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ChatGPT New Deep Research Update: Business Impact and Five Precision Use Cases

OpenAI’s latest update to ChatGPT’s Deep Research feature is not just a UI refresh—it fundamentally shifts how company leaders can interrogate internal and external data to surface actionable insights. This article extracts precise, non-obvious value from the new Deep Research rollout, focusing on advanced controls, data prioritization, and five business use cases that streamline complex workflows.

Deep Research Platform Upgrade: Pinpoint Control for Business Data

OpenAI’s Deep Research tool no longer behaves as a mere chatbot—it is now a full research platform. The update, currently available to ChatGPT Plus and Pro users, empowers business users to take granular command over source selection. The ability to prioritize and narrow research to specific sites, including a company’s internal knowledge base, streamlines fact-finding and cuts down on irrelevant or unverified results. This means business owners can direct the tool to focus strictly on key knowledge assets, competitor sites, or trusted industry domains.

The visual overhaul offers practical enhancements: a full-screen split-view interface, dynamic table of contents, and dedicated source-citation panels. The interface is now designed for extensive, multi-step research projects rather than chat-based Q&A. Such improvements simplify the task of cross-referencing, auditing, and reviewing large outputs—critical for leaders who expect transparency in AI-driven reporting.

Precision Data Ingestion and Real-Time Steering

One of the most significant advances is real-time "steering" during a research session. Users can pause, re-direct, or supplement data context mid-stream without restarting the research, enabling iterative guidance and correction. Files and internal documents can be uploaded both at the initiation and while research is underway—making it feasible to anchor reports on authoritative, up-to-date company data instead of relying solely on general training corpora or the open internet.

This mid-task interruption minimizes wasted queries and helps leaders inject clarification or priority adjustments, enhancing both speed and accuracy of deliverables.

Upgraded AI Model: Enhanced Synthesis for Complex Research

Powered by OpenAI’s latest GPT-5.2 model, Deep Research delivers improved reasoning on multi-source, context-heavy queries—features necessary for market-facing strategies, operational overviews, and competitor benchmarking. The synthesis and pattern detection capabilities are notably stronger, allowing the system to spot trends across months of presentations, emails, or industry releases when anchored to an organization’s own data sources or specific web domains.

Five Business Use Cases for Immediate ROI

1. Strategic Planning Anchored in Company Memory

By integrating ChatGPT’s memory and history features, Deep Research can assimilate prior discussions, strategic goals, and evolving pain points to generate medium-term roadmaps. Owners can prompt the system to analyze previous communication and suggest six-month strategic focuses, uncovering actionable gaps and emergent priorities invisible through manual review.

2. Internal Knowledge Search with Source Control

Businesses can restrict Deep Research to only internal data—such as Google Drive, SharePoint, or private company wikis. This creates an on-demand, context-aware knowledge engine. Unlike unstructured document searches, this process delivers fully-sourced, synthesized briefs drawing exclusively from company assets. The value: tailored answers, confidence in source provenance, and time saved compared to legacy searches or information siloes.

3. Competitor Analysis Customized to Business Context

By feeding Deep Research both company and competitor URLs, plus internal KPIs, owners receive competitor profiles mapped directly to their own organization’s strengths and vulnerabilities. Rather than generalized “market overviews,” the output cross-references competitive activity with internal goals, product lines, or unique customer feedback, forming the backbone for executive decision decks.

4. Industry SWOT with Real-Time Market Data

Expanding to industry benchmarks, Deep Research can aggregate relevant sector news and filter it through the lens of company-specific history and positioning. If leaders supply URLs from trusted news outlets alongside internal data and company history, the resulting SWOT analysis reflects both up-to-the-minute market developments and tailored business context. The tool’s handling of Boolean search parameters supports granular information targeting.

5. Operational Follow-up Engines via Inbox and Calendar Synchronization

For owners managing large volumes of communication, Deep Research can comb connected inboxes (Gmail, Outlook) and calendars, emphasizing sent items to distinguish critical threads from inbound noise. Cross-referencing meetings and correspondence, the tool identifies stalled opportunities, dropped follow-ups, and suggests actionable outreach—flagging timely action items based on day-to-day reality, not just CRM records.

Limitations and Transparency for Business Integration

It’s essential to note that Deep Research retains read-only access when linked with business systems or external accounts; it cannot execute actions or modify records. This ensures no unintended automation or data loss—making it a safe analytical layer, not an autonomous agent.

Current limitations include proprietary model transparency and, for some legacy users, the loss of detailed clarifying questions before a research run. Leaders may want to pair Deep Research with context-stacking prompt strategies to maintain rigorous, iterative quality in deliverables.

Tangible Business Impact: From Manual Synthesis to Scalable Knowledge Work

The new Deep Research mode blends specialized source targeting, real-time workflow adaptation, and end-to-end traceability—directly addressing bottlenecks in manual research and cross-app knowledge transfer. For business leaders, time spent reconciling documents, audits, or reports across multiple silos can now be focused on oversight, validation, and decision-making, freeing intellectual bandwidth for growth.

