AI Strategy Planning

"We need an AI strategy!" 𝘙𝘦𝘤𝘰𝘳𝘥 𝘴𝘤𝘳𝘢𝘵𝘤𝘩 Hold up. That's the wrong question. The right question? "What business problem are we actually trying to solve?" I've sat in countless board meetings where executives demand AI initiatives – not because they've identified a problem AI can solve, but because they're afraid of being left behind. This FOMO-driven approach is precisely how companies end up in what I call "perpetual POC purgatory" – running endless proofs of concept that never see production. Here's the uncomfortable truth: Your goal isn't to use AI for the sake of AI. Your goal is to solve real business problems. Sometimes the best solution is a regular hammer, not a sledgehammer. So when leadership pushes AI without purpose, redirect the conversation: → "What business outcome are we trying to drive?” → “What’s the actual problem we’re solving?” → “Is AI the most effective tool for that — or just the most exciting one?” Next, how do you determine if AI is the right solution? I recommend this straightforward approach that keeps business problems at the center: 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗽𝗿𝗲𝗰𝗶𝘀𝗲𝗹𝘆 - What specifically are you trying to solve? The more precisely you can articulate the problem, the easier it becomes to evaluate whether AI is appropriate. 2. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗳𝗶𝗿𝘀𝘁 - Could existing technology or processes handle this faster, cheaper, and more reliably? 3. 𝗟𝗲𝗮𝗻 𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝘁𝘀 - If the problem seems AI-suitable, validate it with people who’ve delivered outcomes — not just hype. 4. Be brutally realistic about your organization's maturity - Do you have the data infrastructure, talent, and risk tolerance necessary for an AI implementation? Remember this fundamental truth: AI is not a silver bullet. Even seemingly simple AI projects require time, focus, alignment, and resilience to implement successfully. The companies winning with AI aren't the ones with the flashiest technology. They're the ones methodically solving pressing business challenges with the most appropriate tools—AI or otherwise. 𝗜’𝗱 𝗹𝗼𝘃𝗲 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂: What business problem are you trying to solve that might (or might not) actually need AI?

𝗦𝘁𝗼𝗽 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝘀𝗼 𝗺𝗮𝗻𝘆 𝗔𝗜 𝗽𝗶𝗹𝗼𝘁𝘀. 𝗦𝘁𝗮𝗿𝘁 𝗴𝗼𝗶𝗻𝗴 𝗱𝗲𝗲𝗽. Right now, many organisations are doing the same thing: “Let’s test AI everywhere.” “Every team should run a pilot.” “More experiments must mean faster progress.” It feels bold, but it rarely works. 𝗠𝗼𝘀𝘁 𝗔𝗜 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝗳𝗮𝗶𝗹 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗳𝗼𝗰𝘂𝘀 𝗶𝘀 𝘀𝗽𝗿𝗲𝗮𝗱 𝘁𝗼𝗼 𝘁𝗵𝗶𝗻. Dozens of small pilots don’t build capability. They create noise, confusion and isolated wins that never scale. If everything is a priority, nothing becomes a success. 𝗧𝗵𝗲 𝗽𝗮𝘁𝗵 𝘁𝗼 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝘄𝗶𝗱𝗲 𝗽𝗶𝗹𝗼𝘁𝗶𝗻𝗴. 𝗜𝘁’𝘀 𝗮 𝗱𝗲𝗲𝗽, 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁. Choose one domain where data, processes and outcomes are connected. Build capability there first. Create standards, clarity and a repeatable model others can adopt. Depth delivers: →↳ Trust →↳ Adoption →↳Real capability →↳Repeatable wins →↳ Momentum that compounds Breadth delivers: + High costs + Fragmentation + Slow progress +“Pilot purgatory” Depth forces discipline. Discipline creates impact. Impact is what scales. 𝗜𝗳 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 𝗮𝗯𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝗔𝗜 𝘄𝗼𝗿𝗸: →𝗦𝘁𝗲𝗽 𝟭: Pick one domain with connected value streams →𝗦𝘁𝗲𝗽 𝟮: Prioritise opportunities that build long-term advantage →𝗦𝘁𝗲𝗽 𝟯: Sequence work so each stage strengthens the next →𝗦𝘁𝗲𝗽 𝟰: Keep watching the competitive and tech landscape 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵: 𝗔𝗜 𝘀𝗰𝗮𝗹𝗲𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗴𝗼 𝗱𝗲𝗲𝗽𝗲𝗿, 𝗻𝗼𝘁 𝘄𝗶𝗱𝗲𝗿. So pause. Reflect. Ask yourself: 👉 Where can we go deep enough, 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆, 𝘁𝗼 win? 🔁 Follow for more on AI strategy, transformation and building future-ready organisations. #AITransformation #DigitalStrategy #FutureReadyBusiness #AIDrivenGrowth #EnterpriseAI

