How to Define Your Organization's AI Ambition

In January, everyone signs up for the gym, but you're not going to run a marathon in two or three months. The same applies to AI adoption. I've been watching enterprises rush into AI transformations, desperate not to be left behind. Board members demanding AI initiatives, executives asking for strategies, everyone scrambling to deploy the shiniest new capabilities. But here's the uncomfortable truth I've learned from 13+ years deploying AI at scale: Without organizational maturity, AI strategy isn’t strategy — it’s sophisticated guesswork. Before I recommend a single AI initiative, I assess five critical dimensions: 1. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Can your systems handle AI workloads? Or are you struggling with basic data connectivity? 2. 𝗗𝗮𝘁𝗮 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: Is your data accessible? Or scattered across 76 different source systems? 3. 𝗧𝗮𝗹𝗲𝗻𝘁 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Do you have the right people with capacity to focus? Or are your best people already spread across 14 other strategic priorities? 4. 𝗥𝗶𝘀𝗸 𝘁𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲: Is your culture ready to experiment? Or is it still “measure three times, cut once”? 5. 𝗙𝘂𝗻𝗱𝗶𝗻𝗴 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Are you willing to invest not just in tools, but in the foundational capabilities needed for success? This maturity assessment directly informs which of five AI strategies you can realistically execute: - Efficiency-based - Effectiveness-based - Productivity-based - Growth-based - Expert-based Here's my approach that's worked across 39+ production deployments: Think big, start small, scale fast. Or more simply: 𝗖𝗿𝗮𝘄𝗹. 𝗪𝗮𝗹𝗸. 𝗥𝘂𝗻. The companies stuck in POC purgatory? They sprinted before they could stand. So remember: AI is a muscle that has to be developed. You don't go from couch to marathon in a month, and you don't go from legacy systems to enterprise-wide AI transformation overnight. What's your organization's AI fitness level? Are you crawling, walking, or ready to run?

Most companies say they want to “get better at AI.” But what does that actually mean? For anyone trying to move beyond vague ambitions to real, measurable progress— this AI Maturity Model from Hustle Badger and Susannah Belcher is worth bookmarking. It’s more than a framework. It’s a roadmap to becoming an AI-ready organization across strategy, culture, tools, and trust. Here’s how it works: Step 1️⃣ : Diagnose your starting point Rate your organization across 6 categories—like data readiness, governance, and leadership mindset—from Level 1 (Limited) to Level 5 (Best-in-class). Step 2️⃣: Visualize your maturity scorecard Get a snapshot of strengths, gaps, and hidden risk factors (like weak AI governance or untrained teams). Step 3️⃣: Align on what matters This isn’t about maxing every score. It’s about identifying which dimensions actually move the needle for your business and customers. Step 4️⃣: Build your AI development canvas Assign clear owners, define target maturity levels, and create specific actions and timelines to get there. Step 5️⃣: Repeat and evolve Because AI isn’t static—your maturity model shouldn’t be either. 🧠 What I loved most:  This framework creates shared language and accountability around AI. It’s not just a tech team thing—it touches leadership, hiring, operations, and product delivery. Whether you’re early in the journey or already shipping AI-powered products, this model offers a smart way to: ▸ Run internal audits ▸ Create realistic roadmaps ▸ And scale AI capability without chaos 🔗 Worth a read if you're building AI into your org's future: https://lnkd.in/ejVSwmAW 👉 Curious—has your company done an AI maturity assessment yet? What category do you think most teams are underestimating? #AI #ProductBuiding #OrgMaturity

🚀 Where’s the Value in AI? 🚀 Despite all the buzz around artificial intelligence (AI), only 4% of companies are creating substantial value with it, according to new research by BCG. If you're wondering how to move beyond pilots and proofs of concept to drive real impact, this is the playbook you've been waiting for. Here’s what sets AI leaders apart: 🎯 Big Ambitions, Bigger Targets: Leaders aim for transformational outcomes—think billions in cost savings and revenue growth. 🤝 People & Processes First: It’s not just about the tech; leaders prioritize workforce enablement and reimagining processes. 📈 Focused Investments: Instead of spreading resources thin, leaders invest strategically in high-priority opportunities. ⚡ GenAI Ready: From content creation to qualitative reasoning, leaders are leveraging generative AI to innovate faster. 📊 The Results? AI leaders are delivering: 45% more cost reduction than others. 60% higher revenue growth. A 2x higher ROI on AI initiatives. 🏆 How You Can Join the 4%? BCG outlines a 7-step playbook to shift your AI trajectory: 1 - Set a bold strategic commitment from the top. 2 - Maximize the potential value of AI with initiatives that include streamlining everyday business processes, transforming entire business functions, and developing new offerings. 3 - Implement one to three high-value, easy-to-implement initiatives to fund the journey. 4 - Ensure that the minimal viable infrastructure required for these initiatives exists. 5 - Perform an AI maturity assessment to baseline current critical capability gaps versus peers. 6 - Ensure that implementation governance focuses on people and processes over technology and algorithms. 7 - Set up guardrails to deploy AI responsibly. Source: "Where’s the Value in AI?", BCG, October 2024 👉 Let’s discuss: What’s your biggest challenge in scaling AI for impact? #AI #generativeAI #bcg #marcelointech #artificialintelligence

