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5 Actionable AI Strategies to Optimize Workflow Using Google Tools
Modern businesses are facing the challenge of integrating AI without unnecessary complexity. Recently, simple AI strategies that leverage Google’s ecosystem have emerged, providing concrete benefits for everyday workflows. The following insights outline five highly practical and immediately applicable approaches, each backed by real examples, that enable business owners and decision-makers to maximize the impact of AI in their organizations.
Deep Research with Gemini: Accelerate Market and Competitive Analysis
Gemini’s Deep Research feature dramatically reduces the effort and time required to synthesize large volumes of information. Rather than manually scouring multiple sources, Deep Research taps into Google Search, reviews over a hundred relevant sites, and consolidates findings into coherent, actionable reports—including data visualizations and concise summaries.
For scenarios such as market research, competitor analysis, or evaluating new business strategies, Deep Research enables fast decision cycles. The tool’s synthesis of diverse perspectives also provides a built-in mechanism to surface potential blind spots, challenge assumptions, and encourage more rigorous strategic thinking.
Tactical Business Value:
Reduce research from days to minutes for projects like vacation planning, technical investigation, or market mapping.
Upload proprietary documents or datasets to contextualize research according to organization-specific needs.
Instantly generate summaries, charts, and exportable reports ready for presentation or further analysis.
Contextual Learning via NotebookLM: Transform Information Management
NotebookLM offers a platform for businesses to organize internal and external data—including company documents, web links, and YouTube content—and make it instantly explorable. Unlike stand-alone LLM chatbots, NotebookLM grounds every response in the specific source materials uploaded.
For onboarding, training, or large-scale information management, NotebookLM allows teams to create living documents that employees can interrogate, reformat, or summarize according to personalized preferences. This moves information management from static repositories to dynamic, interactive learning experiences.
Tactical Business Value:
Streamline onboarding with personalized, conversational agents that reference your company’s own policies, org charts, or past project materials.
Convert complex documentation (legal agreements, T&Cs, technical specs) into digestible Q&A formats, summaries, or flashcards.
Enable always-current knowledge bases without requiring additional coding or infrastructure.
Gemini CLI & Code Assist: Lower the Barrier to Custom Tool Creation
With Gemini CLI and Code Assist, the ability to build software and automations is no longer confined to technical roles. Through natural language prompts and guided assistance directly in the command line or within code editors, business analysts, product managers, and non-traditional “builders” can quickly generate scripts, prototypes, and even lightweight applications.
The focus shifts from technical syntax to problem intent: describe what you need, iterate with the tool, and produce operating prototypes far more efficiently than traditional development cycles allow.
Tactical Business Value:
Quickly create data summaries, automations, or reporting scripts to bridge gaps in business software.
Empower non-technical staff to validate operational improvements before involving engineering for production solutions.
Save resources by reducing the period from idea to prototype and supporting the “demos over memos” mentality for innovation.
Background Task Automation with Google JUULS: Delegate & Coordinate at Scale
Google’s JUULS exemplifies the use of background AI agents for handling routine or specification-driven tasks. By providing clear, structured directives, organizations can automate the creation of documents, code, or quality control checks, freeing up staff to focus on strategic priorities.
JUULS integrates with code repositories and project workflows, supporting asynchronous collaboration where work is handed off to AI agents, reviewed upon completion, and iterated as needed. The approach enhances throughput and supports parallelization of multiple projects.
Tactical Business Value:
Increase productivity by setting up multiple concurrent AI-driven projects (documentation, feature development, compliance checks).
Maintain oversight and quality control by coordinating agent outputs, while delegating repetitive technical tasks.
Scale the impact of small teams without overreliance on headcount growth.
Embedded AI Functionality Across Google Workspace: Everyday Operational Uplift
AI capabilities are surfacing natively within widely used Google Workspace tools—ranging from Sheets with AI-powered table building to natural language processing in Cloud BigQuery and enhanced Google Search interfaces. Instead of learning new platforms, users benefit from advanced automation and intelligence within the familiar environments they already use.
This ongoing integration enables businesses to flatten the learning curve and encourage organization-wide uptake.
Tactical Business Value:
Allow team members to create dashboards, perform analytics, or extract insights with simple prompts rather than specialized training.
Systematically reduce the operational burden of manual, repetitive digital tasks across departments.
Foster a culture where expressing intent and deriving insights is easier than ever, making AI an everyday part of decision-making.
