI of MLOps, or is it still treated as a technical checkbox? #MLOps #AI #MachineLearning #DevOps #DataOps #BusinessValue #AIEngineering #ArtificialIntelligence #DigitalTransformation #CloudComputing #FutureOfAI #Leadership

"I told my teenager: master soft skills—not just tech—to thrive in the AI age." AWS CEO Matt Garman recently shared some powerful guidance he gave his high school–aged kid: in an era of AI and automation, the true staying power comes from creativity, adaptability, emotional intelligence, human insight, and above all, critical thinking. I agree with him. And I tell my kids similar things, too. Here’s why it matters: ➡️ Critical thinking is irreplaceable. Machines can crunch numbers and parse patterns, but they can’t ask why, challenge assumptions, or connect the dots, so to speak. ➡️ Soft skills are strategic assets. Teams that can understand data drive real change, not just automation. Critical thinking isn’t going away anytime soon. And as technology evolves, we have to make sure we’re asking the right questions and challenging assumptions. What’s your take? Let me know in the comments. #automation #AI #criticalthinking #AWS #CEO

Training AI is easy. Deploying it is where most fail. Here are 5 reasons why 80% of AI projects never make it past the PoC stage and how to fix them. The AI Deployment Dilemma 🚨  Training AI models has never been easier.   ✅ Cloud services   ✅ Open-source libraries   ✅ Pre-trained models  But here’s the truth no one tells you:  ❌ Nearly 80% of AI models never make it to production.   They stay stuck in Jupyter notebooks or proof-of-concepts, never delivering real business value.  Why does this happen? Here are the 5 biggest roadblocks to AI deployment, we see at The Logic Lounge ⬇️  1️⃣ The Data Pipeline Problem Messy, ever-changing data kills models.   👉 Build automated pipelines + feature stores from day one.  2️⃣ The Model-Operations Gap Data science ≠ production.   👉 Use MLOps (CI/CD for AI) with monitoring & versioning.  3️⃣ Scalability Bottlenecks Models break under real-world loads.   👉 Optimize with quantization, distillation & hardware acceleration.  4️⃣ Regulatory & Ethical Challenges Compliance, fairness, and transparency are often ignored.   👉 Adopt AI governance & bias audits early.  5️⃣ Lack of Business Alignment   High accuracy ≠ real value.   👉 Start with business goals & define success beyond accuracy.  💡 The takeaway?   AI isn’t just about models. It’s about data, pipelines, MLOps, scalability, and business impact.  🚀 Companies that master deployment will lead the next wave of innovation.   👉 What’s been YOUR biggest challenge in moving AI from research to production? #AIEngineering #MLOps #DevOps #MachineLearning #AIInProduction #CloudComputing #TheLogicLounge

AI in Enterprises: How Do We Move Beyond Pilots to Production? What’s the Real Roadmap for AI? . . Enterprises often find themselves stuck in the “pilot purgatory” of AI — proof-of-concepts look promising, but scaling to production is where momentum breaks. The key lies in bridging vision with execution. Here’s a strategic-to-practical framework for navigating the AI adoption journey: 🔹 1. Anchor Pilots in Strategy Executives must link AI use cases directly to growth levers — reducing churn, accelerating claims, detecting fraud. For practitioners, this means defining success metrics (AUC, precision, latency) that ladder up to business KPIs. 🔹 2. Manage Technology Debt Early CIOs and CTOs see this as modernizing legacy ecosystems; practitioners tackle it by cleaning data lakes, deprecating redundant ETL pipelines, and adopting modular architectures. 🔹 3. Build on Scalable Cloud Infrastructure AWS provides the enterprise backbone — Amazon SageMaker for model lifecycle, Bedrock for GenAI, Redshift for analytics, QuickSight Q for self-service insights. Teams can experiment fast, while leaders get the assurance of security, compliance, and cost governance. 🔹 4. Operationalize AI with MLOps Executives think in terms of governance and risk; practitioners automate retraining pipelines, monitor model drift, deploy CI/CD for ML. Together, they ensure AI doesn’t just launch — it sustains. 🔹 5. Translate Outcomes into ROI Business leaders want efficiency gains, revenue growth, or risk mitigation. Practitioners measure throughput, inference cost, and adoption rates. Marry both lenses to articulate ROI in boardroom language and technical dashboards. ✨ The result? AI is not a side-project — it becomes a core enterprise capability, embedded into workflows, strategy, and culture. 👉 Where do you see the biggest roadblock today — in strategy alignment, infrastructure readiness, or scaling MLOps? 👉 For those working hands-on with AWS or MLOps pipelines, what’s the toughest challenge in moving from POC to production? 👍 𝗟𝗶𝗸𝗲 🔁 𝗥𝗲𝗽𝗼𝘀𝘁 💬 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 ➡️ 𝗙𝗼𝗹𝗹𝗼𝘄 Dyuti Lal for such posts on Data Analytics, Data Science, Artificial Intelligence #AIAdoption #EnterpriseAI #FromPilotToProduction #MLOps #AWSCloud #DataScience #MachineLearning #GenerativeAI #BusinessStrategy #AIInnovation #CloudComputing #DyutiLal