The GenAI wave is real, but most engineers still feel stuck between hype and practical skills. That’s why I created this 15-step roadmap—a clear, technically grounded path to transitioning from traditional software development to advanced AI engineering. This isn’t a list of buzzwords. It’s the architecture of skills required to build agentic AI systems, production-grade LLM apps, and scalable pipelines in 2025. Here’s what this journey actually looks like: 🔹 Foundation Phase (Steps 1–5): → Start with Python + libraries (NumPy, Pandas, etc.) → Brush up on data structures & Big-O — still essential for model efficiency → Learn basic math for AI (linear algebra, stats, calculus) → Understand the evolution of AI from rule-based to supervised to agentic systems → Dive into prompt engineering: zero-shot, CoT, and templates with LangChain 🔹 Build & Integrate (Steps 6–10): → Work with LLM APIs (OpenAI, Claude, Gemini) and use function calling → Learn RAG: embeddings, vector DBs, LangChain chains → Build agentic workflows with LangGraph, CrewAI, and AutoGen → Understand transformer internals (positional encoding, masking, BERT to LLaMA) → Master deployment with FastAPI, Docker, Flask, and Streamlit 🔹 Production-Ready (Steps 11–15): → Learn MLOps: versioning, CI/CD, tracking with MLflow & DVC → Optimize for real workloads using quantization, batching, and distillation (ONNX, Triton) → Secure AI systems against injection, abuse, and hallucination → Monitor LLM usage and performance → Architect multi-agent systems with state control and memory Too many “AI tutorials” skip the real-world complexity, including permissioning, security, memory, token limits, and agent orchestration. But that’s what actually separates a prototype from a production-grade AI app. If you’re serious about becoming an AI Engineer, this is your blueprint. And yes, you can start today. You just need a structured plan and consistency. Feel free to save, share, or tag someone on this journey.

Most businesses talk about AI transformation. → They attend conferences. → Read whitepapers. → Schedule vendor demos. But here's what 73% of executives won't admit: *️⃣ They're paralysed by the possibilities. Great AI adoption doesn't just automate tasks. → It transforms workflows. → It amplifies human potential. → And you can measure the ROI. Data will show you what's possible, but strategic thinking is what gets you results. 💡 Here's what most leaders keep getting wrong (and can't seem to break free from): – 68% of companies still approach AI as a technology solution rather than a business transformation, despite MIT research showing that workflow decomposition increases success rates by 3x. – 54% of AI pilots fail because businesses skip the cost-benefit analysis, yet Gartner data proves that systematic evaluation frameworks reduce implementation costs by 40%. – Leaders invest 80% of their AI budget in high-stakes applications without human oversight, even though Forbes analysis shows that 85% of successful implementations start with low-risk, quick-payback projects. So, if you're ready for transformation, here's a proven roadmap to break through: → Decompose before you deploy. → Break every workflow into discrete tasks. → Map what's repetitive, creative, or time-consuming using tools like ONET Online. → Run the numbers ruthlessly. → Calculate licensing costs, adaptation efforts, and error correction mechanisms. → Compare against traditional methods. → Accuracy requirements vary—marketing copy can tolerate errors, medical diagnoses cannot. ✳️ Start small, think big. Launch pilots with pre-built solutions, commercial models like GPT-5, or open-source options like DeepSeek. Build human-in-the-loop systems from day one. - Use the 2x2 matrix. - Plot use cases by risk versus demand. - Focus on low-risk, high-demand applications like routine customer inquiries before tackling legal document drafting. This systematic approach helps businesses avoid the common trap of being overwhelmed by AI possibilities and instead focus on use cases that align with their strategic priorities and resource constraints. ↳ Train beyond the data team. ↳ Involve employees across the organisation. ↳ They'll spot opportunities your data scientists miss. Build enterprise-wide AI literacy around concepts like RAG and data quality. At successful companies, they don't separate AI strategy from business strategy. Every implementation serves both. Are you making these fundamental mistakes? - Go systematic. - Balance methodology with bold experimentation. That's how you build AI advantage that competitors can't replicate. ↳ Could it be easier said than done? ↳ Or will it be another missed opportunity? ↳ How strategic will your next AI move be?  Don't let your competitors outmaneuver you.

