Ep 520: IBM Think 2025 – AI Updates that could shape Enterprise Work

Topics Covered in This Episode:

1. IBM Think Conference 2025 Highlights
2. IBM's Watson AI Platform Updates
3. Enterprise Workflow with watsonx Orchestrate
4. Build Your Own AI Agents Features
5. Prebuilt Domain Agents Overview
6. New Agent Catalog with 50+ Agents
7. IBM and Salesforce AI Collaboration
8. IBM's Partnership with Oracle for AI

Keywords:

IBM Think 2025, AI updates, Enterprise work, IBM Watson, Generative AI, Enterprise organizations, IBM products, Watson AI platforms, AI news, Amazon Kiro, Code generation tool, AI agents, Technical design documents, OpenAI, Google's Gemini 2.5 Pro, Web app development, Large Language Models, Enterprise systems, Dynamic enterprise data, Enterprise-grade versions, Meta's Llama, Mistral models, Granate models, Small language models, IBM watsonx, AI agent creation, Build your own agents, Prebuilt domain agents, Salesforce collaboration, Oracle Cloud, Multi agent orchestration, watsonx data intelligence, Unstructured data, Open source models, Consumer grade GPU, Data governance, Code transformation, Semantic understanding, Hybrid cloud strategy.

Podcast Transcript

Jordan Wilson [00:00:16]:
On this show, we talk a lot about consumer AI. Those are the AI chatbots that we as individuals go in and log in and start using, right? The ChatGPTs and the Geminis and the Claudes of the world. But I understand that that's not always how work gets done at work. Right. Because I know even in our audience, that's not always technical. Many of you are working in enterprise organizations. And even if you might have access to those tools, maybe on a team or enterprise plan, there's a good chance that you're tapping into a much more robust way to work in specifically with generative AI. And there's a good chance that to do that, you're using IBM's products.

Jordan Wilson [00:01:07]:
So that's why today I'm excited to talk about what was just released at the IBM think conference in Boston and, some AI updates that I think could shape enterprise work. Alright. What's going on y'all? My name is Jordan Wilson, and I'm the host of Everyday AI. This it's for you. This is your daily livestream podcast and free daily newsletter helping us all not just learn what's happening in the world of AI, but how we can actually leverage it to grow our companies and our careers. So if that's what you're trying to do, welcome. You're in the right place. It starts here on the live stream of podcast.

Jordan Wilson [00:01:44]:
But where you're actually gonna leverage what you learn is on our website. That's youreverydayai.com. There you can not just sign up for our free daily newsletter. We'll be recapping all the AI news for today and everything we talk about on this very episode, but you can also go listen to more than, I don't know, 510 now episodes, from some of the leading, some of the world's leading experts in AI sort of by category, so no matter what you care about. Alright. So make sure you go do that if you haven't already. Alright. Before we get in and we talk about what's new in IBM's Watson AI platforms, let's start off as we do most days by going over the AI news.

Jordan Wilson [00:02:29]:
A lot going on as always. So first, Amazon is reportedly developing, an AWS, an AI powered code generation tool called Kiro that aims to produce code in near real time by leveraging AI agents. Kiro is designed to support both web and desktop applications with multimodal capabilities and compatibility with third party AI agents, potentially broadening its usability in diverse coding environments. So beyond generating code, hero can also, reportedly generate technical design documents, identify potential issues in existing code, and optimize, entire code bases. So Amazon already offers an AI coding assistant named Q Developer in their Amazon Q platform, which is similar to GitHub Copilot, but Kero, appears to be a more advanced tool with broader functionality. So initial plans suggested a launch for Kiro towards the June 2025, though those timelines might have shifted, reflecting the fast evolving nature of AI tool development. The emergence of Kero highlights the growing competition in the AI assisted coding space, with companies like Cursor and Windsurf attracting massive investments. And, Cursor also, or sorry, Windsurf also just reportedly being acquired by OpenAI for $3,000,000,000.

