How to Scale Genai in Organizations

In 2025, deploying GenAI without architecture is like shipping code without CI/CD pipelines. Most companies rush to build AI solutions and create chaos. They deploy bots, copilots, and experiments with no tracking. No controls. No standards. Smart teams build GenAI like infrastructure. They follow a proven four-layer architecture that McKinsey recommends with enterprise clients. Layer 1: Control Portal Track every AI solution from proof of concept to production. Know who owns what. Monitor lifecycle stages. Stop shadow AI before it creates compliance nightmares. Layer 2: Solution Automation Build CI/CD pipelines for AI deployments. Add stage gates for ethics reviews, cost controls, and performance benchmarks. Automate testing before solutions reach users. Layer 3: Shared AI Services Create reusable prompt libraries. Build feedback loops that improve model performance. Maintain LLM audit trails. Deploy hallucination detection that actually works. Layer 4: Governance Framework Skip the policy documents. Build real controls for security, privacy, and cost management. Automate compliance checks. Make governance invisible to developers but bulletproof for auditors. This architecture connects to your existing systems. It works with OpenAI and your internal models. It plugs into Salesforce, Workday and both structured and unstructured data sources. The result? AI that scales without breaking. Solutions that pass compliance reviews. Costs that stay predictable as you grow. Which layer is your biggest gap right now: control, automation, services, or governance?

🧠 Strategy scales GenAI. Culture sustains it. Leadership ignites it. 🚀 GenAI is no longer just a disruptive force; it’s a defining one. But fundamental transformation doesn’t come from deploying another model. It comes from aligning strategy, culture, and leadership to scale innovation responsibly. Over the past few years, I’ve worked closely with organizations navigating the messy middle of GenAI maturity, where potential is high but direction is often unclear. What distinguishes high-impact adopters from others? Clarity across seven core priorities: 📍 1. Benchmark Maturity Map your current state. Understand the gaps across governance, data, infra, talent, and value realization. You can’t scale what you can’t see. 🏗 2. Build a GenAI Center of Excellence Not just a team, a cultural engine that standardizes experimentation, governance, and reuse across the enterprise. ⚖️ 3. Operationalize Responsible AI From model transparency to ethical deployment frameworks, responsible AI is no longer optional; it’s a reputational imperative. 🎯 4. Prioritize Strategic Use Cases Innovation must be intentional. Focus on use cases that enhance resilience, efficiency, and differentiation, not just novelty. 🔌 5. Invest in Scalable Infrastructure Cloud-native, secure, and observable. A robust AI backbone ensures models don’t just work in notebooks; they perform reliably in production. 📚 6. Foster AI Literacy From execs to frontline teams, shared language fuels adoption. Culture shifts when knowledge becomes a company-wide asset. 📊 7. Measure & Communicate Impact Business value is your north star. Track metrics that matter and tell a compelling story around them. 💡 Here’s my lens: GenAI isn't about chasing the next shiny model; it's about building the organizational muscle to adapt, lead, and scale responsibly. 📢 I’d love to hear from others in the space: What’s been your biggest unlock or challenge on the path to GenAI maturity? Let’s keep this conversation strategic. 🤝 #GenAI #EnterpriseAI #CTOLeadership #AITransformation #TechStrategy #InnovationAtScale #AIinBusiness #ThoughtLeadership #DigitalLeadership

