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Google Gemini Enterprise: A Detailed Breakdown for Business Leaders
The AI workplace landscape just took a significant step forward with the introduction of Google Gemini Enterprise and Google Gemini Business. This new offering marks Google’s bid to consolidate its fragmented AI tools into a unified system aimed squarely at enterprise productivity, security, and integration. For organizations grappling with AI adoption, this episode’s insights provide a granular view of Gemini’s capabilities, pricing, and strategic value – all crucial for informed decision-making.
Pinpointing the Gemini Differentiation
Google’s updated Gemini lineup distinguishes between personal tiers (Gemini Pro and Gemini Ultra) and enterprise-focused plans (Gemini Business and Gemini Enterprise). The latter are designed as comprehensive platforms, not just standalone tools, and are priced competitively—Gemini Enterprise at $30/user/month aligns with Microsoft Copilot’s structure, while Gemini Business comes in at $21/user/month for lighter controls and up to 300 seats.
Crucially, Gemini Enterprise goes beyond simply offering access to powerful models. It consolidates agent building, enterprise search, and governance within a single interface, reducing confusion noted with Google’s previous disjointed product lineup. Business leaders have often expressed challenges integrating Google’s AI into existing workflows, especially compared to ChatGPT and Anthropic. Google’s revamped approach demonstrates a commitment to addressing these concerns with a streamlined access point.
Core Features Unpacked: What’s Inside Gemini Enterprise
A closer look reveals six defining elements sourced directly from Google’s documentation:
Access to Leading Models: Most third-party benchmarks currently rank Gemini 2.5 Pro at the top, even surpassing competitors like GPT-4 (OpenAI) in blind taste tests (e.g., LMSys Arena). Enterprises gain direct access to these advanced models.
Agentic Platform with No-Code Builder: Gemini Enterprise enables teams—including marketing and finance—to create custom workflow-automating agents, grounded in their organizational data, without needing to write code. This can significantly accelerate deployment times for bespoke AI applications.
Prebuilt Google Agents: Deep Research, NotebookLM integrations, and coding agents are available out-of-the-box, intended to deliver immediate value.
Extensive Data Grounding: Enterprises can securely connect Gemini to core business systems—Google Workspace, Microsoft 365, Salesforce, SAP, BigQuery—ensuring the AI’s context is always relevant and organization-specific.
Centralized Management and Governance: Administrators benefit from a clear interface for visualizing, auditing, and managing agent and data security—a necessity for compliance-heavy sectors.
Partner Ecosystem and Marketplace: Gemini offers built-in connectors for third-party workflow automation and a marketplace for extended agent functionality.
The Underrated Power of Data Grounding
One standout insight centers on Gemini Enterprise’s grounding ability. Unlike personal Gemini accounts, which operate file-by-file and can be inconsistent in sourcing, the enterprise-grade grounding is robust. For example, when users connect organizational data (Gmail, Google Docs, Calendar) to Gemini Business, the platform integrates and contextually triangulates information across multiple sources. Testing revealed that Gemini Business reliably understood abbreviations and acronyms by cross-referencing multiple apps, a testament to its contextual capabilities.
This approach also contrasts with tools like NotebookLM, which restrict responses strictly to uploaded data, and personal Gemini accounts, which may still hallucinate or misinterpret instructions. For data-driven organizations, this reduces the risk of inaccurate outputs and increases trust in automated decision support.
Multi-Tiered Offerings: Navigating Pricing and Access
Decision-makers evaluating Gemini must understand the tier limitations:
Gemini Pro (Personal): $20/month, bundled within Google One subscriptions, but features can be limited depending on account type (personal vs. workspace).
Gemini Ultra (Personal): $250/month, comes with premium perks but has restricted features if used via workspace accounts.
Gemini Business: $21/user/month, capped at 300 seats, suitable for small teams needing grounded AI and basic agent functionality.
Gemini Enterprise: $30/user/month, supports advanced connectors, broader agent features, and is pitched at large organizations requiring stringent controls and data integrations.
Many existing Google customers may not realize their workspace subscriptions already grant access to Gemini Pro. However, core capabilities such as Gems (prompt templates) and Canvas Mode (visual agent building) are currently exclusive to personal plans and not yet available in the enterprise tier.
Performance: Early Impressions and Testing
Side-by-side testing of Gemini Business against Gemini Pro, ChatGPT Pro, and Anthropic Claude on identical prompts and connected data streams produced telling results. Gemini Business consistently produced accurate, well-sourced responses, proving particularly adept at cross-referencing calendar events, emails, and documents. By comparison, personal Gemini and other LLMs struggled with data consistency and sourcing, especially with complex organizational queries.
