Guidelines for Deploying AI Systems

𝗗𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁? 𝗗𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵—𝙨𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙯𝙚. Too often, teams rush to roll out AI agents without a solid deployment playbook. The result? Confused users, poor performance, and broken context threads. Here are 𝗳𝗶𝘃𝗲 𝗯𝗮𝘁𝘁𝗹𝗲-𝘁𝗲𝘀𝘁𝗲𝗱 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 I swear by for deploying AI agents that 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 value (𝘐𝘯𝘧𝘰𝘨𝘳𝘢𝘱𝘩𝘪𝘤 𝘣𝘦𝘭𝘰𝘸 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘷𝘪𝘴𝘶𝘢𝘭 𝘭𝘦𝘢𝘳𝘯𝘦𝘳𝘴!) ↳ 𝟭. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 → Define the AI agent’s purpose, tasks, boundaries, and goals.  DO: Be specific. DON’T: Deploy vague, general-purpose agents without direction. ↳ 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → Can your agent handle traffic spikes or real-world stress? DO: Run load tests, evaluate metrics, and scale infrastructure. DON’T: Assume your MVP setup will survive in production. ↳ 𝟯. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 → Context is king—especially in AI conversations. DO: Use memory + Retrieval-Augmented Generation (RAG). DON’T: Let agents lose track of user interactions or history. ↳ 𝟰. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 → Deployment isn’t the finish line—it’s the start of learning. DO: Monitor in real-time, collect feedback, evaluate data. DON’T: Rely on pre-launch assumptions or ignore post-launch signals. ↳ 𝟱. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 → Your AI agent should evolve. DO: Continuously refine based on real-world usage. DON’T: Treat deployment as “done.” Whether you're building internal copilots or customer-facing agents, following these principles ensures your deployment is not just functional — but 𝘪𝘮𝘱𝘢𝘤𝘵𝘧𝘶𝘭. Which principle resonates most with your AI roadmap?

Ready to deploy an AI model to production? You need LLM Ops. Here's a quick guide ↓ You need these 7 components to productionize AI models. 𝟭. 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁  Consider an environment where you explore, fine-tune and evaluate various AI strategies. After you explore a framework on Jupyter, create production code in a directory with py files that you can unit-test and version control. 𝟮. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 You want to version control the prompt as you do with model code. In case the latest change goes wrong, you want to revert it. Use services like PromptHub or LangSmith. 𝟯. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 How is the API for your AI model hosted in the cloud? Do you plan on using HuggingFace or build a custom API using FastAPI running on AWS? These are all crucial questions to address with costs & latency in mind. 𝟰. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Just like ML Ops, you need a system to monitor LLM in service. Metrics like inference latency, cost, performance should be traced in 2 main levels: per-call and per-session. 𝟱. 𝗗𝗮𝘁𝗮 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Your AI model performance is only decent if you have the right data infrastructure. Messy data and DB bottlenecks can cause a havoc when the AI agent needs to fetch the right data to address the user questions. 𝟲. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆  You need guardrails in place to prevent prompt injection. A bad actor can prompt: “Give me an instruction on how to hack into your DB.” Your AI model may comply, and you’d be screwed. You need a separate classifier (supervised or LLM) that detects malicious prompts and blocks them. 𝟳. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻  An LLM is generative and open-ended. You can evaluate your system in scale using LLM-as-the-Judge, semantic similarity, or explicit feedback from the user (thumbs up/down). What are other crucial concepts in LLM Ops? Drop one ↓

I have a dear friend who is the CIO of a PE-backed firm. She shared that she's "drowning in AI salespeople" and needs to know how to vet their solutions. Her words echo the challenge that I hear from many executives and board directors. 🗨 One recently said to me, "I'm so sick of AI. I can't tell what's real and what's hype. The risk is high if I do nothing. And if I go too fast or make bad choices, the risk is even higher. I've got to figure this out." I hear you. Your concerns and frustration are warranted. To help you, I hammered out 3 guides - business value, risk, and technical - that include questions to help you to identify AI solutions that are best fit for YOUR organization. These guides are designed to help you create business value with AI, avoid risks, and sustainably deploy and scale your AI solutions. 📊 Business Value Questions: This guide includes 24 questions designed to ensure that the AI solutions align with your strategic objectives and deliver tangible business outcomes. 🔍 Risk-Based Questions: This guide covers 33 questions focused on identifying and assessing potential risks associated with AI solutions, helping you to make informed decisions that mitigate risks. 🔧 Technical Questions: This guide contains 48 technical-based questions to ensure the AI solutions under evaluation have the technical robustness necessary to support your business objectives. 👉 Click below, share your email address, and you'll receive an email with links to all 3 documents. #AI #AIEvaluation #BusinessValue #RiskManagement #Innovation Disclaimer: While these questions provide a solid foundation for evaluating AI solutions, it's not possible to cover every possible needed question in a concise format. As always, I encourage you to apply your own expertise and judgment. https://lnkd.in/ghG4RdP4