Conclusion: Proactive Participation Unlocks Competitive Advantage
Adoption of these Google AI strategies does not require wholesale disruption or vast technical knowledge. The most significant gains emerge when organizations actively experiment with free, readily available tools, continually update their workflows, and promote a culture of curiosity and humility. By embedding AI into research, learning, prototyping, and everyday operations, organizations gain measurable speed, flexibility, and depth—instead of being passive recipients of change, they become active drivers of their own business evolution.
Topics Covered in This Episode:
- Five Simple Google AI Workflow Strategies
- Gemini Deep Research for Fast Analysis
- NotebookLM Grounded AI Learning Exploration
- Gemini CLI and Code Assist for Builders
- Google Jewels Autonomous AI Coding Agents
- AI-Powered Context Integration in Google Workspace
- AI Tools for Change Management and Productivity
- AI Interface Revolution Across Google Products
Episode Transcript
Jordan Wilson [00:00:46]:
I think sometimes when we think about implementing AI into our day to day workflows, we overcomplicate things. Right? We think sometimes we have to be very technical or it's a big endeavor to get started, and that's definitely not the case. And today on everyday AI, I'm excited for today's show because we're gonna be going over five simple AI strategies to supercharge your workflow with Google. So I'm excited. I hope you are too. Let's get into it. Welcome to Everyday AI. If you're new here, Everyday AI, it's an unedited, unscripted livestream podcast helping everyday business leaders like you and me not just keep up with everything that's happening in the world of AI because it is hard to do, but this show helps us make sense of it and grab the information that we actually need to grow our companies and our careers.
Jordan Wilson [00:01:38]:
If that's what you're trying to do, awesome. Starts here. But if you wanna take it to the next level, make sure you go to our website, youreverydayai.com. Sign up for the free daily newsletter. We're gonna be be recapping the five simple strategies we're gonna be going over today as well as keeping you up to date with everything else happening in the world of AI, so make sure you do that. But without further chitchat from me, let's bring on, an expert from Google to help walk us through this. So I'm excited. And livestream audience, if you could, please help me welcome, to the show.
Jordan Wilson [00:02:10]:
We have Richard, Saroder, who is the, senior director and chief evangelist at Google Cloud. Richard, thank you so much for joining the Everyday AI Show. Yeah. Really happy to be here, Jordan. Alright. So tell us, like, what the heck do you do at Google Cloud? Because it sounds like you do a lot, but walk us through your day
Richard Seroter [00:02:28]:
to day. Yeah. I definitely can't even explain it to my parents. Right now it's a problem. But, look. I lead, teams like developer relations, our technical docs team, our open source program office. Just anybody who's about how do we inspire and activate builders on Google Cloud. How do you give people the confidence? Right? There's a lot of information out there, but how do you give them the confidence they can do it too So they can use cool open source stuff, cloud services, AI stuff.
Richard Seroter [00:02:52]:
I spend most of my day talking to customers, working with my team, going hands on. I still code a decent amount. I'm not good at it, but enough to use the products that I don't think we should be talking about, products we don't know how to use.
Jordan Wilson [00:03:04]:
And, you know, I'm curious. So throughout your years at Google, right, and, obviously, on the AI side, Google's been there for a very long time before the large language model, surged from a couple of years ago. But I'm curious. The people that that you're talking to specifically about AI, is it more, yes, the technical dev, type people? Are you talking to the everyday business leader? And is it changing as AI, especially generative AI, becomes more and more accessible?
Richard Seroter [00:03:32]:
Yeah. I mean, I've been in this field now too long. I don't guess my age. But this is the I mean, I think this we're at Internet level in terms of people who care about this outside of IT. No one outside of IT cared about Kubernetes, serverless, arguably cloud computing, maybe mobile, but we're back to, like, Internet level conversation of, like, the people I talk to are sometimes in marketing, sometimes in HR, sometimes in CIO roles. It's not just builders. Of course, for tech folks, it's awesome. But the difference is this isn't just about how do I improve my day to day work with tech stuff.
Richard Seroter [00:04:02]:
Some of it's like, how do I change my business mix and products they offer? How do I change how my team works? I don't think this is just about what you can do. I think it's about how you do it. And we haven't had a change like that industry wide in a long time.
Jordan Wilson [00:04:14]:
Yeah. That's a that's a good point. And, you know, I think people, when making comparisons about generative AI and and what it can accomplish, they're not always going back to, you know, cloud or mobile. They're saying, like, electricity. Right? Like, so much bigger than that. You you know, I'm curious even for you before we get into our five strategies. Like, how how big or how much of an impact, has Gemini and just Google AI in general had on how you work personally?
Richard Seroter [00:04:44]:
Yeah. I mean, look. On one hand, I don't wanna be one of these, wacky AI influencer types who says everything's unbelievable. Everything changes with AI. Like, look. It's still, hopefully, good people using good tools. You still need human thought. You still need human creativity.