Agent builder functionality in Gemini Business is straightforward but currently less flexible than OpenAI’s recently launched agent builder. However, the platform’s core performance and deep research agent—enabling dynamic searches across multiple connectors—provide immediate utility for teams prioritizing accuracy and speed over customization.
Enterprise Impact and Use Cases
Pilot programs at organizations like HCA Healthcare and Best Buy quantify Gemini’s impact:
HCA Healthcare: Nurse handoff automation with Gemini is estimated to save millions of annual hours.
Best Buy: Achieved a 200% increase in customer self-service and 30% higher inquiry resolution rates post-implementation.
These cases confirm that Gemini’s integrated approach can lead to significant operational efficiencies when grounded in organizational data.
Market Positioning: Can Gemini Compete with ChatGPT Enterprise and Copilot?
The current competitive landscape still favors ChatGPT for user volume (800 million weekly users) and Microsoft Copilot for deep enterprise penetration (100 million monthly users), particularly among Windows-first organizations. However, Google Gemini’s rapid improvements and effective grounding capability position it as a credible option, particularly for companies not fully entrenched in the Microsoft ecosystem or seeking alternatives to OpenAI’s pricing.
Google’s consolidated, feature-rich Gemini Enterprise now addresses many prior connectivity and fragmentation criticisms. Its ability to integrate with Microsoft 365 and major business platforms widens the addressable market beyond existing Google Workspace customers.
Strategic Takeaways for Decision Makers
- Evaluate Tier Fit: Organizations need to assess seat count, integration needs, and data sources when selecting between Gemini Business and Enterprise.
Prioritize Grounded AI: The differentiated approach to data grounding can meaningfully improve accuracy, trust, and automation outcomes.
Monitor Feature Expansion: Features like Gems and Canvas Mode may soon migrate to business tiers, potentially expanding customization and productivity benefits.
Google Gemini Enterprise is no longer an afterthought in enterprise AI. Its unified interface, powerful benchmarking, competitive pricing, and robust data grounding are timely solutions to the persistent pain points of fragmented AI adoption. Companies seeking to upgrade their productivity platforms should closely examine this new offering’s specific benefits and ongoing developments.
For further details on Gemini’s feature comparisons and use case demos, consult the official product materials or subscribe to specialized AI business updates.
Topics Covered in This Episode:
- Google Gemini Enterprise vs Business Comparison
- Gemini Enterprise Unified Workplace AI Launch
- Gemini Pricing vs ChatGPT Enterprise & Copilot
- Agent Builder & No-Code Platform Features
- Grounded Data & Enterprise Search Capabilities
- Prebuilt Agents: Deep Research & Data Science
- Microsoft 365, Salesforce, SAP Data Integration
- Role-Based Access Control & Security Layers
- Real-World Use Cases: HCA Healthcare, Best Buy
- Gemini Enterprise vs ChatGPT & Copilot Adoption
Episode keywords
Google Gemini Enterprise, Google Gemini Business, Gemini Pro, Gemini Ultra, Enterprise AI, AI-powered workplace, Unified workplace AI system, $30 per user per month, AI pricing comparison, Microsoft Copilot, ChatGPT Enterprise, Anthropic, Agent builder, No code AI agent, Deep Research Agent, Google Workspace integration, Microsoft 365 integration, Data grounding, Enterprise data security, Role aware access controls, Central governance layer, Multisystem integration, Real-time data access, Salesforce integration, SAP integration, BigQuery, LM Arena, AI model benchmarks, Third party benchmarks, Generative AI, Personalized AI answers, Agentic platform, Automated workflow, Data stores, NotebookLM, HCA Healthcare, Best Buy AI use case, Customer self-service, Data federation, Comprehensive contextual understanding, Data permissioning, Google Workspace apps, Grounded generative answers, AI system performance, File selection AI, SaaS systems integration, Cross-platform workflow, Prebuilt AI agents, AI-powered calendar, Gmail integration, Google Docs integration, Cloud AI, AI in healthcare, AI in customer engagement, Small business AI, Medium business AI, AI for Fortune 500, Enterprise user adoption, Business tier AI solution, AI user metrics, AI hallucination reduction, AI trust and transparency
Podcast Transcript
Jordan Wilson [00:00:47]:
Just when you thought we had enough large language models, we technically have another new offering from Google. That's because they just announced Google Gemini enterprise and Google Gemini business, technically different from their Google Gemini Pro and Google Gemini Ultra offerings. So not only on today's show, are we gonna uncover what all that means and help you differentiate between those kind of personal and now enterprise versions of Google Gemini. But we're also gonna look at, okay, is Google Gemini gonna be a key player in enterprise AI? Well, I think so, but let's dive in and talk about it. I'm excited for today's show. I hope you are too. What's going on, y'all? Welcome to Everyday AI. My name is Jordan Wilson, and this is your daily livestream podcast and free daily news that are helping everyday business leaders like you and me, not just keep up with AI, but how we can get ahead to grow our companies and our careers.
