At its core, strategy is the deliberate design of a long-term path to success. It aligns resources, capabilities, and choices to achieve competitive advantage. You can read more here: [[What is business strategy]] In business, a strategy answers: - **Where to compete** (markets, products, customer segments) - **How to win** (value proposition, pricing, brand) - **What capabilities to build** (people, data, technology) - **What systems to scale** (operations, governance, culture) A **great strategy** is not a static plan but a dynamic, evolving framework that guides decisions in a fast-changing world. As you might imagine, beside business strategy, it's mandatory today to also have an AI strategy. ## **2. Why Is AI So Important?** Artificial Intelligence (AI) is not just a technology trend—it is a **transformational force**. Like electricity or the internet, AI reshapes the infrastructure of industries. You need an AI strategy to: 1. **Stay Competitive** – AI enhances speed and intelligence in decision-making. 2. **Boost Efficiency** – Automate the mundane, unleash human creativity. 3. **Enhance Customer Experience** – Personalization at scale. 4. **Improve Decision-Making** – Leverage data in real time. 5. **Cut Costs** – Optimize workflows and resource usage. 6. **Drive Revenue** – Enable smarter marketing, pricing, and product strategies. 7. **Future-Proof the Enterprise** – The pace of AI adoption is only accelerating. 8. **Survive** – **Companies that do not implement AI will likely go out of business.** The AI divide is growing. Businesses failing to adopt will suffer from higher costs, slower response times, poorer insights, and declining customer loyalty. Just as the internet disrupted analog businesses, AI will make non-AI-native firms obsolete. ## **3. What Is an AI Strategy?** An **AI strategy** is a blueprint for how a company will use AI to achieve business outcomes. It is both **technological and transformational**. It outlines: - The AI vision and ambition. - Where AI creates the most value. - How to build data infrastructure. - Talent, culture, and ethical frameworks. - Governance and risk controls. - A roadmap for scaling across the enterprise. ## **4. Why Do You Need an AI Strategy?** Despite the hype, 76% of companies still deploy AI in only 1–3 use cases. Without strategy, businesses risk: - Fragmented pilot projects. - Shadow AI usage with compliance risks. - Wasted spend on vendors/tools. - Slow, unscalable progress. An AI strategy aligns leadership, IT, operations, and front lines. It bridges the gap between ambition and execution, ensuring every AI investment drives ROI and long-term competitive advantage. ## **5. What Is an AI-First Company and Why Will They Win?** An **AI-first company** is not one that merely uses AI; it’s one that **thinks AI-first in every decision, every product, every process**. Characteristics of an AI-First Company: - **Empowers humans with AI** rather than duplicating their work. - **Does not perform any task manually that AI could do better or faster.** - **Builds tools, not tasks** – AI agents, copilots, and workflows become standard. - **Automates the routine**, freeing humans for creativity, relationships, judgment, and vision. - **Leverages data as a flywheel** – Every customer interaction trains smarter systems. - **Cultivates a culture of experimentation**, with AI embedded in product development, marketing, operations, and sales. - **Designs for adaptability** – product launches, service models, and org structures are all agile. The companies that win will be those that **rethink the business from the ground up** through the lens of intelligent systems. Why they will win: - Lower cost to operate - Faster speed to market - Superior customer experience - Exponential data leverage - Continuous innovation via feedback loops ## **6. How to Develop an AI Strategy (And Who to Include)** Developing an AI strategy is a **cross-functional executive mandate**, not just an IT initiative. The key stakeholders are: - **CEO**: Sets the vision and communicates the urgency. - **CIO/CTO/CDO**: Owns AI infrastructure, tooling, and data governance. - **CHRO**: Drives upskilling, AI literacy, and future-of-work readiness. - **CFO**: Controls AI investment and ROI metrics. - **Business Unit Heads**: Identify and own specific AI use cases. - **Compliance/Legal**: Ensures ethical, fair, and legal AI usage. - **Board of Directors**: Must be AI literate and supportive of long-term transformation. Best Practice: - **Appoint a Chief AI Officer (CAIO)** or similar leadership role to own