Most Scrum Product Owners Are Using AI Wrong in 2026 (3 UX Research Tools That Actually Work)

Most product owners are using AI wrong. They're pasting user stories into Robo or Copilot, asking Zoom to summarize their sprint review notes and calling it a day. And look, that's fine. But in 2026, that's the floor, not the ceiling. The product owners pulling ahead. They're using AI as a research engine, and they know which tool to reach for depending on the job. Because here's the thing nobody talks about. Not every AI assistance is built the same. The wrong tool gives you the wrong answer. But the right tool gives you a competitive edge. Today I'm going to show you three types of research. Deep research, deep search, and wide research using three different AI assistants. For each one, I will show you the capabilities, the benchmarks that back it up, and the exact product owner use cases it's best for. By the end of this video, you'll know exactly which one to reach for and what prompt to give it. Let's go. The first type of research is deep research. This is not about getting quick answers from AI. Deep research is about going all the way down the rabbit hole on a single topic. It's a linear autonomous process. The AI plans its own research, run dozens of

searches, reads real pages, and synthesizes everything into one coherent report. My go-to research tool for this is Gemini, specifically the Gemini 3. 1 Pro models. Here's why Gemini wins at this. Two reasons. First, live search mastery. Gemini has direct access to Google search. So, it's not working from still training data. Deep Research Max can run upwards of 160 iterative web searches for a single task. It's reading and synthesizing in real time. Second, the context window. You need a lot of room to hold all of that data. Gemini 3. 1 Pro has 1 million token context window. That means it can cross reference hundreds of pages of search results simultaneously without losing the thread. Bonus point, multimodal capability. Gemini doesn't just read text. It can analyze PDFs, YouTube videos, even complex charts it find during the research. So nothing in the rabbit hole gets missed. And the benchmark backs this up. Gemini 3. 1 Pro leads on the Arc AGI2 with 77. 1% and on the GPQA Diamond, which measures expert level reasoning, it scores 94. 3%. That last one matters most for product owners. When you ask Gemini about technical feasibility or regulatory requirements, it's not just pattern matching, it's reasoning like a subject matter expert. Here's how I prompt Gemini for deep research. The magic keyword you need to put in your prompt is deep research max. When you kick off a task, Geminina will propose a research plan first and always review its proposal and refine the scope before telling the agent to spend 15 minutes digging down the rabbit hole. As a product owner, the use cases go well beyond competitor analysis. Use Gemini to do a technical gap analysis. Ask it to find the best API for a new feature and cross reference its security history. or use it for compliance guardrails. Feed it the EU AI act and ask it to tell you exactly which checkboxes your team needs to hit before the release date. Or ask it for vendor evaluation. Ask it to deep dive a SAS tool your team is considering covering features, pricing, security posture, and customer reviews all in one report. It's like having a senior architect and a legal consultant in your pocket on demand. The second type of research is deep search. And this is where things get interesting. Where Gemini goes deep on one topic with exhaustive verified information, Gro's deep search is built for something different. It actively searches the live web and X feed in real time. Then it synthesizes everything into clear actionable insights, usually within a couple of minutes. And that is Grock's superpower. No other major AI has native real-time access to X data at this level. Here's why Grock 4 wins at this. Three reasons. First, native real-time X-axis. Grock taps directly into full X data stream, trending topics, live events, raw social conversations in a way no other major AI can replicate. When you need the unfiltered voice of the customer, this is the only tool that actually has it. Second, speed and synthesis. Deep search

