Best Practices For Scaling AI In Large Companies

After reviewing dozens of enterprise AI initiatives, I've identified a pattern: the gap between transformational success and expensive disappointment often comes down to how CEOs engage with their technology leadership. Here are five essential questions to ask: 𝟭. 𝗪𝗵𝗮𝘁 𝘂𝗻𝗶𝗾𝘂𝗲 𝗱𝗮𝘁𝗮 𝗮𝘀𝘀𝗲𝘁𝘀 𝗴𝗶𝘃𝗲 𝘂𝘀 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿𝘀 𝗰𝗮𝗻'𝘁 𝗲𝗮𝘀𝗶𝗹𝘆 𝗿𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗲? Strong organizations identify specific proprietary data sets with clear competitive moats. One retail company outperformed competitors 3:1 only because it had systematically captured customer interaction data its competitors couldn't access. 𝟮. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗼𝘂𝗿 𝗰𝗼𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗷𝘂𝘀𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀? Look for specific examples of fundamentally reimagined business processes built for algorithmic scale. Be cautious of responses focusing exclusively on efficiency improvements to existing processes. The market leaders in AI-driven healthcare don't just predict patient outcomes faster, they've architected entirely new care delivery models impossible without AI. 𝟯. 𝗪𝗵𝗮𝘁'𝘀 𝗼𝘂𝗿 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝗻𝗴 𝘄𝗵𝗶𝗰𝗵 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗿𝗲𝗺𝗮𝗶𝗻 𝗵𝘂𝗺𝗮𝗻-𝗱𝗿𝗶𝘃𝗲𝗻 𝘃𝗲𝗿𝘀𝘂𝘀 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰𝗮𝗹𝗹𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱? Expect a clear decision framework with concrete examples. Be wary of binary "all human" or "all algorithm" approaches, or inability to articulate a coherent model. Organizations with sophisticated human-AI frameworks are achieving 2-3x higher ROI on AI investments compared to those applying technology without this clarity. 𝟰. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗲𝘁𝗿𝗶𝗰𝘀? The best responses link AI initiatives to market-facing metrics like share gain, customer LTV, and price realization. Avoid focusing exclusively on cost reduction or internal efficiency. Competitive separation occurs when organizations measure algorithms' impact on defensive moats and market expansion. 𝟱. 𝗪𝗵𝗮𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗵𝗮𝘃𝗲 𝘄𝗲 𝗺𝗮𝗱𝗲 𝘁𝗼 𝗼𝘂𝗿 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗼 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘃𝗮𝗹𝘂𝗲 𝗼𝗳 𝗔𝗜 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀? Look for specific organizational changes designed to accelerate algorithm-enhanced decisions. Be skeptical of AI contained within traditional technology organizations with standard governance. These questions have helped executive teams identify critical gaps and realign their approach before investing millions in the wrong direction. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: V𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 own 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴.

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.

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA

As a Global Capability Center(GCC) Leader, the Onus Is on You—Will You Drive AI Transformation or Get Left Behind? Most GCCs were not designed with AI at their core. Yet, AI is reshaping industries at an unprecedented pace. If your GCC remains focused on traditional service delivery, it risks becoming obsolete. The responsibility to drive this transformation does not sit with IT teams or innovation labs alone—it starts with you. As a GCC leader, you must push beyond cost efficiencies and position your center as a strategic AI hub that delivers business impact. How to Transform an Existing GCC into an AI-Native GCC This shift requires clear, measurable objectives. Here are five critical OKRs (Objectives & Key Results) to guide your AI transformation. 1. Embed AI in Core Business Processes Objective: Move beyond AI pilots and integrate AI into everyday decision-making. Key Results: • Automate 20 percent or more of manual workflows within 12 months. • Deploy AI-powered analytics in at least three business-critical functions. • Reduce operational decision-making time by 30 percent using AI insights. 2. Reskill and Upskill Talent for AI Readiness Objective: Develop an AI-fluent workforce that can build, deploy, and manage AI solutions. Key Results: • Train 100 percent of employees on AI fundamentals. • Upskill at least 30 percent of engineers in MLOps and GenAI development. • Establish an internal AI guild to drive AI innovation and best practices. 3. Build AI Infrastructure and MLOps Capabilities Objective: Create a scalable AI backbone for your organization. Key Results: • Implement MLOps pipelines to reduce AI model deployment time by 50 percent. • Establish a centralized AI data lake for enterprise-wide AI applications. • Deploy at least five AI use cases in production over the next year. 4. Shift from AI as an Experiment to AI as a Business Strategy Objective: Ensure AI initiatives drive measurable business value. Key Results: • Ensure 50 percent of AI projects are directly linked to revenue growth or cost savings. • Develop an AI governance framework to ensure responsible AI use. • Integrate AI-driven customer experience enhancements in at least three markets. 5. Change the Operating Model: From Service Delivery to Co-Ownership Objective: Position the GCC as a leader in AI-driven transformation, not just an execution arm. Key Results: • Rebrand the GCC internally as a center of AI-driven innovation. • Secure C-level sponsorship for AI-driven initiatives. • Establish at least three AI innovation partnerships with startups or universities. The question is not whether AI will reshape your GCC. It will. The time to act is now. Are you ready to drive the AI transformation? Let’s discuss how to accelerate your GCC’s AI journey. Zinnov Mohammed Faraz Khan Namita Dipanwita ieswariya Mohammad Mujahid Karthik Komal Hani Amita Rohit Amaresh

