AI Strategy Planning

We hear all about the amazing progress of AI BUT, enterprises are still struggling with AI deployments - latest stats say 78% of AI deployments get stall or canceled - sounds like we’re still buying tools and expect transformation. But those that have succeeded? They don’t just license AI, they redesign work around them. Because adoption isn’t about the tool. It’s about the people who use it. Let’s break this down: 😖 Buying AI tools just adds to your tech stack. Nothing more, nothing less! Stat you can’t ignore: 81% of enterprise AI tools go unused after purchase. (Source: IBM, 2024) 🙌🏼 But adoption, adoption requires new workflows, new roles, and new routines - this means redesigning org charts, updating SOPs, and rethinking “a day in the life.” Why? Because AI should empower decisions—not just automate tasks. It should amplify human strengths—not quietly sideline them. That’s where the 65/35 Rule comes in! 65% of a successful AI deployment is redesigning business processes and preparing the workforce. Only 35% is tools and infrastructure. But most companies still do the reverse. They invest 90% in tech and 10% in training… and wonder why they’re stuck in “perpetual POC purgatory” (my term for things that never make production. It’s like buying a Formula 1 car and expecting your team to win races—without ever learning to drive. Here’s the better way: Step 1: Start with the “day in the life” Map how work actually gets done today. Not hypothetically. Not aspirationally. Just reality. Step 2: Identify friction points Where do delays, errors, or bad decisions happen? Step 3: Redesign with intent Now—and only now—do you introduce AI. Not to replace the human. But to support and strengthen them. Recommendation #1: Design AI solutions with your workforce, not just for them. Co-create roles, rituals, and reviews. Recommendation #2: Adopt the 65/35 Rule as your north star. If your AI strategy doesn’t spend more time on people and process than tools and tech… it’s not ready. ⸻ AI doesn’t fail because it’s flawed. It fails because the org using it is unprepared. #AI #FutureOfWork #DigitalTransformation #Leadership #OrgDesign #HumanInTheLoop #AIAdoption #DataDrivenDecisions #Innovation >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Sol Rashidi was the 1st “Chief AI Officer” for Enterprise (appointed back in 2016). 10 patents. Best-Selling Author of “Your AI Survival Guide”. FORBES “AI Maverick & Visionary of the 21st Century”. 3x TEDx Speaker

𝐀𝐈 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐢𝐧 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬: 𝐅𝐨𝐮𝐫 𝐋𝐞𝐯𝐞𝐥𝐬 𝐟𝐫𝐨𝐦 𝐂𝐮𝐫𝐢𝐨𝐬𝐢𝐭𝐲 𝐭𝐨 𝐀𝐈-𝐍𝐚𝐭𝐢𝐯𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 Most companies think they are further along in AI adoption than they actually are.  This framework maps four distinct levels and being honest about where you sit is the first step to moving up. LEVEL 1: INDIVIDUAL USAGE (Curiosity-Driven) Goal: Individuals experiment with AI to save time. • Quick Tasks: Used for emails, brainstorming, and summaries • No AI Strategy: No formal company policy or direction • Personal Tools: Employees use different AI tools individually • Manual Workflows: Outputs are copied manually between tools • Early Exploration: High curiosity but inconsistent results • No Data Governance: Sensitive data may be shared without safeguards LEVEL 2: TEAM-LEVEL EXPERIMENTATION (Process Exploration) Goal: Teams begin applying AI to real work processes. • AI Content Creation: Used for emails, posts, reports, and documents • Meeting Automation: AI summarizes meetings and extracts action items • Workflow Automation: Simple AI chains automate repetitive tasks • AI Research Support: Helps analyze competitors and summarize reports • Tool Consolidation: Teams narrow down to a few preferred AI tools • Manager-Driven Adoption: Leaders encourage AI adoption LEVEL 3: DEPARTMENTAL AI INTEGRATION (Structured + Scalable) Goal: AI use becomes standardized across teams. • AI Playbooks: Defined workflows for each department • Data Pipelines: Clean, structured data feeds AI systems • Prompt Libraries: Shared prompts ensure consistent results • AI Team Champions: Each team has someone responsible for AI adoption • Security Controls: Data protection policies and tool vetting in place • ROI Tracking: Teams measure productivity gains and cost savings LEVEL 4: AI-NATIVE OPERATIONS (Autonomous + Self-Improving) Goal: AI is embedded in every workflow and continuously improves. • AI-Driven Decisions: AI guides strategy, hiring, pricing, forecasting • Connected AI: AI systems across teams work together automatically • Self-Learning: Models improve continuously using new data • AI Governance: Policies ensure ethical and secure AI use • Custom Models: Internal data trains specialized AI models • Revenue from AI: AI creates new products and services MY RECOMMENDATION At Level 1: Establish an AI strategy and basic data governance immediately. At Level 2: Consolidate tools and appoint AI champions per team. At Level 3: Build data pipelines and prompt libraries before scaling further. At Level 4: Focus on connected AI systems and self-learning loops. Which level best describes your organization right now? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #EnterpriseAI #AgenticAI #AIGovernance

