How to Use Prototyping in Business Strategy

AI ENABLES PERMISSIONLESS INNOVATION The review gauntlet that product orgs use to "ensure quality" often kills breakthrough ideas before they see the light of day. Strategy reviews, product committees, design approvals—each layer of gatekeepers favors safe, consensus-driven concepts over the risky, opinionated bets that create real innovation. AI prototyping is changing this dynamic entirely. Smart PMs are now bypassing traditional approval processes by building functional AI prototypes themselves. Instead of pitching abstract concepts to committees, they're: - Creating working prototypes in hours or days - Testing directly with real customers - Gathering concrete feedback and usage data - Iterating based on actual user behavior - Walking into review meetings with proof, not just PowerPoints The result? They're presenting stakeholders with tangible experiences and customer validation rather than hypothetical arguments. It's much harder to kill an idea when users are already loving the prototype. The new playbook: Build first, get permission later. When you have a bold product idea, don't let it die in committee. Use AI to prototype your vision, validate it with real users, then use that momentum to navigate the approval process from a position of strength. What innovative ideas are you sitting on that could benefit from this approach?

Product leaders, stop hiding behind docs! If your team is still spending all their time in PRDs and product strategy docs, they're not operating in 2025. AI prototyping has literally changed the game. Here's how teams should do it: — THE OLD WAY (STILL HAUNTS MOST ORGS) 1. Ideation (~5% actually prototyped) “We should build X.” Cool idea. But no prototype. Just a Notion doc and crossed fingers. 2. Planning (~15% use real prototypes) Sketches in Figma. Maybe a flowchart. But nothing a user could actually click. 3. Discovery (~50% try protos) Sometimes skipped. Sometimes just a survey. Rarely ever tested with something interactive. 4. PM Handoff (~5%) PM: “Here’s the PRD.” Design: “Uhh… where’s the prototype?” PRDs get passed around like homework. 5. Design Design scrambles to build something semi-clickable, just so people stop asking “what’s the plan?” 6. Eng Start Engineering starts cold. No head start. They’re building from scratch because nothing usable exists. — WHAT HAPPENS - Loop after loop. Everyone frustrated. - Slow launches. Lots of guesswork. - And no one truly understands the user until it’s too late. — THE NEW WAY (THIS IS HOW WINNERS SHIP) 1. Ideation PMs don’t just write ideas. They prototype them. Want to solve a user problem? Click, drag, test. There. No waiting. No “someday.” You build it, even if it’s ugly. 2. Planning Prototypes are the roadmap. You walk into planning with a live flow, not a list of features. And everyone’s like: “Oh. THAT’S what you meant.” 3. Discovery Real users. Real prototypes. You send them a flow and you watch them break it. You’re not guessing anymore. You’re observing. 4. PM Handoff PMs don’t just hand off docs. They ship working demos alongside the PRD. No more “interpret this paragraph.” Just click and see it work. 5. Design Designers don’t start from scratch. They take what’s already tested, validated, and tweak it. Suddenly, “design time” is “refinement time.” 6. Eng Start Engineers don’t wait around. They start with something usable. If not, they prompt an AI tool to build it. And we’re off to the races. — If you want to see how AI prototyping actually works (and learn from expert Colin Matthews), check out the deep dive: https://lnkd.in/eJujDhBV

Day 80: AI Prototyping for Businesses This is a timely topic, as Chris Wolf posted about VMware Private Cloud for AI RAG Starter using NVIDIA software. The ability to build an AI pipeline is essential for prototyping projects and building what Dell Technologies calls an AI Factory. More Insights from ChatGPT AI prototyping is a critical step in the development process, allowing businesses to test AI models and validate their potential before full-scale deployment. Prototyping helps organizations mitigate risks, refine AI models, and ensure that AI projects deliver measurable value. Here’s an overview of how to approach AI prototyping in a business context. Key Steps in AI Prototyping 1. Define the Problem and Objectives Definition: Clearly outline the business problem you want to solve with AI and define the goals of the prototype. Application: Establish measurable objectives, such as improving operational efficiency, enhancing customer experience, or reducing costs. This ensures that the prototype is aligned with the organization’s strategic goals. 2. Select the Right Data: Definition: Choose the relevant data sources needed for the prototype and ensure that the data is clean and well-organized. Application: Data is the foundation of any AI model, so it’s essential to use high-quality, representative data for the prototype. Data governance and privacy considerations should be part of this step. 3. Choose the Right AI Model: Definition: Select an AI model that is appropriate for the problem at hand. This could be a machine learning model, natural language processing (NLP) algorithm, or another AI approach. Application: Depending on the business problem, different AI models will be more or less suited to the task. Use tools like AutoML to experiment with different models and select the most effective one. 4. Build and Train the Model: Definition: Develop a working AI model and train it using historical data. Application: Training is a crucial phase where the AI model learns patterns from the data to make predictions or decisions. The accuracy and effectiveness of the model will depend on the quality of data and the robustness of the algorithm. 5. Test and Validate: Definition: Test the prototype using real-world data to assess its performance and validate its outcomes. Application: Measure the model’s performance against predefined metrics, such as accuracy, precision, recall, or ROI. This helps ensure the model meets business expectations before moving to full deployment. 6. Iterate and Refine: Definition: Use feedback from testing to refine and improve the prototype. Application: Prototyping is an iterative process. Analyze the results, identify areas for improvement, and adjust the model to improve performance. Iterate until the model is ready for deployment.