In the past few months, we've worked with partners who've run into the same challenge with AI adoption. They rolled out policies or guidelines without bringing people into the conversation first—no workshop, no consensus building, just documents that needed signatures or implementation. Unsurprisingly, the result was frustrated staff expected to enforce or follow rules they had no part in creating, and leaders facing resistance instead of adoption. Both AI policies and guidelines are critical for responsible AI adoption, but they have to be built intentionally, with stakeholders driving consensus, or they most likely won't work. After working with hundreds of districts, we've created the resource below. Here are the best practices we recommend. Policies are your compliance layer and are designed to protect your district. We suggest adaptations to existing: ✔️ Acceptable use policies ✔️ Data privacy/FERPA protections ✔️ Academic integrity standards ✔️ Cyberbullying policies (to add deepfakes) Guidelines are your change management layer. They are the "why" that brings people along. We recommend including the following in your AI guidelines: 💡 Vision for GenAI adoption across your district 💡 GenAI misuse/academic integrity response protocols 💡 GenAI chatbot and EdTech tool vetting processes 💡 Digital wellbeing, data privacy, and student safety practices 💡 Implementation tips and instructional supports 💡 AI Literacy training opportunities and expectations What matters most is that both policies and guidelines should be built with stakeholders, not handed down to them. They should evolve with feedback, evidence of impact, and technical advancements. In all of our guideline and policy development work, we always start with AI literacy. It's important to build foundational understanding across stakeholders so that when policies and guidelines are developed, people can contribute meaningfully to the process and understand the "why" behind what they're being asked to implement. Intentional stakeholder engagement isn't a nice-to-have. It's what we've seen drive adoption. #AIforEducation #GenAI #ChangeManagement #AI

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

If you’re learning AI automation without a roadmap, you’re guaranteed to get overwhelmed. People usually “learn AI automation” by jumping straight into tools… and then wonder why nothing works consistently. Real automation requires structure - thinking, logic, testing, and a gradual build-up of skills. This 18-day roadmap breaks down the exact sequence to go from zero → confidently building automations with AI, APIs, tools, and no-code platforms. Here’s the full breakdown, day by day: Day 1 - AI Automation Fundamentals Learn what automation really means, how it differs from AI and agents, and see real examples. Day 2 - Automation Thinking Break work into steps, triggers, and outcomes - the mindset behind every good automation. Day 3 - APIs & Webhooks Basics Understand how apps communicate and how events trigger workflows. Day 4 - No-Code Automation Platforms Explore Zapier, Make, n8n - and how no-code tools actually run workflows. Day 5 - Build Your First Automation Create a simple trigger-action workflow and connect two apps. Day 6 - Data Handling Pass data between steps, map fields, and work with text, numbers, and dates. Day 7 - Logic & Error Handling Add filters, conditional logic, retries, and fallbacks to keep automations reliable. Day 8 - AI Model Basics Learn prompts vs system instructions, tokens, limits, and LLM behavior. Day 9 - Using AI Inside Automations Insert AI steps into workflows and parse structured AI outputs. Day 10 - Prompt Design for Automation Write consistent prompts and reduce hallucinations with JSON outputs. Day 11 - Text-Based Task Automation Automate email replies, summaries, CRM updates, and document tasks. Day 12 - Knowledge Automation (RAG Basics) Connect AI to internal documents and fetch accurate answers from real data. Day 13 - AI Agents Basics Understand agent planning, tools, and identify use cases for agents. Day 14 - Business Use Case Automation Automate lead qualification, ticket routing, and internal processes. Day 15 - Sales & Marketing Automation Personalize outreach, repurpose content, and automate follow-ups. Day 16 - Operations Automation Manage approvals, notifications, and repetitive operational tasks. Day 17 - Monitoring & Optimization Track workflow success, cut costs, and improve performance. Day 18 - Build & Ship Your System Design, test, document, and finalize a complete end-to-end automation. You don’t master AI automation by learning tools, you master it by learning systems thinking, data flow, and structured execution. Follow this roadmap, and you’ll build automations that are reliable, scalable, and business-ready.

Too many AI strategies are being built around the technology instead of the business challenges they should solve. The real value of AI comes when it is directly tied to your goals. I have arrived at seven lessons on how to align your AI strategy directly with your business goals: 1. Start with the "why," not the "what." Before discussing models or tools, ask what business problem you need to solve. It could be speeding up product development, or cutting operational costs. Let that answer be your guide. 2. Think in terms of business outcomes. Measure AI success by its impact on metrics like revenue growth or employee productivity not by technical accuracy. 3. Build a cross-functional team. AI can't live solely in the IT department. Include leaders from all relevant departments from day one to ensure the strategy serves the entire business. 4. Prioritize quick wins to build momentum. Identify a few small, high-impact projects that can deliver results quickly. This builds organizational confidence and makes people ready to take on larger initiatives. 5. Invest in data foundations. The best AI strategy will fail without clean and well-governed data. A disciplined approach to data quality is non-negotiable. 6. Focus on change management. Technology is the easy part. Prepare your people for new workflows and equip them with the skills to work alongside AI effectively. 7. Create a feedback loop. An AI strategy is not a one-time plan. Continuously gather feedback from users and analyze performance data to adapt and refine your approach. The goal is to make AI a part of how you achieve your objectives, not a separate project. #AIStrategy #BusinessGoals #DigitalTransformation #Leadership #ArtificialIntelligence