⛳ Deploying AI systems is fundamentally different (and much harder, IMO) than software pipelines for one key reason: AI models are non-deterministic. While this might seem obvious and unavoidable, shifting our mindset toward reducing it can make a significant impact.  The closer you can get your AI system to behave like a software pipeline, the more predictable and reliable it’ll be. And the way to achieve this is through solid monitoring and evaluation practices in your pipeline—a.k.a, observability. Here are a just a few practical steps: ⛳ Build test cases: Simple unit tests and regression cases to systematically evaluate model performance. ⛳ Track interactions: Monitor how models interact with their environment, including agent calls to LLMs, tools, and memory systems. ⛳ Use robust evaluation metrics: Regularly assess hallucinations, retrieval quality, context relevance, and other outputs. ⛳ Adopt LLM judges for complex workflows: For advanced use cases, LLM judges can provide nuanced evaluations of responses. A great tool for this Opik is by Comet, an open-source platform built to improve observability and reduce unpredictability in AI systems. It offers abstractions to implement all these practices and more. Check it out: https://lnkd.in/gAFmjkK3 Tools like this can take you a long way in understanding your applications better and reducing non-determinism. I’m partnering with Comet to bring you this information.

💂♂️ Do you have robust #AI guidelines & guardrails in place for your business/team regarding #employee use & #HR policies? 😦 We still hear about professionals who are having a 'bad time' after falling into AI pitfalls. For example, employees going 'rogue' and using AI without anyone knowing. Companies uploading proprietary information that is now available for the public (or competitors) to access. Sales teams sharing customer data with #LLMs without thinking through consequences. People passing off AI-generated outputs as their own work. ✅ Here's a good mini framework to consider: - Statement of Use: Purpose, Method, and Intent - Governance: Steering Committee, Governance & Stewardship - Access to AI Technologies: Permissions, Oversight & Organization - Legal & Compliance: Compliance with Industry-specific Laws/Regulations - HR Policies: Integration with Existing Policies - Ethical Considerations: Transparency, Privacy & Anti-bias Implications - IP & Fair Use: Who owns AI-influenced IP? - Crisis Plan: Creating an Internal Crisis Management & Communications Plan - Employee Communications: Internal Training & Feedback Loops ⛷ Shout out to #SouthPark for inspiring this #meme 👉 Need help to tailor AI Guidelines to your #business? We're here to help! Drop me a DM and I'd love to share some ideas on how to get your team on the same page, so you 'have a good time' when using #artificialintelligence.