For those tasked with operational agility and competitive foresight, these precision features translate to less time on information gathering and more cycles for analysis, strategic planning, and execution.

Topics Covered in This Episode:

1. OpenAI's Deep Research Platform Update Overview
2. New Full-Screen Deep Research Report Viewer
3. Real-Time Deep Research Steering and Pausing
4. Upgraded GPT-5.2 Model in Deep Research
5. Connecting Apps and Targeting Company Data
6. Restricting Deep Research to Selected Websites
7. Context Stacking for Enhanced Research Results
8. Top Five Deep Research Use Cases Explained

Episode Transcript 

Jordan Wilson [00:00:15]:
Imagine having complete control over your company's information and the web. Let's be honest. Whether you're an AI native worker or not, when it comes to retrieving the correct information, whether it's from your company's files and folders, your cloud, or the Internet, it takes a long time. But what if you had like a Jarvis esque enabled, tool that could find high value insights from AI models without all of that nonsensical back and forth or those pesky hallucinations that come with using AI models. So while deep research tools have given us glimpses of this type of informational control that you really only see in superhero movies, I think even the most powerful options have left a bit to be desired. Well, that may have changed this past week with OpenAI's latest update to its deep research platform. And if I'm being honest, I think a lot of people missed it because of everything that was going on in the AI world. I mean, Microsoft was like, oh, we're not gonna use OpenAI's models anymore, and OpenAI and Anthropic are kind of fighting in this OpenCloth thing.

Jordan Wilson [00:01:28]:
It's been crazy. So I think the majority of people missed this huge announcement from OpenAI because they just kinda rolled it out as a little tweet. But it is a new deep research update, and we're gonna be going over it today and the five ways that you can use it starting now. So I hope you're excited. I am too. This is everyday AI, but this is our putting AI to work on Wednesday's series. So for the past year or so, we've been doing this every single week, a practical and actionable hands on walk through for a new AI tool or model. So let's get into it.

Jordan Wilson [00:02:06]:
And if you are brand new here, well, welcome to Everyday AI. My name is Jordan Wilson, and we do this every day, not just Wednesdays, Monday through Friday with the unedited, unscripted daily livestream podcast and a free daily newsletter, helping business leaders like you, yeah, sift through all of that information better, save time to grow your companies and your careers. So if that's what you're trying to do, starts here, but make sure you take it to the next level by going to our website at youreverydayai.com. Sign up for the free daily newsletter. We're gonna be recapping the highlights from today's show as well as all of the important AI news today that is going to impact your company tomorrow and in the future. Alright. Speaking of impacting you and your company now and in the future, if you haven't already, I cannot tell you enough times. Make sure you go listen to episode seven twelve and seven thirteen.

Jordan Wilson [00:02:55]:
That is our twenty twenty six AI predictions and roadmap series. Trust me, it is literally thousands of hours of conversations, over the past year, all into two episodes that you can't miss. Alright. But let's talk about what we're here for. The new Chad GPT updated deep research, and, you know, apologies to our livestream audience. If you're listening on the podcast, you didn't know this. Sometimes when I say unedited, unscripted yeah. Tried to do this earlier today.

Jordan Wilson [00:03:22]:
Audio went haywire. So sorry about that. This is take two. But regardless, let's get in and learn about the new deep research. So what we're gonna be going over out today show the single upgrade, one small little thing that I think turns, now ChatGPT's deep research from chatbot into a true research platform, why your company data just became deep research's most powerful source. And last but not least, I'm gonna be going over five practical use cases that can replace hours of manual research that you can do today. Alright. So here's what's new.

Jordan Wilson [00:03:58]:
OpenAI announced this last week with a tweet. Nothing else. No live stream. No big hoopla. And I think it's pretty big. So it is available right now for plus and pro users, and I think it's going to be rolling out here soon to free users on chat g b t. So here's the biggest updates. Visually, you'll notice it right away if you're using the new version of deep research, and I'll show you how to choose between the two.

Jordan Wilson [00:04:24]:
But reports now appear in a full screen viewer with a split screen citation checking as well as a nice little table of contents on the left side. So visually, it looks better, it's easier to work with, and it's just a better experience reading these research reports. So there's also you can upload files both in the beginning and during the course of the deep research without interrupting it, which is huge, and then using that as your primary context for research. The other thing kinda related to that is live steering, lets you pause or redirect the deep research agent logic in real time without having to start over. And then the one thing this one little feature that I think is actually turning, deep research from a nice little, you know, tool to a complete research platform is the ability to choose which websites it does go to, which is big. And then the most obvious update, but probably the biggest one there is the updated model. So now this is running GPT 5.2. So this is OpenAI's, technically their latest series of models available in chat GPT.