Richard Seroter [00:04:59]:
Some of these things don't work as they advertise. Some things are better. Some things are worse. It's all great. These are tools. These are ways we do better work. Now they're transformative tools for some teams. And so for myself, and we'll talk through some of these strategy things, how I research, how I learn, how I build, how I do some of my day to day things.
Richard Seroter [00:05:16]:
Absolutely. And, look, there's other areas where I am purposely staying low tech. I write a daily newsletter and I write every word. I don't want AI to do it for me. And, like, I I learned by writing. I learned by doing that work. And so I think all of us wanna make sure we hold closely to those things we actually love doing and make sure that we're building depth, not just sort of shallow knowledge because we've outsourced all our thinking to the AI. So use this as a tool to augment yourself, not replace yourself.
Jordan Wilson [00:05:41]:
No. Like what Richard just said there, like, take take that away. That's so important. Don't just, you know, kick everything over to AI. You still have to practice those skills, the human side, if you really wanna augment to the level that you can. Right? So real quick, I'm gonna give everyone the five different strategies, and then we'll unpack them, have Richard unpack them one by one. So number one, Gemini deep research for analysis. Number two, notebook l m for exploration.
Jordan Wilson [00:06:08]:
Number three, Gemini CLI and code assist to build. Number four, jewels for background work. And five, AI just kind of rolling out everywhere. So let's get into them. Let's start at the top. A tool that I love and use all the time, Gemini Deep Research. Richard, can you explain it for us, and how can people what's a good strategy to put this into the to your daily workflow? Yeah.
Richard Seroter [00:06:32]:
Yeah. I mean, with all of these and the the ones you called out there, these aren't just about using new tools to me. These are about forming new habits. Mhmm. And that's the hard part. That's the change management piece. Right? Like, you could one off use any of these and then never use it again. So what we're all trying to do is almost reprogram ourselves and be like, when I get a hard question, what do I do first? To me, that's what AI first means.
Richard Seroter [00:06:51]:
AI first does not mean I use AI for everything. It means that when a situation comes up, I ask myself, very help AI can do here? No? Fine. Do your thing. Yes? Do it. So Gemini Deep Research is part of the Gemini app, and we've added a ton of stuff to that over this year, whether that's helping you vibe code an app or, you know, build a storybook for your kids, which is crazy. All sorts of cool things. But deep research is awesome. And there's other deep research y things out there, but I'll focus on this one.
Richard Seroter [00:07:17]:
The idea and I just used it, last week. I had a complex problem. I was trying to actually bias it and say, like, look. We're at Google. We're only focused on this part of the application delivery right now. Is the rest of it kinda boring? Should I ignore it? And so what deep research did, it was it went to because it's connected to Google search, which is awesome. It went and looked at I think I count it was over a 150 sites, synthesized it all, gave me a report that repudiated me constantly saying I was missing the boat, which was amazing. Fables, charts, all this stuff in about six minutes.
Richard Seroter [00:07:49]:
So this was work legitimately that would have taken me two to three days. I would have gone to a Google search. I would have typed in words. I would have clicked blue links. Keep doing that. We need that ad money. Like, it is gotta keep the lights on. But if I'm doing a research project, why in the world would I do that today? Instead, I'm going to deep research.
Richard Seroter [00:08:05]:
I'm getting a really good synthesis, and now you can upload your own files. You can redirect that research. The results of that research, I can turn into different forms and export to a doc. And so I think we change how we do research. No one should say I need weeks and weeks from normal projects that we might be doing personally, planning a vacation, understanding competitors, doing analysis of a market, figuring out a business scheme, figuring out a technical architecture. Instead of just kinda going in and doing all that yourself, how about you kick start it? To me, AI is the best thing for blank pages. Like, it's the easiest way to now go from I don't know where to start to here's something I can start with. And throw it away, keep 5% of it, but none of us just wanna stare at a blank page going, what do I do next? So deep research is an amazing way to go.
Richard Seroter [00:08:52]:
I have a question. Might be involve a lot of different angles. Can you give me a a look at that and get it back in minutes and go, that's not it at all? Or shoot. I'm even asking the wrong question. I don't wanna find that out three days later, a month later after my giant research project. I wanna know now. And so the ability to learn faster might be the only remaining professional competitive advantage out there. And so how do we all just learn faster? That that's how you stand out.