Jordan Wilson [00:01:47]:
If that's what you're trying to do, you're in the right place. Starts here with the unedited, unscripted livestream podcast. But to take it to the next level. Go to our website at youreverydayai.com. Make sure you sign up for the free daily newsletter. We're gonna be recapping the highlights of today's show, as well as keeping you up to date with all the other AI news happening today. But y'all the big news is Google Gemini enterprise. You know what? I think it's about time.
Jordan Wilson [00:02:14]:
I think there's been so much confusion and I can see how this actually might be more confusing because even as we speak, I've been playing around with the, the new Google Gemini, business version. So there's kind of an enterprise and a business version, but we'll say it's in the enterprise tier. So I'm technically using, a Google Gemini Pro, and a Google Gemini Ultra, which are, personal plans. And then I have a Google Gemini business, and it's completely different. So in some instances, you might say, this is actually a bad move for Google because now they have even more product offerings and it's a little confusing, but I'm actually gonna say it's a good move for Google. I can't tell you how many times I do. I do trainings. Right.
Jordan Wilson [00:03:05]:
And, and one question I always ask, because I'm curious, I have people raise their hands. I say, you know, hey. Who in here uses, you know, Microsoft Outlook, you know, in Microsoft products? And, you know, I don't know, maybe two thirds. And then I say, okay. What about the rest of you? Google? And then, you know, the rest of the audience raises their hands. And with those people that have their hands up, I say, how many of you use Gemini consistently? And obviously, you know, over the last, you know, quarter or two, the number of hands in the room would usually stay up, But earlier on, no. Right. Like a year ago, even, you know, when there would be dozens of people or more that would be Google customers.
Jordan Wilson [00:03:51]:
No one was using Google Gemini. And I think one of the reasons is, well, it was disjointed. It was confusing, and even Google at one point had worse connections, to Google Docs and and Gmail and, all the Google products then ChatGPT and Anthropic did. But I'm gonna tell you this. I've been very impressed. Alright? But I'm gonna wait on my reactions, and let's just get straight into what we're gonna be going over. So, today, we're gonna simplify the unique features debuting in Google Gemini enterprise, and there's one that I think is the key feature. I'm gonna explain the differences between the new tiers, which are enterprise and business versus the previous Gemini offerings, Pro and Ultra.
Jordan Wilson [00:04:36]:
And I'm gonna give you my prediction on if Google Enterprise will compete with Chad GPT Enterprise and Microsoft Copilot. So what the heck's new? What is this Gemini Enterprise? Well, they just released it, yesterday as their, first big take at a unified workplace AI system. So it costs $30 per user per month, matching Microsoft Copilot, three sixty five's offering, but they do have the cheaper offering with the business plan at $21 a month, and I'll show you the difference. And, you know, some people, if you're listening, you might think, oh, like, what's the big deal? Right? Well, pricing is a big deal. Right? Especially when you have the enterprise organizations that have tens of thousands of employees. So the difference between paying, $30 a month versus, you know, Chad GPT Enterprise, the last pricing I saw for that's around $60 a month. A lot of companies might have to look at this. Right? Also Gemini enterprise consolidates agent building, enterprise search, and governance in a single interface, and I think that's an important step forward.
Jordan Wilson [00:05:52]:
I'm gonna share, a little bit how, Google, has told the story of Gap, Figma, and HCA Healthcare, and how they've, been able to get some measurable gains using Google Gemini Enterprise. And, we're gonna talk about some users, the chase for users here in a little bit as well. All right. There's essentially, I think six key elements, and this is from Google that kind of constitute, Google Gemini enterprise. So one would be the world class models. Right? Most third party benchmarks, do have Gemini 2.5 Pro as the world's most powerful model. Depending on what benchmark you look at, sometimes it's GPT five Pro from OpenAI, but many, including probably the most important or the one that I give the most credence to, which is LM Arena, which is essentially the blind taste test of AI models. Gemini 2.5 pro has been on top for a while, so you get obviously access to the world's most powerful models.
