Key Steps for AI Project Implementation

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗮𝘁 𝗔𝗜 𝗶𝘀 𝗮 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁 𝗽𝗮𝘁𝗵 𝗳𝗿𝗼𝗺 𝗱𝗮𝘁𝗮 𝘁𝗼 𝘃𝗮𝗹𝘂𝗲. The assumption: 𝗗𝗮𝘁𝗮 → 𝗔I → 𝗩𝗮𝗹𝘂𝗲 But in real-world enterprise settings, the process is significantly more complex, requiring multiple layers of engineering, science, and governance. Here’s what it actually takes: 𝗗𝗮𝘁𝗮 • Begins with selection, sourcing, and synthesis. The quality, consistency, and context of the data directly impact the model’s performance. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 • 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Exploration, cleaning, normalization, and feature engineering are critical before modeling begins. These steps form the foundation of every AI workflow. • 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: This includes model selection, training, evaluation, and tuning. Without rigorous evaluation, even the best algorithms will fail to generalize. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 • Getting models into production requires deployment, monitoring, and retraining. This is where many teams struggle—moving from prototype to production-grade systems that scale. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 • Legal regulations, ethical transparency, historical bias, and security concerns aren’t optional. They shape architecture, workflows, and responsibilities from the ground up. 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰. 𝗜𝘁’𝘀 𝗮𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 𝘄𝗶𝘁𝗵 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗿𝗶𝗴𝗼𝗿 𝗮𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗮𝘁𝘂𝗿𝗶𝘁𝘆. Understanding this distinction is the first step toward building AI systems that are responsible, sustainable, and capable of delivering long-term value.

𝐖𝐚𝐧𝐭 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝? Here is a proven 7-part strategy to move from random prompts to fully functional autonomous agents 𝐅𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐞𝐬𝐞 𝐬𝐭𝐞𝐩𝐬 𝐭𝐨 𝐝𝐞𝐬𝐢𝐠𝐧 𝐬𝐦𝐚𝐫𝐭𝐞𝐫, 𝐠𝐨𝐚𝐥-𝐝𝐫𝐢𝐯𝐞𝐧 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝟏. 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 Start by identifying the real problem Map pain points, define user behavior, and clarify what value the agent should deliver 𝟐. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞 𝐃𝐞𝐬𝐢𝐠𝐧 Design where and how the agent will be used Think beyond chatbots - automate workflows, perform research, summarize docs, or handle scheduling 𝟑. 𝐒𝐤𝐢𝐥𝐥 𝐌𝐚𝐩𝐩𝐢𝐧𝐠 Define what the agent should be able to do From reasoning and planning to making decisions, generating outputs, and working with APIs 𝟒. 𝐓𝐨𝐨𝐥 𝐚𝐧𝐝 𝐌𝐨𝐝𝐞𝐥 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 Choose the right LLM and supporting tools Use orchestration frameworks, select tools (APIs, DBs), and decide how the agent will think (RAG, embeddings, rule-based) 𝟓. 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐚𝐧𝐝 𝐌𝐞𝐦𝐨𝐫𝐲 Let your agent stay intelligent over time Simulate real-world tasks, handle errors, recall context, and optimize latency and cost 𝟔. 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 Continuously improve your agent Collect feedback, run A/B tests, monitor performance, and integrate reward-based learning 𝟕. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐂𝐡𝐚𝐧𝐧𝐞𝐥𝐬 Launch where it adds the most value Whether it is in Slack, CRMs, mobile apps, dashboards, or voice assistants - deploy where users already in work Smart agents are not built in one go, they are designed with systems integrated thinking Save this strategy as your go-to roadmap for AI agent development ♻️ Repost this to help your network get started ➕ Follow Prem N. for more

In enterprise AI - '23 was the mad rush to a flashy demo - '24 will be all about getting to real production value Three key steps for this in our experience: - (1) Develop your "micro" benchmarks - (2) Develop your data - (3) Tune your entire LLM system- not just the model 1/ Develop your "micro" benchmarks: - "Macro" benchmarks e.g. public leaderboards dominate the dialogue - But what matters for your use case is a lot narrower - Must be defined iteratively by business/product and data scientist together! Building these "unit tests" is step 1. 2/ Develop your data: - Whether via a prompt or fine-tuning/alignment, the key is the data in, and how you develop it - Develop = label, select/sample, filter, augment, etc. - Simple intuition: would you dump a random pile of books on a student's desk? Data curation is key. 3/ Tune your entire LLM system- not just the model: - AI use cases generally require multi-component LLM systems (eg. LLM + RAG) - These systems have multiple tunable components (eg. LLM, retrieval model, embeddings, etc) - For complex/high value use cases, often all need tuning 4/ For all of these steps, AI data development is at the center of getting good results. Check out how we make this data development programmatic and scalable for real enterprise use cases @SnorkelAI snorkel.ai :)