Jordan Wilson [00:03:52]:
Alright. Next piece of AI news, an interesting one. So in a groundbreaking use of AI, a victim impact statement was delivered in court by an AI generated video of Chris Pelkey, a man that was killed in 2021 in a road rage shooting in Arizona. So Helgi, who died at age 37, appeared on screen via an AI avatar, addressing his killer and expressing forgiveness and regret over the tragic encounter. And this was a message created by feeding AI models, with, Palki's past video and audio. So this marked possibly the first at least reported time that AI was used to represent a deceased victim's, likeness in a courtroom, raising new questions about how AI could be integrated into legal proceedings. So it was Pelkey's sister who helped create the AI representation, describing the process as a quote, unquote Frankenstein of love, aiming to capture what Chris might have said if alive. So the judge praised the AI statement, noting it conveyed genuine forgiveness despite the anger surrounding the case and sentenced, the, killer in the case to ten and a half years in prison on manslaughter charges.

Jordan Wilson [00:05:17]:
Alright. Our last piece of AI news. There's a new king of the hill when it comes to large language models. Google has introduced a special edition of its Gemini 2.5 pro, preview called the IO edition. So this updated version of its Gemini 2.5 pro model aims to improve coding performance and web app development ahead of its annual IO developer conference, here in about two weeks actually. So the new model is accessible through the Gemini API, Google's Vertex API, Google AI Studio platforms, and the Gemini chatbot app on web and mobile. So Gemini 2.5 preview, this one's pretty important. So it now leads the web dev arena leaderboard.

Jordan Wilson [00:06:08]:
That's one of the few benchmarks that Google had previously not been number one in, although they've been number one overall in the LM arena, which kind of pits, you know, blindly two different chat bots against each other. You pick a response across all these different categories. So, pretty, pretty noteworthy that now Google is essentially dominating in every category that it can compete in. And it's now 37 elo points above the second place model in OpenAI's o3 and forty points in front of the third place model OpenAI's GPT four o. So a lot of improvements so far in the, new two five pro I o, version. That's surprisingly not a mouthful as some of the other names that we've seen in the past. But some of those improvements include better coding skills, especially in code transformation and editing, along with reduced errors and improved function calling trigger rates based on developer feedback. So Google highlights the model balances, aesthetic web development, and steerability, meaning it can follow user instructions effectively while producing attractive results.

Jordan Wilson [00:07:12]:
And, yeah, it looks like a lot of these, updates can be seen in Google's Canvas, Canvas tool. So we might have to do another update on this pretty soon. Alright. Let's get straight into it. IBM, I think, is happening right now. Actually, if you're watching on the, watching on the live stream in my hotel room, I'm about to go down and, watch the key, the the next round of keynotes, here in less than an hour. So, luckily, I was able to partner, with IBM, to get a quite literal front row access, to you all to see what is happening, with IBM. I was able to attend, CEO keynote yesterday, you know, asking as I often do when I get to attend conferences, you know, talking to the people that are actually building these products that we all use, you know, talking with executives.

Jordan Wilson [00:08:03]:
I'll probably have an interview, you know, with, someone at IBM dropping on the podcast either later this week or next week. So a lot of big announcements happening at IBM Think twenty twenty five. So here is a quick highlight, at least, of the things that we're gonna be going over in today's episode. And, we're gonna be going over also just a reminder. Right? And maybe if you aren't, overly familiar with Watson, x watsonx AI, offerings. We're gonna be recapping those as well. So don't worry if I'm going over some of these what's new in Watson and you're like, wait. What does that part of Watson do? We're gonna be covering that.

Jordan Wilson [00:08:42]:
But here's, at least for our audience, right, kind of the the nontechnical, business leaders of the world. I know a lot of y'all are technical, but, you know, it's it's one, it's one area where people are always saying, like, hey, Jordan. Love everything that you talk about on everyday AI, but, you know, I'm the CEO of the company or I'm a, you know, CMO. And, you know, I'm not always the one in there, you know, fine tuning models and, you know, I'd I'd love to know a little bit more of the nontechnical things at the enterprise level. So here we go. Here's what's new. So we have some new updates in watsonx Orchestrate in the build your own agents capabilities. Also inside watsonx Orchestrate, we have prebuilt domain agents.