Jordan Wilson [00:05:32]:
There is a GPT five three codex, but that's only available in their codex, platform, which, by the way, codex is absolutely insanely good even for, you know, nontechnical, non coding work. Anyways, this new deep research is powered by their latest model available in chat g p t, which is the g p t five two family of models. So, unfortunately, we don't know exactly what flavor or variation that they're actually using for this. We just know it's GPT five two. I assume it's probably GPT five two thinking. I don't know if it's the g p t five two pro model. Hopefully, OpenAI will release a little bit more information, but from my use case, my testing, obviously, using chat g p t way too much every single day. My thought is it's a it's an extended thinking version and not the pro version.

Jordan Wilson [00:06:22]:
Speaking of that limits, who gets it? How much? So if you are a pro user on that $200 a month plan like I am, you get a 125 full model and then a 125 lightweight queries. So again, you know, like the last version of Deep Research was technically powered by a dual model approach, o three and o four. So those models aren't really used anymore. So I believe it was the o three full and then the o four mini. So presumably, there's two different versions of five two. So once you hit your queries on the full version, then you get the lightweight. So, yeah. Hopefully, opening eye, releases a little bit more information on that because I think it's super important.

Jordan Wilson [00:07:03]:
If you are on the normal $20 a month or a team's plan, you get 10 full model, runs and then 15 lightweights for every thirty days. So, you know, essentially, you get one every work day of the month, between the heavy and the light. And then free users are expected to get limit limited queries in late February twenty twenty six. So I've checked my different free accounts. Don't have access to it yet. Obviously, I have access on my pro account, my plus account, my team's accounts, all my other accounts. Alright. Here's why I think it's no longer just a mode, and now this is a fully deep research platform.

Jordan Wilson [00:07:41]:
Aside from the end goal is much better, the model is exponentially better. Right? And not just that, but the ability to pause, redirect, and interrupt, this model in the in the middle is such a huge, game changing option or feature. Right? So if you are a power chat GPT user, you'll know that actually OpenAI rolled out this feature, I think, a couple of months ago to pro users. Right? So if you're using GPT five two Pro, which is probably my favorite model, I do have to give, Gemini, three deep think a little more, a little more time to see if that, you know, can kind of take the crown in at least in my personal usage. Right? But with GPT five two Pro, it can often take twenty, thirty minutes, an hour longer. Right. So deep research queries, if you haven't used it, the new model is actually a little slower, which I think is not a bad thing, than the previous deep research. But, deep research query might run anywhere from, you know, eight minutes to forty five minutes.

Jordan Wilson [00:08:48]:
Right? It really just depends on, number one, what you're asking it. Number two, the data that you're giving it, the complexity of your query, you know, any steering that you do in the middle of the query. So it really depends. And, you know, I'll say this. If you're not running, whether it's on Chad GPT, Google, Gemini, their new deep research powered by Gemini three Pro is absolutely bonkers good. Right, Claude's research tool, not deep research, their research tool, perplexity, whatever. If you're not using a deep research tool daily and connecting your data on the front end, I'm telling you, you are absolutely missing out. It is the best way to consume, synthesized, well sourced information at scale that is personalized for you, your use case, your businesses, viewpoint, etcetera.

Jordan Wilson [00:09:42]:
It is literally sometimes, right, I've worked with, consultancies in the past. Right? Some deep research queries, if you give them enough information, enough, context in that context window, I mean, it is like you hired a consulting company. If you do it right, right, it is an amazing output. So let's talk a little bit, about why the five two model matters. Well, it's enhanced reasoning capabilities for complex multisource research tasks, just smarter research planning and improve synthesis across multiple sources. And you can still use the old model if you want to, but for the most part, there is actually one caveat that I'll share here in a little bit. But for the most part, I do think that users are going to get a much better experience from the new five two model. The document viewer is also great.

Jordan Wilson [00:10:28]:
So I am gonna show that. Right? It's our, putting AI to work at Wednesday. So I will, grab the screen here in a little bit and do some live walk throughs. What could go wrong? Right? Aside from, my audio not working like the first attempt today. But research reports are now open in a dedicated full screen interface. So it's just a a nicer, way to consume the information. It's less cluttered. The table of contents on the left is really cool.

Jordan Wilson [00:10:53]:
I like that. It's great to have. And then you also have the dedicated source panel on the right for easier and faster fact checking. And then the great thing is is that real time control. So you can obviously, like, when you're using a thinking model, you can kind of monitor the chain of thought, but it's kind of a two pronged approach, and I can show you. So it's going to both, show you what sources it's looking at. It's going to technically, there's three different things that you can see by kind of watching this in real time. There are sources it looks at, which is usually hundreds.

Jordan Wilson [00:11:22]:
There are sources that it will use, which is usually dozens. And then you can also see kind of the steps or the thinking, that the model takes. So it's great to be able to see that. A little more granular control with this new version of deep research versus the last version, and then being able to adjust, deep research in the middle with new sources or follow-up instructions midway like you could with g p d five two pro. It's just such, it is it is huge. Right? Even if you don't have the thirty minutes to sit around and watch the computer screen, if I'm being honest, schedule some time that you can with a meaningful deep research query. Right? Especially if you're, you know, you know, on the the normal paid plan where it's somewhat limited. If you do that earlier on in the process, it's going to pay its dividends later.