SMBs are facing a critical challenge: how to maximize efficiency, connectivity, and communication without massive resources. The answer? Strategic AI implementation. Many small business owners tell me they're intimidated by AI. But the truth is you don't need to overhaul your entire operation overnight. The most successful AI adoptions I've seen follow these six straightforward steps: 1️⃣ Identify Immediate Needs: Look for quick wins where AI can make an immediate impact. Customer response automation is often the perfect starting point because it delivers instant value while freeing your team for higher-value work. 2️⃣ Choose User-Friendly Tools: The best AI solutions integrate seamlessly with your existing technology stack. Don't force your team to learn entirely new systems. Find tools that enhance what you're already using. 3️⃣ Start Small, Scale Gradually: Begin with focused implementations in 1-2 key areas. This builds confidence, demonstrates value, and creates organizational momentum before expanding. 4️⃣ Measure and Adjust Continuously: Set clear KPIs from the start. Monitor performance religiously and be ready to refine your AI configurations to optimize results. 5️⃣ Invest in Team Education: The most overlooked success factor? Proper training. When your team understands both the "how" and "why" behind AI tools, adoption rates soar. 6️⃣ Look Beyond Automation: While efficiency gains are valuable, the real competitive advantage comes from AI-driven insights. Let the technology reveal patterns in your business processes and customer behaviors that inform better strategic decisions. The bottom line: AI adoption doesn't require disruption. The most effective approaches complement your existing workflows, enabling incremental improvements that compound over time. What's been your experience implementing AI in your business? I'd love to hear what's working (or not) for you in the comments below. #SmallBusiness #AI #BusinessStrategy #DigitalTransformation

From AI Consumers to Intelligence Architects: are you on this journey yet? AI success depends on organizational clarity, decision design, and the ability to operationalize intelligence at scale. The most forward-thinking companies are no longer just purchasing AI capabilities; they’re taking an intelligence-first approach -- treating intelligence as a core design principle, not an afterthought. Deloitte’s 2024 Tech Trends report highlights that organizations viewing AI as an architectural challenge -- integrating AI deeply into their core systems, processes, and talent strategies -- are better positioned to scale AI adoption enterprise-wide. In my latest Forbes Technology Council article "Building True Intelligence: Beyond Models and Compute", I share what I believe are fundamental enablers of enterprise AI's transformative potential. 1. Decisions Drive Value, Models Scale It Instead of starting with tools, start with mapping which decisions matter. AI must augment or automate frequent, impactful, and structured decisions. Model selection comes after decision clarity. 2. Data Must Be Connected in Context The volume of data is less important than its structure and relevance. Success lies in integrating signals meaningfully in the context of specific decisions. 3. Operational Readiness is Non-Negotiable Most AI failures happen in day-2 operations, not during development. AIOps and lifecycle management are essential to sustained performance and ROI. 4. Institutional Knowledge is the Differentiator Encoding domain expertise -- escalation thresholds, compliance logic, etc. -- makes AI systems enterprise-relevant. This makes intelligence explainable, trustworthy, and actionable. 👇Link to the article in the comment section below.

