How to Update Business Strategy for Generative AI

Generative AI Guidance for the Forward-Thinking CEO Here’s a leadership-first framework, no hype, for navigating GenAI responsibly and strategically. GenAI is more than a consulting engagement, a pilot, and an AI-First press release. 1. This is a CEO-level responsibility, not a tech deployment. GenAI is not a tool. It’s a new layer of organizational infrastructure, reshaping how your company thinks and builds trust. In a recent PwC survey, 73% of CEOs said they expect AI to significantly change how they create value in the next 3 years⁽¹⁾. This cannot be delegated. 2. Refine your company’s soul. This is an extraordinary opportunity to exert leadership and examine the essential purpose of the company you lead. What do you do better than anyone else? Why should employees, customers, or society care? Why should stakeholders believe in you? 3. Define Three First Principles. Too many goals kills clarity. Research shows execution improves 2x with 3 or fewer⁽²⁾. If GenAI doesn’t serve all three, it’s not strategy.  It's performance art and a slide show. 4. Build your stakeholder-wide coalition, then lead like a candidate. Find people you trust who aren’t afraid to speak truth to power. You don’t need cheerleaders. You need grounded, future-focused judgment. And you don't need a high-paying, sycophantic consultants sharing templates they've used since they shared them with Henry Ford back in the industrial revolution. Then communicate like it’s a campaign, like you are running for office and need everyone's vote. Edelman data shows CEOs with a visible AI vision earn 16% higher stakeholder trust⁽³⁾. Start with your board. Then your employees. Then your customers. 5. Put effort into a strategic thought leadership campaign. This is often overlooked. You must get out in front of this. Lots of communications. 6. Design the platform last. Flexible. Cross-functional.  Avoid vendor lock-in. Build around open-source adaptability, modularity, and secure data stewardship. 80% of early GenAI pilots fail due to poor integration and data issues⁽⁴⁾. Build a team that spans IT, legal, HR, compliance, and ops from day one. 6. Understand GenAI’s limits, and lead accordingly. Generative AI does not think. It predicts⁽⁵⁾. It’s prone to error, hallucination, and false confidence⁽⁶⁾. Use it to augment, not replace, your people. Generative AI is the defining moment of your career. Lead with grace. Lead with courage. Win the day. ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light. Stephen Klein is Founder & CEO of Curiouser.AI, the only values-based Generative AI platform, strategic coach, and organization-first advisory designed to augment individual and corporate human intelligence. He also teaches AI Ethics, Strategy, and Entrepreneurship at UC Berkeley. To sign up, visit curiouser.ai or connect on Hubble: https://lnkd.in/gphSPv_e

🚨 95% of GenAI pilots are failing, but not for the reasons you think. Stop blaming the AI. Start fixing the rollout. Too often, we launch AI like it’s plug-and-play. But success isn’t about the tool . It’s about the system you build around it. Here’s your AI Launch Readiness Checklist 👇 ☐ 1. Start with Strategy ↳ AI without a business outcome is just an expensive science project. ↳ Define the “why” before you buy. ☐ 2. Build Human Readiness ↳ Employees don’t fear AI they fear being left behind. ↳ Upskill, reskill, and explain the why at every step. ☐ 3. Resist the Vendor Hype ↳ Leaders often chase market buzz instead of checking internal readiness. ↳ Buying tools before defining use cases = expensive underuse. ☐ 4. Fix the Foundations ↳ Bad data in = bad insights out. ↳ Data quality, governance, and access matter more than models. ☐ 5. Rethink Workflows, Not Just Tools ↳ AI must slot into the way people already work. ↳ Otherwise, adoption stalls. ☐ 6. Pilot with Purpose ↳ “Test everything” = wasted time. ↳ Pick 1–2 high-impact use cases and scale only what works. ☐ 7. Establish AI Guardrails ↳ Clear policies on risk, compliance, & ethics build trust. ↳ No guardrails = no scale. ☐ 8. Lead from the Top ↳ Culture follows leadership. ↳ If execs treat AI like a gadget, employees will too. ☐ 9. Measure What Matters ↳ Set KPIs that connect to business impact, not vanity metrics. ↳ If you can’t prove ROI, you can’t scale. ☐ 10. Keep Iterating ↳ AI isn’t a “set it and forget it” project. ↳ Continuous feedback and tuning separate pilots from success stories. The lesson? AI doesn’t fail because it’s weak tech. It fails because we built weak systems around it. ♻️ Repost if you’re investing in people, not just tech. Follow Janet Perez for Real Talk on AI + Future of Work --- Source: MIT report via Fortune