Jordan Wilson [00:12:07]:
I kid you not. One of the easiest things to do to get better results out of any large language model is to watch the chain of thought. You know, write down notes as it goes along. Look at the output. Compare, your input, your notes as you go along in the output, and then run it again. Right? Such an easy shortcut to get much better. And I think with the new version of deep research, aside from the fact that you can interrupt it midway, always running a second or third time is gonna give you better results. Alright.

Jordan Wilson [00:12:34]:
So now let's, do this live because I do wanna, show everyone here a little bit on, kind of the new setup, the new layout. So I'm gonna share, my screen here. Live stream audience, thank you for, letting me know earlier that my that my audio wasn't working. So let's let's try this again. What could go wrong? Right? Alright. So let's bring up my chat GPT window. AI moves too fast to follow, but you're expected to keep up. Otherwise, your career or company might lag behind while AI native competitors leap ahead.

Jordan Wilson [00:13:14]:
But you don't have 10 a day to understand it all. That's what I do for you. But after 700 plus episodes of Everyday AI, the most common questions I get is, where do I start? That's why we created the Start Here series, an ongoing podcast series of more than a dozen episodes you can listen to in order. It covers the AI basics for beginners and sharpens the skills of AI champions pushing their companies forward. In the ongoing series, we explain complex trends in simple language that you can turn into action. There's three ways to jump in. Number one, go scroll back to the first one in episode six ninety one. Number two, tap the link in your show notes at any time for the start here series, or you can just go to starthereseries.com, which also gives you free access to our inner circle community where you can connect with other business leaders doing the same.

Jordan Wilson [00:14:08]:
The Start Here series will slow down the pace of AI so you can get ahead. Alright. There we go. And, you know, podcast audience, I'm gonna do my best to describe this, but if you wanna see the video version, you can always do that on our website at youreverydayai.com. You can always listen to the podcast there on as well. Each episode page, listen to the, the video version, etcetera. Okay. So let's go ahead.

Jordan Wilson [00:14:35]:
I'm gonna clear my my little computer interface out a little bit here. Okay. So, I just started a deep research query, but I'm gonna walk you through, how this new version works. So right now, I am on my looks like I'm on my team's plan because I have this company knowledge button, which is a little different than if you're on a normal plan. It's not super important. But, so now, to start a deep research query, you'll see kind of what I'm doing here. You're gonna look in the, the prompt bar, click the plus button. Okay? You're gonna see the deep research option in the menu that pops up.

Jordan Wilson [00:15:09]:
So here's the thing. A lot of people are overlooking this. Now there's a drop down menu after you select deep research. And from there, you can select the version. The new updated version is just called deep research. The older version is called legacy. So why would you wanna use that legacy version? Right? It's an old model. It's o three and o four mini.

Jordan Wilson [00:15:32]:
Right? You wanna use five two for the most part. There's all these new features that you just told me about, Jordan. Why would you ever use legacy? I'm being honest. There is one little thing I actually like better. So in the old model. So in the legacy model, right before it got started, before the deep research got started, it would usually ask you three to five clarifying questions. Okay? Which is always nice because what if you, you know, just type something that's nonsensical, if you make a mistake. Right? You don't wanna have to wait in the old version a long time for it to be done.

Jordan Wilson [00:16:03]:
So I like that it would ask follow-up questions. So in the new version, there is a similar, feature. Essentially, it puts the plan together, and you just have to approve it. Right? Technically, I like the old version better because those questions that it would ask you, really, I think, lead to a better first version. So what I would, recommend, and if you've taken our free prime prompt polish course, you know, which is just updated, it's free inside of our, inner circle community, you know this concept of context stacking. I would contact stack first, before starting a deep research query FYI, two to three times better results easily. Alright. So now let's go ahead and, jump in here.

Jordan Wilson [00:16:48]:
So, I have a prompt up on my screen, a deep research query, and it's already working. So I'm gonna show you what's happening. So I essentially said, I use Canva every single day for building slides for the Everyday AI Show. Please do not look at these titles, as sometimes I do not always update the titles. So, essentially, in my deep research query, you can choose the different apps that it has access to. Right. Last time I checked, there's 60 some, different apps that you can connect your data to. So I use Canva every single day for my, you know, ugly slides that I put up on the screen here on the live stream.

Jordan Wilson [00:17:24]:
So I have 720 some, Canva decks that have a wealth of information. Right? And then I'm also giving deep research access to my website. So this new feature, you can click on manage sites. Alright? And you can choose a specific site, which is great. And then there's also a new toggle option. So I have it on my screen here, but it's very easy to see this. And then it says prioritize these sites, but allow full web search. So, essentially, you can require deep research to go to, you know, your site first or, you know, a series of sites that you trust, your competitor sites, etcetera, but you don't have to limit it.