Some of the best AI breakthroughs we’ve seen came from small, focused teams working hands-on, with structured inputs and the right prompting. Here’s how we help clients unlock AI value in days, not months: 1. Start with a small, cross-functional team (4–8 people) 1–2 subject matter experts (e.g., supply chain, claims, marketing ops) 1–2 technical leads (e.g., SWE, data scientist, architect) 1 facilitator to guide, capture, and translate ideas Optional: an AI strategist or business sponsor 2. Context before prompting - Capture SME and tech lead deep dives (recorded and transcribed) - Pull in recent internal reports, KPIs, dashboards, and documentation - Enrich with external context using Deep Research tools: Use OpenAI’s Deep Research (ChatGPT Pro) to scan for relevant AI use cases, competitor moves, innovation trends, and regulatory updates. Summarize into structured bullets that can prime your AI. This is context engineering: assembling high-signal input before prompting. 3. Prompt strategically, not just creatively Prompts that work well in this format: - “Based on this context [paste or refer to doc], generate 100 AI use cases tailored to [company/industry/problem].” - “Score each idea by ROI, implementation time, required team size, and impact breadth.” - “Cluster the ideas into strategic themes (e.g., cost savings, customer experience, risk reduction).” - “Give a 5-step execution plan for the top 5. What’s missing from these plans?” - “Now 10x the ambition: what would a moonshot version of each idea look like?” Bonus tip: Prompt like a strategist (not just a user) Start with a scrappy idea, then ask AI to structure it: - “Rewrite the following as a detailed, high-quality prompt with role, inputs, structure, and output format... I want ideas to improve our supplier onboarding process with AI. Prioritize fast wins.” AI returns something like: “You are an enterprise AI strategist. Based on our internal context [insert], generate 50 AI-driven improvements for supplier onboarding. Prioritize for speed to deploy, measurable ROI, and ease of integration. Present as a ranked table with 3-line summaries, scoring by [criteria].” Now tune that prompt; add industry nuances, internal systems, customer data, or constraints. 4. Real examples we’ve seen work: - Logistics: AI predicts port congestion and auto-adjusts shipping routes - Retail: Forecasting model helps merchandisers optimize promo mix by store cluster 5. Use tools built for context-aware prompting - Use Custom GPTs or Claude’s file-upload capability - Store transcripts and research in Notion, Airtable, or similar - Build lightweight RAG pipelines (if technical support is available) - Small teams. Deep context. Structured prompting. Fast outcomes. This layered technique has been tested by some of the best in the field, including a few sharp voices worth following, including Allie K. Miller!

Most AI teams are building RAG systems the hard way. They're stitching together 15+ tools, spending months on infrastructure, and burning through runway before they ship their first feature. Here's the 9-step blueprint that successful AI companies use instead: 1/ Ingest & Preprocess Data → Firecrawl for web scraping → Unstructured.io for document processing → Custom connectors for your data sources 2/ Split Into Chunks → LangChain or LlamaIndex for intelligent chunking → Test semantic vs. fixed-size strategies → Context preservation is everything 3/ Generate Embeddings → text-embedding-ada-002 for reliability → BGE-M3 for multilingual support → Cohere Embed v3 for specialized domains 4/ Store in Vector DB & Index → Pinecone for managed simplicity → Weaviate for hybrid search → Qdrant for self-hosted control 5/ Retrieve Information → Dense vector search for semantic matching → BM25 for keyword precision → RRF for hybrid fusion 6/ Orchestrate the Pipeline → LangChain for rapid prototyping → LlamaIndex for production workflows → Custom orchestration for scale 7/ Select LLMs for Generation → Claude for reasoning tasks → GPT-4o for general purpose → Llama 3 for cost optimization 8/ Add Observability → Langfuse for prompt tracking → Helicone for usage monitoring → Custom metrics for business KPIs 9/ Evaluate & Improve → Automated evaluation metrics → A/B testing frameworks → Human feedback loops The companies shipping fastest aren't building everything from scratch. They're choosing the right tool for each job and focusing on what makes them unique. What's your biggest RAG challenge right now? P.S. If you're tired of managing infrastructure and want to focus on your product, Rebase⌥ handles the DevOps complexity so you can ship AI features faster.

Your AI journey shouldn’t start with models. I’ve helped several enterprises avoid one of the biggest AI pitfalls: → Jumping straight to advanced models before building a strong foundation. Instead, we follow a proven "Crawl → Walk → Run" framework  to help you scale Enterprise AI Automation the right way. Here’s how it works👇🏻 𝗣𝗵𝗮𝘀𝗲 1: 𝗖𝗿𝗮𝘄𝗹 – 𝗕𝘂𝗶𝗹𝗱 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 Start with low-cost, low-risk projects.  Your goal? Learn fast and build core capabilities. ✅ Automate routine tasks using RPA (invoice processing, data entry) ✅ Organize and clean your data for downstream AI use 📌Key Insight: Don’t chase ROI yet. Chase readiness. Train teams. Prove small wins. 𝗣𝗵𝗮𝘀𝗲 2: 𝗪𝗮𝗹𝗸 – 𝗔𝗱𝗱 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗦𝘁𝗮𝗿𝘁 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 Once your foundation is solid, step into mid-complexity AI with moderate investment. ✅ Predictive Maintenance: Reduce equipment failures with ML ✅ AI Chatbots: Improve CX while lowering support costs 📌Key Insight: Let technical and business teams work closely together. Use real learnings from the crawl phase to guide decisions. 𝗣𝗵𝗮𝘀𝗲 3: 𝗥𝘂𝗻– 𝗗𝗿𝗶𝘃𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗪𝗶𝗱𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 Now you’re ready for high-stakes, high-reward transformation. ✅ Personalization Engines: Tailored experiences = loyal customers ✅ Executive Decision Support: Fast insights for strategic calls 📌Key Insight: Establish strong governance. Track ROI. Let AI shift from a support role to a strategic driver. Skipping these foundations breaks momentum. This approach is sustainable, and that’s how real AI transformation happens. Curious where your organization stands in this journey? Let’s connect… happy to share how we approach this at Pronix Inc #AIAutomation #AutomationStrategies #PronixInc