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

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

95% GenAI Failure rate? Okay, let me jump in with my opinion and recommendations for organizations that do not want to be part of that 95%. The fundamental challenge lies in the fact that there is so much fascination with the impressive GenAI capabilities (like making a dog walk on its hind legs), and not enough focus on understanding the role that GenAI could play in driving meaningful business and operational outcomes. How does one avoid the 95% GenAI Trap? - Prioritize Business and Operational Outcomes: Begin by clearly identifying the targeted business problem or challenge, desired outcomes, and the metrics against which you will measure outcomes effectiveness. - Embrace the "AI-Human Edge": Recognize that humans are the key, and that putting "AI-in-the-Middle" can transform your employees into superstars that can deliver more value to customers, operations, and society. - Shift from a focus on "Productivity" to a focus on "Effectiveness": Don't just focus on making existing processes faster. Leverage GenAI to unlock creativity and innovation to do things better, not just faster. - Focus on Causal Relationships (Causal AI): Understand the cause-and-effect relationships within your business to build AI models that can adapt and deliver more meaningful, relevant, responsible, and ethical outcomes. - Convert Front-Line Employee into AI Engineers: Empower all team members - especially those at the front lines of customer engagement and operational execution - to participate in the definition and design of the GenAI solutions that are designed to help them. Analytics adoption starts in the definition stage, not the delivery stage. - Embrace an Economics-based, Value-Driven Approach: Shift the focus from data-driven to value-driven, ensuring all AI initiatives are aligned with creating AI and data economic assets that deliver tangible business value. #GenAI #ArtificialIntelligence #BusinessStrategy #DigitalTransformation #DataScience #BillSchmarzo #FailureRate #BusinessOutcomes #Innovation

Instead of thinking “AI first,” focus on where #AI delivers the use cases with the most value. J&J is moving “from the thousand flowers to a really prioritized focus on GenAI” after finding that only 10% to 15% of use cases were driving about 80% of the value. It seems odd to have to say point out that we should focus on where AI best delivers, but with each new tech hype cycle, we need to be reminded that being tech first is never the solution. Focus on what customers and the business most needs, then figure out what tech best delivers. At J&J, employees had been pursuing nearly 900 individual use cases, but the company found it got the most significant value by using generative AI for drug discovery, supply chains, and internal chatbots. The internet was promised as a cheap and easy “24/7 storefront,” but only added to competitive pressures. Social media was promoted as “free advertising,” but created new challenges to managing reputation and new channels to be maintained. Like past tech, AI is not a plug-and-play solution to the complexities your organization faces. AI will best enhance the business of organizations who know their customers, have a sound and disciplined strategy, and are prepared to evaluate where AI best fits and where the costs, risks, and capabilities make AI an unwise an investment (for now). https://lnkd.in/gYK4e9gU

Are you in AI pilot purgatory? 🪨🪨🪨 CIO is out with "3 ways to avoid the generative AI ROI doom loop" by Bryan Kirschner, VP of Strategy at DataStax. Along with recommending a growth mindset and ambitious North Stars, the article strategizes how to maximize ROI from AI investments and avoid common pitfalls: 1. “Attack workflows, not just use cases”. Instead of focusing solely on specific use cases, organizations should look at entire workflows to identify where AI can add value. This includes both substantive and procedural aspects of workflows. 2. “Make 'soft metrics' matter”. It's important to consider qualitative benefits, such as reducing the cognitive load and stress embedded in workflows. Also, improving employee satisfaction and teamwork, alongside quantitative metrics. 3. “Think about ROI in terms of value proposition, not nickels and dimes”. Encourage process, product, and experience owners to view genAI as a way to enhance the overall value proposition of their workflows, focusing on meaningful improvements rather than just cost savings WHY THIS MATTERS: Workstream automations need to matter more to organizations who wish to retain trained staff. Setting contextual and qualitative North Stars helps by addressing employee satisfaction through pre- and post- AI deployment surveys, and L&D paths for upskilling staff to new ways of working smarter not harder with genAI, in addition to quantitative capacity increases, cost savings, efficiency gains, etc. Are you setting soft metrics to gauge AI deployment success when automating workstreams? Join this important discussion in comments- #ai #llm #genai #marketing #automation #promptengineer Image credit: CIO / DataStax

I’ve noticed a pattern in conversations with leaders: while there’s a lot of enthusiasm around GenAI, many feel their investments aren’t yielding the impact they expected. I think one reason for this is that many organizations aren’t clear on where GenAI truly fits within their business. At LatentView Analytics, we created the RAISE framework to bridge this gap and help organizations move from GenAI vision to GenAI reality with measurable impact. Here’s the breakdown: 1️⃣ Retrieve - Enhances knowledge discovery and data retrieval, so you have the insights you need at your fingertips. 2️⃣ Analyze - Summarizes data for actionable insight generation, enabling quicker, smarter decision-making. 3️⃣ Implement - Supports automation with AI agents, freeing teams to focus on strategic work. 4️⃣ Sync & Execute - Manages complex workflows, with multi-agent collaboration driving seamless operations across functions. The path to GenAI success isn’t just having advanced tools—it’s about knowing how to embed them where they’ll actually make a difference. Using our RAISE framework, you can classify the right business areas for GenAI implementation. We categorize our bespoke GenAI solutions with this framework, enabling leaders to make informed decisions. With the right approach, GenAI can deliver powerful results across different functions. The equation is simple (even if the execution isn’t): a clear vision + a solid data strategy = profitable GenAI. Together, let’s rAIse the bar for what is possible through GenAI! #GenAI #RAISEframework #LatentView