Jordan Wilson [00:06:57]:
The agentic platform, so you could chat with Gemini to search and analyze information and orchestrate agents to automate workflows. They do have a no code, agent builder, which I've used. And anyone from marketing to finance can build their own custom agents grounded in their data. Next, you can enjoy, a suite of Google made agents like Deep Research, a notebook LM agent, and coding agents delivering value from day one. Again, this is from the Google website. Also, you can ground agents in your business reality so you can securely connect your company's data wherever it lives in Google Workspace and even Microsoft three sixty five, huge advantage there, and as well as business apps like Salesforce and SAP or data stores like BigQuery, to give your agents relevant context. Also, you can deploy and manage with confidence according to Google. You can centrally visualize, secure, audit, and govern all your agents with Gemini Enterprise, helping you to meet your security compliance in, sovereignty requirements within your organization.
Jordan Wilson [00:08:07]:
And you can leverage Google's rich agentic AI partner ecosystem. So you can automate cross platform workflows using Google's built in connectors to partner apps, accelerate your AI journey with their service partners, and find innovative partner solutions at the agent marketplace. So a little bit of copy there, from Google's, homepage on Google Gemini enterprise. So why? Alright. So reading this from Google again. So they say that it connects content across your organization to generate grounded and personalized answers. That's the thing that's gonna be interesting once it rolled out once it rolls out, and once we start getting some real world, public feedback on this. So, they mentioned this in their keynotes, but that over time, that Google Gemini for Enterprise is going to become predictive.
Jordan Wilson [00:08:59]:
It's going to know and understand what you want and what you, are going to ask based on, personalization, and just the, sheer amount of data that it has access to. So you can obviously process large volumes of enterprise data. You can sync and search, your data across different SaaS systems, such as, Salesforce, Jira, and Confluence. You can enforce access controlled search results in generative answers at scale. Yeah. So a lot of, great, features. So what Google is trying to put this out as as the new front door for AI, that is their words. And I kind of respect that positioning because the problem is Google's had like 10 side doors.
Jordan Wilson [00:09:52]:
And I think Microsoft has like 50 back doors. Right? What, like I field questions all the time from, you know, small businesses, entrepreneurs, all the way up to, you know, Fortune 100 companies. And these are the questions I get all the time. Right? People are like, oh, well, what's the difference between using Google Gemini? You know, if if I have a pro account, if I'm using Google Gemini in Workspace, if I'm using Google Gemini in Word, if I'm using Google Gemini in Vertex. Right? So technically, you could make the argument that in the same way that I have, you know, two windows open right now with two technically different versions of Google Gemini, my personal account and now my business account. But I think it makes sense. I think, eventually, Google may start consolidating, certain things inside the platform. So I like that.
Jordan Wilson [00:10:49]:
And also what they're trying to do is replace the fragmented tools that they have right now with one interface for all workplace AI interactions. Yes. You get access to all the latest models as well, as b o three video generation and image generation as well as their no code workbench. So you can build multi step agents without any, programming skills. I do wanna see under the hood a little bit more on that. I've been using it for a little bit, you you know, so far. I haven't been super impressed, with the no code builder. You know, maybe as I get more reps in, I'll be a little bit more, impressed.
Jordan Wilson [00:11:31]:
You know, I kind of been comparing it to, OpenAI's, agent builder that they just released. So it, OpenAI's seems to have more, features a little bit more, you know, bells and whistles. The Gemini one doesn't seem that great, but ultimately it comes down to performance. So we'll see how it performs. And I'm sure that, Google is going to add on to this over time. Also, they do have the prebuilt Google agents for deep research, which I'm gonna talk about that here in a little bit. I do like one new feature, in this as well as prebuilt, agents for data science and customer engagement workflows as well. And well, even if you're a Microsoft three sixty five organization, you can, connect that enterprise data to Google, Gemini enterprise.
Jordan Wilson [00:12:22]:
So let's quickly break down the different tiers. Alright? So on the personal plan side, so let's just say non team. You have your Google Gemini Pro, that costs $20 a month that also you know, most people have that and they don't even know it. So essentially, if you're paying, for Google One, so if you have a, you know, paid workspace account. Right? You have your, you know, Google Drive storage. Right? If you have a paid business account, you have access to Gemini Pro and you might not know it. The problem is and one of the it's been one of my biggest gripes, since day one with Google Gemini, which it's gotten better, over the last, like, four four or five months. But so many things.
Jordan Wilson [00:13:09]:
I've always had to use my personal Gmail. I couldn't use my work account, in the same way that I could use my personal account with Google Gemini. There were certain features and functions that just didn't work. Right? So even I pay for a Google Ultra, Gemini Ultra, which is $250 a month, but I use that on my personal Gmail. Because in, Gemini Ultra, there's certain features that just weren't available if you had a workspace account. So now I like that Google is finally trying to hopefully tear that door down, and I hope that we see these other, features and modes, and I throw modes out there in particularly. I I I hope we see these other modes and features make their way into Gemini enterprise and Gemini business. Specifically, two ones that I use so many times every single day, would be their gems, Google gems.