People reaching out to Ranjani Mani and me for guidance on putting together a 30-60-90 day plan to start their AI journey might find the note below helpful. This is a high-level framework you will need to customise according to your career goals, the domain you work in, and the stage of your career. 📍 30-Day Plan: 1️⃣ Self-Assessment and Learning: Understand AI Fundamentals: Start by diving into the basics of artificial intelligence. Learn about machine learning, neural networks, and natural language processing. Online Courses and Tutorials: Enroll in online courses. Many large corporations like Microsoft, Google, IBM, and Oracle offer free courses. Focus on topics like Python programming, data science, and AI frameworks (e.g., TensorFlow, PyTorch). 2️⃣ Networking and Research: LinkedIn Networking: Connect with professionals in the AI field. Join relevant LinkedIn groups and participate in discussions. Research AI Companies: Identify companies that work on AI projects. Understand their products, services, and technology stack. 3️⃣ Hands-On Projects: Kaggle Challenges: Participate in Kaggle competitions to apply theoretical knowledge to real-world problems. Personal Projects: Work on small AI projects (e.g., sentiment analysis, image recognition) to build a portfolio. 📍 60-Day Plan: 1️⃣ Deepen Technical Skills: Advanced Machine Learning: Study advanced ML techniques such as deep learning, reinforcement learning, and transfer learning. Implement Algorithms: Code and implement algorithms from scratch to gain a deeper understanding. Explore Cloud Platforms: Familiarize yourself with cloud platforms like AWS, Google Cloud, or Microsoft Azure. 2️⃣ Industry Insights: Attend Webinars and Conferences: Participate in webinars and conferences related to AI. Stay updated on the latest research and trends. Read Research Papers: Dive into research papers published in top AI conferences (e.g., NeurIPS, ICML). 3️⃣ Build a Strong Portfolio: GitHub Repository: Create a GitHub repository showcasing your AI projects, code, and contributions. Blog Posts: Write blog posts about your learnings, insights, and experiences in AI. 📍 90-Day Plan: 1️⃣ Explore AI Roles: Search: Start searching for AI-related job openings. Customize Resume: Tailor your resume to highlight relevant skills and projects. Prepare for Interviews: Practice technical interviews, behavioral questions, and case studies. 2️⃣ Certifications: Certified AI Professional: Consider pursuing certifications like “Certified AI Professional” from reputable organizations. 3️⃣ Mentorship and Networking: Find a Mentor: Seek guidance from experienced AI professionals. Attend Meetups: Attend local AI meetups and network with industry experts. Feel free to leave your questions in the comments section, and we will try to address them in the next set of videos. 🚀🤖💡 #AI #CareerTransition #MachineLearning #TechLearning #AIJobs #Networking #TechSkills #CareerDevelopment #LearningPath #AIProjects #Certifications

Many people are talking about the Bloomberg story on former McKinsey, BCG, and Bain consultants training AI models to automate parts of the strategy consulting work (The link to the article is in the comments). Some see this as the beginning of the end for the consulting industry. It is not. It is the end of one model of consulting and the emergence of another. For decades, the consulting value chain was built on analysis: gather data, benchmark competitors, synthesize findings, deliver a deck. Today, AI can perform much of this faster, cheaper, and at scale. If consulting was only about analysis, then yes, AI would replace it. But strategy was never just analysis. The real work has always been about judgment, interpretation, decision-making, alignment, mobilization, execution, and building strategic capability inside the organization. This is the shift I wrote about in "Strategy Consulting Reinvented: A New Partnership Model" (The link to my article is in the first comment) - AI is commoditizing data and insights - The differentiator is now the ability to help organizations think strategically - Clients no longer want answers delivered to them - They want capacity built with them The future of strategy consulting will be defined by: (1) Partnership, not prescription Strategy is co-created, not handed over. (2) Contextual intelligence, not generic best practices What works in Silicon Valley does not automatically work every where else. (3) Capability building, not dependency The goal is to leave behind stronger leaders and stronger strategic muscles. (4) Continuous strategy, not episodic projects Strategy becomes an ongoing system of sensing, learning, and adjusting. So yes, AI will replace a certain kind of consulting. The kind that equates thinking with slide production. The kind that confuses frameworks with judgment. The kind that treats strategy as analysis rather than synthesis and leadership. But the consulting firms and advisors who will shape the next decade are those who help organizations build strategic capability: the ability to embrace complexity, navigate uncertainty, resolve ambiguity, explore futures, make trade-offs, act with agency, and learn continuously. The question is no longer: Can we get the analysis? The question is: Can we think strategically, together, in a world where the answer keeps moving and generates more questions? The future of strategy will belong to those who learn faster, adapt faster, and co-create the path forward. #Strategy #Consulting #Leadership #CapabilityBuilding #StrategicThinking