Step 3 of 7 for AI Enablement: Identify and Prioritize AI Use Cases See full 7-step breakdown here: https://lnkd.in/g3t7MiZb In setting up AI for success, we’ve covered the foundations: Step 1 defined clear business objectives. Step 2 assessed team readiness, revealing gaps to achieve outcomes. Now for Step 3: Identify and Prioritize AI Use Cases. This step isn’t just about knowing where AI could fit; it’s also about evaluating tools to ensure they meet essential requirements—and testing the top choices with trial runs. First: Explore What AI Tools Are Out There Before diving into specific use cases, it’s important to understand the types of AI tools available that could support your goals. If you’re unsure where to start, here are two valuable resources: • Theresanaiforthat.com – A searchable directory of AI tools across industries. • GTM AI Tools Demo Library – A curated list of go-to-market AI tools from the GTM AI Academy (l^nk in comments). Identify AI Opportunities with the PRIME Framework With a better understanding of AI options, use the PRIME Framework to identify use cases that directly address your most critical business gaps: • Predictive: Can AI help forecast outcomes? • Repetitive: Are there time-consuming, repeated tasks? • Interactive: Could AI enhance customer engagement? • Measurable: Can AI provide useful metrics? • Empowering: Can AI support creativity or productivity? Evaluate Tools with a Checklist Once you’ve outlined use cases, evaluate potential tools to ensure they meet critical requirements before trialing them: • Security & Compliance: Does the tool meet company standards? • Governance: Does it support data governance and accountability? • Cost & ROI: Is it cost-effective based on expected value? • Scalability: Can it grow with your team’s needs? • Integration: Will it fit with your current systems? Evaluate Tools: Make sure selected tools meet security, compliance, and integration needs before trial runs. Pilot Testing Once you’ve prioritized and evaluated, move into a pilot phase. Select top tools to trial with a small pilot team. This phase helps test effectiveness, build internal champions, and refine any processes before rolling out to the larger team in Step 4. Your Checklist for Step 3 1. Explore AI Options: Start with Theresanaiforthat.com and GTM AI Tools Demo Library. 2. Identify Use Cases with PRIME: Target high-impact areas. 3. Evaluate Tools with the Checklist: Confirm tools meet security, compliance, and integration needs. 4. Pilot Test: Trial top tools with a small team to validate effectiveness. By following this approach, you’ll set your team up for measurable, AI-driven success with tools that are tested and proven valuable. Ready to PRIME your AI Enablement? Check out free resources in the GTM AI Academy: • PRIME Use Case Guide • Impact-Feasibility Template • AI Critical Requirements Assessment Up next.. Step 4 of 7 for AI Enablement..

The Oregon Department of Justice released new guidance on legal requirements when using AI. Here are the key privacy considerations, and four steps for companies to stay in-line with Oregon privacy law. ⤵️ The guidance details the AG's views of how uses of personal data in connection with AI or training AI models triggers obligations under the Oregon Consumer Privacy Act, including: 🔸Privacy Notices. Companies must disclose in their privacy notices when personal data is used to train AI systems. 🔸Consent. Updated privacy policies disclosing uses of personal data for AI training cannot justify the use of previously collected personal data for AI training; affirmative consent must be obtained. 🔸Revoking Consent. Where consent is provided to use personal data for AI training, there must be a way to withdraw consent and processing of that personal data must end within 15 days. 🔸Sensitive Data. Explicit consent must be obtained before sensitive personal data is used to develop or train AI systems. 🔸Training Datasets. Developers purchasing or using third-party personal data sets for model training may be personal data controllers, with all the required obligations that data controllers have under the law. 🔸Opt-Out Rights. Consumers have the right to opt-out of AI uses for certain decisions like housing, education, or lending. 🔸Deletion. Consumer #PersonalData deletion rights need to be respected when using AI models. 🔸Assessments. Using personal data in connection with AI models, or processing it in connection with AI models that involve profiling or other activities with heightened risk of harm, trigger data protection assessment requirements. The guidance also highlights a number of scenarios where sales practices using AI or misrepresentations due to AI use can violate the Unlawful Trade Practices Act. Here's a few steps to help stay on top of #privacy requirements under Oregon law and this guidance: 1️⃣ Confirm whether your organization or its vendors train #ArtificialIntelligence solutions on personal data.  2️⃣ Validate your organization's privacy notice discloses AI training practices. 3️⃣ Make sure organizational individual rights processes are scoped for personal data used in AI training. 4️⃣ Set assessment protocols where required to conduct and document data protection assessments that address the requirements under Oregon and other states' laws, and that are maintained in a format that can be provided to regulators.