Richard Seroter [00:09:19]:
My
Jordan Wilson [00:09:21]:
so so much to unpack there, Richard. We're like we might have to just cancel the other four because I wanna talk to you just about that for for multiple hours, but I won't. But, you know, a couple things that I heard there that I I really wanna zoom in on. Is this really talking about, like, change management? Right? So even for me personally, this is how I use deep like, deep research. If I wake up and I'm like, oh, I'm gonna go grab a coffee and sit down, I sit down first, have Google Deep Research start on something, get my coffee, and come back. So this is like making those little changes, but something else you said, intentionally have it, like, not challenge your thinking, but sometimes go against a preconceived notion. Right? But doing it with Google Deep Research. What's maybe a a strategy that you can leave people with on how they can use deep research for maybe either challenging their thinking or when you are just, you know, having to take on a big project instead of staring at a blank page? What's what's maybe a a piece of advice you have? People?
Richard Seroter [00:10:18]:
Yeah. I would say stop thinking of AI as a great way to get answers. Think of it as a way to get great questions. And we don't use it that way. But if you go to deep research and even or to, frankly, Google AI mode, go to google.com/ai, go to any of our AI tools and say, I have this issue. What questions should I be asking, or what should I be thinking about? I've done this with, I use some tools sometimes if I get a really technical doc from a team, and I know they're just trying to show me up. I can pass that doc and go, what are really smart questions to ask about this doc in my review? Now do I take them all? I don't know. But you might have sparked something with me.
Richard Seroter [00:10:51]:
Go, oh, that's a good angle. I should think about that. So generative AI is pretty good at that. And so sometimes you just don't know what to ask. And so it's great to sometimes give adversarial questions to a deep research thing going, this is what I think, but tell me why this is wrong, or tell me what I'm missing, or look for counter views. Because, honestly, I think we do our best strategic work when we take these three sixty views of an issue and don't just get myopic about the preconceived solution we thought. And, you know, I
Jordan Wilson [00:11:19]:
don't think we'll have time to go feature by feature and update by update. But one thing that I think is important for our audience to know, a a new, update in Gemini Deep Research that I am loving is now the ability for it to, you know, go through your calendar, to go through, you know, if you choose to connect it, right, if you enable that, to go through your email. Because that's one thing I struggle with so much. And then to combine it with normal, kind of deep research across the web, you you know, help us understand what that can unlock for people because even for me personally, that unlocks so much.
Richard Seroter [00:11:54]:
I mean, that's the contextualization of all LLMs are amazing, and we'll all keep shipping amazing things, and that's awesome. But this world of more agent stuff and agent stuff, really, just how do you give kind of overlay these models with things like tools which access other real time data or your personal data? And you have long running conversations, not just stateful one offs with an LLM. And so things like deep research are agents and be able to pass in data that might be your inbox or your whole set of style guides that you wanna feed in and ask it for ideas on a look and feel. The model doesn't know that by itself. And so being able to give this thing context, that's why you hear this term context engineering. I just don't wanna write a clever prompt. That's cool, and that's a skill, but it's insufficient when I wanna give it a bunch of things like, hey. You know, here's a bunch of SOP documents.
Richard Seroter [00:12:42]:
Help me figure out new standards for my team. You can't figure it out by itself. But once you give it that context, you get something pretty awesome back. So thinking about your context, what do I need to to tell this thing so it can properly give me what I need and not just assume it's a magic robot who knows all this stuff? Give it a little help. This is again where you stay in control. I think that's what we've learned even the last twelve months. There was a lot of fear of this thing's just gonna do all our work. These things don't know as much as we do, regardless of what some influencer types say.
Richard Seroter [00:13:10]:
Like, they need context. They need certain things. You have that. You're the orchestrator. You're the engineer. Every individual is now becoming a manager because you are managing the work of these things. You gotta change your mindset to think about that.
Jordan Wilson [00:13:23]:
Yeah. It's it's a great call out, and even just the concept of providing more and more context is going to make kind of the agentic work from Gemini Deep Research, much more fruitful in the long run the more context you share. Right? Speaking of sharing context, I mean, notebook LM for exploration. Like, I I can't and anyone that's listened to the show, I've talked for literally countless hours about how much now I just rely on NotebookLM and how it's completely changed, not just how I work, but how I think and how I organize myself. Right? Richard, maybe, for those who haven't heard me talk for hours about NotebookLM, explain a little bit what it is, what it does, and then let's maybe, dive in a little bit deeper after that.
Richard Seroter [00:14:11]:
Yeah. I didn't understand what it was when we first announced the Google IO whenever it was. Like, that's neat. What the heck is the use case for that thing? And then I finally kinda I had some light bulb moments. But how do you have a bunch of data that you collect on your terms? Could be your own data, could be links, could be YouTube videos, could be now it connects to, like, Drive and pull in your own stuff. And then how do you then turn this into a form that you can learn however you want? That's the big takeaway is at this point, 2025, you can learn what you want how you want to. And I don't think I don't think you and I are gen z. Let's pretend we're not.
