Optimizing Workflow Processes

𝗢𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗠𝗢𝗦𝗧 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗲𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝗛𝗼𝘄 𝘁𝗼 𝗽𝗶𝗰𝗸 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲? The LLM landscape is booming and choosing the right LLM is now a business decision, not just a tech choice. One-size-fits-all? Forget it. Nearly all enterprises today rely on different models for different use cases and/or industry-specific fine-tuned models. There’s no universal “best” model — only the best fit for a given task. The latest LLM landscape (see below) shows how models stack up in capability (MMLU score), parameter size and accessibility — and the differences REALLY matter.  𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: ⬇️ 1️⃣ 𝗚𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝘁 𝘃𝘀. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁: - Need a broad, powerful AI? GPT-4, Claude Opus, Gemini 1.5 Pro — great for general reasoning and diverse applications.   - Need domain expertise? E.g. IBM Granite or Mistral models (Lightweight & Fast) can be an excellent choice — tailored for specific industries.  2️⃣ 𝗕𝗶𝗴 𝘃𝘀. 𝗦𝗹𝗶𝗺:  - Powerful, large models (GPT-4, Claude Opus, Gemini 1.5 Pro) = great reasoning, but expensive and slow. - Slim, efficient models (Mistral 7B, LLaMA 3, RWWK models) = faster, cheaper, easier to fine-tune. Perfect for on-device, edge AI, or latency-sensitive applications.  3️⃣ 𝗢𝗽𝗲𝗻 𝘃𝘀. 𝗖𝗹𝗼𝘀𝗲𝗱   - Need full control? Open-source models (LLaMA 3, Mistral, Llama) give you transparency and customization.   - Want cutting-edge performance? Closed models (GPT-4, Gemini, Claude) still lead in general intelligence.  𝗧𝗵𝗲 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆? There is no "best" model — only the best one for your use case, but it's key to understand the differences to make an informed decision: - Running AI in production? Go slim, go fast. - Need state-of-the-art reasoning? Go big, go deep. - Building industry-specific AI? Go specialized and save some money with SLMs.  I love seeing how the AI and LLM stack is evolving, offering multiple directions depending on your specific use case. Source of the picture: informationisbeautiful.net

“What AI skill should my team and I actually learn right now?” I will scream this from the rooftops of NYC. ➡️ Learn agent delegation Target a dedicated workflow or task. Assign an AI agent said role, define the outcome, set constraints, and schedule review gates. Treat it like a junior teammate and give it work, while monitoring so you can review for accuracy. Here’s my do-this-now stack, and how I’d run it with a team ⏬ If you’re a beginner: Start with ChatGPT Agent Mode. Open a new ChatGPT chat and change the dropdown to ‘Agent Mode’. It can plan tasks, execute steps, and return cited outputs for market scans, vendor comparisons, executive briefs, and decision memos. Kick off the job, let it run, WATCH IT RUN, and then review the completion. If you’re more technical or ops-heavy: Use Claude Code when the work requires operating UIs or your computer - clicking through portals, filling forms, wrangling spreadsheets, saving down documents. Expect more upfront setup and ownership, so keep a step-by-step prompt checklist, add automatic reruns for failing steps, and update the checklist only when the site’s labels or paths change. If you’re living in Google Workspace: Turn on Google connectors (Drive, Gmail, Calendar) inside ChatGPT or Claude. Ask the model to find your team’s file, summarize threads, compare document versions, prepare for and schedule meetings, or draft from past emails. This lets your agent pull context and act on it without manual hunting. How to turn this into outcomes in 30 days ⏬ → Twice a week, use Agent Mode to produce a one-page brief with citations and a recommendation on a real business question. Track cycle time and data/citation quality, and, where relevant, use Claude Code to automate in parallel. At the end of the month, you should know where a few agents can tackle real work and have the data to support what to scale. #AIinWork