Jordan Wilson [00:14:07]:
And I love that you can use Google gems as an example inside of Google Sheets. So I don't see gems inside of my, Google business account, and I didn't see any mention of them, in, Gemini, enterprise. So Gemini, business or Gemini enterprise. And then the other one, canvas mode. Right? So I would love to see canvas mode, but don't see those so far. Alright. So you have your Gemini Pro personal plan, $20 a month. Gemini Ultra personal plan, $250 a month.
Jordan Wilson [00:14:36]:
It comes with a bunch of, you know, other, perks as well. You get 30 terabytes of storage. Who can use 30 terabytes of storage? My gosh. I think you also get, like, YouTube premium, some other things if you're on that Gemini Ultra plan. But now let's go to the, kind of team or business tiers, the new ones that were just announced yesterday. So, your Google business, Google Gemini business, that's the one that I'm on, cost $21 per month per user, and this is for small teams who need lighter controls. And then the Google, Gemini enterprise that starts at $30 per month per user, and that also allows you to connect to, some higher tier data such as Microsoft three sixty five, Salesforce, and SAP. So I don't have all those, connection options on the business plan, but I also don't think I'm a small business.
Jordan Wilson [00:15:28]:
I don't think I'll qualify for the Gemini enterprise plan. I reached out to the sales team. Haven't heard anything back, but I did, kind of get off the wait list for Gemini business, fairly quickly. So I do have a little, comparison chart here. But the the biggest, things I think between the Gemini business and the Gemini enterprise, well, you get, other, agent prebuilt agents. You get other, data sources, like I said, on the enterprise plan. So I do have a comparison here, and I'm gonna be sharing it in the, the newsletter as well. So if you wanna check that out, but you you know, I'll say this.
Jordan Wilson [00:16:12]:
If you're a smaller, medium sized business, you just may not qualify for Gemini enterprise. Right? And there's a limit to 300 seats on the business plan. So it depends on, you know, will you even qualify for the Gemini enterprise? You know? Yes or no? Who knows? But if not, I do think with Gemini business, you're gonna get a bulk of, you know, what everything that was announced. Right? The grounded, data, which I'm gonna talk about, the agent builder, and just the new platform, kind of the the new, you know, Google front door to AI. You will get access to all of that. And speaking of grounding, that is the thing I'm most excited about and I'm gonna talk about. But first, gonna pause for
Steven Johnson [00:16:58]:
a quick word from our partners. This podcast is supported by Google. Hey, folks. Steven Johnson here, cofounder of NotebookLM. As an author, I've always been obsessed with how software could help organize ideas and make connections. So we built NotebookLM as an AI first tool for anyone trying to make sense of complex information. Upload your documents, and NotebookLM instantly becomes your personal expert, uncovering insights and helping you brainstorm. Try it at notebooklm.google.com.
Jordan Wilson [00:17:31]:
Here is the one feature. And speaking of notebook l m, it's grounding. Okay? And let me I'm gonna oversimplify this. Alright. So, if there's any, data dorks in the audience, please, please, excuse me. I'm gonna simplify this. Okay. If you've used notebook LM and then you've used Google Gemini, you you probably can understand the difference between, grounding versus true generative answers.
Jordan Wilson [00:18:03]:
Let me give you an example. Actually just did a, you know, show on, notebook l m, recently. I think that was, yesterday or the day before. And I talk about how big of a deal grounding is because it cuts down, on the hallucination rate, and it increases trust and transparency. Right? So grounding is when you have to enter or rely on your data for a response. So the best example is notebook lm. If you upload, I don't know, a bunch of sources about, the nineteen ninety three bulls, one of the best teams ever, you know, I'm from Chicago. So, and my name's Jordan.
Jordan Wilson [00:18:50]:
So, so if you upload in notebook LM, a bunch of information about the ninety three bulls, and then you ask it about the ninety four bulls, it'll say, don't know. If you ask it, what's the weather today in Chicago? It'll say, don't know. Right? If you upload that same information into Google Gemini, into a gem or something like that, and you ask about the 94 bulls, it'll tell you. If you ask it about the weather in Chicago, it'll tell you. Right? And then the problem and this isn't a problem with Google Gemini. It's a problem with all large language models, ChattGPT, Claude, etcetera, Copilot, everything. Right? Even when you upload your data. And if you have, specific instructions.
Jordan Wilson [00:19:33]:
Right? If you're using a project or a GPT or even a just in the body of a chat, and you upload, a file, if you upload your documents, even if you tell the model, hey, Only use this. Don't use your own training data. Don't, you know, query the Internet. Half the time, it's not gonna pay attention to you. Right? Unless you're pretty decent at prompting. That's why grounding is so important. It starts and it filters through the ground up through your data. So personal Gemini, accounts don't have that.