The new Gartner Hype Cycle for AI is out, and it’s no surprise what’s landed in the trough of disillusionment… Generative AI. What felt like yesterday’s darling is now facing a reality check. Sky-high expectations around GenAI’s transformational capabilities, which for many companies, the actual business value has been underwhelming. Here’s why.… Without solid technical, data, and organizational foundations, guided by a focused enterprise-wide strategy, GenAI remains little more than an expensive content creation tool. This year’s Gartner report makes one thing clear... scaling AI isn’t about chasing the next AI model or breakthrough. It’s about building the right foundation first. ☑️ AI Governance and Risk Management: Covers Responsible AI and TRiSM, ensuring systems are ethical, transparent, secure, and compliant. It’s about building trust in AI, managing risks, and protecting sensitive data across the lifecycle. ☑️ AI-Ready Data: Structured, high-quality, context-rich data that AI systems can understand and use. This goes beyond “clean data”, we’re talking ontologies, knowledge graphs, etc. that enable understanding. “Most organizations lack the data, analytics and software foundations to move individual AI projects to production at scale.” – Gartner These aren’t nice-to-haves. They’re mandatory. Only then should organizations explore the technologies shaping the next wave: 🔷 AI Agents: Autonomous systems beyond simple chatbots. True autonomy remains a major hurdle for most organizations. 🔷 Multimodal AI: Systems that process text, image, audio, and video simultaneously, unlocking richer, contextual understanding. 🔷 TRiSM: Frameworks ensuring AI systems are secure, compliant, and trustworthy. Critical for enterprise adoption. These technologies are advancing rapidly, but they’re surrounded by hype (sound familiar?). The key is approaching them like an innovator...  start with specific, targeted use cases and a clear hypothesis, adjusting as you go. That’s how you turn speculative promise into practical value. So where should companies focus their energy today? Not on chasing trends, but on building the capacity to drive purposeful innovation at scale: 1️⃣ Enterprise-wide AI strategy: Align teams, tech, and priorities under a unified vision 2️⃣ Targeted strategic use cases: Focus on 2–3 high-impact processes where data is central and cross-functional collaboration is essential. 3️⃣ Supportive ecosystems: Build not just the tech stack, but the enablement layer, training, tooling, and community, to scale use cases horizontally. 4️⃣ Continuous innovation: Stay curious. Experiment with emerging trends and identify paths of least resistance to adoption. AI adoption wasn’t simple before ChatGPT, and its launch didn’t change that. The fundamentals still matter. The hype cycle just reminds us where to look. Gartner Report:  https://lnkd.in/g7vKc9Vr #AI #Gartner #HypeCycle #Innovation

It was great sitting down with Maggie McGrath at Forbes to unpack one of the biggest risks in enterprise AI: scaling without a solid data foundation. It’s easier than ever to build AI agents. But building high-quality, impactful, and governed AI? That’s where most companies struggle - with Massachusetts Institute of Technology reporting that 95% of GenAI pilots are failing. That’s why Snowflake is so critical at this moment. Our customers are among the most AI-ready organizations in the world because their data already lives in a secure, well-governed platform, and we bring AI directly to that data. For any enterprise looking to scale AI successfully, here are a few key lessons from our conversation: ❄️ Start with real business problems. Avoid chasing the hype. Focus on high-friction, time-consuming tasks where AI can drive efficiency. One example: our internal GTM AI Assistant, built on Snowflake Intelligence, saves our sales team weeks of manual effort for each customer meeting — surfacing insights in minutes. ❄️ Lead with customer impact. AI needs to show clear ROI. We’ve seen great results with customers like WHOOP, who deployed a conversational app to democratize insights across their teams, and TS Imagine, who built an AI agent that automated 4,000+ hours of manual work. ❄️ Build trust and accountability from day one. Responsible AI isn’t optional. From testing for hallucinations, to deploying tools like Cortex Guard, to enforcing strong governance, we talked about how safety, clarity, and control need to be built into every deployment. Catch the full Forbes interview here: https://lnkd.in/g7-gpHiT Thanks for having me, Maggie, and for the thoughtful conversation.