AI Transformation involves multiple layers across technology, people, and processes. Here are the most relevant components for a successful AI transformation at the enterprise level: 1. Strategic Alignment - AI Vision & Goals: Clear definition of how AI supports the organization’s mission. - Executive Sponsorship: Leadership buy-in to drive funding, priorities, and culture. - Use Case Prioritization: Business-driven selection of high-impact, feasible use cases. 2. Data Foundation - Data Strategy: Governance, quality, privacy, and availability planning. - Data Infrastructure: Modern data platforms (data lakes, warehouses, vector databases). - Labeling & Annotation: Especially important for supervised learning and fine-tuning. 3. Technology Stack - Model Layer: Foundation models (e.g., GPT, Claude), custom ML models, MLOps. - Infrastructure: Scalable compute (cloud, on-prem, hybrid), APIs, and edge support. - Integration Layer: Connectors to business systems (ERP, CRM, ITSM, etc.). 4. Talent & Capabilities - Cross-functional Teams: Data scientists, ML engineers, domain experts, and DevOps. - Training & Upskilling: Programs to enable AI literacy and advanced capabilities. - External Partnerships: Vendors, academia, or consultants to bridge capability gaps. 5. Governance & Risk Management - AI Ethics & Policy: Bias mitigation, explainability, and fairness guidelines. - Compliance & Privacy: GDPR, HIPAA, or industry-specific regulations. - AI GRC: Governance, risk, and compliance tailored to AI lifecycle. 6. Operationalization (MLOps / LLMOps) - Model Lifecycle Management: From experimentation to deployment and monitoring. - CI/CD for AI: Automating testing, retraining, and releasing of models. - Monitoring & Evaluation: Observability for performance, drift, and cost. 7. Change Management - Process Reengineering: Adapting or redesigning processes to leverage AI. - Stakeholder Engagement: Ensuring alignment and reducing resistance. - Communication Strategy: Educating stakeholders on impact and benefits. 8. Agentic & Autonomous Systems (for advanced orgs) - Multi-agent Architectures: AI agents interacting with tools, people, and data. - Tool Orchestration: Dynamic use of APIs, functions, and external systems. - Evaluation Frameworks: Guardrails and alignment metrics for autonomy. 💡 My Takeaway AI Transformation is not just about AI. Behind every successful AI initiative lies a robust foundation in data, automation, and cloud infrastructure. Enterprises that treat AI as a siloed capability often stumble—because scalable, reliable, and secure AI requires more than just models. From infrastructure-as-code to MLOps, from data pipelines to secure deployment, true transformation demands an integrated architecture where AI, cloud, and automation work in harmony. 🎯 That’s the mindset I believe in: AI is the tip of the spear—but it's the foundation that makes it fly. #DigitalTransformation #ArtificialIntelligence #EnterpriseAI

72% of enterprises adopted traditional AI over 8 years. Generative AI hit ~70% in just 3. Agentic AI is already at 35% in 2. (MIT Sloan + BCG, 2025) Your organization is almost certainly investing in all three. But if your leadership team can’t articulate what each does, where each belongs, and where one ends and the next begins, you’re not investing in AI. You’re misallocating capital across the fastest-moving technology shift in decades. The CXO’s Field Guide to Enterprise AI: 1/ Traditional AI → Rules-based systems, predictive models, classification engines → Trained on historical data to optimize specific, narrow tasks → Think: fraud detection, demand forecasting, recommendation engines This is still where the majority of measurable AI ROI comes from today. 2/ Generative AI → Creates new outputs: text, code, images, summaries → Understands and produces language—not just numbers → Think: drafting reports, summarizing calls, accelerating code Widespread adoption, minimal enterprise impact. Most deployments improve individual productivity, not business workflows. 3/ Agentic AI → Plans, reasons, uses tools, and executes multi-step tasks → Acts on goals, not just prompts → Think: monitoring supply chains, resolving disruptions, updating systems autonomously Gartner predicts 40% of enterprise apps will embed AI agents by 2026. 4/ Where Most AI Strategies Break Down → Vendors are “agentwashing” — relabeling assistants as agents → “We use ChatGPT” gets confused with “we have an AI strategy” → Budget follows the buzzword, not the business problem Gartner has already flagged “agentwashing” as the most common misconception in enterprise AI. 5/ The Portfolio Questions Your CFO Should Be Asking Most AI budgets are being allocated without answering these: → Traditional AI: Are our models still driving ROI? → Generative AI: Are we reducing workflow cycle time? → Agentic AI: Do we have the data quality, governance, and observability to let AI act autonomously? 43% of companies are already directing more than half their AI budgets toward agentic systems. 6/ The Maturity Test: Can You Sequence? Most organizations should be running all three simultaneously. → Traditional AI for optimization → Generative AI for augmentation → Agentic AI for automation The mistake is deploying the right AI in the wrong order. 7/ The Two-Year Window 93% of IT leaders plan to deploy autonomous agents within two years. The reality: Most companies are using AI. Very few are operationalizing it. The gap between pilots and production is widening every quarter. 8/ What This Means for Your Next Board Conversation → Break AI spend into traditional, generative, and agentic, with different ROI expectations → Audit your vendors for agentwashing → Assign metrics that matter The companies that win the next 3 years won’t be the ones that spend the most on AI. They’ll be the ones that know: what to deploy, where to deploy it, and in what sequence.