the strategy, coordinate execution, and ensure AI is a board-level priority. ## **7. What Should the AI Strategy Cover? ** A complete AI strategy document should serve as both **vision** and **operating plan**. It should include: **Executive Summary** - Vision and urgency - Competitive threats - Strategic outcomes **Strategic Pillars** - Business model transformation - AI in decision-making - Workforce augmentation - Responsible AI and trust **Use Case Portfolio** - Heatmap of opportunity vs feasibility - Prioritized roadmap (12–36 months) - Business KPIs per use case **Data & Infrastructure** - Data liquidity and interoperability - Modern data stack (cloud, lakehouse, APIs) - MLOps, prompt engineering, observability **Technology Stack** - Model architecture (LLMs, fine-tuning, open vs proprietary) - Tools and platforms (internal, SaaS, open source) - AI agents, copilots, plugins, orchestration layers **People & Talent** - AI literacy by role (basic → expert) - Learning paths, certifications, academies - Hiring plans: ML, data, prompt engineering, AI ops **Workforce Transformation** - Empower employees with AI copilots - Redesign workflows for AI-native work - Define new roles and eliminate low-value ones **Governance & Risk** - AI ethics principles and policy - Risk taxonomy (privacy, bias, drift, hallucination) - Human-in-the-loop controls - Vendor and model transparency **Operating Model** - AI Center of Excellence (CoE) - Decentralized federated ownership (business units) - RACI charts and escalation trees **KPIs & Value Realization** - Value by use case (cost reduction, revenue lift) - AI maturity index by function - Model ROI vs traditional solutions ## **8. How to Implement an AI Strategy?** Here’s a **practical playbook** tailored for **small and medium businesses** looking to get started without massive resources: **AI Strategy Execution Plan** - Write your AI vision in 1 page. - Appoint an AI lead, even if it’s a dual-role. - Identify 3–5 business pain points that are repetitive or slow. - Map use cases to business value: customer support, invoicing, lead generation, etc. **Build the Foundations** - Ensure your data is structured, digitized, and accessible. - Centralize files and documents into cloud storage. - Audit internal tools and APIs for AI integration potential. **Select Tools** - Choose a small set of high-impact AI tools (e.g., ChatGPT, Notion AI, Salesforce Einstein, Copilot for Office). - Test low-code platforms to build internal agents. **Educate Your People** - Launch basic AI literacy programs (free or paid). - Identify 2-3 AI champions in each department. - Create a shared internal prompt library. **Quick Wins & Pilots** - Use GPT to automate customer support responses. - Generate product descriptions or ads. - Build a chatbot for FAQs. - Use AI to analyze sales patterns or churn. **Review, Expand, Improve** - Review outcomes monthly. - Double down on wins. - Add complexity only after success with simplicity. ## **AI Strategy Is a Strategic Imperative, Not a Tech Choice** You are not competing against AI - you are competing against **companies using AI better than you**. Here are some leading patterns of today's AI companies: - **Augmented Board & CEO**: AI dashboards, risk scenarios - **AI Agents Across Org**: HR, IT, finance, legal, R&D - **Democratized Tools**: Prompt libraries, no-code builders - **Model Catalogs**: Internal LLM registry by use case - **RAG (Retrieval-Augmented Generation)** for search and knowledge AI is the **greatest business transformation tool** of our generation. But like electricity, it only powers what you plug into it. Waiting is not a neutral choice - it’s a decision to fall behind. The winners are not the companies with the biggest models or fanciest labs. The winners are: - Those that **move early and learn fast**. - Those that **teach every employee how to use AI tools daily**. - Those that **organize their work to maximize AI leverage**. - Those that **build AI into the DNA** of their workflows, products, and culture. Last, but not least, the common pitfals when it comes to implementing AI: - Strategy owned only by IT - Focus on tools instead of outcomes - No upskilling programs - Shadow AI with no governance - Ethics and safety as afterthought **If you're not building an AI-first company, you're building a legacy company.