Jordan Wilson [00:20:07]:
Right? Yes, you can connect kind of their, apps. I I believe is what they're called. Right? So you can, you know, speak or chat with Gmail or speak or chat, with a a a certain Google Drive, document. But it's not always super accurate. Right? And I've detailed that over the the last, you know, year and a half on this channel, maybe a little bit more on the YouTube channel. It's gotten much better, but there's a huge difference between, you know, essentially connect like connecting a, a file to your chat and having an enterprise, large language model that is grounded in your data. That's why I love notebook ln. It is literally only going to look at the data that you give it and nothing else.
Jordan Wilson [00:20:55]:
It's not quite the level that we're looking at, with Gemini Enterprise, but I've been extremely impressed in my very limited testing so far and its ability to ground answers in the data that it has access to. Okay? And you know what? I wasn't planning on this, but, you you know, I'm probably do a quick demo. Actually, I'll have I won't be able to do that one live. Right? It's it's tough to do some demos live that are connected to your personal data. Right? Because I don't have, the ability to connect all these other third party platforms because I don't use them. So a lot of it's like my Gmail and, you know, my Google Docs, and I don't wanna accidentally, you know, put someone's email out there. But I'll do another show maybe in the coming weeks and just have screenshots and show the comparisons. So anyways, the Gemini enterprise connects securely to your company wide data system.
Jordan Wilson [00:21:47]:
So anything obviously on the Google side as well as Microsoft three sixty five, Salesforce, SAP and others. And the enterprise platform includes role aware access controls ensuring employees only see what they have authorized, access to see. That's huge as well. Because when you talk about enterprise, you have to understand data security, you know, user permission, authorization, all of those things. And there's also inside this, there's a central governance layer that also manages all agents, data connections, and security from a single council. So I have a little, visualization here on the data access approaches. So if you've used Google Gemini's apps, if you've used, Google Gemini in workspace apps, or if you've just used it from the personal side of Google Gemini, essentially how it works. Yeah.
Jordan Wilson [00:22:42]:
Even when you're connecting via an app. Okay. So if you connect, inside Google Gemini and you're chatting with your Gmail app, as an example, what it does is it will go through, and semantically search for a message that you give it based on a keyword. Right? And normally it'll do an okay job, but essentially what it's doing is it's doing a manual file selection. It has a limited context access, and it's a fairly basic integration. If you're using the personal version of Google Gemini and you're connecting even to your Google products via their connected apps. It's really just a file by file basis, which if you know exactly what you're looking for, if you have all your settings correct, and if you prompt Google Gemini, the personal version, well, you'll usually get an accurate response, but those are big ifs, right? On the Google Gemini enterprise side is completely different. I mean, we're talking about automated data federation, comprehend comprehensive contextual understanding.
Jordan Wilson [00:23:53]:
In my couple examples, I was honestly kinda shocked. Right? I was. I was like, my gosh. This is really, really good. Right? The difference with notebook LM, is you're manually adding all of your files one by one. So you almost have, like, an expectation that, hey. It's like it's almost like I helped build this thing, so I know what's, you know, I know what's under the hood. When I was using the Google Gemini business, you know, I just connected everything.
Jordan Wilson [00:24:22]:
And I was like, this is this is actually pretty impressive. It has a multi system integration, as well as the real time data access, and it is grounded. So if you're asking it, a question about your data, it just knows, right? I was even trying to trick it and trip it up using abbreviations that I didn't think it would know. And it got it surprisingly so because, it looked through multiple, it looked through my, my calendar. It looked through my, Gmail and it looked through my Google docs and it connected, it kind of triangulated a certain acronym or abbreviation that I didn't think it would be able to figure out and it did. And I was fairly impressed by that. Right. It's not often I'm impressed by these things.
Jordan Wilson [00:25:14]:
All right. Google did give a couple of use cases. I'll just mention one here because you can go read about the rest. They mentioned how HCA Healthcare, one of their pilots, in the, Google Gemini enterprise, said that the nurse handoff automation estimated to save millions of annual hours, millions of annual hours, by using, Google Gemini on the enterprise side. Also the Best Buy one, pretty cool. They said they achieved a 200% increase in customer self-service in 30% more resolved inquiries. All right. Enough chatting up the features.
Jordan Wilson [00:25:53]:
Let's get to the good stuff. Is it too little too late for Google Gemini? Let me be honest. Fifteen months ago, eighteen months ago. I think most people, including myself would say, you know, Google Gemini might not even be top three. Right. I think there was a period of time, maybe about, early twenty twenty four, where it was definitely OpenAI number one, Anthropic Claw number two, and probably Microsoft Copilot number three. Right? And Google was like, yeah. Alright.