Richard Seroter [00:14:43]:
You and I learned from teachers teaching us in class one way to 30 kids. We read books, maybe had some CDs, watched maybe an online training. That wasn't personalized. That was whatever the heck would be delivered to the masses. And if you fell behind, you fell behind. If you had a dumb question, you'd either ask or keep it to yourself. This is the first time where you and I can learn how we want to. I can use notebook l m to turn piles of information into a fifteen minute podcast.
Richard Seroter [00:15:08]:
Listen to it on the way to work while I'm walking the dog. I can turn that into a video podcast. Maybe I'm a visual learner. Flashcards because I'm about to get tested on it. Sounds good. Have a chat with the data going, I don't understand this or make sense of this. There's no company that should have the same onboarding process they have today in two years because they're all terrible. Like, all the onboarding is just here's a pile of information, study it, and get to work.
Richard Seroter [00:15:31]:
Why are we doing that? You should be giving every new hire a link to your notebook l m instance going, here's our business. What do you wanna know about it? Oh, you wanna understand vacation? We'll give you all the details. Just chat with it. You wanna figure out, you know, how the org is set up? It'll figure out the org chart for you, tell you how like, all of a sudden learn it on your terms. And so it's free. We've made it available to students. It's amazing student tool, personal tool. But, again, this is we talked first about changing how you research.
Richard Seroter [00:15:57]:
This is changing how you learn. And it's a it's, again, it's a new habit. It's a new muscle. But all of a sudden, we're all learning the hard way until we
Jordan Wilson [00:16:04]:
use things like this. Once maybe, you know, even going back to your initial reaction when you heard about it at IO and you're like, okay. What's the use case? Right? Now fast forward that it's been out for, you know, a year and a half, two years, you know, maybe what's an important takeaway, that you've maybe experienced or your team has experienced using notebook l m that you think, would be helpful for our audience? Yeah. Look.
Richard Seroter [00:16:30]:
I saw I think it's a couple of the events Google ran over the summer where the result of it was a notebook l m because no one pays attention to the 200 announcements we just made or all the videos or the 15 blog posts or whatever. And so even as you're doing big complex things, guess what? No one's paying attention. It doesn't matter if it's Google's event, Amazon's event, your crazy awesome launch. No one cares about it as much as you do. Awesome. Give them a way to synthesize it then. So first off, for every big complex thing even you or your business does, give an easy way for people to digest it all on their terms and learn about it. And then the other way is I think all of these tools are amazing at taking really complex things and helping us finally understand what they mean.
Richard Seroter [00:17:11]:
Whether that's AI in Chrome or whether you use NotebookLM or Gemini app, whatever it is, give it the terms and conditions to your credit card, which no one in every history of time has ever read, or your employment agreement where you're like, this maybe this is fine, but this is 15 pages. Give that to the AI and go, what's a weird thing in here that I should be aware of? Awesome. Where else can you do that? So look for those applications to either make the complex very simple or to take a very big distributed set of announcements and turn that into something that anybody can figure out. Yeah.
Jordan Wilson [00:17:41]:
And it probably would have been helpful for me to set the stage a little bit and just, you know, for people that aren't familiar with NotebookLM, the concept of of grounding. Right? Because I think sometimes people are like, well, okay. Notebook l m is powered by Gemini. Why wouldn't I just use Gemini for these things? So, Richard, could you just kind of explain kind of the concept of how notebook l m just grounds answers in the information that you give it?
Richard Seroter [00:18:01]:
Yeah. It's like we said. Look. A lot of these things are all gonna be based on the same, let's even say, the same Gemini model or whatever your favorite model is. It's about the experiences on top. That's where the magic's happening now. And so can I solve similar problems with different tools in different ways? Maybe. But notebook LM is purpose built to say, let me take a bunch of your information, your preferences, your links, ground it on your truth data, and turn it into a form that you can consume.
Richard Seroter [00:18:24]:
And that's just what it's good at. Do I would I use that to look up the latest baseball scores? I don't think so. And maybe you could even do it, but that would be a weird use of it. I would jump to the Gemini app or whatever. So just knowing what these things are good for and even Google Cloud customers have it all baked into Gemini enterprise. It can be private just for you. Google doesn't train on it, all that sort of stuff. So you have enterprise versions of notebook l m, the Gemini deep research experience, all in Gemini enterprise for corporate customers.
