In this week’s episode, the ProductivityCast team continues their conversations about how AI is embedded across many of the tools that we currently use and how to integrate them into the places we already work.
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In this Cast | AI in Everything: Maximizing AI Within Your Existing Platforms and Tools Part Two
Ray Sidney-Smith
Augusto Pinaud
Art Gelwicks
Francis Wade
Show Notes | AI in Everything: Maximizing AI Within Your Existing Platforms and Tools Part Two
Resources we mention, including links to them, will be provided here. Please listen to the episode for context.
AI Models, Assistants & Specialized Tools
Gemini (Google)
CoPilot (Microsoft)
ChatGPT
Claude
NotebookLM (now Gemini Notebook)
Evernote AI Assistant
Productivity, Office & Note-Taking Software
Google Workspace
Google Drive
Google Sheets
Google Docs
Microsoft 365 / Microsoft Office suite
Microsoft Excel
Microsoft Word
Microsoft Outlook
Clipchamp
Capacities
Evernote
Search Engines & Historical Web Tools
AskJeeves
Wayback Machine
Publications, Media & Platforms
CliffsNotes
Raw Text Transcript
Raw, unedited and machine-produced text transcript so there may be substantial errors, but you can search for specific points in the episode to jump to, or to reference back to at a later date and time, by keywords or key phrases. The time coding is mm:ss (e.g., 0:04 starts at 4 seconds into the cast’s audio).
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Ray Sidney Smith | 00:00
Are you ready to manage your work and personal world better to live a more fulfilling, productive life? Then you’ve come to the right place. Welcome to ProductivityCast, the weekly show about all things personal productivity. Here are your hosts, Ray Sidney-Smith and Augusto Pinaud with Francis Wade and Art Gelwicks.
Ray Sidney Smith | 00:17
Welcome back, everybody, to ProductivityCast, the weekly show about all things personal productivity. I’m Ray Sidney-Smith.
Francis Wade | 00:24
I’m Francis Wade.
Art Gelwix | 00:26
And I’m Art Gelwicks.
Ray Sidney Smith | 00:27
Welcome, gentlemen. Welcome to our listeners and onward with this episode from our last episode where we were talking about basically how AI is being embedded in all of the tools that we currently use. It is becoming… Somewhat of a commodity feature in many of the tools. And I think that there’s a value to that. And so we’re talking through what that really means and what So at this point in our discussion, I want us to turn our sights on the more practical ways in which we can use these embedded tools. So that we can actually figure out where in our productivity stacks the tools can be most helpful. And I always think about it from really the simplest of things, right? And journey of a thousand miles begins with the first step, right? And I think that most people want to change the world with AI. And I just want to send the next email. And so I just really want it to do very simple things to augment my life. As I frequently say now to audiences, technology is there to empower human endeavors. It should always and forever be a tool to empower human endeavors. And so I don’t need AI to live my life. I need it to augment my life in these little small ways that help my life be better and the lives of people around me. So let’s talk through this. What are those ways in which you’ve implemented AI inside of your embedded work? AI tools within maybe Gemini and Google Workspace or within another tool or Copilot within Microsoft 365 or even ChatGPT or Cloud, which connects to many of the Microsoft products?
Art Gelwix | 02:16
I’ll start with a couple of examples. The application of AI within our, what I want to say, our daily driver apps is I think has caused some consternation for a lot of people because It’s like this is a new feature, but I don’t know what to do with it. I don’t know how to derive a value. I’ve approached it from the standpoint of start with what you’re doing already, which is looking for information and synthesizing information. You need to use it to leverage the information that you already have that you know is sound, sane, and… Validated So for example, within Copilot, within Microsoft space, if you’re licensed for that, you can use it looking at your document assets. You can use it within things like Excel to generate new formula structures, things that you’re already familiar with. Within capacity, the tool I use all the time. Being able to generate a chat around a topic that creates the note base that I validate that extends on the thinking around a topic that I’ve been working on, which integrates then into scheduled planned activities that I need to pursue. Why is that so critical? One, because you’re doing something that you are comfortable to do. But verify what the AI system in that tool is giving you back. You’re just not taking it at its face value. You look at it and you go, yeah, that’s not right. Or yeah, that’s really good. The second part is to look at it as search 2.0, to be able to go past that initial search prompt to say, hey, find me this stuff. But then get into it further. What else about this thing? Tell me more about this thing. Let’s expand on that a little bit further. Getting comfortable with that process in the tools that we already use is a safe space to do it. Because again, we’re familiar with what the end result should be. If we start to get familiar with that in just generic open spaces… We’re not entirely sure what the result is, so we either have to take it at face value, Or we have to kind of doubt how things are coming back, have that constant question. I think back to the early days of, say, Google or Ask Jeeves, if you really want to go back into the Wayback Machine, is a great example of that. When they first started entering into the world of natural language questions, that was just earth-shaking. Everybody thought this was the coolest thing ever. I didn’t have to put in keywords and connector parameters and ands or ors. I could just We just have the ability to ask more questions. We have the ability to have personas and structures that look at things a specific way. But starting from an application that you already know, When you look at the result that comes back, you can go yeah, it did look at it the right way, or I need to tune it a little bit better, or I need to adjust accordingly.
Ray Sidney Smith | 05:20
I found myself going into Google Drive and now having these conversations with the files in Google Drive. And it’s actually quite helpful to have that embedded conversational component that is very different than doing an advanced operation search, right? And I know all of the advanced operators in Google Drive, at least the ones that are publicly known to folks. And I use them very effectively to find things. But it’s been really helpful to have a fuzzy search approach when I know that file is in there, I just don’t remember X or Y about it and be able to just natural language ask for it is helpful. And then the other side is when you want to be able to say, “Okay, everything in this folder I want to talk about everything in this folder in kind of the same way you would with Notebook LM, which is a little bit more grounded in the data. Now you are similarly grounded in the data that is already in your system. So you didn’t have to go to Notebook LM and add each individual file. They’re just there and you’re able to go ahead and engage with it. So it’s really powerful.
Francis Wade | 06:27
There’s a mindset shift there. I believe is coming, which is away from changes being driven by what’s available as opposed to what’s needed. I’m riffing off of what Art said. But he said, you got to start with what we are doing already. And I think we should almost be skeptical or stingy about a new possible change, let’s put it that way. Simply because partly because there’s more changes that are being suggested to us that are being advertised to us than we can possibly implement in a lifetime. So this is not going to get better, it’s going to get worse. Decide to play the odds and say, you know what, I can’t implement all of the embedded AI options that are available in my I don’t have enough lifetime to do it. And the question is, do you experiment until you find something or do you take? שעוד פרק, שזה I’m not taking any suggestions whatsoever, which is a bit. Draconian, but… I’m going to look for the one or two changes that are going to move the needle the most in my productivity. Which would mean that I need to have an understanding of the bottlenecks to my productivity and focus my efforts on them. Believing that if I “Focus on the few.” they’ll move the needle the most, that there’s some Pareto effect in here somewhere. And I somehow believe that our future is going to be Less raw experimentation and more diagnosis, analysis and targeted searching. And again, partly it’s because We’re going to have to say way more no’s than yes’s to suggestions that we include an embedded AI in our productivity stack. We’re going to… 99% of the time, we’re going to, we got to say no. We got to, but the question is. Where do you get the Pareto effect from the 1% and how do you find the 1%? How do you find a few things that are going to make you 10 I believe that there’