Jordan Wilson [00:26:36]:
Yeah. They're, you know, number four, maybe they can catch up top three. And I think one of the reasons was that they didn't have a front door to AI. It was a very fragmented approach. And I think one of the reasons why when I would go around to both in person rooms and digital rooms and, you know, asking all the people who used Google. Hey. How many of you use Google Gemini? And so few hands went up. And I think one of the reasons is it had problem connecting to its own services.
Jordan Wilson [00:27:10]:
Google Gemini had a ton of issues connecting to its own services, even Google search, right? Even Google search. I've I've detailed this, way, like way too much, probably, especially in late twenty twenty three and early twenty twenty four. So you have to think, is it too little, too late? Like I said the enterprise platform looks impressive. Everything on paper, you heard the features that, you know, they're checking all the boxes. But Chad GPT is the user leader by a lot and Microsoft is the literal operating system owner. Right. And I don't think you have to worry about apple. So is it too little too late for Google Gemini to be a, the top tier enterprise AI company? Let's look.
Jordan Wilson [00:28:05]:
So right now, Chad GPT says they have 800,000,000 weekly active users. Alright? Google Gemini, last reports 450,000,000, but they are gaining, and they are gaining especially across their different platforms, like notebook, notebook l m, nano banana. Right? They're coming out with all these, you know, other products that, you know, I do see now that they're integrating into the enterprise platform as well. The enterprise platform has a, direct tie in into notebook LM. You can use, their very viral v o three, their image generator as well. Then you have Microsoft Copilot with a 100,000,000 monthly active users. So a 100,000,000 Microsoft Copilot, Google Gemini across their apps, four fifty million. But can anyone catch ChatGPT? Well, you know, ChatGPT, that's just total users, 800,000,000.
Jordan Wilson [00:29:09]:
Microsoft Copilot, I would guess that the majority of theirs, not a 100,000,000, but they have a pretty high percentage of paid users because of their, early on stranglehold under the enterprise. They were the first, enterprise company to come out with AI for the masses. But Google Gemini, I would say is far behind. You know, most enterprise companies I talk to, they're on Microsoft Copilot. Many of them aren't fans. Many of them are also using chat GPT at the same time. So, you know, I think it what it boils down to is, number one, can Google Gemini convert a lot of their, kind of middle tier businesses? Ones that maybe aren't quite big enough to want or need Microsoft Copilot. You know, can they win over the, Mac crowd? Right? Because if you're Windows PC organization, which so many, especially in The US, so many enterprises run on, you you know, Windows PC.
Jordan Wilson [00:30:15]:
So for everyone else. Right? So you think, okay. Who's who's the Mac crowd? Right? Startups, small medium businesses. A lot of them were early on flocking to ChattGPT. And ChattGPT's enterprise, right, I I think, they've reported that, I think it's more than 90% of the Fortune 500 use ChattGPT. So can Gemini cut into each of their lead? And I think, yeah, maybe they can. But before I'm gonna end on that before I'm gonna give you some of my thoughts so far, by using, Gemini business. So, the agent builder, it's okay.
Jordan Wilson [00:30:57]:
I don't have a ton of practice in it so far. It's a little limited, in how you can build the agents. Right. It's not one of those, you know, when you see the the kind of nodes. Right? That's what I have on my screen. It's similar if, like, if you're looking at a screenshot, it's similar to, OpenAI's, agent builder that they just released last week, but, or this week. But the agent builder has way more configuration, instructions. Right? And I think it's also like when you're working with nodes, like, right, these drag and drop little boxes on a canvas, you assume you can move them all over and, you know, have all these tiers.
Jordan Wilson [00:31:38]:
And, I don't know. Maybe I'm just not good at it yet, but it seems, very limited, in functionality, so far in terms of what you can do to customize the agents. However, the performance in my limited testing is pretty good. Right. So it's maybe one of those things if I had, you know, if I had, you know, SAP data, if I had, you know, Salesforce, if I had these other platforms, maybe I would get more utility out of the agent builder, but it's simple, straightforward, and the performance is good so far may not be super robust and flexible, but for what you need, it may do the job. One of their agents, I like obviously is their deep research agent. And one of the reasons I like this is because it also connects to any of your connectors. Right? So, you know, whether it's the Google products, Confluence, Jira, OneDrive, Outlook, etcetera.