Richard Seroter [00:18:52]:
So a lot of this is just about what's the interface you need to solve a given problem. And notebook l m is amazing if you just say, I wanna learn from a lot of material that I've curated, and that could be my curriculum for this class this semester. That could be about my business, but it saves it. And then it's something where I can just keep growing it or removing it or pairing it, learning about it different ways. Such a unique experience. It's free to use. Everybody can use it on their phone, web app, party on.
Jordan Wilson [00:19:20]:
Yeah. And it's, the the the fact, number one, that it's free. But number two, the fact that this technology exists and is this easy is still bonkers to me. Right? Like, thinking back, like, two years ago, and, like, before notebook LM and then seeing what we can do with it now, I'm just sometimes, like, how is this so easy, and how is it available for everyone? Right? Absolutely. No.
Richard Seroter [00:19:41]:
I mean, you're gonna look back even in a year ago. We've just all been doing it the hard way, and that's okay. Sometimes you have to learn it the hard way, back to, like, skills and things. And so it's good to know the hard way to do research. Like, we shouldn't just have the easy way. Maybe people don't go to libraries anymore, and no kid under 30 knows what the Dewey Decimal System is. But, like, that was a big part of how you and I probably had to do real research. And it's good to know that because you gotta hunt and you gotta figure stuff out.
Richard Seroter [00:20:04]:
But let's do it easier now.
Jordan Wilson [00:20:07]:
Alright. So we just, unveiled two of our first five, maybe for more nontechnical people. Our next two, maybe if you wanna get a little technical, we're gonna get there. But before we do, just a real quick break for a word from our sponsors. Alright. So let's get into it, Richard. Number three on our list, talking about Gemini, CLI, and Codasys. So explain what is what is it, how does it work, and who should be paying attention? Because I'm even experimenting with this a little bit even though I'm not a developer or code.
Jordan Wilson [00:20:43]:
We're all developers now, Jordan.
Richard Seroter [00:20:44]:
I mean, I think that's the most exciting thing is we're all builders. You don't have to wait for an engineer to build the thing for you to at least see it in action. Now would you put build that in production? No. But you everybody's a builder now, and that's I can use the Gemini app to vibe code a web app and see what that looks like and all that stuff. But for these things, you know, we talked about learning differently. We talked about researching differently. It's about building different and say, how do I use tools that help me as a developer, write software, learn my system? So the CLI or the command line interface is something that sits in the terminal. You know, think a a lost prompt or whatever.
Richard Seroter [00:21:18]:
Being able to come in there and have access to the Gemini model, being able to reference a bunch of extensions so I can reach into third party systems, and maybe I would use that to do something like, hey. I'm trying to, take this really old app and make it new. Okay. And then I think it passed that into Gemini, update it, iterate back and forth. It's really powerful or could be as simple as because it's Internet connected. Hey. I'm trying to build a chart of, today's stock rankings, put them into a table though, and factor this in. And it'll can reach out to the Internet, synthesize it all, put it back into language.
Richard Seroter [00:21:49]:
So if they're not connected, I can use it to build. I can use it to connect to third party systems. But for some people, it's a really good hardcore programming way and and system administrative way to manage things without point and click GUI. Right? Plenty of people are just faster with the keyboard, easier to build, full power of Gemini, giant free tier that anybody can mess with. Just give us an email address, and that's it. And party on. And use corporate versions if you want to too. But then sometimes if you're a coder, use things like an integrated development environment or IDE.
Richard Seroter [00:22:20]:
I wanna see my code. I wanna write it. I wanna do things. And things like Gemini code assist can help you complete a line of code or say, hey. I just need a function that reverses, you know, or adds two numbers together, and it'll write the function for you. That sounds good. Or I got this giant code from somebody. They just retired.
Richard Seroter [00:22:36]:
I don't understand any of this. What does this application even do? And get back an answer in a second versus four days. And so how do I understand code, write code, change code? Again, you're seeing, at this point, I think 90% of devs are using some of these tools at this point, but so can everybody to some extent. I could use the Gemini CLI as a business analyst or a financial analyst to maybe, hey. Can you look at this spreadsheet and make some, you know, updates to this? I might jump into Gemini code assist and go, you know, I have this kinda cool idea for an app. I'm not a programmer, but here's what I want. And that's for builders nowadays, I think the biggest takeaway is we've moved away from you having to know everything to do anything, so you have to know your intent. We all know our intent.
Richard Seroter [00:23:17]:
What am I trying to do? Now, again, don't take those things that you don't understand and then push it all the way to production. You'll get hacked or something will be screwy. But to prove your ideas faster, there's a motto in my product area right now. I'm in a product area in engineering that focuses on all these dev tools. And one of our leaders, Ryan and Scott, they both kinda coined demos over memos. Build stuff. Stop writing so many freaking docs. Build your idea out.