👋 Say hello to GenOps! As organizations move to deploy Generative AI solutions at scale, they often face operational challenges. GenOps, or MLOps for Gen AI, addresses these challenges. GenOps combines DevOps principles with ML workflows to deploy, monitor, and maintain Gen AI models in production. It ensures Gen AI systems are scalable, reliable, and continuously improving. 𝗪𝗵𝘆 𝗶𝘀 𝗠𝗟𝗢𝗽𝘀 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗶𝗻𝗴 𝗳𝗼𝗿 𝗚𝗲𝗻 𝗔𝗜? Gen AI models present unique challenges that make the traditional MLOps practices insufficient: 1. Scale: Billions of parameters require specialized infrastructure. 2. Compute: High resource demands for training and inference. 3. Safety: Need for robust safeguards against harmful content. 4. Rapid evolution: Constant updates to keep pace with new developments. 5. Unpredictability: Non-deterministic outputs complicate testing and validation. Think of a Gen AI model as the tip of an iceberg. Generative AI systems are complex and contain numerous hidden interconnected elements. The different elements in GenOps for pre-trained and fine-tuned models: 1. Gen AI experimentation and prototyping: Experiment and build prototypes using enterprise models like Gemini, Imagen and open-weight models like Gemma2, PaliGemma etc. 2. Prompt: Below are the various tasks involving prompts: - Prompt engineering: Design and refine prompts for GenAI models to generate desired outputs  - Prompt versioning: Manage, track and control changes to prompts over time.  - Prompt enhancement: Employ LLMs to generate an improved prompt that maximizes performance on a given task. 3. Evaluation: Evaluate the responses from the GenAI model for specific tasks using metrics or feedback 4. Optimization: Apply optimization techniques like quantization and distillation to make models more efficient for deployment. 5. Safety: Implement guardrails and filters. Models like Gemini have inbuilt Safety Filters to prevent harmful responses by the model. 6. Fine-tuning: Adapt pre-trained models to specific domains/tasks through additional tuning on specialized datasets.  7. Version control: Manage different versions of GenAI models, prompts and dataset versions. 8. Deployment: Serve GenAI models with scaling, containerization and integration.  9. Monitoring: Track model performance, output quality, latency and resource usage in real-time. 10. Security and governance: Protect models and data from unauthorized access or attacks and ensure compliance with regulations. 📖 Read the complete article. I'm leaving the link in the comments. -- Image: Sample architecture for GenOps 👨💻 👉 I post daily about data science and data engineering. I also share some tips and resources you might find valuable. Follow David Regalado and smash the 🛎 in my profile for more content. 👍 Like, 🔗 share, 💬 comment, 👉 follow #GenOps #MLOps #LLM #GCP #GoogleCloud #GoogleCloudPlatform

𝐃𝐨𝐧’𝐭 𝐨𝐮𝐭𝐠𝐫𝐨𝐰 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧. 𝐋𝐞𝐚𝐫𝐧 𝐡𝐨𝐰 𝐜𝐮𝐬𝐭𝐨𝐦 𝐆𝐞𝐧𝐀𝐈 𝐚𝐝𝐚𝐩𝐭𝐬 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐞𝐬 𝐚𝐥𝐨𝐧𝐠𝐬𝐢𝐝𝐞 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬. AI isn’t a one-size-fits-all solution. To stay competitive, your AI strategy must evolve. Here are 7 ways to ensure your GenAI scales effectively:   1 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐜𝐥𝐞𝐚𝐫 𝐠𝐨𝐚𝐥𝐬. ↳ Define how AI fits your strategic vision. ↳ Identify measurable outcomes for success. ↳ Align stakeholders on a shared roadmap.   2 𝐂𝐡𝐨𝐨𝐬𝐞 𝐟𝐥𝐞𝐱𝐢𝐛𝐥𝐞, 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐭𝐨𝐨𝐥𝐬. ↳ Select systems that grow with your business. ↳ Build modular features for easy updates. ↳ Avoid rigid solutions that limit innovation.   3 𝐂𝐫𝐞𝐚𝐭𝐞 𝐚 𝐬𝐞𝐚𝐦𝐥𝐞𝐬𝐬 𝐮𝐬𝐞𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. ↳ Make interfaces intuitive and accessible. ↳ Train teams for smooth adoption. ↳ Deliver outputs that solve real problems.   4 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲. ↳ Audit for accuracy and diversity. ↳ Build adaptive, robust data pipelines. ↳ Address biases before scaling operations.   5 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐡𝐮𝐦𝐚𝐧 𝐀𝐈 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧. ↳ Empower AI to amplify human expertise. ↳ Redefine roles as technology evolves. ↳ Build trust by showing AI’s complementarity.   6 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐚𝐧𝐝 𝐢𝐭𝐞𝐫𝐚𝐭𝐞 𝐜𝐨𝐧𝐬𝐭𝐚𝐧𝐭𝐥𝐲. ↳ Use real-time analytics for insights. ↳ Test and adjust workflows regularly. ↳ Stay ahead of trends in AI innovation.   7 𝐂𝐨𝐦𝐦𝐢𝐭 𝐭𝐨 𝐞𝐭𝐡𝐢𝐜𝐚𝐥 𝐀𝐈 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬. ↳ Ensure transparency in decisions and operations. ↳ Address biases with proactive interventions. ↳ Build trust through responsible AI use.   Your AI shouldn’t just work today it should grow with tomorrow. What steps will you take to scale your GenAI effectively? ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation. #ArtificialIntelligence #AIInnovation #GenAI #FutureOfWork #ScalableSolutions

