Best Practices for Global Leadership in AI Model Development

"five building blocks — conceptual and technical infrastructure — needed to operationalize responsible AI ... 1. People: Empower your experts Responsible AI goals are best served by multidisciplinary teams that contain varied domain, technical, and social expertise. Rather than seeking "unicorn" hires with all dimensions of expertise, organizations should build interdisciplinary teams, ensure inclusive hiring practices, and strategically decide where RAI work is housed — i.e., whether it is centralized, distributed, or a hybrid. Embedding RAI into the organizational fabric and ensuring practitioners are sufficiently supported and influential is critical to developing stable team structures and fostering strong engagement among internal and external stakeholders. 2. Priorities: Thoughtfully triage work For responsible AI practices to be implemented effectively, teams need to clearly define the scope of this work, which can be anchored in both regulatory obligations and ethical commitments. Teams will need to prioritize across factors like risk severity, stakeholder concerns, internal capacity, and long-term impact. As technological and business pressures evolve, ensuring strategic alignment with leadership, organizational culture, and team incentives is crucial to sustaining investment in responsible practices over time. 3. Processes: Establish structures for governance Organizations need structured governance mechanisms that move beyond ad-hoc efforts to tackle emerging issues posed in the development or adoption of AI. These include standardized risk management approaches, clear internal decision-making guidance, and checks and balances to align incentives across disparate business functions. 4. Platforms: Invest in responsibility infrastructure To scale responsible practices, organizations will be well-served by investing in foundational technical and procedural infrastructure, including centralized documentation management systems, AI evaluation tools, off-the-shelf mitigation methods for common harms and failure modes, and post-deployment monitoring platforms. Shared taxonomies and consistent definitions can support cross-team alignment, while functional documentation systems make responsible AI work internally discoverable, accessible, and actionable. 5. Progress: Track efforts holistically Sustaining support for and improving responsible AI practices requires teams to diligently measure and communicate the impact of related efforts. Tailored metrics and indicators can be used to help justify resources and promote internal accountability. Organizational and topical maturity models can also guide incremental improvement and institutionalization of responsible practices; meaningful transparency initiatives can help foster stakeholder trust and democratic engagement in AI governance." Miranda Bogen, Kevin Bankston, Ruchika Joshi, Beba Cibralic, PhD, Center for Democracy & Technology, Leverhulme Centre for the Future of Intelligence

If you’re leading AI initiatives, here is a strategic cheat sheet to move from "𝗰𝗼𝗼𝗹 𝗱𝗲𝗺𝗼" to 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘃𝗮𝗹𝘂𝗲. Think Risk, ROI, and Scalability. This strategy moves you from "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗺𝗼𝗱𝗲𝗹" to "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝘀𝘀𝗲𝘁." 𝟭. 𝗧𝗵𝗲 "𝗪𝗵𝘆" 𝗚𝗮𝘁𝗲 (𝗣𝗿𝗲-𝗣𝗼𝗖) • Don’t build just because you can. Define the Business Problem first • Success: Is the potential value > 10x the estimated cost? • Decision: If the problem can be solved with Regex or SQL, kill the AI project now. 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 (𝗣𝗼𝗖) • Goal: Prove feasibility, not scalability. • Timebox: 4–6 weeks max. • Team: 1-2 AI Engineers + 1 Domain Expert (Data Scientist alone is not enough). • Metric: Technical feasibility (e.g., "Can the model actually predict X with >80% accuracy on historical data?") 𝟯. 𝗧𝗵𝗲 "𝗠𝗩𝗣" 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗩𝗮𝗹𝗹𝗲𝘆 𝗼𝗳 𝗗𝗲𝗮𝘁𝗵) • Shift from "Notebook" to "System." • Infrastructure: Move off local GPUs to a dev cloud environment. Containerize. • Data Pipeline: Replace manual CSV dumps with automated data ingestion. • Decision: Does the model work on new, unseen data? If accuracy drops >10%, halt and investigate "Data Drift." 𝟰. 𝗥𝗶𝘀𝗸 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 (𝗧𝗵𝗲 "𝗟𝗮𝘄𝘆𝗲𝗿" 𝗣𝗵𝗮𝘀𝗲) • Compliance is not an afterthought. • Guardrails: Implement checks to prevent hallucination or toxic output (e.g., NeMo Guardrails, Guidance). • Risk Decision: What is the cost of a wrong answer? If high (e.g., medical advice), keep a "Human-in-the-Loop." 𝟱. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Scalability & Latency: Users won’t wait 10 seconds for a token. • Serving: Use optimized inference engines (vLLM, TGI, Triton) • Cost Control: Implement token limits and caching. "Pay-as-you-go" can bankrupt you overnight if an API loop goes rogue. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Automated Eval: Use "LLM-as-a-Judge" to score outputs against a golden dataset. • Feedback Loops: Build a mechanism for users to Thumbs Up/Down outcomes. Gold for fine-tuning later. 𝟳. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 (𝗟𝗟𝗠𝗢𝗽𝘀) • Day 2 is harder than Day 1. • Observability: Trace chains and monitor latency/cost per request (LangSmith, Arize). • Retraining: Models rot. Define when to retrain (e.g., "When accuracy drops below 85%" or "Monthly"). 𝗧𝗲𝗮𝗺 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 • PoC Phase: AI Engineer + Subject Matter Expert. • MVP Phase: + Data Engineer + Backend Engineer. • Production Phase: + MLOps Engineer + Product Manager + Legal/Compliance. 𝗛𝗼𝘄 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗺𝘆 𝗮𝗱𝘃𝗶𝗰𝗲): → Treat AI as a Product, not a Research Project. → Fail fast: A failed PoC cost $10k; a failed Production rollout costs $1M+. → Cost Modeling: Estimate inference costs at peak scale before you write a line of production code. What decision gates do you use in your AI roadmap? Follow Priyanka for more cloud and AI tips and tools #ai #aiforbusiness #aileadership