Jordan Wilson [00:18:06]:
But you can if you want. Right? So in this use case, I'm actually going to do that. Alright? And then it gave me a kind of a research plan here. So it says I'm gonna extract livestream decks from the user's Canva account using the Canva connector. It's funny that OpenAI is still calling it a connector, even though it's not what it's called anymore. It's called an app. Anyways, let me get back to my original prompt. Sorry.

Jordan Wilson [00:18:32]:
Got sidetracked here. So I said, please look through the last six months of Canva documents that appear to be live stream presentations for the Everyday AI Show. This is important. Alright? Because I obviously use Canva for dozens of other projects. So I'm saying ignore those. Right? The names are all over the place. So go in, use your best judgment smart model with computer vision, and find those that look like that they're livestream documents. And then I'm saying cross reference those Canva decks with the web pages on my website, youreverydayai.com.

Jordan Wilson [00:19:05]:
Then create an easy to digest report that goes over the 50 most popular trends, categories, stories, news, happenings, events, LLM updates, new AI models, etcetera. This should be angled as a starting guide for someone who is newer to AI, but who wants to double down on their knowledge. Right? So even the last fifth right? I said six months, it's like a 150 episodes. That's a ton. Even myself, I've probably forgot 80% of what was covered here. So this is, hopefully gonna be a good guide. But now you see, deep research kind of creates this plan. So it's a couple steps.

Jordan Wilson [00:19:40]:
It gives me, the different steps that it's gonna do. It's gonna extract information, then it's gonna cross reference it with the website. It's gonna survey additional high quality web sources. It's gonna identify and rank the top 50 trends, and then it's gonna draft an easy to dig to digest starter guide. So if I want to update that at any time, well, there is an update button. Then essentially, what's going to happen here, I can click, to add, files, or I can just essentially send a follow-up prompt. Alright. So I'm not gonna do that because we're gonna give it some time, to cook.

Jordan Wilson [00:20:10]:
But now you see how this works in real time. You see some of the new features already, and we're gonna check-in this, at the end, and kind of see some of the other features that are on the back end once a report has been produced. But you can see, it's going. I can also click, I wish OpenAI made this a little more prominent. People don't know, but there's essentially this small little gray text at the bottom. So if you click on that, that's how you watch, that's how you watch it work live. Right? So now I can see its research activity. I can see the steps that it's going through.

Jordan Wilson [00:20:49]:
This is important because sometimes maybe one of your apps that you have connected, maybe the connection is stale and you need to go reconnect it and you think it's connected, but it's not. Right? So essential especially if you're, using a lot of apps, you always wanna keep an eye early on. Right? Because you don't wanna come back forty five minutes later and, like, how freak. Right? My oh, I changed my password two weeks ago and forgot to update the app. And, you know, a lot of times, deep research and AI models, when that does happen, they'll try, you know, crazy things to try to make up for not having access to certain information that you told it it had access to, and it'll try for sometimes way too long. Alright. We'll check back in on that later. So, let's go ahead and get back to learning.

Jordan Wilson [00:21:37]:
Alright. So like I said, you can connect the apps and target specific websites, And this is huge. Alright. So, I'm gonna give you my use cases here in a minute, and I've already started to, but this is where I need you to think. What is it you do? I think so many knowledge workers out there, that's probably most of people listening, to this podcast. If you're sitting in front of a computer all day, you know, you're probably using different apps, different pieces of software. Right? As an example, maybe you're using Salesforce as your CRM. Maybe you're using HubSpot for email marketing.

Jordan Wilson [00:22:12]:
Maybe you're using, I don't know, ClickUp for project management. Right? All of those that I just mentioned, they all have connectors. Right? Maybe you use, you know, SharePoint in OneDrive or, I don't know, Gmail, Google Calendar. All of those things have connectors. So what do we as knowledge workers do? Well, we go visit Salesforce. We go look at Slack. We go look inside HubSpot at our last campaigns. Right? We go check the project in ClickUp.

Jordan Wilson [00:22:42]:
We go, you know, log in to our, you know, our our our OneDrive, our SharePoint to check these files and folders. All of those things that we do. Large language models, especially ones that are extremely powerful, like this new deep research from OpenAI. They do it better. They do it faster. They do it at scale. I don't care. Better than me, better than anyone.

Jordan Wilson [00:23:10]:
This is what, right? This is what I've been doing for twenty plus years of my professional career. Use different software, go find the information. Right? Essentially, you are synthesizing, personalizing, and carrying context over from app to app. Maybe you're taking notes. Maybe you're working on a document as you go along, but that's what we do. Right? That's all we do. But now this is what deep research does. Right? And it's not just OpenAI's version.

Jordan Wilson [00:23:36]:
Right? Infropix version, very similar. Google's version, very similar. Perplexity's version, very similar. You know, they all have different, you know, apps or connectors. But one thing to keep in mind, which is very different than an agent. Right? Because technically, at least when OpenAI announced this, they said it's a deep research agent. Deep research, this only has read access. Okay? So it's not going to perform actions for you.