Richard Seroter [00:23:42]:
Prove if it makes sense. And then when it does, write the document. But stop wasting months pixel pushing a doc and tables and perfect prose when your idea is not right. Build it. Build demos. Prove your ideas. Everybody can do that. And then once you have a solid idea, you jump into real building and real scaling.
Richard Seroter [00:24:00]:
But get that first experiment and learn stage done faster before the next one. That's a culture change. Like, that transforms a business from being paralyzed by these giant release stages. So, like, let's all be builders. Let's all prove ideas and and move the blockers. Mhmm. Rishard, I think
Jordan Wilson [00:24:16]:
what you said there is really important. Just responding with, like, Jordan, no. We're all builders. Right? It reminds me when a a a story when I, had Paige Bailey from Google on the show, and I do suggest people go listen to that episode six nineteen. She talked about now at hackathons, it's nontechnical people that are winning AI hackathons. Right? And and I love what you said. They're demos over memos and just encouraging people to build it. Do you think it's gonna become whether it's using, you know, Gemini CLI and and Codesys or, you know, other kind of vibe coding tools.
Jordan Wilson [00:24:50]:
Is it gonna become common, or is it already common? And maybe I don't know because I don't, live in California where everyone's just building their own, you know, solutions. Right? Like, oh, this this piece of software stinks or was spending hours, you know, a week just for this one figure. I should just build something. Is that gonna be almost the de facto way to work? We'll probably swing the pendulum that far, and we're all just gonna be building everything.
Richard Seroter [00:25:15]:
And you might build personal software. You might be like, I'm just trying to track my recipes better because I keep forgetting, what I make, and let me just build an app for myself. We're gonna all do I think we're gonna see an explosion of software. Now this would be an explosion of software that replaces production grade software. I'm not sure. Like, there's gonna be times where could you write your own customer relationship management system? My goodness. You could. It's not feeling like that's gonna be a competitive differentiator for you or something you wanna maintain.
Richard Seroter [00:25:40]:
So I think we're gonna swing the pendulum too far for a while where we all just build everything because we can. It's super easy. And then we'll find, as usual, that middle ground of still buy, you know, buy commodity and build differentiators. Like, be careful. Don't accidentally build everything and then realize you took your eye off the ball of your business because you were geeking out on something that actually doesn't matter to your success.
Jordan Wilson [00:26:02]:
Alright. So let's let's move from the terminal Yep. To the autonomous AI coding agent, Google jewels. So explain, you know, explain the real, big use case here, Richard, and specifically the concept of, you know, having a background teammate?
Richard Seroter [00:26:19]:
Yeah. I mean, this is the world. Look. If you're a builder now, there are things that you do I mean, think of it as a when I talk about the CLI, that's really almost like working with a junior engineer. Oh, you're you're collaborating. You're hanging out at the same time. You're both working the same shift. Amazing.
Richard Seroter [00:26:34]:
When you work with things like JUULS, you're actually working around the sun or you're working with an outsource agent or a partner and saying, let me write a quality spec. You'll hear the term spec driven development. Let's write a specification that's machine readable. Still natural language, but maybe organized really effectively. Let me iterate on a spec with this background agent and then hand it off. And I might go to lunch. I might go home. Might have your cup of coffee.
Richard Seroter [00:26:56]:
And when it's done, it it gives me a pull request or gives me the changes going, here's what I did. Check it out. You can go, do you like that? No. Not doing that. Let's iterate that again. But it's almost like having work that you can truly offload. You can you're still in control. You're still the engineer.
Richard Seroter [00:27:11]:
You're still the orchestrator. You're still the coordinator. But the idea of having a bunch of background work, this is the next cultural change. I think the future for a lot of builder teams is that you're gonna have multiple of these going at the same time. If you're a software developer, here's an agent. Can you go write the docs? I'll check back with you. You add some tests to this code? Got it. And you add this new feature because I'm I'm wondering about this.
Richard Seroter [00:27:31]:
And you're just coordinating responses and things, and maybe it won't be that extreme for everyone. But this idea of multiple independent agents doing work that you've directed, which then you pull back together when it's done, that's part of the future, one way or the other. And so how do you get ready for that? Look. The most important programming language for the next few years is English or whatever, your spoken language. How do you communicate intent to these agents? Because if you write terrible specs, you will get back terrible responses from these agents. So how do I convey my intent effectively? How do I think about communicating guardrails? Hey. Hey. Don't do this.