AI adoption isn't a one-time event. It's an ongoing process. Most organizations jump to tools and think that will solve the problem. It's not about the technology, it's about the people. AI adoption is all about following a sequence that builds on one another. They include 4 phases: 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 1. Executive Sponsorship — leaders must visibly own AI. Not just approve budgets. 2. Business-Aligned Strategy — connect AI to specific business goals. Define your North Star. 3. Readiness Assessment — understand people, process, data, and technology before selecting tools. 𝗘𝗻𝗮𝗯𝗹𝗲𝗺𝗲𝗻𝘁 4. Data Foundation — clean, accessible, governed data is a prerequisite. Not a nice-to-have. 5. Governance Before You Scale — establish guardrails early. Not after an incident. 6. High-Impact Pilots — identify 2–3 workflows that demonstrate measurable value quickly. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 7. Redesign Workflows — embed AI into reimagined processes. Not just existing ones. 8. Change Management — address job displacement fears directly and transparently. 9. Train and Upskill — executives, managers, and front-line employees need different skills. 𝗦𝗰𝗮𝗹𝗲 10. AI Champions — internal advocates who bridge IT and the business. 11. Track KPIs and ROI — define success beyond accuracy. Measure adoption and time saved. 12. Scale What Works — expand proven pilots. Treat AI as an evolving operating model. At the core, AI adoption starts with people. Yes, you need executive sponsorship. But, more importantly, it's about having everyone on the same page. The fastest way to derail adoption is to build on a foundation of mistrust. Transparency is key here. Focus on trust and value, and don't lead with technology. ♻️ Share if this resonates ➕ Follow Jason Moccia for more insights on AI and leadership.

𝑨 𝑪𝑬𝑶’𝒔 𝑨𝑰 𝑻𝒐-𝑫𝒐 𝑳𝒊𝒔𝒕: 𝑭𝒓𝒐𝒎 𝑩𝒖𝒛𝒛𝒘𝒐𝒓𝒅 𝒕𝒐 𝑩𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝑽𝒂𝒍𝒖𝒆 – AI is no longer a futuristic add-on—it’s a boardroom imperative. But most CEOs still struggle to move beyond the hype and translate AI into tangible business value. A practical and strategic roadmap for executives ready to lead with intelligence, not just implement tools. 𝑲𝒆𝒚 𝑻𝒂𝒌𝒆𝒂𝒘𝒂𝒚𝒔: • 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐂𝐚𝐬𝐞 𝐁𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐞 𝐓𝐞𝐜𝐡 Instead of chasing shiny AI tools, start with a clear business problem. Ask: What process can we optimize? What decision can we augment? • 𝐄𝐦𝐩𝐨𝐰𝐞𝐫 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐓𝐚𝐥𝐞𝐧𝐭 𝐌𝐢𝐱 Success in AI isn’t just about data scientists. It requires collaboration between domain experts, tech teams, and leadership to align goals and ensure adoption. • 𝐓𝐫𝐞𝐚𝐭 𝐀𝐈 𝐚𝐬 𝐂𝐡𝐚𝐧𝐠𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐓𝐞𝐜𝐡 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 AI initiatives often fail because of resistance, fear, or misunderstanding. CEOs must actively manage this transformation, just like any other major change—through effective communication, upskilling, and fostering inclusion. • 𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐄𝐭𝐡𝐢𝐜𝐬 𝐄𝐚𝐫𝐥𝐲 Don’t wait for a PR crisis. Responsible AI requires clear governance, transparency, and ethical safeguards from the outset—especially for models that make decisions affecting people. • 𝐏𝐢𝐥𝐨𝐭, 𝐋𝐞𝐚𝐫𝐧, 𝐒𝐜𝐚𝐥𝐞 Start small. Use controlled pilots to prove value, then scale what works. Avoid trying to “AI everything” at once. 𝑩𝒐𝒕𝒕𝒐𝒎 𝑳𝒊𝒏𝒆 AI can unlock enormous value—but only when CEOs stop seeing it as an IT initiative and start owning it as a core business transformation strategy. The winners will be those who combine vision with discipline, ethics, and real-world impact. https://lnkd.in/gJ46fRu3