Jordan Wilson [00:24:02]:
Obviously, with their agent mode, you can do that if you want to, and still using a lot of those apps. It's much slower. So deep research is not going to, you know, delete those files, right, in your CRM, or it's not gonna change the status of an important project in your project management tool. It is read only, never write actions. That's important. Alright. So let's go over now now that you know how it works, and we'll check-in on, our little project to see if it worked. Now let's go over the use cases.

Jordan Wilson [00:24:33]:
So I have five that I think are great, and I already gave you kind of my, you know, an example of my use case. But as I go over these five use cases, I want you to think about your work. Where do you spend your time? Even as you are using AI tools, right? Where are those inefficiencies still? I think with deep research, a lot of those are gonna go away. I think it's an underused aside from canvas modes, right, in OpenAI's, CHEDGBT, canvas mode in Google Gemini, or the artifacts mode in Claude. Right? I think canvas modes or artifacts is underused and deep research is underused. And the biggest thing, right, is when you select those apps that you want to use or the websites, you can select multiple. You can select 10. These are the 10 apps that I use every day.

Jordan Wilson [00:25:25]:
Again, assuming you have permission to connect all these apps to your chat g p t account. Right? Always do that first. That's what we do all day. We carry information from app to app, website to website, and well, we create something in the end. So this is where it's huge. So use case number one is a memory powered planning for your next steps. Sometimes I like being very open ended, with ChatGPT in terms of what you're working on. And this becomes especially powerful as you give access to ChatGPT to more information about you, about your goals, about your team, what you're working on, your company, etcetera.

Jordan Wilson [00:26:02]:
So this is if you have the personalization turned on, the memory turned on. Right? This is great, and you'd be surprised. So my example and I invite you to try something like this. Just say based on everything you know about me, including memory, chat history, etcetera, please channel please plan out my next six months of what I should be focusing on, in my case, to grow everyday AI. Right? Start open ended. Don't give it access to everything else. Right? Start open ended, and then you can do a follow-up prompt if you want to based on what it suggests, and then give it access to certain information. I think one of the biggest mistakes people make when working with extremely powerful large language models is we think that we know the right answer.

Jordan Wilson [00:26:40]:
I always stay say, start wide, work your way to narrow. Okay? It's you're gonna find out some great insights. Because one thing that I always say, especially if you're a power user, this is why I think advertising on the ChatGPT platform is gonna be, bananas good. They find gaps that you don't even know about. Right? If you're asking about a, b, c, d, e, f. Right? A large language model is going to be able to connect patterns, across things that you may not even know about yourself personally, professionally, career wise, your team, etcetera. Right? Because it understands the intent of what you're asking over and over. It's gonna be able to spot patterns that you used to ask about but no longer do.

Jordan Wilson [00:27:19]:
You you know, it's gonna connect these dots that you may not necessarily even know are there to connect. So start wide. Use case two, essentially brag company search. Right? This is not again, this is not as good as a complete fully fledged vector database, but this is huge. And I think that, you know, using the deep research mode is going to give you a much more accurate and better cited report than using a normal thinking model and then using apps that way. So in this case, I say restrict deep research to search only your whatever it is, you know, your Google Drive and your company website as an example. And then the connected document stores, just become these searchable sources for focused internal research. And then you can get a synthesized report built entirely from your organization's own files and your files only.

Jordan Wilson [00:28:10]:
So my example of this, well, that's what I just showed you. Right? I can't tell you, if I'm being honest, how valuable I think my Canva account is and our website. Right? There's a lot of inaccurate information out there when it comes to covering AI. Right? And that's why I love this new sites feature as well. And you can include sites because I know as an example, you know, there's a lot of sites out there, web publications that you think, oh, by looking at their name, but then there's different versions of those. Right? Different countries, different publications under that umbrella. I know at least a dozen that you would think would be very reputable. I know that they just turn out AI slop.

Jordan Wilson [00:28:52]:
It's full of hallucinations. Anytime I'm doing a normal search, I say, oh, can't include that. Right. I do hope that OpenAI, allows you to instead of include websites one by one, I hope it allows you to exclude or blacklist websites one by one. That would make it a lot better. I did suggest that, you know, on Twitter, and they actually liked the reply, which they normally, you know, don't go through and like reply. So maybe that means it's coming. Maybe it means nothing.

Jordan Wilson [00:29:18]:
But, that's a great example is, you know, give it access to all of your company's data, only your company's website, and that's it. Go to town. Right? This is a very quick version. Not as good. Again, it's a full retrieval. I'm gonna generation, set up. Right? But it's 80% of the way there and 1% of the time. The other thing that's great, or something that's important to understand and know.

Jordan Wilson [00:29:49]:
Where does data come from? There's three sources. Training data. Right? And we don't necessarily have control over that. We can kinda prompt our way around it. So large language models have training data. Then there's number two, the data that we connect, whether that's through, you know, apps formerly known as connectors. It's it's something we upload in a chat window, a project file, etcetera. Then there's three websites.

