Steps to Create an AI Roadmap

Every company has an "AI strategy" now. But 90% suck. Here's step-by-step how to build one that doesn't: AI strategy is different from regular product strategy. This is the battle-tested framework Miqdad Jaffer & I use. We've used at Shopify, OpenAI, & Apollo: — 1. SET CLEAR OBJECTIVES At Shopify, Miqdad killed dozens of technically cool AI projects... And doubled down on inventory management. Why? That’s where merchants were losing money. No business impact = no AI initiative. Simple as that. Look for pain points humans consistently fumble, impact their growth, and first solve that with AI. — 2. UNDERSTAND YOUR AI USERS Users don’t adopt AI the same way they adopt a button or a new flow. They don’t JUST use it. They test it, build trust with it, and only then rely on it. So, build something that empowers them throughout their journey with your product. — 3. IDENTIFY YOUR AI SUPERPOWERS Not everyone has access to the same behavior signals... User context, or proprietary data that make outputs smarter over time. That’s your moat, the data nobody else can use. Not the fancy models. Not the MCPs. Not even revolutionary AI agents. Your goal is to build around your moat, not your product or models. — 4. BUILD YOUR AI CAPABILITY STACK In AI, speed beats pride. Think of it this way: A team spends 9 months building their own LLM. Meanwhile, a smaller competitor ships with OpenAI and captures the market. So, did you make the smartest move by trying to build everything yourself? Great PMs lead when to build and when just to leverage. — 5. VISUALIZE YOUR AI VISION In 2016, Airbnb used Pixar-level storyboards to communicate product moments. Today? Tools like Bolt, v0, and Replit make it possible in hours for a fraction of a cost. Create visiontypes that show: → Before vs. after (and make the “after” impossible to do manually) → Progressive learning and smarter experiences → Human + AI collaboration in real workflows — 6. DEFINE YOUR AI PILLARS At this stage, you’re building a portfolio of some safe and some big bets: → Quick wins (1–3 months) → Strategic differentiators (3–12 months) → Exploratory options (R&D, future leverage) And label each one clearly: Offensive = creates new value Defensive = protects from disruption Foundational = unlocks future bets — 7. QUANTIFY AI IMPACT If your AI strategy assumes flat, linear returns - you’re modeling it wrong. AI compounds with usage. Every interaction trains the system, feeds the flywheel, and lifts the entire product. Even Sam Altman shared that just adding a “thank you” feature increased OpenAI’s operational cost by millions.... — 8. ESTABLISH ETHICAL GUARDRAILS One biased result. One hallucination. One misuse. And the entire product feels unsafe. Set guardrails around every part of the process to make it safe... From all the hallucinations that disrupt your trust! — Making a great strategy is still hard. But these steps can help.

AI is coming for your team's jobs. 𝘞𝘳𝘰𝘯𝘨! That's the narrative of fear and redundancy. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆? AI is a massive opportunity to multiply your existing talent, not just replace it. But many companies are getting it wrong. They're either: ► Freezing all spending, scared of making the wrong move. ► Looking at AI as a pure cost-cutting tool (i.e., who can we fire?). Both are paths to slow-growth and eventual failure. There's a 3rd option: 𝗧𝗵𝗲 𝗛𝘂𝗺𝗮𝗻-𝗔𝗜 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝘂𝗻𝘄𝗮𝘆. It's a 90-day strategic plan to turn your current employees into an AI-augmented, high-leverage workforce. Instead of firing your Instructional Designers, you turn them into Human-Machine Performance Analysts. How? ► 𝐒𝐭𝐞𝐩 𝟏: Audit Tasks, Not People. Map every task your team does. Then, use a framework like the Human-AI Task Scale to classify them. What's fully manual? What can AI support? What can be fully automated? ► 𝐒𝐭𝐞𝐩 𝟐: Find the Skill Gaps. You know what can be automated. Now, what adjacent skills does your team need to manage that new reality? This isn't a mystery. The path is from creator to orchestrator. ► 𝐒𝐭𝐞𝐩 𝟑: Execute a 90-Day Runway. Week 1-2: AI Foundations (Prompting, etc.) Week 3-4: Task Automation (Automate one core workflow) Week 5-6: Skill Pivot (Start an adjacent-skill project like data analysis) ...and so on. The result? 𝘠𝘰𝘶'𝘳𝘦 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 "𝘴𝘢𝘷𝘪𝘯𝘨 𝘮𝘰𝘯𝘦𝘺." You're building a team with a 4x output multiple. You're getting a 3x-10x ROI on your investment. (Links to the research in the comments.) You're keeping the institutional knowledge you'd lose from layoffs. 𝗦𝘁𝗼𝗽 thinking about replacing people. 𝘚𝘵𝘢𝘳𝘵 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘢𝘶𝘨𝘮𝘦𝘯𝘵𝘪𝘯𝘨 𝘵𝘩𝘦𝘮. The companies that do this will win the next decade. The others will become a footnote. Need a visual? I mocked up an application (still in progress) illustrating the steps and the ROI. You can find the link in the comments. 👇🏻