Inspired by a post from Vin Vashishta, I decided to comment on it a genAI use case we've been tackling lately, which seemed to have sparked some thoughts with others who have then reached out asking further questions. I believe that AI notetakers are by far the biggest 2025 secret weapon to uncovering VALUABLE generative AI use cases, and scalable agentic workflows (and I'm shocked that more companies haven't fully realized this, yet...) below is a simple playbook/diagram that will explain my thoughts on why: → Build a proprietary AI notetaker: Invite it to every internal and external meeting. Let it capture every insight, question, and feedback point. Store all transcripts in a backend database with encryption and configured data usage rules for deeper analysis. → Train a company-specific LLM: Funnel these transcripts into your LLM, fine-tuned for pattern detection and insights. For a sales use case, tag your transcript uploads by signaling outcomes like which meetings led to closed deals and which did not. Let the LLM uncover blind spots—like overlooked objections, key phrases that resonate, or missed opportunities in your proposal readouts. → Discover transformative insights: Find patterns in question sequences, objection handling, and narrative structures that convert clients. Enrich your dataset w/ personas to your dataset, learning exactly what your clients really want. And also... assess your workforce lol how skilled are the consultants that you're paying ($$$) for in real-time? Where can they improve? → Build a scalable, agentic workforce & iterate: Deploy agents that can be available 24/7 to your clients, agents that can train your junior staff and prepare them for more senior level roles/projects. Focus on creating that feedback loop powerhouse, continuously improving and delivering what clients need and what your workforce needs and your business will evolve, amplifying human performance and driving growth. 💡If anything, just remember this..... 1) AI notetakers are the ears. 2) Documentation transcripts are the memory. 3) AI agents are the brain. In 2025, companies who adopt this methodology will lead BIG TIME. Those who don’t... well, I think they will be wondering how they fell behind. Curious to hear others thoughts on this. #AI #AgenticAI #Agents #ArtificalIntelligence #GenAI #GenerativeAI #LLMs #UseCase #LLM