doesn't hand you a list of links. It reads, reasons, and hands you a structured cited brief before you can act on it immediately, usually in minutes, not hours. Third, conflict resolution. Grock actively reasons through contradictory sources and tell you which signals to trust. That matters when public sentiment on your product or your competitors is noisy and mixed. The benchmark backs this up. Gro 4 heavy is the first model to score 50% on humanity's last exam. A benchmark specifically designed to be a final closeended academic benchmark of its kind. And on the vending bench agentic test, Grock 4 dominates with a net worth of $4,694, vastly outpacing cloth 4 at $2,77. Strong reasoning matters here because deep search isn't just fetching data. It's making judgments about what's real users actually mean. Here's how I prompt Grock to invoke its deep search capability. The keyword you need to tell Grock is to use deep search. And within a few minutes, you get a full report, including direct quotes from X. That is a gamecher for voice of the customer research. Beyond competitive intel, use GROP for voice of the customer. Get unfiltered real-time feedback on your product or your competitors straight from users. Not polished G2 reviews, but raw, honest reactions the moment they happen. Use it for a feature demand validation. Before you put a feature on the road map, ask Rock where the real users are already asking for it on X. And use it for trend and sentiment monitoring. Catch emerging technologies, shifting user behaviors, or negative sentiment spikes around your product before they hit the mainstream media. One honest caveat, ex conversations can be noisy and polarized. So for anything high stakes, cross-ch checkck the important claims and Grock has a strong personality which some corporate users find it a bit direct but for speed and raw insight nothing even comes close. The third type of research is wide research and this one is a completely different category. If deep research is about going deep on one thing, wide broad across hundreds of things simultaneously. Think of it as horizontal scaling. Instead of one query, one report, you're running hundreds of independent research tasks in parallel. It's ideal for high volume jobs like mapping an entire industry, comparing hundreds of products at once or benchmarking the Fortune 500. My research tool for this is Manis, the startup that sparked a bidding war earlier this year, and for a good reason. Here's what makes Manis different. Well, three reasons. First, parallel sub aents at scale. Unlike Gemini which runs one deep report at a time, Manis enables up to 100 parallel sub agents to operate simultaneously significantly reducing execution time for research intensive task. Each sub agent is a complete manage instance running in its own virtual machine. They all work at the same time. That's a gamecher. Second, autonomous task execution. Mattis doesn't just research

it acts. It visits websites, extracts structured data, populates comparison matrices, and delivers a formatted output without you lifting a finger between the prompt and the result. Third, structured readytouse deliverables. Whether it's a polished report, slides, a dashboard, or even a website, Madness delivers organized formatted outputs, not just raw data dumps. The results go straight into your next sprint planning session. And the benchmark backed this up on the GIA benchmark, which assesses an AI's ability to reason, use tools, and automate real world tasks end to end across multiple steps. Manis achieve state-of-the-art results, outperforming OpenAI's GPT4. GIA is the right benchmark here because it tests agentic performance, not just reasoning in isolation. And why research is at its core an agentic task. Here's how I prompt manise to invoke its wide research capability. The keyword you need to use here is wide research. As a product owner, the applications are massive. Instead of manually checking five competitors, use wide research for feature parity analysis at scale. Analyze 50 simultaneously with each agent visiting a site, extracting the feature list, and populating a comparison matrix. Use it for pricing intelligence. Monitor and compare pricing models, tiers, and hidden fees across an entire industry segment in one pass. Or use it for release monitoring. Track the what's new pages of dozens of competitors simultaneously to catch emerging feature releases the moment they drop. This is the kind of competitive coverage that used to take a team of analysts a week to pull together. So, three types of research, three different AI assistants. Each one optimized for a specific job. Gemini 3. 1 Pro for going deep on complex factual single topic research. Gro 4 for real-time sentiment, social intelligence and voice of the customer and manners for high volume parallel research across an entire market. The key takeaway for you, the tool matters, but the prompt is everything. Knowing which tool to reach for and how to ask it the right question is what separates a good product owner from an awesome one in 2026. If you want to learn how product owners can integrate AI into their full process, I run a product owner AI essentials course

through scrum. org link in the description below. Go check it out. And if you find this video useful, subscribe now. There's a lot more coming on product ownership and AI on this channel. I will see you in the next video. Bye, folks.