I love it when AI works out, because when it does - it’s magic. Here is my personal 5-step readiness checklist so you succeed with it. 𝗦𝘁𝗲𝗽 𝟭: 𝗔𝘂𝗱𝗶𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 Before any AI conversation, ask: "Is our data clean, accessible, and flowing properly?" - Map your current data sources and quality. - Identify gaps between systems. - Ensure data governance policies are in place 𝗦𝘁𝗲𝗽 𝟮: 𝗔𝘀𝘀𝗲𝘀𝘀 𝗬𝗼𝘂𝗿 𝗧𝗲𝗮𝗺'𝘀 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗖𝗼𝗺𝗳𝗼𝗿𝘁 𝗭𝗼𝗻𝗲 Meet your people where they are, not where you want them to be. - Evaluate current tool proficiency (Are they Excel natives? Advanced analytics users?) - Identify the skills gap between current state and AI requirements. - Plan bridge training programs. 𝗦𝘁𝗲𝗽 𝟯: 𝗕𝘂𝗶𝗹𝗱 𝗔𝗜 𝗟𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝗔𝗰𝗿𝗼𝘀𝘀 𝗬𝗼𝘂𝗿 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Create understanding before implementation. - Run AI awareness sessions for leadership and end-users. - Define AI terminology and use cases relevant to your industry. - Address concerns and misconceptions upfront. 𝗦𝘁𝗲𝗽 𝟰: 𝗦𝘁𝗮𝗿𝘁 𝗦𝗺𝗮𝗹𝗹 𝘄𝗶𝘁𝗵 𝗣𝗶𝗹𝗼𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 Test the waters before diving in. - Choose one high-impact, low-risk use case. - Select a team that's excited about innovation. - Measure adoption rates, not just performance metrics 𝗦𝘁𝗲𝗽 𝟱: 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗮𝗻𝗱 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽𝘀 Define what winning looks like. - Set clear ROI expectations. - Create channels for user feedback and iteration. - Plan for scaling successful pilots Organizations that complete this readiness checklist see 3x higher adoption rates and significantly better long-term ROI. AI implementation isn't a sprint, it's a strategic marathon. Where is your organization in this readiness journey? What step are you focusing on right now?

Ever wondered how a real AI project actually works ? A successful AI project goes through 7 structured steps, each led by different experts. From defining the business problem to continuous improvement after deployment, every role plays a part in making AI work in the real world. Here’s a cheat sheet that breaks down the end-to-end AI project lifecycle with clear steps, leaders, and responsibilities. ✅ AI Project Steps Covered: 🔹Step 1: Defining the Problem → Led by business analysts & product managers. Identify real problems, set objectives, align business & tech needs. 🔹Step 2: Preparing the Data → Led by data engineers & analysts. Collect raw data, clean, standardize, and split into training, validation, and test sets. 🔹Step 3: Building the Model → Led by ML engineers & data scientists. Choose algorithms, engineer features, train models, tune hyperparameters, and compare best fits. 🔹Step 4: Testing & Evaluation → Led by data scientists & ML researchers. Validate with unseen data, use metrics (accuracy, recall, AUC), stress-test, and decide if model is production-ready. 🔹Step 5: Deployment → Led by MLOps engineers & software developers. Package models into APIs, use Docker/Kubernetes, integrate with apps, enable predictions, and ensure reliability before going live. 🔹Step 6: Validation & Monitoring → Led by validators, ethicists, QA teams. Monitor accuracy, detect drift, check bias, log failures, and trigger alerts if performance drops. 🔹Step 7: Continuous Improvement → Led by data scientists, PMs, domain experts. Gather feedback, add new data sources, retrain, optimize pipelines, and push regular updates. Save this guide and share with others, and hopefully this will help to understand how AI projects work, step by step, role by role! #AI