Don't let your company become an AI "Pilot Graveyard." Change the game now. The hard truth about #AI today: most companies know how to build a pilot, but few know how to engineer scalable value. As an AI Strategy leader, I see this pattern repeat constantly. The tech works, the model is incredible, yet the project dies right after "Go Live." Why? Because the "organizational scaffolding" was missing. The failure is rarely technical (code or #data); it is cultural and process-based. I recently read about the "5Rs" framework in Harvard Business Review, and it resonated deeply with the work we are doing at IBM. It is a simple yet powerful operating system to turn isolated pilots into real P&L impact. If you want to lead digital transformation, you need to master these 5 pillars: 1️⃣ Roles: Absolute clarity on who does what. Eliminate the "gray zones" between tech and business teams. Without a defined owner, the project dies at handover. 2️⃣ Responsibilities: Accountability doesn't end at launch. AI models learn and drift. Who owns the ongoing success and model retraining? 3️⃣ Rituals: You can't run cutting-edge tech with 1990s management. You need a cadence of operational and executive reviews to unblock issues fast. 4️⃣ Resources: Stop reinventing the wheel for every project. Templates, governance frameworks, and reusable architectures can accelerate delivery by up to 50%. 5️⃣ Results: Vanity metrics don't pay the bills. Success must be tied to business impact (churn reduction, EBITDA #growth), not just technical model accuracy. The lesson for leaders: AI isn't "plug-and-play" magic. It is a capability that must be managed. The question is no longer "if" AI will change your company, but if you are building the operating model to support that change. Do you feel your organization is stuck in the "pilot phase," or have you managed to scale for real results? 👇

AI Agents: The Next Leadership Imperative AI agents aren’t side projects anymore—they’re fast becoming the new operating system of work. For executives, the question isn’t “Can we build them?” but “How do we lead with them?” Here are five priorities I believe every leadership team must get right: 1. Tie to outcomes — Link agents to growth, customer retention, and cycle times, not pilots. 2. Fix the data — Agents are only as strong as the data they can reach. 3. Redesign roles — Free people from low-value tasks, grow talent density. 4. Empower business units — Don’t keep AI locked in IT. 5. Ensure trust — Auditability, oversight, and responsible use from day one. I’ve seen this play out firsthand—launching zero-to-one products, scaling SaaS platforms to millions of users, and helping global companies embrace AI in their talent and technology strategies. The same lesson holds: success comes from linking technology adoption to leadership clarity and measurable outcomes. Agents will define competitive advantage this decade. The leaders who act decisively today will future-proof their organizations for tomorrow.