Jordan Wilson [00:09:25]:
That one is exciting. Also, yeah, a lot new inside watsonx Orchestrate. Also new in a new agent catalog with a 50 plus agents. We also have new IBM and Salesforce collaboration for AI agents. Also some big, partnership announcements with IBM and Oracle in working on, the the new availability to have multi agent orchestration on Oracle Cloud. We saw a preview of IBM Granite four point o, IBM's new open source model, but the tiny version. So we'll probably see the larger versions of those released, at a different time. And last but not least, the watsonx data intelligence for AI ready enterprise data.

Jordan Wilson [00:10:16]:
There is a lot more that was announced y'all. There's the, zero copy integration with IBM z, the, I mean, the support for the MCP protocol, very big watsonx integration with, Meta's llama stack, IBM web methods, hybrid integration. So there was a ton, a ton that was announced as as you you know, most of these conferences, there's more than I could cover in any one show. So, yeah, make sure you continue to just stay tuned to the newsletter. We'll be sharing a lot more, about even the things we aren't gonna go into too deep, today. So let's start at the beginning. You know, what's new now. Let's talk about, what is watsonx? If you don't know, well, it's the AI model foundation, that powers business applications with intelligence inside of IBM's platform.

Jordan Wilson [00:11:09]:
So it, provides enterprise grade versions of technology similar to chat g b t for specific use cases all based on your data. Right? That's the biggest thing. You know, if you're using even if you're using enterprise version of the consumer LLM tools. Right? So, you know, the ChatGPT's and the clods, etcetera. Right? You're not working with your dynamic data. And when your data lives in all of these different enterprise tools, right, two I already named. Right? Salesforce, Oracle, etcetera. It's hard to work with your up to date data, which is extremely important.

Jordan Wilson [00:11:46]:
Right? Those, companies, those AI labs, the anthropics and open AIs are working at, bringing more dynamic enterprise data into their platforms. But if you need it now, right, if you need something flexible, that's what a lot of people have been using watsonx, for years. Right? IBM has been a leader in artificial intelligence technology for decades. Right? Going back to literally, Watson. Right? The, the the the famous AI that defeated the world champion in in, in jeopardy, you know, back more than more than ten years ago. So IBM has been a key player in their watsonx platform is really kind of, the the ground or the area where you can go in and connect all of your business data and use different models, to change how you work, right, to rethink how you work. So this includes IBM's Granite models, which are optimized for business tasks and industry knowledge. But there's a lot of other models that you can use inside as well.

Jordan Wilson [00:12:52]:
So, you you know, some of their, most up to date integrations, you can work with other open models like, Meta's llama, like the Mistral models, and just a lot of now, you know, more powerful, smaller open models as well. So even Google's Gemma three, which is an open source model, you you know, watsonx has the ability to connect with that as well. So and and even talking on this whole, you know, open, you know, open model in, small language model movement. Right? So this is, this is something that that was talked about extensively, in the keynotes. So, IBM, CEO and chairman, Arvind Krishna, talked about this. And, I I I I actually shared a video, you know, kind of from the keynote floor right after on on LinkedIn, and I shared it in our newsletter as well. So, going over three of my biggest takes. But one of these that I didn't really get to is really just how much, Arvin was talking about, this this surgeons of of small language models, which if you listen to the show, you know I've been extremely bullish on small models for a very long time.

Jordan Wilson [00:13:59]:
Even before they were good. Right? Two years ago, you you know, the the gap between small language models and large language models seemed, like an insurmountable gap. It seemed like they would never be on the the same page and, you know, fast forward to today, they are. Right? You have even open, you know, small language models such as Meta's new llama four, such as some of some of Mistral's models, you know, even as an example, Google's Gemma three, their small open model. Right? They're on par. You know, maybe they're not in the one a, you know, tier, you know, with the Gemini 2.5 pros, with the, you know, OpenAI o3, but they're definitely in that one b. Right? Where before they weren't in the first tier, they probably weren't even in the second tier two years ago. So, this is important to know that, you know, Watson, watsonx's AI platform, is is really, set up to bring in a variety of different models, and and suitable for different enterprise needs.