Jordan Wilson [00:32:37]:
Right? Any of your connectors, you can run their deep research agent across all of that data and it's dynamic. So overall my thoughts personally, the first, I mean, if you look back at it, the first iterations of Google's main AI products, aside from notebook LM, haven't been very good. Some of them have been bad. Right? Remember Bard Bard wasn't good. First version of Google Gemini. Wasn't good. When Google rolled out Gemini and workspace, it was bad. Not that it wasn't good.
Jordan Wilson [00:33:21]:
It was bad. It got better. Right? Google AI studio was was okay, but it was clunky. Now it's a beast. Right? Google Gemini itself when it came out was was bad. Right? Like I said, but Google enterprise or Google, you know, Gemini enterprise or Gemini business, it's actually starting off really good. Right? First version of ChatGPT also full disclosure. Absolutely terrible.
Jordan Wilson [00:33:50]:
Absolutely terrible. I hated it. I didn't use it. Right? Or I was I I was using other versions of that technology, you know, at the time through other platforms. First version of ChatGPT was awful. First version of Cloud was awful. Right? So it's not like I'm picking on on Google's first iteration of of AI products. That's just the truth.
Jordan Wilson [00:34:10]:
But when I'm using Gemini enterprise, I'm like, wait, this is pretty impressive. Right? And let me give you just one example. So I ran the same prompt connected to, and I ran it across four different platforms. So I ran it across my, Google Gemini pro, account, my new Google Gemini business account. That's grounded keyword there. Okay. Then, my chat GPT, pro account and then my paid Claude account too. I forget if that's a pro or what the heck it's called.
Jordan Wilson [00:34:51]:
Alright. Same prompt. I connected all of the same things. I connected my Gmail, my Google calendar, and my Google drive. Ran the same prompt. Couple of times, different variations, trying to trick each system each time. I couldn't trick Google Gemini business. I couldn't.
Jordan Wilson [00:35:16]:
It didn't get anything wrong either. And the sourcing, because it's a grounded model, was extremely impressive. It was markedly better in at least for me, because I suck at keeping up with things. And, because of the podcast, I get so many emails. I can't keep up. One of my kind of recurring prompts or actions is always, you know, checking my, my calendar against my Google drive against my Gmail. And now I'm going to be using, I'm going to be using this new Google Gemini business for that. And it is ridiculously good.
Jordan Wilson [00:36:00]:
All right. So final take. Well, I don't think that Google Gemini overnight is going to take away very much market share, from Chad GBT or Copilot. However, I do think they're going to take small market share from each of them because it makes sense. I think for certain companies, it makes sense. I think for companies, even that are Microsoft, you know, PC companies. Well, I think Google Gemini can win share from them because unless they are deeply ingrained, and have a great internal Microsoft, training program at your company, well, Google Gemini integrates, with the Microsoft three sixty five, you know, suite of products, not all of them, but the main ones. Right.
Jordan Wilson [00:36:59]:
So I think they can take market share from them. Chat. What about chat GBT? Right? Well, the fact that you are grounded in data. Right? So for me, for me, I use Gmail. I love Chad GPT. The fact that I can ground everything in this new Gemini, business in Gemini enterprise means that I'm going to be using Chad GPT less. The difference between a large language model being grounded in your data versus it, you know, going through, kind of more of a, you know, going out and looking at files individually. It's a huge difference.
Jordan Wilson [00:37:44]:
And from a pricing perspective too, right, Gemini enterprise, half the cost of Chad GPT enterprise. So for me personally, I would still probably use both. Right? But I know many enterprises, right? If if you're paying for tens of thousands or, you know, thousands of seats, you gotta make a decision. So a lot of people, when they roll out an enterprise account with Chatt GPT, as an example, they start with, you know, a 100 seats, and then they go up to 500, and then they go up to a thousand. So I think that Google Gemini actually has a legit shot at picking off a decent amount of business. I think they can pick off from Microsoft Copilot just because their models are better, overall, top to bottom. And Microsoft is starting to diversify their models, from just not just OpenAI. And I think they may pick off some, either current, enterprise customers or prospective, enterprise customers from OpenAI as well on price.
Jordan Wilson [00:38:46]:
But the big takeaway here, Google before this, I don't think that they were a top two player in team enterprise AI. Right? For developers, IT, right? Their their their Vertex platform, Google AI studio, but for teams, I don't think before this, Google was a top two player. Now they can be. All right. And it's going to be more competition at the top, which means that we all win. So that's my final take. I hope this show was helpful as we went over the new features in Google Gemini enterprise. So, if you missed anything or if you just need a little more clarification, don't worry.
Jordan Wilson [00:39:37]:
We're gonna have it all in the newsletter, so make sure you go to youreverydayai.com. Sign up for the free daily newsletter. If this was helpful, if you're listening, on the podcast, please make sure to subscribe, like, and follow the show. Thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.