Prioritizing Gen AI Projects for Engineering Teams

I still remember my first big project. Beth and I had just moved to Chicago, and I’d started at GE Capital Consulting. I dove in headfirst—excited, motivated, and maybe a little naive. As the project wrapped up, it felt like it would never end. That experience pushed me to level up my skills. One of the first books I picked up was The Software Project Survival Guide (https://lnkd.in/et9iUGCt). It taught me a critical lesson: process matters. In traditional software projects, skipping process discipline early on leads to chaos later—endless meetings, defect-fixing marathons, missed deadlines, and sometimes total collapse. AI projects are no different. The waste is just harder to see. Without defining value and ROI upfront, teams chase technical wins—model accuracy, novel algorithms, flashy demos—that don’t translate into business impact. By the time leadership asks, “What value has this delivered?”, the investment is already sunk. Here’s how to avoid “AI thrashing”: 🔹 Front-load clarity: Define the business problem and success metrics before training your first model. 🔹 Score use cases: Evaluate AI opportunities based on impact, feasibility, adoption likelihood, and time-to-value. 🔹 Prioritize ruthlessly: Don’t spread resources across dozens of pilots. Focus on the few that can truly move the needle. 🔹 Measure ROI differently: It’s not about model accuracy—it’s about reducing service calls, accelerating collections, boosting upsell, or mitigating risk. The cost of disciplined value assessment is minimal compared to the cost of a failed AI project. Just like in software, the real overhead isn’t process—it’s cancellation. If you want your AI program to scale, assign value and ROI as deliberately as you assign data scientists and GPUs. That’s how you keep projects moving forward—without thrashing.

Introducing RICE-A, a prioritization framework for AI products. Traditional frameworks like RICE excel at helping teams evaluate feature ideas based on Reach, Impact, Confidence, and Effort. However, when it comes to AI products, the unique challenges of data collection, model training, and deployment require a nuanced approach. I see Product Managers sometimes including these challenges within ‘Effort’ but I don’t believe that this is the right approach... That’s why I am proposing RICE-A, an enhanced prioritization framework tailored specifically for AI-driven features. RICE-A will help product managers make data-informed decisions, balancing innovation with execution feasibility. ✨ What Is RICE-A? RICE-A builds on the RICE framework by introducing a fifth factor: AI Complexity (A). This additional layer captures the unique effort required by the AI lifecycle - to design, train, and deploy AI models, ensuring AI-specific challenges are weighted appropriately. ✨ The RICE-A Formula (look at the image) Each component evaluates a specific aspect of the feature's feasibility and potential: →Reach: What percentage of your target audience will benefit from this feature? →Impact: How significant is the impact for the target user? →Confidence: How certain are you about the accuracy of your assumptions and ability to deliver? →Effort: What is the engineering effort needed to implement the feature? →(the new part) AI Complexity (A): What are the data and computational demands for collecting the right dataset, training a robust model, and ensuring scalability? ✨ Why Add "AI Complexity"? AI features present unique challenges that aren't captured by traditional effort metrics. For example... -Data Challenges: Collecting, cleaning, and labeling high-quality datasets is often a monumental task. -Training Costs: Model training requires substantial computational resources, hyperparameter tuning, and infrastructure setup. -Deployment & Monitoring: AI systems demand post-deployment monitoring, retraining, and bias detection to ensure sustained performance. I'm expanding this more on the first link in the comments, I also included 11 AI Product Management jobs I would apply to if I were looking for anyone interested. <><><><><><><><><><><><><><><><> Follow Marily Nika, Ph.D for the #1 AI Product Management certification. Best way to support my work is if you like & share 🔄 my content.

“We’re only focused on AI tooling right now.” I keep hearing this from engineering leaders. But when I ask: - What problems are you trying to solve? - How will you measure impact? - How will you know which use cases to scale? Crickets. The race to adopt AI is causing companies to skip critical steps—burning budget with nothing to show for it. Meanwhile, the smartest teams are taking a different path: ✅ Establish a baseline: What’s slowing devs down? ✅ Identify where GenAI can help in the SDLC ✅ Test + measure impact before full rollout ✅ Track outcomes post-implementation and iterate Buying AI tools ≠ becoming an AI-driven org. AI is not the strategy. Solving real problems is. The companies that win will be the ones that apply AI intentionally—with measurement, iteration, and purpose. #dx #developerproductivity #genAI #devprod

ICYMI.. Hard truths by McKinsey & Company on scaling #Gen #AI Requires a strategic approach that focuses on integration, cost management, and creating value-driven teams. By addressing these challenges, companies can move past the pilot phase and achieve significant business value from Gen AI. - Eliminate the Noise, Focus on the Signal: Cut down on experiments and focus on solving important business problems. Most companies spread resources too thinly across multiple gen AI initiatives - Integration Over Individual Components: The challenge lies in orchestrating the interactions and integrations at scale, not in the individual pieces of gen AI solutions - Manage Costs: Models account for only about 15% of the overall cost. Change management, run costs, and driving down model costs are crucial. - Tame the Proliferation of Tools and Tech: Narrow down to capabilities that best serve the business and take advantage of cloud services while preserving flexibility - Create Value-Driven Teams: Teams need a broad cross-section of skills to build models and ensure they generate value safely and securely - Target the Right Data: Invest in managing the data that matters most for scaling gen AI applications - Reuse Code: Reusable code can increase development speed by 30 to 50% - Orchestration is Key: Effective end-to-end automation and an API gateway are crucial for managing the complex interactions required for gen AI capabilities - Observability Tools: These tools are necessary for monitoring gen AI applications in real-time and making adjustments as needed - Cost Optimization: Tools and capabilities like preloading embeddings can reduce costs significantly - ROI Focus: Investments in gen AI should be tied to return on investment (ROI), with different use cases requiring different levels of investment Source: https://lnkd.in/ezYN5chb