Jordan Wilson [00:15:00]:
And the biggest thing is it just integrates with your existing systems. Right? You you know, not having to worry about, oh, is there is there a connector for this? Is there an integration for this? Oh, if I'm building a a a project, you you know, I have to make sure to, you know, upload all my documents, you know, every every quarter or every month or every week. Right? That you have to right now with some of the more consumer models that don't have that, you you know, that true integration that you may need, to to have your data that's from now, not from, you know, last week or last month. Alright. So that's watsonx. watsonx is where it starts. Well, what about watsonx AI? So this is the AI model foundation that powers, business applications with intelligence and watsonx AI provides those inner, enterprise grade versions of the technology, similar to ChatGPT and it includes, like we talked about, IBM's granite models. Alright.

Jordan Wilson [00:16:03]:
Now orchestrate. Alright. We're gonna be talking a lot about orchestrate because I think this is where, a lot of our audience, you know, if if your company does have access, to IBM watsonx, this is probably where you're gonna be excited to go look inside the orchestrate platform. So first, what is watsonx orchestrate? So this is where you can create AI assistance and agents that can actually complete tasks across your business systems, and you can connect multiple enterprise applications to provide unified workflow automation, and you can transform AI from just answering questions, performing valuable business actions. So, you know, orchestrate is where you can actually put, like, AI to work, right, by building AI agents and, you know, automating, repetitive workflows. So if you haven't seen it, you you know, we'll probably be sharing, a link to a couple pages in our newsletter where you can go see it, you know, for yourself. But this is where there's a really, like, it's it's it's low code. Right? There's low code and even no code options to connect your enterprise data.

Jordan Wilson [00:17:13]:
So, you know, think if you've used something like Zapier, right, where you can build these visual flows. Right? Like, oh, if I get an email in Gmail, and if it has this type of subject line, then, it should trigger, you know, update something in a spreadsheet, then, it should update that same, line of data in our CRM. Right? So there's there's been marketing automation for very, like, for a very long time. Right? So think of that, but for your internal data. Right? And when you're using all of these different, enterprise systems. So within orchestrate, this is where you can really build, you know, visual agents in no code and low code using a variety of models. Right? You can use different models for different tasks. And this is where you can really just unify, your workflow automation without having, you you know, to, you know, try to patch something together, you know, in Zapier or, you know, using multiple AI tools.

Jordan Wilson [00:18:13]:
Right? This is where it's just it's live. It's it's living, inside your company's dynamic data. So, a couple examples, IBM said that their internal HR implementation handled 94% of company wide HR, requests. So, you know, being able to build something like that and working off of these as templates as well. So even inside Orchestrate, when we talk about the agents and we're gonna get to the, agent catalog, here in a minute. You you know, just the ability to work with templates, I think, is extremely, important. So, this was, during another keynote presentation I watched yesterday. You know, these agents are described as more doers, that act autonomously and orchestrate workflows across your enterprise.

Jordan Wilson [00:18:59]:
Right? Versus a lot of the AI, you know, especially the large language models that we use right now. We don't think of them as necessarily autonomous, because we, the humans, still, for the most part, have to go in and feed them information. Right? Whereas with walk watsonx orchestrate is much more agentic. Right? It is creating, flows that actually do the work and aren't always waiting, for the human to go in and set off one of those integrations. Alright. Next, the build your own agent capabilities. So some new updates to this inside watsonx orchestrate. You can create customized AI agents in under five minutes with no coding required.

Jordan Wilson [00:19:45]:
That part to me is extremely impressive. Right? Even things right? I'm not an I'm not an enterprise. Let let me start that at like, let me start this out now. And this is probably why I haven't talked more, about watsonx. Right? I'm not an enterprise. It's it's not always right if you're a super, small business, if you're an entrepreneur, you know, if there's 10 employees, watsonx probably isn't something that's for you because, the reason is is you don't have a lot of that enterprise data, that bigger enterprise companies would need. Right? So that's why I think I haven't even talked as much about watsonx, but I wish. Right? Maybe I have to ask ask my friends at IBM, like, hey.