At Amazon, I’ve built pipelines that move thousands of gigabytes of data. At Amazon, I’ve also built platforms used by hundreds of teams across the organization. But do you know how I got the opportunity to do these things? → It was because of one simple mindset shift: I stopped thinking like a pipeline builder. And started thinking like a product builder. Here’s what that shift looks like in real life 👇 1. Optimize for adoption, not just execution A fast Spark job is nice. But a pipeline that any team can deploy, monitor, and debug without you? That’s a game-changer. If your internal users are struggling, that’s a UX bug. 2. Design APIs, not one-off scripts Your Airflow DAGs and Glue jobs should feel like APIs. Versioned, observable, with clear inputs/outputs. That’s how you build trust at scale. 3. Surface friction like a PM If people keep pinging you for creds, schemas, or weird Athena errors, that’s a signal. Treat those moments like product bugs. Fix them once, and fix them for everyone. 4. Metrics = feedback loops In product, you track conversion. In data platforms, track usage: → How many teams use your tools? → How often do they fail? → Who’s stuck? These are your feature requests. 5. Think enablement > control Great platforms don’t block, they enable. Guardrails should guide, not restrict. Make it easy to do the right thing. I’ve learned this the hard way. When you think like a product builder, your work scales. It doesn’t stop at you. It becomes a system that helps others move faster. So next time you're building a data pipeline, ask yourself: What would this look like if it were a product? Let’s build platforms that people actually want to use.

Your talent is worthless if you can't balance it with real-world demands. Most people chase success the wrong way: - Overworking during the week - Trying to be creative on weekends - Burning out trying to do both This approach is destroying both your potential and peace of mind. Instead, here's what actually works: 1. Integration over Separation Blend creative thinking into every professional task Make every meeting a chance to innovate Turn routine work into creative experiments 2. Balance through Boundaries Set clear limits for both work and creative time Create transition rituals between different modes Respect your energy levels above all else 3. Consistency over Intensity Small creative acts daily beat big weekend projects Regular professional development trumps sporadic sprints Sustainable practices win over heroic efforts The most successful professionals I've worked with don't try to be two different people - they become one balanced individual. Ready to transform how you approach your work and life? Pick one routine task today and approach it with creative intent. ✍️ Your insights can make a difference!

When working with multiple LLM providers, managing prompts, and handling complex data flows — structure isn't a luxury, it's a necessity. A well-organized architecture enables: → Collaboration between ML engineers and developers → Rapid experimentation with reproducibility → Consistent error handling, rate limiting, and logging → Clear separation of configuration (YAML) and logic (code) 𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗧𝗵𝗮𝘁 𝗗𝗿𝗶𝘃𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 It’s not just about folder layout — it’s how components interact and scale together: → Centralized configuration using YAML files → A dedicated prompt engineering module with templates and few-shot examples → Properly sandboxed model clients with standardized interfaces → Utilities for caching, observability, and structured logging → Modular handlers for managing API calls and workflows This setup can save teams countless hours in debugging, onboarding, and scaling real-world GenAI systems — whether you're building RAG pipelines, fine-tuning models, or developing agent-based architectures. → What’s your go-to project structure when working with LLMs or Generative AI systems? Let’s share ideas and learn from each other.

Are your lawyers doing too much admin? I watched a senior lawyer spend 45 minutes chasing signatures on a straightforward NDA. Email after email. Follow-up after follow-up. That's not legal work. That's administrative housekeeping. And it's costing you a fortune. Here's the maths that should terrify every GC - if your senior lawyer earns £150k and spends even 20% of their time on administrative tasks, you're burning £30k per year on work that doesn't require legal qualification. Multiply that across your team and the numbers become eye-watering. The problem isn't that your lawyers are inefficient. It's that you've built a function where qualified lawyers are the only people who can do anything. Need a contract reviewed? Lawyer. Need a signature chased? Lawyer. Need a report compiled? Lawyer. Need a file organised? Lawyer. It's like using a surgeon to take your temperature. The most efficiently run legal functions I've worked with operate completely differently. They've identified which tasks genuinely require legal judgement and which are just process. They've built capacity around their lawyers - paralegals, legal ops professionals, business support. They've implemented technology that handles the routine stuff automatically. E-signatures that don't need chasing. Templates that don't need reviewing. Reports that compile themselves. The result? Lawyers who actually get to be lawyers. And here's what's remarkable - when you free lawyers from administrative burden, they don't just become more efficient. They become more strategic, more commercial, more valuable to the business. But it requires courage. The courage to invest in proper operational support. The courage to implement technology properly. The courage to redesign how work flows through your function. Most legal teams know their lawyers are drowning in administrative work. Few have the backbone to actually fix it. What percentage of your team's time is spent on work that doesn't require legal qualification? #legaloperations #inhouselegal #generalcounsel