Crossing the GenAI Divide: Why 95% of AI Pilots Fail and How Leaders Can Fix It According to the new State of AI in Business 2025 report, 95% of enterprise GenAI pilots deliver essentially zero P&L impact. In other words, only about 5% of projects are producing millions in measurable value. This divide isn’t due to AI models or regulations, but how companies approach implementation. The report finds that the few who succeed do three things differently: they buy versus build, embed AI deeply into workflows, and focus on high-ROI use cases. Key insights for leaders: * Buy, don’t build: Top-performing firms partner with AI vendors instead of developing tools entirely in-house. In our interviews, twice as many vendor-supplied solutions reached full deployment (66% vs 33%) compared to internal projects. Treat AI providers as strategic collaborators (think BPO models, not just software licenses) and require solutions to learn from your data and adapt to your processes. * Prioritize back-office automation: Nearly half of GenAI budgets currently flow to marketing and sales, but the highest ROI often lies in operations, finance, and other support functions. Automating mundane admin tasks, report generation or customer support workflows can deliver clear cost savings (for example, $2–10M annual BPO spend reduction in best-in-class cases). Don’t chase shiny front-office demos at the expense of these workhorse opportunities. * Align with real needs: Only deploy AI that integrates seamlessly into existing workflows and drives measurable outcomes. Successful buyers demand deep customization to their processes, benchmarking tools on operational metrics – not just model features. If a GenAI tool can’t learn from user feedback or fit into the day-to-day, users will abandon it. Takeaway/Call to action: Enterprise leaders must rethink their GenAI strategy. Shift spending from one-off pilots to strategic buys: select learning-capable AI systems that remember and evolve with your business. Start with high-ROI, back-office use cases and empower front-line managers to drive adoption. Hold vendors accountable to real business KPIs, and insist on deep workflow integration. By buying the right tools (not building static proofs of concept) and aligning them with concrete needs, you can cross the GenAI divide and turn AI pilots into profit. Chinmay Hegde | Chandrashekar SK [CSK] #GenAI #AI #ArtificialIntelligence

Everyone celebrates the AI skyline. Almost no one wants to invest in the foundation. That foundation is data governance. Not as a policy exercise, but as an operating discipline. When governance is weak, AI looks impressive at first: fast demos clever outputs early wins Then reality shows up: inconsistent answers hidden bias teams arguing over whose data is “right” leaders quietly losing trust in the system That’s not an AI failure. It’s a foundation failure. Here’s the practical playbook I’ve helped organizations use to fix it: 1) Assign real ownership, not committees Every critical data domain needs a clear owner with actual decision rights. If no one owns the data, the model ends up guessing. → Leader question: Who is accountable when this data misleads a decision? 2) Define “good data” in business terms Quality only matters in context. Accuracy, timeliness, and completeness must be tied to how the data is used, not how it’s stored. → Leader question: What decision breaks if this data is wrong or late? 3) Design guardrails before scale Not every dataset should feed every model. Governance is about boundaries: what AI can see, what it can influence, what it can automate. → Leader question: Where must humans stay in the loop, no matter how good the model gets? 4) Treat data pipelines like production systems Monitoring, lineage, versioning, and rollback aren’t optional. If you can’t trace an output back to its source, you can’t trust it. → Leader question: Could we explain this answer six months from now? 5) Build governance where work actually happens Policies on slides don’t scale. Embedded checks in workflows do. → Leader question: Is governance preventing rework later, or just slowing teams down today? AI doesn’t fail because it’s too advanced. It fails because the groundwork was never finished. If you want a skyline that lasts, build where no one is looking. 📌 Save this if AI reliability is now a leadership issue 🔁 Repost to shift the conversation from demos to durability 👤 Follow Gabriel Millien for grounded insight on Enterprise AI and transformation

