The Importance of an AI Roadmap

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

AI integration can be daunting, but the path becomes a lot clearer with a roadmap. Here's a sneak peek at what you'll find in my comprehensive AI Integration Checklist: 1️⃣ 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 ↳ Define your AI goals to tackle key organizational challenges. 2️⃣ 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 ↳ Assess your tech infrastructure and data readiness. 3️⃣ 𝗔𝗜 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 ↳ Decide between nurturing in-house talent or partnering externally. 4️⃣ 𝗧𝗲𝗰𝗵 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 ↳ Choose AI tools that align with your objectives, starting with pilot projects. 5️⃣ 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝗮𝘁��𝗴𝘆 ↳ Prioritize robust data management for AI success. 6️⃣ 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 ↳ Form a cross-functional team for holistic integration. 7️⃣ 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 𝗼𝗳 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 ↳ Cultivate an environment that embraces AI and continuous learning. 8️⃣ 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 ↳ Lead with responsibility in AI application. 9️⃣ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 ↳ Measure, iterate, and scale your AI initiatives. Check out the complete checklist and take a significant step towards transforming your organization with AI. #AI #Innovation #AIIntegration #DigitalTransformation

Everyone is investing in AI. Very few are moving beyond pilots and prototypes. According to Gartner’s AI Roadmap, long-term success depends on alignment across strategy, governance, data, engineering, and people—not just models and tools. Here’s where most organizations stall: ❌ Rushed in without a defined an AI vision ✅ Instead: Measure AI strategy success and refine it based on outcomes ❌ Launched pilots ✅ Instead: Prioritize an AI product portfolio and implement best practices ❌ Appointed an AI leader ✅ Instead: Build cross-functional teams and establish external AI partnerships ❌ Launched AI awareness campaigns ✅ Instead: Monitore workforce readiness and build AI literacy into the culture ❌ Purchased AI tools ✅ Instead: Build a strong data foundation and implement observability Gartner reports: 80% of AI projects stall at proof of concept Less than 15% make it to production with measurable ROI The difference between experimentation and enterprise value? Execution, consistency, and cross-functional readiness. Ask yourself: 1. Are you measuring what matters? 2. Is your AI strategy embedded—or isolated? 3. Is your foundation strong enough to scale? Because deploying a model is easy. Building AI that actually works across the business is the challenge. #artificialintelligence #ai #technology

#AI – Choose your path: innovate, accelerate, or follow fast. Whether you're a small business or a large enterprise, generative AI revolutionizes industries. Here's your strategic roadmap to fully leverage generative AI within your organization: >>> 1. Establish a Center of Excellence for AI (CoE): Form an AI CoE as the central command for all AI-related initiatives. This hub will facilitate the smooth deployment of AI and encourage cross-departmental collaboration. By centralizing expertise and resources, the CoE ensures efficient project execution and maximizes the benefits derived from AI investments. >>> 2. Embrace Change and Nurture Continuous Learning: • Restructure for Agility:  Modify team structures to include AI-focused roles, preparing your organization to capitalize on AI advancements. • Cultivate a Culture of Innovation:  Promote continuous learning and improvement, drawing lessons from past experiences to refine future AI projects. • Invest in AI Capabilities:  Dedicate resources to AI technology and training, equipping your team to innovate and implement effectively. >>> 3. Unlock Transformational Benefits:  • Market Leadership:  Harness AI to identify new market opportunities and develop informed strategies, establishing your company as an industry leader.  • Empowered Workforce:  Continually enhance your team's AI skills, ensuring your workforce is resilient and future-ready.  • Strategic Insights:  Utilize AI for data-driven decision-making, guiding your company towards its objectives. Together, let's leverage the power of generative AI to propel our businesses forward! ★ Redefining tomorrow, today with AI ★

I’ve been thinking a lot about how we actually deliver value with AI not just experiment, but go from idea to impact. I came across this framwork from open AI outlines a simple but solid approach to making that happen in 3 months. While it’s from OpenAI, the structure honestly applies to most AI projects: 🗺️ 5 clear phases: 1) Ideation & Scoping – Align on use cases, success metrics, and architecture 2) Development – Get hands-on with prototyping, workshops, and feedback loops 3) Testing & Evaluation – Run A/B tests, talk to users, and fine-tune guardrails 4) Production – Plan the rollout, secure it, and scale responsibly 5) Maintenance – Keep improving accuracy, cost, and overall performance It’s a good reminder that in AI projects, the process is just as important as the technology. With how fast things move, aligning stakeholders and delivering value quickly is critical. Plus, with tools evolving so rapidly, yesterday’s features or use cases can become obsolete in no time—making a solid execution plan even more essential. 💡 Key Enablers: - Early visibility into evolving AI capabilities - Embedded collaboration across technical and business teams - Clear success metrics from day one ( VERY IMPORTANT and it can't be a marketing or cool gimmick ) #data #ai #openai