I’m a Data Engineer. Why should I care about DevOps? Because waking up to a Slack alert at 2:07 AM that says “pipeline failed” is not a career goal. Neither is explaining broken numbers to leadership on Monday. 𝘋𝘢𝘵𝘢 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘧𝘢𝘪𝘭 𝘲𝘶𝘪𝘦𝘵𝘭𝘺. 𝘐𝘵 𝘧𝘢𝘪𝘭𝘴 𝘪𝘯 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯. Data tells you what happened. CI/CD decides what happens next. 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗖𝗜/𝗖𝗗 → Manual deployments = bottlenecks and broken pipelines → Data quality issues surface after dashboards fail → Scaling means rebuilding from scratch → One bad commit takes down prod (and your weekend) 𝗪𝗶𝘁𝗵 𝗖𝗜/𝗖𝗗 → Automated testing catches bugs before merge → Pipeline-as-code deploys changes safely and consistently → Monitoring & alerts flag issues before users notice → Rollbacks happen in seconds, not hours 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗜/𝗖𝗗 𝗦𝘁𝗮𝗰𝗸 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀  • Version Control: Git, GitHub/GitLab  • CI/CD: Azure DevOps, GitHub Actions, Jenkins  • IaC: Terraform, ARM Templates  • Testing: pytest, Great Expectations, dbt tests  • Monitoring: Datadog, Prometheus/Grafana, Azure Monitor  • Orchestration: Airflow, Prefect with CI/CD integration Best habits that pay off fast: → Treat pipelines as code → Test data logic early, → Keep environments separate → Automate deploys → Never push blind changes DevOps isn’t a side skill anymore for data or cloud engineers. It’s how your work survives contact with production. Remember - If a change scares you to deploy, your pipeline is teaching you what to fix next. CI/CD doesn’t make you faster. It makes you safer. Speed shows up right after. Willing to upskill with DevOps skills? Here's an amazing repository to dive in by Dr Milan Milanović - https://lnkd.in/dK4SErK6 “𝘠𝘰𝘶 𝘣𝘶𝘪𝘭𝘥 𝘪𝘵, 𝘺𝘰𝘶 𝘳𝘶𝘯 𝘪𝘵.” Kudos to Nikki Siapno for the amazing illustration on CI/CD Pipeline!

Operating Models: The Bridge from Strategy to Execution Many organizations struggle when turning strategy into action. The gap between planning and execution can derail growth, slow innovation, and cause misalignment. A well-designed operating model is the blueprint that connects strategy to day-to-day operations. It defines how resources are deployed, decisions are made, and performance is managed. When built well, it drives clarity, agility, and results. What Makes an Effective Operating Model? According to Bain & Company, five key elements define high-performing operating models: 1. Structure Define clear boundaries between business units, shared services, and centers of expertise. Optimize the size and shape of the organization to strike a balance between scale and flexibility. 2. Accountabilities Clarify who owns what—across P&L, decisions, and cross-functional roles. Align responsibilities and incentives with strategic priorities. 3. Governance Create forums and processes that support fast, high-quality decisions. Use dashboards and key metrics to keep teams focused and leadership aligned. 4. Ways of Working Foster cultural norms that support speed, collaboration, and ownership—especially across teams and functions. Remove bottlenecks and eliminate unnecessary layers. 5. Capabilities Build repeatable, high-impact capabilities using the right people, processes, and technologies. Ensure the entire operating model reinforces these strengths. Execution Best Practices Bring the model to life with these practical guidelines: 1. Align Structure with Value Creation Organize around where and how value is created. Enable better decisions by balancing scale with local autonomy. 2. Design Around the Customer Don’t just optimize for internal efficiency. Make sure the operating model reflects and prioritizes customer needs. 3. Build to Win Identify the few things your company must do exceptionally well—and structure teams, systems, and processes to deliver them at scale. 4. Use Principles, Not Bureaucracy Empower teams with simple, clear decision-making principles. Avoid rigid rules that slow execution. Agility is a competitive advantage. The Bottom Line An effective operating model translates strategy into action—faster, more effectively, and with staying power. It enables better decisions, stronger execution, and sustained growth. Let your operating model be more than a plan. Make it your bridge from strategy to execution—and the engine of high performance.