Most Enterprise AI failures are governance fails Not model glitches or technical flaws Here are 8 Best Practices of AI governance that separate high-performing AI teams from others: 1. Build an operating model (who decides what) ↳ One executive owner for AI risk and value. Clear RACI across Product, Engineering, Legal, Security, Risk, Audit ↳ Three lines of defense: product/engineering owns delivery → risk/legal sets standards → audit tests evidence 2. Maintain an AI inventory (you can't govern what you can't see) ↳ Track where AI is used, what model, what data it touches, impact if wrong ↳ This is foundation for risk tiering 3. Risk-tier every use case (don't treat autocomplete like credit scoring) ↳ Low: non-sensitive summarization ↳ Medium: internal copilots with sensitive data ↳ High: hiring, lending, healthcare, safety, legal decisions Attach controls per tier 4. Put controls across the full lifecycle (not just pre-launch) ↳ Pre-deploy: data checks, evals, red-team tests ↳ Deploy: guardrails, logging ↳ Operate: monitoring, drift checks, incident response ↳ Retire: decommission plan 5. Make evidence a first-class output ↳ Model cards (purpose, limits, risks) ↳ Data docs (sources, gaps) ↳ Eval reports (metrics, findings) ↳ Approval records ISO/IEC 42001 pushes orgs toward auditable governance 6. Monitor like it's production software (because it is) ↳ Track quality, safety, drift, incidents ↳ Playbook: detect → triage → contain → notify → fix → learn 7. Govern vendors and model supply chains ↳ Most orgs are AI assemblers, not builders ↳ Vendor due diligence, contractual controls, IP clarity, change logs are critical practices for success at scale 8. Train people on acceptable AI use ↳ Biggest attack surface: confused or uncertain humans ↳ Role-based training, rules for sensitive data, escalation paths --------- I am Priyadeep Sinha and I help you go from AI Anxiety to AI Expertise one strategy at a time Every week, I share one complete AI workflow system for leaders, consultants and knowledge workers in my newsletter Work in Beta: https://lnkd.in/gPqYEzaJ

𝐀𝐈 𝐩𝐢𝐥𝐨𝐭𝐬 𝐝𝐨𝐧’𝐭 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 𝐚𝐫𝐞 𝐰𝐞𝐚𝐤. 𝐓𝐡𝐞𝐲 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐢𝐬. The whitepaper I wrote for the The Data Institute, University of San Francisco makes this plain: C-suites are discovering that AI isn’t a technology upgrade. It’s a business transformation. And that’s where the gap lies. 𝐓𝐡𝐞 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐚𝐛𝐥𝐞 𝐟𝐚𝐢𝐥𝐮𝐫𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬: 1. 𝐏𝐢𝐥𝐨𝐭 𝐭𝐫𝐚𝐩. Models in the lab, never in production. 2. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐦𝐢𝐬𝐚𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭. AI because “others are doing it,” not because it solves a business problem. 3. 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐫𝐞𝐬𝐢𝐬𝐭𝐚𝐧𝐜𝐞. Systems that work, but people who won’t. 4. 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐢𝐧𝐚𝐝𝐞𝐪𝐮𝐚𝐜𝐲. Data stacks built for reporting, not real-time AI. 𝐓𝐡𝐞 𝐩𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐟𝐨𝐫 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐰𝐡𝐨 𝐰𝐢𝐧: 1. 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧. Anchor AI in one critical business problem before chasing use cases. 2. 𝐓𝐫𝐞𝐚𝐭 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐚𝐬 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐚𝐥. Train, adapt roles, reset culture. 3. 𝐁𝐮𝐢𝐥𝐝 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬, 𝐧𝐨𝐭 𝐩𝐨𝐢𝐧𝐭 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬. Reuse infrastructure, governance, and data across functions. 4. 𝐋𝐞𝐚𝐝 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐂-𝐬𝐮𝐢𝐭𝐞. CEO for patience and vision. CTO for scalable architecture. CFO for funding with accountability. CHRO for workforce evolution. The first 120 days decide everything. Either you build alignment, assess capabilities, plan realistically, and start building systematically… Or you drift into the same wasteland where 95% of enterprise pilots go to die. AI advantage compounds. Those who scale early will lock in moats that laggards cannot cross. 𝐂𝐡𝐨𝐢𝐜𝐞 𝐟𝐨𝐫 𝐞𝐯𝐞𝐫𝐲 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞: Commit to transformation, or accept decline. Half-measures won’t save you. 𝐒𝐡𝐨𝐰 𝐮𝐩 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐭𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐥𝐚𝐛. 𝐎𝐫 𝐝𝐨𝐧’𝐭 𝐬𝐡𝐨𝐰 𝐮𝐩 𝐚𝐭 𝐚𝐥𝐥. If you want to move beyond experiments, explore the AI-First Organizations program at University of San Francisco Data Institute. And if your enterprise is struggling with this transition, reach out to me directly. Paul Intrevado Thomas Maier Ph.D Jamie Wheeler Elisabeth Merkel Baghai