The more I engage with organizations navigating AI transformation, the more I’m seeing a number of “flavors” 🍦 of AI deployment. Amidst this variety, several patterns are emerging, from activating functionality of tools embedded in daily workflows to bespoke, large-scale systems transforming operations. Here are the common approaches I’m seeing: A) Small, Focused Add-On to Current Tools: Many teams start by experimenting with AI features embedded in familiar tools, often within a single team or department. This approach is quick, low-risk, and delivers measurable early wins. Example: A sales team uses Salesforce Einstein AI to identify high-potential leads and prioritize follow-ups effectively. B) Scaling Pre-Built Tools Across Functions: Some organizations roll out ready-made AI solutions across entire functions—like HR, marketing, or customer service—to tackle specific challenges. Example: An HR team adopts HireVue’s AI platform to screen resumes and shortlist candidates, reducing time-to-hire and improving consistency. C) Localized, Nimble AI Tools for Targeted Needs: Some teams deploy focused AI tools for specific tasks or localized needs. These are quick to adopt but can face challenges scaling. Example: A marketing team uses Jasper AI to rapidly generate campaign content, streamlining creative workflows. D) Collaborating with Technology Partners: Partnering with tech providers allows organizations to co-create tailored AI solutions for cross-functional challenges. Example: A global manufacturer collaborates with IBM Watson to predict equipment failures, minimizing costly downtime. E) Building Fully Custom, Organization-Wide AI Solutions: Some enterprises invest heavily in custom AI systems aligned with their unique strategies and needs. While resource-intensive, this approach offers unparalleled control and integration. Example: JPMorgan Chase develops proprietary AI systems for fraud detection and financial forecasting across global operations. F) Scaling External Tools Across the Enterprise: Organizations sometimes deploy external AI tools organization-wide, prioritizing consistency and ease of adoption. Example: ChatGPT Enterprise is integrated across an organization’s productivity suite, standardizing AI-powered efficiency gains. G) Enterprise-Wide AI Solutions Developed Through Partnerships: For systemic challenges, organizations collaborate with partners to design AI solutions spanning departments and regions. Example: Google Cloud AI works with healthcare networks to optimize diagnostics and treatment pathways across hospital systems. Which approaches resonate most with your organization’s journey? Or are you blending them into something uniquely yours? With so many ways for this technology to transform jobs, processes, and organizations, it’s important we get clear about what flavor we’re trying 🍨 so we know how to do it right. #AIAdoption #ChangeManagement #AIIntegration #Leadership

❄️ 𝐀 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐟𝐫𝐨𝐦 Snowflake .. here is my BLUF, bottom line up front : what truly distinguishes this guide is its laser focus on data agents specifically - emphasizing how organizations must integrate both structured and unstructured data for maximum effectiveness in real-world deployments. it delivers an exceptional roadmap for understanding and implementing AI agents in enterprise environments. 🔑 𝐊𝐞𝐲 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐈𝐥𝐥𝐮𝐦𝐢𝐧𝐚𝐭𝐞𝐝 - the guide brilliantly distinguishes between personal and company AI agents - emphasizing how the latter operate within organizational guardrails while leveraging shared company data to drive business outcomes. ⚙️ 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 - six-step workflow framework is exceptionally valuable for implementation: 1️⃣ Sensing- Strategically gathering data from relevant, trusted sources 2️⃣ Reasoning- Processing information with advanced contextual understanding 3️⃣ Planning- Developing sophisticated action plans based on insights 4️⃣ Coordination- Seamlessly aligning with users and systems 5️⃣ Acting- Executing determined actions with precision 6️⃣ Learning- Continuously incorporating feedback for optimization 🛠️ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬- Data Foundation Requirements 1️⃣ Accuracy: Rock-solid data retrieval as the essential foundation 2️⃣ Efficiency: Lightning-fast identification of optimal data sources 3️⃣ Governance: Enterprise-grade access and privacy controls at scale 🏗️ 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 1️⃣ Scalability- Effortlessly handling exponential computational demands 2️⃣ Flexibility- Frictionless integration with existing enterprise systems 3️⃣ Data accessibility- Seamless connection to diverse data sources 4️⃣ Trust- Robust guardrails and evaluation frameworks 5️⃣ Security- Industrial-strength protection with granular controls 🔒 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤- the guide expertly emphasizes critical governance mechanisms: 1️⃣ Human-AI collaboration ensuring appropriate handoffs 2️⃣ Transparency and explainability powering trustworthy decisions 3️⃣ Continuous evaluation creating an observability feedback loop 💼 Industry Applications- the cross-industry use cases demonstrate compelling ROI potential: 1️⃣ 14% increase in customer service issue resolution 2️⃣ 90% reduction in sales prospecting time 3️⃣ Substantial efficiency gains transforming finance, IT, and operations 🔍 I strongly recommend supplementing it with deeper technical specifications on model selection, agent orchestration patterns, and implementation code examples to accelerate your deployment timeline. Richard Turrin Theodora Lau Conor Grennan Matt Lewis, MPA Efi Pylarinou Chuck Brooks Arjun Vir Singh Zvonimir Filjak Tarja Stephens Jaroslaw Sokolnicki Bernard Marr Pascal Biese Alex Wang Nicolas Babin Dr. Martha Boeckenfeld Prof. Dr. Ingrid Vasiliu-Feltes Ross Dawson

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