Jordan Wilson [00:30:12]:
So training data, data we connect, and websites. So this is a great way to control the latter two by just restricting them, and then you have a version of rag company search. Alright. So let's go ahead, let's see how, ours did. So, yeah, unfortunately, it's still it's still working. Alright. I wanted to be able to see if we could, see some results here. Luckily, like any good, you know, person trying to cook in the kitchen with some AI stuff, I do have a version of this done.

Jordan Wilson [00:30:44]:
So, yeah, the example I gave that was kind of the, you know, everyday AI retrieval on meta generation. Right? Just pay only look at our wealth of Canva decks in our website. It's still going. Right? Still going. It's been going for, quite a bit here. And I can kinda check on it. Still cooking. But let's go ahead.

Jordan Wilson [00:31:03]:
Let's look at the finished version here because we do have a finished version. Right? I put one cake in before we start it. So this did take thirty five minutes, which is probably one reason why we couldn't get it done in the live stream. Sometimes I've had ones that take, you know, forty minutes, and then the second time, it takes twenty minutes. So I was giving it a try. But anyways, let's look now, live at some of the new features and options. So there is this new kind of full screen, view here. And then on the right upper right hand side of the screen.

Jordan Wilson [00:31:32]:
Right? So, hopefully, podcast audience, simple enough to follow along. So you, when the deep research is done, you're gonna click on it. It's gonna put it into live, sorry, full screen mode. In the upper right hand corner, you can download it. You can copy the contents. There's this little squiggly line. If you click that, that's how you get your sources. So these are the sources used.

Jordan Wilson [00:31:53]:
Okay? And then you can click the activity, which is it's kind of summarized chain of thought. This is how it thought about things, went through the sources. And this to me is fascinating. I spend way too much time reading, summarized chain of thought more than probably most humans. And then on the left hand side, this is the new table of contents, which is really cool. So So especially if you have longer reports, you can just hover over these, little toggles here on the screen, and then you can scroll down. And then if you wanna click something, it's like a jump link. So I can click that and it goes straight there.

Jordan Wilson [00:32:23]:
So actually, let me see. Let's, see how this turned out. Okay. So we have an executive summary. Let's see if it did everything we asked. Okay. So this is the great thing. It's giving sources.

Jordan Wilson [00:32:33]:
So, this is meta. All right. There is something about deep research, that just came up from my website in the deep research report. So I can click it. And then on the right hand side, it has all the sources that are used. So if there's ever anything that you want to verify, you can always click that, look at the sources, and verify it. Alright? This is not your initial Chatt GPT hallucination. Right? 2020 this is not it.

Jordan Wilson [00:33:03]:
The always cite, cited and sourced very well. Alright. So let's go down. Let's see how it did, according to, the directions I gave it. Alright. So scope sources and cross referencing. So it's telling me what it did, how it worked. Alright.

Jordan Wilson [00:33:19]:
It kind of mapped out all the different Canva decks. That's really cool too. So I can go check those out if I ever have any questions. Okay. This is nice. It gave me, a visual synthesis of the six month landscape. I didn't even ask about this. This is pretty cool.

Jordan Wilson [00:33:35]:
Maybe kind of a visual of how things have changed over the last six months based on all of the data, which is very helpful. Alright. It gave me a, kind of what themes were talked about the most. Unfortunately, the x axis is a little messed up and it's overlapping. But if I wanted to, I could look at the code and kinda figure out what it's doing. So, you know, I didn't even ask it to create these visuals, and it actually did a really good job of making this document more interactive. Right? It made some charts. Here's the ranked top 50 items with side ready briefs.

Jordan Wilson [00:34:08]:
Sorry. Slide ready briefs. Okay. This is this is very cool. So found the 50 trends that have, you know, the 50 biggest trends over the last, six months, at least according to things that we've been talking about here on the website. This to me is such a valuable resource. Right? Imagine doing this for your company's website, for a competitor's website, for, you know, 10 industry websites that you follow all the time. Right? Maybe you've been out of the loop, been busy on a couple projects, been on vacation.

Jordan Wilson [00:34:37]:
Right? This is such a good way, to do some simple sentiment analysis, and catch up quickly. So it gave me kind of the recurrence signal strength, you know, things that were very high. So AI as an operating system, that's a trend, the number one trend. AI agents becoming the default workflow units, very high trend. So this is really cool. So this is really good. Okay. And then we have slide ready briefs, for ranks one through 50.

Jordan Wilson [00:35:03]:
So it gave me a nice write up on all 50 of those. Sheesh. This is good. This is nice. My gosh. Okay. I'm gonna read this. Right? As crazy as it sounds like I told you, I don't remember everything I've talked about over the past six months.