** ## **Read more: A Playbook for Crafting an AI Strategy – MIT Technology Review Insights (2025)** ![[MIT_A playbook for crafting an AI strategy 2025.pdf]] This report, developed by MIT Technology Review Insights and sponsored by Boomi, offers a comprehensive guide for organizations transitioning from AI experimentation to enterprise-wide implementation. Based on a global survey of 205 C-suite and senior data executives and enriched with expert interviews, the report outlines key trends, challenges, and strategies for embedding AI at scale across industries. The focus is on aligning AI initiatives with business goals, ensuring data readiness, and managing costs, risks, and regulatory compliance while fostering partnerships and sector-specific use cases. **Key Insights** - **AI Adoption Is Widespread but Not Yet Scaled**: While 95% of companies use AI and nearly all plan to, 76% have only applied it in one to three use cases. Moving from pilot projects to full-scale integration requires strategic infrastructure, robust data governance, and vendor ecosystems. - **Spending to Support AI Growth Is Set to Surge**: Although AI investment was flat or modest through 2023, 90% of firms plan to increase spending in 2024, especially on data readiness (platform modernization, cloud migration) and supporting transformations (strategy, culture, business models). - **Data Liquidity and Quality Are Critical**: Seamless access and contextualization of data are essential for AI success. However, data quality issues - particularly in large firms with legacy systems - remain a major bottleneck. - **Customized AI Use Cases Offer Greatest Value**: Firms are increasingly prioritizing industry-specific or business-unique applications over general-purpose solutions, which are easier to deploy but yield less strategic differentiation. - **Financial and Technical Costs Are High**: AI development and operations incur significant infrastructure, energy, and talent costs. Mid-sized firms face particular constraints, often stuck between the affordability of large enterprises and the agility of startups. - **ROI Measurement Is Evolving**: Beyond cost savings, companies are focusing on AI’s potential for revenue growth and employee productivity. “Hard ROI” (e.g., efficiency gains) and “soft ROI” (e.g., experience improvements) are both important. - **Risk Management Slows Adoption**: Governance, security, and privacy are the top inhibitors of speed in AI deployment, especially for large enterprises. Risks include AI hallucinations, data privacy breaches, cyberattacks, and regulatory penalties. - **Regulation and Compliance Are Rising Challenges**: Global legislation is increasing, with the EU’s AI Act and U.S. executive actions leading the way. Firms must adopt AI assurance practices such as algorithm audits and emphasize explainability. **Actionable Takeaways** - **Invest in Data Foundations**: - Prioritize data quality, infrastructure upgrades, and cloud migration. - Ensure data lineage, liquidity, and metadata strategies are in place. - **Adopt Business-Centric AI Applications**: - Focus on tailored use cases that solve specific problems or create competitive advantages. - Avoid overinvestment in general-purpose models with limited differentiation. - **Manage Costs Strategically**: - Plan for recurring infrastructure expenses and energy consumption. - Consider partnerships to reduce model development costs and maximize efficiency. - **Prepare for Regulation**: - Stay informed on global AI laws and build internal compliance capabilities. - Implement human oversight and explainability protocols in all AI systems. - **Balance Speed with Risk**: - Prioritize safety over first-mover advantage; 98% of firms support this. - Use AI itself to bolster cybersecurity defenses. - **Build Cross-Functional AI Literacy**: - Encourage collaboration across technical and business units. - Focus on cultural transformation to align AI with broader corporate strategy. **Notable Quotes** - _“No job, no function will remain untouched by AI.”_ — SP Singh, Infosys - _“If you don’t have the necessary expertise and resources to make a significant investment, it’s better to fine-tune and optimize off-the-shelf models.”_ — Kevin Collins, Charli AI - _“AI is not cheap. A lot of our customers are shocked when they realize how much infrastructure they have to have to get the performance they need.”_ — Kevin Collins, Charli AI - _“The mindset is shifting to using AI as an enabler for revenue growth, not just cost savings.”_ — Amy Machado, IDC - _“Organizations with high data liquidity—the ability to get the right data at the right time and place—will be most successful with AI.”_ — Matt McLarty, Boomi - _“The biggest factor holding back AI implementation is people not knowing where to start.”_ — Matt McLarty, Boomi