Blindly Trusting Vendor Data Is a Costly Engineering Mistake Blindly trusting vendor data is one of the most common—and most expensive—mistakes in process engineering. Vendor datasheets are not wrong, but they are not automatically right for your process. As process engineers, we often receive neatly prepared datasheets showing: → Guaranteed performance → High efficiencies → Compliance with standards But here’s the uncomfortable truth 👇 Most equipment failures don’t happen because vendors lied. They happen because engineers stopped questioning. ⚠️ Where Blind Trust Goes Wrong → Rated flow assumed as operating flow → Normal case considered, part-load ignored → Turndown and minimum flow not verified → Fouling, aging, and degradation overlooked → Utilities and site limitations not cross-checked A pump that works perfectly on paper can cavitate in the plant. A heat exchanger that meets duty can fail after six months. A control valve sized “as per datasheet” can generate noise and vibration. 🧠 The Real Engineering Mindset Vendors design equipment. Process engineers design systems. Your responsibility is not to approve numbers. Your responsibility is to protect plant operability and reliability. Always ask: → What is the design basis? → What are the operating and off-design cases? → What happens at minimum flow or maximum turndown? → What will change after two years of operation? ✅ Remember This Vendor data is an input, not a conclusion. Verification is engineering. Blind trust is assumption. If you want to grow as a process engineer, challenge the data—before the plant challenges you. #ProcessEngineering #ProcessDesign #ChemicalEngineering #EPCProjects #PlantDesign #EngineeringReality #ProcessEngineer #MyProcessDesign #ProcessEngineering #ChemicalEngineering #ProcessDesign #Engineering #EngineeringLife #EPC #EPCProjects #PlantDesign #OilAndGas #Refinery #Petrochemical #ProcessEngineer #PlantEngineering #DesignEngineering #EquipmentDesign #EngineeringReality #EngineeringCareer #LearningByDoing #ProfessionalGrowth #EngineeringMindset #MyProcessDesign #EngineeringInsights #ProcessDesignEngineering

Quit blaming people. Start questioning the system. When something goes wrong, most plants jump to one question: "Who was running the line?" Wrong question. Because 9 times out of 10, it's not the person. It's one of the 5 Ms. If you want to actually fix the root cause, you need to ask: "Which system failed?" Most leaders use the "Man" (People) category as a dumping ground. They write "Operator Error," retrain the employee, and close the file. That is not root cause analysis. That is lazy management. Here is how I approach the "Man" category using two specific frameworks: TWTTP and HERCA. 1. MAN (The Investigation, Not The Blame) Don't ask: "Did they mess up?" Ask: "Where was the gap?" Step 1: Check the Knowledge (TWTTP) I use The Way To Teach People (TWTTP) methodology to check if the failure was a training issue. - Knowledge: Did they know what to do? - Skill: Could they actually do it without help? - If the answer is No: This isn't an operator failure. It's a training failure. You didn't transfer the skill. Step 2: Check the System (HERCA) If they did have the knowledge and skill but still failed, you must dig deeper. I use Human Error Root Cause Analysis (HERCA). - The Procedure: Was the instruction confusing or ambiguous? - The Interface: Did the design invite the mistake (e.g., two identical buttons)? - The Environment: Was there fatigue, noise, or pressure? Verdict: If a skilled person fails, the system trapped them. Once you clear the "Man," look at the rest of the system: 2. MACHINE Don't just check if it runs. Ask: "Was the machine fighting the operator?" (Micro-stops, jams, constant nursing). 3. MATERIAL Don't just check the spec. Ask: "Did hidden variation in the material force the operator to improvise?" 4. METHOD Don't just check the binder. Ask: "Does the process require reliance on memory, or is it visual?" 5. MOTHER NATURE Don't ignore the context. Ask: "What invisible stressor (noise, lighting, layout) is killing focus?" In reality.. "Operator Error" is almost never the root cause. It is usually just the symptom of a broken training process (TWTTP) or a hostile system design (HERCA). So next time you see "Human Error" on a report... Send it back. And ask to see the system failure behind it. How often do you see "Operator Error" listed as a root cause in your plant? Drop a comment below.