How to Apply Agile Innovation in Data and AI

Some of the best AI breakthroughs we’ve seen came from small, focused teams working hands-on, with structured inputs and the right prompting. Here’s how we help clients unlock AI value in days, not months: 1. Start with a small, cross-functional team (4–8 people) 1–2 subject matter experts (e.g., supply chain, claims, marketing ops) 1–2 technical leads (e.g., SWE, data scientist, architect) 1 facilitator to guide, capture, and translate ideas Optional: an AI strategist or business sponsor 2. Context before prompting - Capture SME and tech lead deep dives (recorded and transcribed) - Pull in recent internal reports, KPIs, dashboards, and documentation - Enrich with external context using Deep Research tools: Use OpenAI’s Deep Research (ChatGPT Pro) to scan for relevant AI use cases, competitor moves, innovation trends, and regulatory updates. Summarize into structured bullets that can prime your AI. This is context engineering: assembling high-signal input before prompting. 3. Prompt strategically, not just creatively Prompts that work well in this format: - “Based on this context [paste or refer to doc], generate 100 AI use cases tailored to [company/industry/problem].” - “Score each idea by ROI, implementation time, required team size, and impact breadth.” - “Cluster the ideas into strategic themes (e.g., cost savings, customer experience, risk reduction).” - “Give a 5-step execution plan for the top 5. What’s missing from these plans?” - “Now 10x the ambition: what would a moonshot version of each idea look like?” Bonus tip: Prompt like a strategist (not just a user) Start with a scrappy idea, then ask AI to structure it: - “Rewrite the following as a detailed, high-quality prompt with role, inputs, structure, and output format... I want ideas to improve our supplier onboarding process with AI. Prioritize fast wins.” AI returns something like: “You are an enterprise AI strategist. Based on our internal context [insert], generate 50 AI-driven improvements for supplier onboarding. Prioritize for speed to deploy, measurable ROI, and ease of integration. Present as a ranked table with 3-line summaries, scoring by [criteria].” Now tune that prompt; add industry nuances, internal systems, customer data, or constraints. 4. Real examples we’ve seen work: - Logistics: AI predicts port congestion and auto-adjusts shipping routes - Retail: Forecasting model helps merchandisers optimize promo mix by store cluster 5. Use tools built for context-aware prompting - Use Custom GPTs or Claude’s file-upload capability - Store transcripts and research in Notion, Airtable, or similar - Build lightweight RAG pipelines (if technical support is available) - Small teams. Deep context. Structured prompting. Fast outcomes. This layered technique has been tested by some of the best in the field, including a few sharp voices worth following, including Allie K. Miller!

Half life of a AI plan seems to be two weeks. I had at least three conversations with enterprise leaders in the past week. The pace of change in AI seems dizzying for them. Big companies are used to plan in quarters and year-long roadmaps. But the terrain keeps shifting: by the time they have consulted experts, crafted their approach, and aligned internal stakeholders, the tech landscape has shifted, rendering their careful thinking irrelevant. Standard enterprise risk frameworks stop working. In such a scenario startup speed is essential. In military strategy terms, this is about operating within the OODA loop Observe: Watch the demos, notice changes in model capabilities, price points, orchestration methods, or integration cost reductions like MCP. Orient: Adjust your use cases from Co-Pilot applications to Agentic ones. Decide: Align everyone on the adjusted direction. Act: Get your team to schedule the release for the next shipping date. Startups, with their weekly shipping cadence and teams agile enough to iterate through tech stacks, use AI above API level, and pivot between approaches (RAG, fine-tuning, or revising their model building), find this OODA rhythm natural. But enterprises, despite their distribution advantages, find themselves like oil tankers in a speedboat race: impressive in scale but impossibly slow to turn. Companies who are getting ahead are doing the following few things. Create small, autonomous teams outside normal approval chains, embrace imperfection with minimum viable products that can be in users' hands next week, and use metrics that reward speed rather than completeness. I've seen companies cut approval layers from six to one, release weekly beta features, and measure teams on days to feedback rather than feature coverage, all with dramatic results

This was one of the most challenging and rewarding projects I’ve ever worked on at Salesforce. It was an AI-powered Slackbot for enterprise cybersecurity at Salesforce, called Ask-IAM. When we first launched the MVP, I was so confident it would blow customers away. But within weeks, user feedback started flooding in, pointing out glaring gaps we hadn’t anticipated. It was humbling, but it forced us into a constant cycle of iteration. For one week, we were refining the natural language processing (NLP) to better understand user queries. Next, we adjusted the bot’s tone to make it feel less robotic and more approachable. It was a rollercoaster, but every tweak made the product better. The takeaway was that success doesn’t come from getting it right the first time; it comes from how fast and effectively you can respond when you don’t. This iterative mindset has stuck with me since then. In AI Product Management, iterative development is the name of the game. Unlike traditional software, AI products evolve rapidly based on continuous data input, requiring constant tweaks. Being a master of iterative development isn’t optional; it’s essential. Here’s how you can master this skill as a superpower: 1. Adopt Agile Frameworks: Learn agile methodologies but tailor them for AI workflows. Understand what “sprints” mean to retrain models, data refinement, and experimentation cycles. 2. Embrace Failure: AI thrives on experimentation. Cultivate a mindset where failed experiments are opportunities to gain insights and improve. Track and document these iterations to build a knowledge base. 3. Collaborate Across Teams: Iterative AI development demands collaboration between PMs, data scientists, and engineers. Sharpen your cross-functional communication skills to lead and align teams during rapid iteration cycles. NavHub AI and APM Club (NavHub AI’s proud community partner!) can help you gain an advantage in learning this skill: 👉 AI-Powered Iteration Practice: Participate in mock project sprints via NavHub AI that simulate real-world AI product development iterations. 👉 Dynamic Feedback Loops: Leverage our mentorship pairing feature to get constant feedback from experienced AI PMs and data scientists on your project iterations. 👉 Live AI Challenge Events: Join hackathons organized by APM Club, designed to mimic high-pressure and iterative AI product development cycles. Iteration isn’t just about doing things fast; it’s about doing them right, with agility and precision. Join our Pilot Program now to turn your skillset into your competitive edge: http://tiny.cc/of15001 #artificialintelligence #upskill #data #productmanagement #communication