Behind the Scenes: Bringing AI to Life at The Inni Group | Webinar | Product Focus скачать в хорошем качестве

Behind the Scenes: Bringing AI to Life at The Inni Group | Webinar | Product Focus

Scroll down for key takeaways... Product Focus: https://www.productfocus.com Join us for a conversation with James from The Inni Group as he shares how they introduced AI into their tax-compliance platform for property investors. We’ll explore the real customer problem, how they evaluated AI against traditional solutions, and the key lessons they learned through the build and rollout. Key takeaways include: When to reach for AI 1. AI is a tool, not a starting point. Start with a valuable problem worth solving. AI should help you solve it — not be the reason you went looking for a problem. 2. Break the problem into small jobs, then find the best tool for each. Don't ask one AI to do everything. Decompose the workflow into specific tasks and pick the right solution for each — sometimes that's AI, sometimes it's rules, sometimes it's good UX. 3. Use simpler logic where it works. Before calling an AI, ask: can I solve this with a rule? With cached user behaviour? Save AI for the jobs that genuinely need inference. This reduces cost, latency, and complexity. 4. Constrain AI outputs. When you need a specific answer from a defined set, don't let the model be creative. Lock down the possible responses. Constraints improve accuracy. Making AI work in practice 5. Simplify the problem so AI can succeed. If AI is struggling with ambiguity, the problem might be poorly framed. Reducing complexity — fewer options, clearer boundaries — often beats adding more AI. 6. Perfection isn't the benchmark. Find out what "good enough" actually means in your domain. The human baseline may be lower than you assume. Don't hold AI to a standard humans don't meet. 7. Be transparent about AI's role. Label what's AI-generated. Let users review, edit, and override. Trust comes from transparency, not from hiding the machinery. Build vs buy 8. Don't build what you can buy. Especially in fast-moving spaces like AI, building your own infrastructure can delay shipping indefinitely. Buy commodity capabilities; invest engineering effort in your differentiation. 9. Evaluate empirically, not based on hype. The best solution for your problem might be boring and unglamorous. Test with real data. Cut through the marketing. 10. Multiple vendors reduce lock-in risk. The AI landscape is competitive. If one vendor disappears or underperforms, you can switch. This is a risk you can manage. Product management fundamentals still apply 11. Discovery before solutions. Understand the job to be done before you design anything. AI doesn't change this — it makes it more important, because you can now build the wrong thing faster. 12. Swim up to the real need. Customers don't want your product. They want the outcome your product enables. Keep asking "why" until you find it. 13. Lead with benefits, not technology. Customers don't care that it's AI-powered. They care that their problem gets solved. Position accordingly. 14. Differentiation doesn't require feature bloat. You can differentiate on service level, simplicity, or focus. Saying no to features can strengthen your value proposition, not weaken it Check out our Product Focus Toolbox, an online store with all our industry-leading best practice resources in one place. It contains our Product Management journals, infographics, webinar recordings, tools, templates, reports, white papers, blogs, and book reviews. It gives you easy access to high-quality resources whenever you need them. Learn more here: https://www.productfocus.com/toolbox/