In this interview, Christoph Schweizer's POV on the impact of #AI in the workforce is both crisp and well-informed. This took me down the rabbit hole with other Boston Consulting Group (BCG) research and I ran across "From Potential to Profit with GenAI," which delivers a substantial amount of deep insight in a bite-size. Both are excellent primers on #artificialintelligence in business. As is commonly said: Mission -> Vision -> Goals Or, as I like to say: Strategy -> Tactics -> Technology You can't tap the real potential of things like #generativeai if you focus on which tech or tactic. You've got to start with the underlying mission of your business, define your vision for your mission in the world of #genai, identify and rank order your goals, and then deploy the tactics/tech in the way that will yield the most impact for your business. Just saying, "#genai will get us efficiency in 2024" is not a vision for operationalizing artificial intelligence. Spec'ing AI for your business is like a S.M.A.R.T. goal for your workforce. The anatomy of your AI strategy comes down to: 1. What are you optimizing for 2. What opportunities are realistic and actionable 3. How are you going to enable and empower your workforce 4. How are you going to hold your team accountable for measurable outcomes 5. How can you accelerate adoption with maximum efficiency and minimal rework as you evolve Deploying AI at scale can have powerful implications, but like many transformative business initiatives, it's not about "should you do it" It's about where to start, why, and when. Thankfully, there are frameworks for this, and I am pumped to be at the ground level of deploying generative AI for a better future. One where we can get the margin gains, create value with the customer, and improve the human experience all at the same time. The BCG report: https://lnkd.in/e3b-gvKZ

I'm thrilled to share The Human Side of #AI: A Leader's Guide to Successful #AIAdoption - our first Prosci Catalyst Report (a 10-page, punchy "research derivative product" designed to delivery engaging and critical insights in a digestible and tasty package). This first Catalyst Report is derived from findings in our recent Enterprise AI Adoption research highlighting four takeaways: 1. Leadership and Cultural Foundations: The Heart of Success 📊 Research Insight: Organizations with strong AI leadership support score +1.65 on a -2 to +2 scale, compared to -1.50 in struggling organizations. 🔑 What this means: AI adoption isn’t just about deploying tools - it’s about leaders modeling adoption and fostering an AI-ready culture. Without visible, engaged leadership, AI remains a side project rather than a strategic transformation. ✅ Operationalize it: Equip leaders with the skills and language to champion AI, define a compelling AI vision, and (perhaps most importantly) use the tools themselves. 2. Balanced Strategic Control: Ambitious Yet Managed 📊 Research Insight: Successful AI implementations balance strong centralized control (+0.82) with bold transformation goals (+1.01). Struggling organizations hesitate, favoring small, incremental steps (-1.86). 🔑 What this means: Overly cautious AI strategies create friction. Organizations that set clear governance structures while embracing big-picture transformation make the most progress. ✅ Operationalize it: Define who owns AI strategy, create a decision framework for AI investments, and ensure AI ambitions extend beyond short-term efficiency gains. 3. External Alignment: Market-Aware Implementation 📊 Research Insight: AI leaders stay ahead by aligning their strategy with industry influence (+1.29) and competitive awareness (+1.11). Struggling organizations report little external orientation (-0.14, -1.17). 🔑 What this means: AI success isn’t just about internal readiness - it’s about understanding the forces shaping AI adoption across industries, competitors, and regulations. ✅ Operationalize it: Build an AI sensing function - regularly track market trends, competitive moves, and regulatory shifts to guide AI strategy. 4. The Critical Role of Change Management 📊 Research Insight: While only 17% of executives cite technical challenges, 56% say workforce capability and organizational change are the biggest barriers to AI adoption. 🔑 What this means: AI adoption depends on human readiness. Without structured change support, even the most powerful AI tools will sit unused. ✅ Operationalize it: Invest in AI change enablement - train teams in AI fluency, upskill employees, and integrate AI adoption into enterprise change frameworks. Big shout out to Scott Anderson, PhD from research and Jasmine Nicol from marketing for the collaboration on the catalyst report product. Enjoy! Share! And reach out to Prosci for AI Adoption research, support, and capability.