Jordan Wilson [00:20:28]:
Hook me up with some, some some access to watsonx here so I can talk about it a little bit more. Right. Because having all these enterprise tools, right, the Salesforce, the Oracle. Right? These are all systems I don't use as an extremely small business. Right? With, with, with, with a handful of people on the team. But when you have these and the ability to think that you can create an agent in less than five minutes that taps into all of your enterprise data. That is not normal. Right? That's not normal.

Jordan Wilson [00:21:02]:
You you you know, a year ago, to think that you could build your own agents with up to date enterprise data, we would have said, oh, you know, a year ago, if we were talking about this, we would have said, oh, that's that's three to five years away. And and here we are in a year with this new build your own agent, capability inside Orchestrate. It's here. So simple drag and drop interface. So, you you know, seeing this demoed, in different sessions on the showroom floor in keynotes, y'all it's it's simple. Right? You don't have to be a a technical person to build some of these agents and they can be tailored, to your unique business processes and requirements. So, IBM says that organizations can start 70% faster, with watsonx Orchestrate, and and really just going beyond and bringing this, AI agent creation beyond people with technical backgrounds. Right? I I I think that's huge in this, you know, build your own, kind of movement that we've seen, not just from IBM, but from lot from a lot of other companies bringing in, you know, no code and low code a jet, agents builders.

Jordan Wilson [00:22:14]:
Right. It's something I've been extremely, pro no code, low code. Right. I've been saying this for a long time that nontechnical people are gonna be building their own agents. They're gonna be building their own applications, you you know, without even necessarily knowing what's going on under the hood. Right? And and this the the the new watsonx orchestrates build your own agent capabilities is another step towards something that I've been talking about, for a long time. So, you know, here for our live stream audience, right, a quick example and something that I love is, you know, you have a familiar panel. So in the middle when you are using this, you you know, build your own agent cape, capability, you can edit your knowledge settings and choose, which, you know, data sources that you want to connect to a particular agent.

Jordan Wilson [00:23:05]:
Maybe you want it to connect to your, you you know, your work day, but maybe you don't want it to connect to, you you know, Salesforce as an example. So you can easily pick and choose, which data sources that it has access to. But the the the thing that I love is you can get a preview of the agent on the right hand side. So you kinda have this dual pane where you can kinda build it, you you know, connect the different settings, upload files, etcetera. But then you can see and preview it on the right hand side. And something that I really like, which is I know sounds like a detail. Right? But you can use reasoning models with these agents, which is extremely important because then, you know, yeah, it's it's fun to see how these, you you know, reasoning models, how they kind of think and how they plan. Right? And that's another thing that can't be understated.

Jordan Wilson [00:23:52]:
The fact that, you know, these agents have access to reasoning models, that's extremely powerful. But even looking to see, right, and look at the reasoning and look how it's accessing your data. Right? When we talk about some of the main problems, with AI, adoption, not even just at the enterprise level. Right? Just even at the consumer level, it's always trust, transparency, data, governance, etcetera. Right? And being able it sounds like a small thing, but being able to see the reasoning and see how a large language model is or is not, accessing your data, to see how it's handling, the query. So to be able to build an agent, and just to see, like, oh, okay. You know, in this example here I have on the screen, it says, you know, what is the active, my screenshot is actually cut off. What is the active return policy for orders, you know, of a certain amount? And then you can see, oh, okay.

Jordan Wilson [00:24:45]:
It's going into a tool. It's it's, getting the get order status. It's analyzing the return policy for an order in transit. It's looking up different, different items that you can set, and then it says, okay. This order is in transit and the thirty day return policy applies. Right? So think of that. Right? Being able to ask a question of an agent that you've built with no code and being able to see it reason, and then you could easily go in and change it if you see something is not performing how it should be based on the reasoning. Extremely powerful updates, in in in this one, in the build your own agent.