Want to accelerate your AI strategy by years? Read this. Johnson & Johnson just gave a rare public look at what it takes to move from early experimentation to true enterprise value with Gen AI. (Link in comments) Yogesh Chavda - Thank you for sharing. To their credit, J&J leaned in early, encouraging teams across the company to experiment and engage directly with the technology. They expected that decentralizing innovation would unleash speed and creativity. Instead, it created fragmentation. Hundreds of use cases popped up, but many lacked clear value, measurable outcomes, executive visibility, and connection to business priorities. Now, J&J is moving toward a more centralized model, complete with governance, curated tools, and a cross-functional steering com. This is a familiar pattern. Early experimentation is important, but without a disciplined approach, momentum stalls. Here’s how to avoid that. It starts with identifying the right use cases. Here’s a simple filter I use with my clients: 1. Start with real tasks: What does your team actually do day to day? 2. Pressure test: Is this task repeatable? Business-critical? 3. Prioritize: Focus on high-impact tasks that create friction 4. AI check: Can GenAI make this faster, smarter, or more effective? If the answer’s no, move on. Then conduct disciplined experimenting. The key word here is disciplined. Here is what that means: ✔️ Define success upfront: Set clear outcomes and a baseline so you can measure real impact. ✔️ Secure a senior sponsor: You need someone with authority to unblock, advocate, and decide. ✔️ Launch within 30 days: Urgency sharpens focus. Avoid over-engineering and just start. ✔️ Progress over perfection: An MVP with the right training is more valuable than a flawless concept no one uses. ✔️ Plan for 90 days: Enough time to learn. Short enough to stay agile. J&J learned it the hard way: experimentation without structure doesn’t scale. Disciplined pilots are what move strategy forward. Are you following these practices or losing time you can’t afford to waste? #WomeninAI #AITrainer #FutureofWork #AIinInnovation #AISpeaker #AIAdvisor

Most technology leaders at larger companies will tell you that implementing AI and generative AI at scale is no small task. Many will also tell you that strong change management is one of several components of a successful implementation plan but the most challenging to get right. As widespread use of generative AI has taken shape, there are a handful of themes I’ve heard consistently about change management as it relates to the technology: ✋🏽 Preparing for resistance: Introducing generative AI may be met with apprehension or fear. It's crucial to address these concerns through transparent communication and consistent implementation approaches. In nearly every case we are finding that the technology amplifies people skills allowing us to move faster versus replacing them. 🎭 Making AI part of company culture and a valued skill: Implementing AI means a shift in mindset and evolution of work processes. Fostering a culture of curiosity and adaptability is essential while encouraging colleagues to develop new skills through training and upskilling opportunities. Failure to do this results in only minimal or iterative change. ⏰ Change takes time: It’s natural to want to see immediate success, but culture change at scale is a journey. Adoption timelines will vary greatly depending on organizational complexity, opportunities for training and—most importantly—clearly defined benefits for colleagues. A few successful change management guiding principles I have seen in action: 🥅 Define goals: Establishing clear objectives—even presented with flexibility as this technology evolves—will guide the process and keep people committed to their role in the change. 🛩 Pilot with purpose: Begin small projects to test the waters, gain insights and start learning how to measure success. Scale entirely based on what’s working and don’t be afraid to shut down things quickly that are not working 📚 Foster a culture of learning: Encourage continuous experimentation and knowledge sharing. Provide communities and spaces for people to talk openly about what they’re testing out. 🏅 Leaders must be champions: Leaders must be able to clearly articulate the vision and value; lead by example and be ready to celebrate successes as they come. As we continue along the generative AI path, I highly suggest spending time with change management resources in your organization—both in the form of experienced change management colleagues and reading material—learning what you can about change implementation models, dependencies and the best ways to prioritize successes.