Jordan Wilson [00:35:21]:
You know, sometimes I don't know. I feel humans, maybe it's because of AI or just attention spans in the internet. But sometimes I feel like, you know, we have, like, a memory of a goldfish. This this report to me is pretty freaking amazing. So, yeah, I don't know. If if if you want access, to the report, just go ahead, repost, today's show, on LinkedIn, and I'll send you this. Because, as I'm scrolling through, this is actually very impressive and a very good resource, to go through and read. So, yeah, if you're listening on the podcast, we always have, the LinkedIn link that you can just go and then repost this, and I'll send it to you.

Jordan Wilson [00:35:58]:
Really cool. Alright. Let's wrap up. Let's go over our next use cases. So use case three, a competitor deep dive using your company context. So similarly, how I started the first use case by saying, hey. Use everything you know about me. Same thing.

Jordan Wilson [00:36:11]:
Use everything you know about my company, but then also combine your personal context, what your role is, and then your synced company data for competitor analysis. Then you can add your competitors' websites, industry websites, etcetera. And then you can get a report that's mapping competitors against what matters to you and your specific company. Again, this is like as if you were hiring a consultant, but for you. Right? Not for your company or for your department. And if you do take the time and even if you go through maybe two or three iterations of providing feedback or, steering it midway through, I think you will literally be shocked at the amount of information that you get out. Have you guys never done this? Sometimes I feel like I'm a crazy man because I'm like, why is no one talking about this and doing it nonstop? It is so good. Right? I can't lie.

Jordan Wilson [00:36:58]:
I think, a a good chunk of whatever, success we've been able to have as a, you know, as a top AI podcast, and I know that's kind of cringe to say. I think a lot of it is from doing things like this, things that I'm telling you to. Right? When I'm when I talk about these use cases, these aren't just, like, random things I read about on the Internet. I'm like, oh, this is cool. Go try it. These are things that I do all the time. And then, like, when I do them, I'm like, holy freak. That's amazing.

Jordan Wilson [00:37:25]:
I need to tell people. Right? So you should be doing this. Use case four, industry SWAT built, from your data and news. So kind of similar to three, but more on the industry versus just competitors and just a SWOT. Right? Strength, weakness, opportunity, threat report. So this can use your chat history and company data to identify sector trends that are impacting you. Deep research can scour, trending industry news filtered through your business contacts and then produce a SWOT analysis grounded in both your data and current market signals. The cool thing and I do have to do a little bit more, research on this, but I'm pretty sure Deep Research does a great job with Boolean URLs, which is amazing.

Jordan Wilson [00:38:03]:
Because without being too dorky, what you can accomplish, with some simple URL hacking, right, you can essentially have a dedicated up to date research assistant that you don't have to keep feeding, you know, different websites. Right? If you know what you're doing, around some simple Google search operators, yeah. It's extremely powerful. Last but not least, or at least I do that all the time with, GPT five two Pro. And I have tested it a little bit in the new deep research, but I got to do a little more testing to see if it is consistently handling that. And then last but not least, use case five, the follow-up assistant that scours your inbox and calendar. This is huge. I miss so many opportunities, so many emails.

Jordan Wilson [00:38:45]:
I stink at it mainly because I get spammed. Right? So my example here, I said, please carefully comb through the last six months of the connected Gmail inbox in Google Calendar. For Gmail, pay specific attention to my outbox as my inbox gets spammed a lot and a good majority of what lands in my inbox is not important. However, if I have replied to something, via my outbox, that means it is generally important. For my Google Calendar, please look to see what which meetings I've had with other people in the last six months and cross reference that with correspondence in my Gmail inbox. The goal is to both follow-up on opportunities for everyday AI where I may have dropped the ball or forgot to respond, as well as to reengage older conversations that may have already closed in theory, but may be worth revisiting. Please keep in mind all the context that you know about me as well as looking at the two attached informational sheets. And what I've attached here is I essentially have these living, breathing markdown files that I always update anytime I'm working in really any large language model, both about everyday AI and about my role, kind of, you you know, my day to day, what it looks like.

Jordan Wilson [00:39:47]:
So I have two different markdown files about everyday AI and then about myself. So that I mean, y'all. I should actually spend way more time on this cause there's no reason for me to suck at email. It's just more or less overwhelming. Right? When I run these, it's like, here's, you know, 85 extremely important emails that you haven't got to. Right? Unfortunately, they can't send or draft replies yet, but, hey, maybe one day. Alright. So that is a wrap.

Jordan Wilson [00:40:13]:
So now you know what is new in OpenAI's new updated deep research and five ways that you can use it today. So, yeah, if you wanna go check out that trend report, make sure to go share this and repost this on LinkedIn. And then seven twelve, seven thirteen. Don't forget those numbers. That is the 2026 AI predictions and road map series. If you haven't listened to those, you have to. And then please go to your everydayai.com. Sign up for the free daily newsletter.

Jordan Wilson [00:40:39]:
Thanks for tuning in, for putting AI to work at Wednesday. Hope to see you back tomorrow and everyday for more everyday AI. Thanks, y'all.