Jordan Wilson [00:25:26]:
Alright. Next, there's prebuilt agents. Yes. This one also, pretty big. So you you you the pre built agents and the, you you know, agent catalog, we're gonna cover them both back to back here. So, pre built domain agents, huge. So as an example, a company like IBM that works with the largest enterprise companies in the world. Right? They know how different, sectors are using their agents.

Jordan Wilson [00:25:54]:
Right? Their agents aren't new. So now when they're announcing these pre built domain agents, these are like agents that have been fine tuned, across the enterprise, right, across, I'm sure millions of of interactions. So, you know, these are, you know, talk about a great starting point. So as an example, an HR agent, right, that can handle employee inquiries, time off requests, and policy questions. That's been some of the coolest demos I've actually seen, from the, from the showroom floor and from keynotes. It's just, you know, all these, like, hey. How like, do I have time off? How can I submit it? Right? All these HR policies that, you know, is probably a pain for employees and maybe for HR people to deal with, to to to be able to see an agent, a domain specific agent based on your company's data that goes in and says, oh, yes. This employee, does have enough PTO, and, yes, it's submitted.

Jordan Wilson [00:26:50]:
Right? So it cuts off the need for back and forth. Oh, is this gonna get approved? Oh, the HR person didn't get back to me. Oh, the HR person. Oh, we 50 people just emailed me about Thanksgiving. Like, what's gonna happen here? Right? So for that to all be automated is huge. The same thing with sales. I think HR sales marketing, are gonna be, agents prebuilt, domain agents that are gonna be extremely popular inside IBM Watson. So the sales agent supports prospecting, account research, and proposal development, procurement agent automates vendor selection, purchase approvals purchase approvals and supply management.

Jordan Wilson [00:27:25]:
So, this is huge. And one example that they talked about, a little bit yesterday was the Better Business Bureau. Right? Most people, especially if you're here, in The US, you know the Better Business Bureau. You know, this is the the organization that essentially gives the the rubber stamp on certain businesses and says, like, okay. This business is legit or, oh, this business is not legit. So the example from the Better Business Bureau, they reported a cost savings of $1,500,000 in, annually, from implementing some of these prebuilt domain agents. So, you know, just one. Right? And you wouldn't think of the Better Business Bureau as like a a a tech organization.

Jordan Wilson [00:28:09]:
I don't know. At least not me. Right. I I I think of the Better Business Bureau. Okay. That's gotta be some, you know, some suits that have, you know, 50 layers of bureaucracy. Right? Nothing against them. It's just that's how I think of the Better Business Bureau and a lot of government.

Jordan Wilson [00:28:23]:
But the fact that, you know, that the Better Business Bureau, you know, which isn't a government agency by the way, but the fact that they were able to just by using these prebuilt domain agents save $1,500,000 already. I'd say that's pretty telling. You know, some other examples, talked about, you know, HR agents have been successfully deployed at organizations like where they provide significant improvements in employee service delivery. So, yeah, a lot of examples and use cases for these prebuilt agents. Similarly, now we have this, new agent catalog, and this is where you can access these 150, ready made agents and tools, from IBM and third party partners. Right? So this is kind of the, the the app store, so to speak, for watsonx orchestrates agents. Right? So all these different, you you know, agents that are ready to go and can tap into your enterprise data. So, you can integrate the AI capabilities with popular, enterprise applications like Salesforce and ServiceNow.

Jordan Wilson [00:29:32]:
And the cool thing, that was just announced as well is there's some new agent observability features provide, you know, that kind of provide governance and performance monitoring. You you know, that's always important being able to trace agentic AI. Right? And that's that's one of the rightfully so. That's a huge concern of many, enterprise leaders when, you know, not even talking about AI. Right? Not even talking about, oh, our humans are gonna go in and use a large language model. Right? There's obviously already a lot of things that you need to keep in mind with, you you you know, transparency, accuracy, trust, data governance, all of those things, ethics. Right? But then when you go to agents, there's a whole another layer because unlike, unlike, agentic AI, when you're talking about a human using a large language model, it's at least a little easier to be like, hey. What happened here? Let's trace back our steps.