A big thank you to the University of Toronto for inviting me to present a talk in the UofT AI conference at the Schwartz Reisman Institute. 💎 In my talk, I outlined a framework for designing and architecting software applications that are powered by #GenerativeAI. 💡 The central theme of my talk was that building #GenAI applications isn’t about putting a fancy wrapper around some “best” model. 🤖 Rather, it’s about designing adaptable systems that are scalable, reliable, and aligned with organizational goals. 🌟 Here are five practical takeaways that underpin this framework that draw from foundational principles in #AI and Decision Science: 1️⃣ No Free Lunch Theorem – There is no one-size-fits-all supreme model that excels at everything. The notion of model quality depends on the problem, constraints, and trade-offs that an organization is willing to make. Chasing some state-of-the-art benchmark-leading model without considering business needs can lead to inefficiencies and misalignment in GenAI applications. 2️⃣ Occam’s Razor – Complexity can undermine scalability. The best GenAI applications aren’t necessarily the most intricate—they are the ones that are focused, efficient, and maintainable. Organizations are well-served by prioritizing long-term governance over unnecessarily complicated architectures or convoluted configurations that can become difficult to manage over time. 3️⃣ Goodhart’s Law – When a metric becomes a target, it stops being a good measure of success. Optimizing a GenAI application purely for perplexity, accuracy, or coherence individually can lead to hallucinations, confabulations, bias, and unintended behaviors. Therefore, the success of a GenAI application should be defined holistically, incorporating business logic and human oversight. 4️⃣ The Bitter Lesson – Scaling computation and data-driven learning typically outperforms handcrafted heuristics over time. While manual tuning and domain-specific customization have their place, GenAI applications should be architected and designed to benefit from self-supervised learning, large datasets, and adaptive optimization techniques rather than relying solely on labor-intensive fine-tuning or ad hoc rule-based customizations on an ongoing basis. 5️⃣ The Amnesia Effect (Gell-Mann Amnesia) – Users over-trust GenAI-generated content, even when they know it can be flawed. This is a major challenge for organizations deploying GenAI applications in high-stakes scenarios. It reinforces the need for transparency, trust calibration, and well-defined human-AI collaboration models. The main message of my talk was that Generative AI isn’t just another technology investment—it’s a strategic capability for driving business model reinvention to create sustained outcomes and build trust. 💯 Organizations that succeed with GenAI will be those that take advantage of applications that are adaptable and aligned—while those that don’t will struggle with trust, governance, and long-term adoption. ✅

In working with many of our AI and Generative AI clients, our @Deloitte teams have pinpointed 13 elements that are key to scaling AI/GenAI solutions into production and delivering sustainable business growth: https://deloi.tt/3BP47uv   We’ve grouped these elements into four main categories, each containing leading practices that point the way to Gen AI value realization:    🟢 Strategy: clear, high-impact use case portfolio, ambitious strategy & value management focus, and strong ecosystem collaboration    🟢 Process: robust governance, agile operating model & delivery methods, and integrated risk management    🟢 Talent: transformed roles, work, & culture, transparency to build trust in secure AI, and acquiring (external) & developing (internal) talent    🟢 Data & Technology: modular architecture & common platforms, modern data foundation, provisioning the right AI infrastructure, and effective model management & operations    Thank you to Lou DiLorenzo, Ed Van Buren, Sanghamitra Pati, Rohit Tandon, Aditya Kudumala, and Jennifer Malatesta for leading the charge with this report! 

How to Lower LLM Costs for Scalable GenAI Applications Knowing how to optimize LLM costs is becoming a critical skill for deploying GenAI at scale. While many focus on raw model performance, the real game-changer lies in making tradeoffs that align with both technical feasibility and business objectives. The best developers don’t just fine-tune models—they drive leadership alignment by balancing cost, latency, and accuracy for their specific use cases. Here’s a quick overview of key techniques to optimize LLM costs: ✅ Model Selection & Optimization • Choose smaller, domain-specific models over general-purpose ones. • Use distillation, quantization, and pruning to reduce inference costs. ✅ Efficient Prompt Engineering • Trim unnecessary tokens to reduce token-based costs. • Use retrieval-augmented generation (RAG) to minimize context length. ✅ Hybrid Architectures • Use open-source LLMs for internal queries and API-based LLMs for complex cases. • Deploy caching strategies to avoid redundant requests. ✅ Fine-Tuning vs. Embeddings • Instead of expensive fine-tuning, leverage embeddings + vector databases for contextual responses. • Explore LoRA (Low-Rank Adaptation) to fine-tune efficiently. ✅ Cost-Aware API Usage • Optimize API calls with batch processing and rate limits. • Experiment with different temperature settings to balance creativity and cost. Which of these techniques (or a combination) have you successfully deployed to production? Let’s discuss! CC: Bhavishya Pandit #GenAI #Technology #ArtificialIntelligence