AI implementation meetings: 5 People. 0 Strategy. Here is where most companies fail. 👉 They jump straight into tools. Vendors. Demos. Dashboards. And call it a strategy. But AI only delivers results when the basics are in place. 📌 A clear business problem 📌 Clean, usable data 📌 Humans who are ready to act Without that? You’re not running a transformation — You’re hosting an expensive guessing game. 7 Moves to Make Your AI Strategy Actually Work: 1. ✅ Define the problem. - AI should solve a specific business need. - If it doesn’t, it’s just a shiny distraction. 2. ✅ Audit your data. - Garbage in, garbage out. - You can’t fake good data. 3. ✅ Pick use cases, not buzzwords. - “GenAI” isn’t a strategy. - “Reduce customer churn by 12%”? That’s a use case. 4. ✅ Loop in your integration team early. - AI isn’t plug-and-play. - Especially not with your 14 legacy systems. 5. ✅ Prep your people. - The biggest blocker isn’t the model. It’s mindset. - Train your team for the change. 6. ✅ Set KPIs before kickoff. - What does success look like? - How will you measure progress? 7. ✅ Assign ownership. - If everyone’s responsible, no one is. - Give someone the wheel. 🧩 Botom Line: If your AI “strategy” fits on a single flip chart… You’re not building transformation — You’re throwing corporate darts at the future. ♻️ Repost if you’re investing in people, not just tech. 👣 Follow Janet Perez for more like this.

Building AI models is not just about choosing the right algorithm. It requires a combination of data quality, model architecture, training workflows, and continuous learning. The most impactful AI systems succeed by treating MLOps, explainability, and deployment as core pillars from day one. Here’s a quick breakdown of the core components involved: 1.🔸Data It starts with collecting, filtering, augmenting, and labeling massive datasets, the raw fuel for training. 2.🔸Algorithms From deep learning and reinforcement learning to zero-shot and probabilistic models this is the brain behind the model’s behavior. 3.🔸 Model Architecture CNNs, RNNs, Transformers, GANs, VAEs… all part of designing how your model learns and processes information. 4.🔶Training Process Where the magic happens, along with loss functions, hyperparameter tuning, mixed-precision training, and more. 5.🔸Evaluation & Validation You can’t improve what you can’t measure. Enter F1 scores, ROC-AUC, cross-validation, fairness audits, and explainability. 6.🔸Inference & Deployment Once trained, the model must serve predictions in real time, on edge/cloud, with Docker containers, optimized for latency. 7.🔸Feedback & Continuous Learning Monitoring, detecting drift, online learning, retraining, and human-in-the-loop corrections, because models never stop learning. 🧠 It’s not one thing that powers an AI model, it’s everything working together. #genai #artificialintelligence

The Ultimate Generative AI Project Structure - Build Like a Pro Building AI applications without proper structure is like constructing a tall building without blueprints. Most developers start with excitement, create a few scripts, and soon find themselves drowning in messy code that's impossible to maintain or scale. Here's a comprehensive project template that transforms chaotic AI experiments into production-ready applications: Key Directory Structure: 1. config/ - Centralized configuration management keeps your settings organized and environment-specific. No more hardcoded values scattered throughout your codebase. 2. src/ - Clean, modular source code with separate clients for different LLM providers. Base classes and utilities ensure consistent patterns across your application. 3. prompt_engineering/ - Dedicated space for prompt templates, few-shot examples, and chain implementations. Version control your prompts like the critical assets they are. 4. utils/ - Essential infrastructure code including rate limiting, token counting, caching, and logging. These utilities prevent common pitfalls in production AI systems. 5. data/ - Organized storage for cache, prompts, outputs, and embeddings. Clear separation makes data management and debugging much simpler. 6. examples/ & notebooks/ - Implementation references and experimentation space. Perfect for testing ideas before integrating them into your main application. Why This Structure Works: This template follows software engineering best practices while addressing AI-specific challenges like prompt management, API rate limits, and result caching. It's designed for maintainability and team collaboration. Getting Started: Clone, configure your model settings, review the examples, experiment in notebooks, then build your custom application. This structure has transformed countless AI projects from proof-of-concepts into scalable solutions. Save this template for your next GenAI project! Follow Satish Goli For More Such Information ! #GenerativeAI #MachineLearning #LLM #AIEngineering #devops #softwareengineering #AIagents #AgenticAI