🚨 95% of GenAI pilots are failing, but not for the reasons you think. Stop blaming the AI. Start fixing the rollout. Too often, we launch AI like it’s plug-and-play. But success isn’t about the tool . It’s about the system you build around it. Here’s your AI Launch Readiness Checklist 👇 ☐ 1. Start with Strategy ↳ AI without a business outcome is just an expensive science project. ↳ Define the “why” before you buy. ☐ 2. Build Human Readiness ↳ Employees don’t fear AI they fear being left behind. ↳ Upskill, reskill, and explain the why at every step. ☐ 3. Resist the Vendor Hype ↳ Leaders often chase market buzz instead of checking internal readiness. ↳ Buying tools before defining use cases = expensive underuse. ☐ 4. Fix the Foundations ↳ Bad data in = bad insights out. ↳ Data quality, governance, and access matter more than models. ☐ 5. Rethink Workflows, Not Just Tools ↳ AI must slot into the way people already work. ↳ Otherwise, adoption stalls. ☐ 6. Pilot with Purpose ↳ “Test everything” = wasted time. ↳ Pick 1–2 high-impact use cases and scale only what works. ☐ 7. Establish AI Guardrails ↳ Clear policies on risk, compliance, & ethics build trust. ↳ No guardrails = no scale. ☐ 8. Lead from the Top ↳ Culture follows leadership. ↳ If execs treat AI like a gadget, employees will too. ☐ 9. Measure What Matters ↳ Set KPIs that connect to business impact, not vanity metrics. ↳ If you can’t prove ROI, you can’t scale. ☐ 10. Keep Iterating ↳ AI isn’t a “set it and forget it” project. ↳ Continuous feedback and tuning separate pilots from success stories. The lesson? AI doesn’t fail because it’s weak tech. It fails because we built weak systems around it. ♻️ Repost if you’re investing in people, not just tech. Follow Janet Perez for Real Talk on AI + Future of Work --- Source: MIT report via Fortune

Testing and piloting AI for sales and marketing can be frustrating. That’s why Jomar Ebalida and I came up with the practical AI roadmap for marketing and GTM ops pros. This roadmap helps you figure out where to start, what to focus on, and how to scale AI initiatives in a way that’s grounded in operational reality. It’s structured in 3 phases: PREP: Evaluate your organization’s current state across data, tools, team skills, and funnel performance. PILOT: Select and test AI use cases based on your actual readiness data. (Diagram shows samples) Avoid guessing by letting the assessment drive decisions. ACTIVATE: Scale the pilots that show promise and embed them into core processes. Here are select projects worth walking through: 🔹 AI Readiness Assessment This project includes evaluating data quality, the state of your CRM, the maturity of your tech stack, and your team’s readiness to work with AI tools. It also includes a bowtie funnel analysis to help identify where your customer journey is breaking down. The outcome is a clear picture of which AI use cases are both valuable and feasible for your team to pursue. 🔹 AI SDR Agent: Outreach and Prospecting This agent is designed to support outbound sales by identifying high-potential accounts, generating personalized outreach messages, and helping SDRs scale without sacrificing relevance. It can help teams boost pipeline without overloading headcount. 🔹 AI QA and Compliance: Brand, Legal, Regulatory This workstream ensures that every piece of AI-generated content or decision logic meets the necessary internal standards. It supports brand consistency, regulatory requirements, and risk mitigation. This process should run in parallel with pilots and activations to ensure safe implementation. 🔹 AI Agents for Ops: QA Checks, Routing, and Campaign Setup This includes AI agents built to handle operational tasks such as verifying UTM links, auto-routing requests, or creating campaign templates. These agents are ideal for improving workflow speed while reducing manual errors and team bottlenecks. At the foundation of all of this is change management. Each phase of the roadmap includes a focus on enablement, training, adoption, metrics, and governance. Tools don’t generate value unless people are set up to use them properly. Which parts resonate with you? What would you change or add? PS: To learn more & access templates, subscribe for free to The Marketing Operations Leader Newsletter on Substack https://lnkd.in/g_3YC7BZ and to Jomar's newsletter at bowtiefunnel(dot)com. #marketing #martech #marketingoperations #ai #gtm