Richard Seroter [00:28:06]:
You should only be scoped here. Or, hey. Don't don't, you know, do things that are insecure. Like, how do I know enough? This is where we still need expertise. So if I give incomplete instructions, I'm gonna get incomplete results. So we gotta keep building our expertise so that we can properly narrate this. And then we're just doing work all over the place at a faster speed and higher quality, but there are prereqs to getting that right.
Jordan Wilson [00:28:29]:
So, that's number four. Let's go straight to number five. So just AI rolling out everywhere, and it's like I kid you not. I have a a working notebook l m document of just everything Google Gemini rolls out because it's hard every day. Every day something's coming out. But tell us, Richard, what does this ultimately mean? Because even right. Like, Google AI mode is, you know, coming out with with with Canvas and and and all these other right. It's it's all these features that I'm using from different products are seemingly being rolled into even just Google search, but just Gemini AI everywhere.
Jordan Wilson [00:29:07]:
What does this look like?
Richard Seroter [00:29:09]:
Part of me hopes that we stop worrying that it's AI pretty soon, and it's just we have smarter things. I mean, I can go to google.com/ai, and I just get a a more interesting way to search. Or I could use Google Sheets and have an AI function that could help me build a table real quick. It's cool. It's awesome. Or I could use our public cloud and Google Cloud and go to BigQuery and just explain what I wanna do and have it turn my natural language into a proper SQL statement, which I forget how to write now. That's amazing. I don't even care that it's AI personally.
Richard Seroter [00:29:38]:
I just care that it's smarter. And, again, I don't have to know everything to do anything. And so as you look across all these tools, I think you're just gonna see these interfaces have changed. Like, the there's a it's the new interface of technology. Right? It's not just UIs or APIs or, you know, it's AI. AI is a new interface. And so how does this just make a smarter way where I can express my intent to some of these systems and build a pivot table, write a resume, understand a document, perform a search? You know? And then when you get into people who build agents, how can we change customer support? How can we change onboarding and knowledge management? And so I think what the future is just gonna look like is is a smarter way to interface with these technology systems without being locked out by our lack of knowledge about the nuance of every programming language and syntax and tool and how do I call this? I don't care. I just wanna go on vacation.
Richard Seroter [00:30:29]:
Lock it in that system. I don't I don't even care what system it is. And so I hope we just keep returning more autonomy to the human by offloading a lot of this different mucking around between systems to the agents and and the AI that can do it.
Jordan Wilson [00:30:44]:
Alright. So, Richard, we've covered a ridiculous amount of great information on today's show. So everything from talking about cultural change to shifting our mindset of being we're all builders, demos over memos. I mean, I think we're literally just going to put up, like, 50 of your quotes in today's newsletter. But, you know, as we wrap up, you know, after going over these five simple AI strategies, maybe what's the one most important takeaway? Because, you know, you and your team are really helping build the future of work. So maybe what is your one strategy or one most important takeaway for people to be able to take advantage of all these things that we've talked about today? Yeah. I don't I mean,
Richard Seroter [00:31:22]:
I think to some extent, either AI is gonna happen to you or you're gonna happen to AI. And I think you have to decide if you're gonna lean in or not because this is all coming in some way, shape, or form. If you wanna be ahead of it and then be in control of it, be smart about it. Understand the best ways to use it. Make it work for you. I don't wanna work for AI. I want it to work for me. But that requires me to lean into it then and understand how to use it well and stay up to date and listen to podcasts like this and lean in.
Richard Seroter [00:31:46]:
Because you know what? Plenty of people won't. And I think you wanna be on the side that's shaping how this industry is going to look and how work is going to look, not just be subjected to what's gonna happen to you. So take some command of the of the scenario by being smart, going hands on. I think everything we talked about today has free tiers of service. Try stuff. You know, to me, the most important two traits every single human should have right now in in professional world is curiosity and humility. Be endlessly curious. Keep learning.
Richard Seroter [00:32:15]:
Don't ever calcify your knowledge because it's changing every week. And, yeah, be super humble because all these opinions you have today are probably wrong next week, and it's okay. So if you have those two things, you are set up for success.
Jordan Wilson [00:32:26]:
My gosh. What an inspiring and, invigorating thirty one minutes from Richard. Richard, thank you so much for taking time out of your busy day to join the Everyday AI Show. We really appreciate it.
Richard Seroter [00:32:38]:
Hey. Thank you so much for having me. Alright, y'all.
Jordan Wilson [00:32:40]:
And if you miss anything, don't worry. We're gonna be recapping it all in our newsletter. So if you haven't, go to youreverydayai.com. Thanks for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks y'all.