DataPebbles-The_Lean_AI_Revolution__How_to_Build_Intelligent_Systems_Without_Breaking_the_Bank_or_the_Planet.pdf

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1. Copyright of DataPebbles The Lean AI Revolution: How to Build Intelligent Systems Without Breaking the Bank (or the Planet) Surajeet Bhuinya datapebbles
2. Copyright of DataPebbles datapebbles one Tool
3. Copyright of DataPebbles datapebbles MYTH Reality Despite an average spend of $1.9 million on GenAI initiatives in 2024, less than 30% of AI leaders report their CEOs are happy with AI investment return Linkedin News GenAI enters the Trough of Disillusionment as organizations gain understanding of its potential and limits
4. Copyright of DataPebbles datapebbles Bigger Model = Better Result Large models: Accurate but expensive, slow, and hard to scale - you lose time, budget, and control. Why companies often go for bigger? • Hype-driven • Convenient • Risk-averse • Skills lacking • Prototype fast Lean models: Right-sized, fast, secure, and efficient - same business value with lower cost and a lighter footprint.
5. Copyright of DataPebbles datapebbles MYTH Solution Solution Name Components Complexity Cost Lean / Sustainable factor Key Benefit 1 Keyword Search + YOLO + Human Verification Text Search + YOLO + human verification Low Low High (minimal infrastructure, low energy use) Low cost, simple implementation 2 Pytesseract (Open-Source OCR) + YOLO + Regex-driven OCR + YOLO + Regex Low-Medium Low High (lightweight, small server load Moderate accuracy with minimal automation 3 Cloud OCR (Azure or similar) + YOLO + Low-Code Platform Verification Cloud OCR + YOLO + Compliance Platform Medium Medium Medium (requires moderate compute) Higher accuracy for product type and text extraction 4 Cloud OCR + Faster R-CNN + Machine Learning–Powered Policy Checks Cloud OCR + 2 Staged Detection + ML Medium-High High Medium (more computation, moderate energy) Handles multiple products per image 5 Cloud OCR + EfficientDet + AI (LLM)-Powered Compliance Rules Cloud OCR + EfficientDet + LLM High Very High Medium-Low (heavy computation) Fully automated, high precision
6. Copyright of DataPebbles datapebbles MYTH AI Will Run the Whole Company Full autonomy is not realistic today. Scoped, orchestrated AI workflows are delivering value. Example: Supply Chain 1.AI spots a delayed shipment. 2.Suggests fixes (reroute, alternative supplier). 3.Human makes the final decision. AI agents: systems that integrate reasoning, decision- making, and execution on behalf of users.
7. Copyright of DataPebbles datapebbles MYTH AI Will Run the Whole Company
8. Copyright of DataPebbles datapebbles AI Transformation Needs Long Timelines 02 Use Case Validation 01 Use Case Discovery 03 POC 04 Implementation 05 Scaling
9. Copyright of DataPebbles datapebbles AI Transformation Needs Long Timelines 02 Use Case Validation 01 Use Case Discovery 03 POC 04 Implementation 05 Scaling
10. Copyright of DataPebbles datapebbles AI Transformation Needs Long Timelines 02 Use Case Validation 01 Use Case Discovery 03 POC 04 Implementation 05 Scaling 1. Define the PoC Scope › Real problem with measurable value. › Narrow but meaningful scope: one process, one region/team, one key outcome. › Time-bound: Max 1–2 months from kickoff to result. › Real and representative data - PoCs with dummy data don’t prove anything. 2. Get Transparency in Solution Design › Understand the tools used and if they are a good choice › Understand where your data lives, how it’s used, and where decisions are made. › Confirm compliance with privacy, security, and regulations. › Look for open standards and portability. 3. Plan Exit & Scale Path › Define success thresholds: if KPIs aren’t hit, you stop. › Know what’s needed to scale: budget, people, tech. › Have a scale/no-scale decision checkpoint agreed upfront.
11. Copyright of DataPebbles datapebbles AI Transformation Needs Long Timelines 02 Use Case Validation 01 Use Case Discovery 03 POC 04 Implementation 05 Scaling Implementation isn’t a final step. You’re no longer validating if AI can help. Now you need to ensure it keeps helping. > Shifting from PoC setups to production-ready architecture with monitoring, versioning, and access controls. > Defining clear ownership across business, data, IT, and support, AI needs a team, not just a single champion. > Embedding AI into daily workflows, not leaving it as a side tool. > Training users and support teams so they trust it, use it, and can maintain it. > Documenting what was built so the system can scale and be handed off without friction.
12. Copyright of DataPebbles datapebbles Detecting Inconsistencies in Healthcare Requirements • End-to-end data security & encryption • Strict access controls & audit trails • Alignment with HIPAA/GDPR standards Problem • Errors in records & dossiers • Manual checks = slow & costly • Risk of fines & patient harm Solution • Auto-check files for inconsistencies & anomalies • Flag errors, duplicate entries, or missing data • Produce tamper-proof, audit- ready logs
13. Copyright of DataPebbles datapebbles Surfacing Enterprise Knowledge Requirements • Unified & secure data integration • Role-based access & audit controls • NLP models tuned to company terminology • Continuous updates to prevent knowledge decay Problem • Knowledge trapped in silos & legacy systems • Employees waste time searching for info • Risk of using outdated or inconsistent data Solution • Intelligent search across data, processes, people • NLP/Q&A to surface the right answer instantly • Context-aware recommendations based on role/task • Secure knowledge graph for enterprise-wide access
14. Copyright of DataPebbles datapebbles Start Small & Scope Smart • Start Small & Scope Smart • Pick one high-friction workflow and one KPI • Involve end-users early to shape requirements • Keep pilot narrow, measurable, and actionable • Look at your entire toolbox to create best solution Efficient & Sustainable Systems • Prefer lightweight, task-specific models • Leverage open-source + cloud APIs • Optimize compute, batch inference, and autoscaling • Track energy usage and environmental footprint Iterate & Scale Pilot → measure → refine → repeat • Reuse modular components, pipelines, and templates • Scale patterns, not one-off projects • Continuously track ROI and operational impact Integrate & Complement • Embed AI into existing workflows and tools • Build AI around use case not other way around • Avoid side processes; ensure AI supports team productivity • Include alerts and exception handling for reliability
15. Copyright of DataPebbles datapebbles Scale smart. Stay in control. Thank you for your attention! If you have more questions visit us at Booth 46