AI is advancing at an extraordinary pace, but adoption is not keeping up. The problem is no longer access to the technology or the quality of the tools. The problem is that organizations are still treating AI like a deployment challenge when it is really an adoption and integration challenge. Value comes when AI is embedded into real work and tied to real outcomes - not when people are simply logging in or "AI snacking" 🍭 with meeting summaries and email drafts. And this puts executives squarely at the center 🎯 of success or failure. This recent Prosci blog organizes executive impact into four levers: Visibility, Vision, Voice, and Value. 👀 Visibility is about leaders being seen substantively participating in pilots, engaging in reviews, and modeling their own learning so AI is experienced as a strategic priority rather than an IT side project. This isn't symbolic sponsorship; it's active involvement.  📍 Vision is about defining a clear why, linking AI to enterprise priorities, and owning a roadmap that balances immediate wins with long-term capability building. Rather than just defining 'an AI strategy' it is the vision of what we look like as an AI-infused organization.  📣 Voice is about communicating with clarity, credibility, and frequency, including addressing uncertainty, listening actively, and reinforcing ethical use in ways that build trust. Leaders must be the voice when the topic is organizational transformation. 💸 Value is about insisting that AI efforts connect to meaningful business outcomes, while also removing the process, skill, and data barriers that keep value from being realized. It's also about elevating the value of the people side of the organization. What I was really trying to emphasize in this blog is that executive leadership is not a supporting factor in AI adoption ⏩ it is a catalytic one. Across our AI adoption research, executive behavior emerges as one of the strongest differentiators between stalled experimentation and scaled impact. The organizations making progress are those with leadership presence, strategic clarity, credible communication, and the organizational conditions that help people actually use AI in their work - above and beyond the tools. The research (and this blog) calls for executives to stop treating AI as something they sponsor from a distance and start treating it as a transformation they lead directly. I encourage you to send the blog along to leaders you know that need to bring Visibility, Vision, Voice, and Value to drive desired AI outcomes. And if you want research-based guidance on leading AI transformation, Prosci is who you'll want to call. Full blog can be found (and passed along to leaders) at: https://lnkd.in/gCiPP4KF

Implementing AI isn’t just about picking tools, it’s about building a strategy that actually delivers value. Too many companies rush into AI with buzzwords and big promises, but no clear direction. The result? Wasted resources and stalled pilots. This 3-phase roadmap breaks down exactly what it takes to go from idea to impact, from identifying the right use cases to building scalable infrastructure and deploying real-world solutions across your organization. 🔍 Phase 1: Evaluation & Planning - Identify high-value opportunities where AI can solve real problems. - Educate leadership on what AI can and can’t realistically do. - Assess your data, tech stack, and team for AI readiness. - Define a clear AI vision aligned with long-term business goals. - Prioritize low-risk, high-impact AI use cases to start with. 🏗️ Phase 2: Foundation & Enablement - Build or partner for top AI talent across data and engineering. - Set up scalable, clean, and real-time data infrastructure. - Choose AI tools that align with your business model. - Establish governance for ethics, bias, and data privacy. - Align tech, ops, and business teams to collaborate on AI. 🚀 Phase 3: Deployment & Scaling - Build and test small-scale AI prototypes (PoCs). - Measure results using clear success metrics and KPIs. - Deploy AI models into production with smooth integration. - Monitor for drift and continuously retrain your models. - Scale successful AI use cases across the organization. 📌 Save this guide for your next AI planning session. Follow me Avani Rajput for more AI insights !