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

🎯 The CIO's Organizational Playbook for the AI Era... I recently spoke with a CIO friend about how IT teams are changing. Our discussion made me think about what sets apart IT teams that succeed with AI from those that don’t. I looked over my research and reviewed my interviews with other leaders. This information is too valuable not to share: ✓ Build AI-Ready Capabilities 🟢 Establish continuous learning programs focused on practical AI applications 🟢 Implement cross-functional training to bridge technical/business gaps 🟢 Prioritize hands-on AI workshops over theoretical certifications ✓ Master AI Risk Management 🟢 Develop processes to identify and mitigate technical failures early 🟢 Create a strategic AI roadmap with clear risk contingency protocols 🟢 Align all AI initiatives with broader business objectives ✓ Drive Stakeholder Engagement 🟢 Build a cross-functional AI coalition (executives, HR, business units) 🟢 Communicate AI initiatives with transparency to reduce resistance 🟢 Document tangible benefits to secure continued buy-in ✓ Implement with Agility 🟢 Replace waterfall approaches with iterative AI development 🟢 Focus on quick prototyping and real-world testing 🟢 Ensure infrastructure scalability supports AI growth ✓ Lead with AI Ethics 🟢 Train teams on bias identification and mitigation techniques 🟢 Establish clear governance frameworks with accountability 🟢 Make responsible AI deployment non-negotiable ✓ Transform Your Talent Strategy 🟢 Enhance IT roles to integrate AI responsibilities 🟢 Create peer mentoring programs pairing AI experts with domain specialists 🟢 Cultivate an AI-positive culture through early wins ✓ Measure What Matters 🟢 Set specific AI KPIs that link directly to business outcomes 🟢 Implement continuous feedback loops for ongoing refinement 🟢 Track both technical metrics and organizational adoption rates The organizations mastering these elements aren't just surviving the AI transition—they're thriving because of it. #digitaltransformation #changemanagement #leadership #CIO

#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 ★

Did you miss it? If you're overwhelmed by all the AI news and innovations in the market, this episode of The CG Hour can help. 1️⃣ Define Strategy: Clearly outline your AI strategy, including use cases and business goals, to guide your implementation efforts. 2️⃣ Establish Governance: Set up a robust governance framework to ensure responsible AI adoption, addressing security, ethics, and regulatory compliance. 3️⃣ Focus on Data: Pay close attention to your data quality, diversity, and bias mitigation strategies to ensure reliable AI outcomes. 4️⃣ Prioritize Training and Education: Invest in comprehensive training programs to educate your organization about AI technologies, potential biases, and responsible usage. 5️⃣ Start Small, Scale Gradually: Begin with pilot projects or proofs of concept to test AI applications before scaling them across your organization, allowing for iterative improvements and adjustments along the way. Some great quotes from our guest panelists: 🔹 Matt Pollard, Senior VP at AI Governance, Data Privacy & Security at CG Infinity: "I believe that AI will absolutely change the world that we're in today. Jamie Dimon, the CEO of JP Morgan Chase actually mentioned it is almost as fundamental as electricity. That's the level that we're talking about from AI disruption standpoint. Pretty exciting times." 🔹 Bhavesh Advani, CISO at City of Tucson & Former VP of Cybersecurity at Fidelity Investments: "There's an element of self-governance... training is number one on the list. Product based training based on job functions and use case based training. I do foresee new role of Chief AI Officer coming up." 🔹 Dan Clarke, President of Truyo | An IntraEdge Company: "You need some type of a governance board. You have to have a set of principles that they want to employ. You need tools around this too because it's going to be really challenging to administer this in an organization. You want to go fast with AI but you need guardrails around it. Consider the bias for discrimination and privacy. Start a bulletin board for people to share use cases." Watch the replay of our discussion here: https://lnkd.in/eG9yqkxB Share your thoughts of where businesses should start when it comes to AI governance in the comments below. ⬇️⬇️⬇️ #technology #ai #artificialintelligence #aigovernance