Jordan Wilson [00:30:22]:
Hey, human. What happened when we were working on this project inside a large language model? With agents, it's a little trickier. Right? Because you can't necessarily, you know, have the same ability to go talk to an agent and be like, yo. What happened here? But this new, agent observability feature, inside watsonx orchestrate does kind of that. Right? You can at least see, you you know, the bullet points of how these agents are working, where they've done work, how they're operating, what may be working, and what doesn't. So it's it's kind of like being able to look, under the hood, that we have, similar, applications for non AI tools. Right? This is what your IT people are always, you know, spending their time on. So, the IT departments, I think, are really, going to like these new observability features, just to be able to see, how these agents are working, especially when you have multi agent orchestration.

Jordan Wilson [00:31:20]:
I think, the, you know, traceability and and being able, to understand how these agents are actually working, without human intervention at times, is extremely important to monitor and ensure accuracy, trust, reliability, all of those things. But I do think, especially if you're already an IBM Watson, you you know, x, customer, If you're wondering, like, okay, Jordan, there's probably a lot that was released, you you know, during, IBM Think, like, where do I begin? If I were you, this is where I would start. And again, all of these, many of these agents are templates as well. So, yeah, you could go find a great one. And maybe it's not your certain CRM or, Right? But maybe everything else is perfect. So maybe all it is is is swapping out, the the the data that it is connected to. Maybe it's just tweaking, you you know, something in the desired output, the output format. Right? But there's now a 50 enterprise ready, ready made agents ready to go that, you know, if if you're using the watsonx platform, there's there's not going to be, you you know, again, you always think things are easier said than done, but, you know, I'm I'm out here on the floor watching live demos.

Jordan Wilson [00:32:49]:
Right? Not, like, prerecorded videos, you know, and asking people, oh, can you go, hey. Go into this flow, you know, change change the data source. And I'm watching it happen in in front of me. Obviously, these are by IBM, employees. A lot of them who work on the product team, but I'm seeing them very easily in a couple of quit clicks, without coding, go in and, customize some of these, pre built agents. So, it's something, that if you are, on the watsonx platform, this is something you need to be paying attention to. So yeah. Even, for our livestream audience, just have a little screenshot of the new, agent catalog.

Jordan Wilson [00:33:30]:
So yeah. You know, you can search, you can search for different things. You know, maybe you want something for, sales, something with your CRM, whatever it may be. So, you know, it is very much like an app store, for watsonx, orchestrate agents. So, very cool. You can also you know, there's toggles and filters, for different, you know, if let's say you're using, Asana or, AWS. Right? You can just go ahead and click those things and stack different filters, to see which of these prebuilt agents, might be a good fit for your workflow. Alright.

Jordan Wilson [00:34:07]:
Let's talk a little bit more about this one because I think this is gonna be another popular, collaboration as well between IBM and Salesforce. So the zero copy integration, connects mainframe data to Salesforce without duplication. So the new sales the updated sales prospecting agent, this collaboration between IBM and Salesforce, pulls leads, or sorry, finds leads, pulls contact info, and can even draft outreach, to certain contacts, in your Salesforce. And then because it integrates with a lot of your, you know, other tools, you know, there's even an employee support agent, in Slack that you can, collaborate with as well. So the Salesforce integration is part of IBM strategy to extend beyond, just the traditional, you know, enterprise systems and into other enterprise software that many, you know, large companies are relying on. So this one's, you know, this one's pretty, like, I gotta see this one more in action because I looked at it. I'm like, okay. This is obviously very powerful.

Jordan Wilson [00:35:14]:
Right? But I'm wondering for those, you you know, for those companies that are both, you know, using watsonx, their AI platform, but maybe also using Salesforce's, Agent Force platform because it looks like there's a lot of crossover here. So, yeah, if that is you, I'd love to hear from you. Right? If if if you've been using Salesforce's Agent Force, and now we see this new and improved, collaboration effort between, IBM watsonx and Salesforce, and these new improved AI agents, you know, some of it's got me scratching my head. Like, okay. So does this mean, like, let's just say some enterprise companies are only, you know, tapping into 10% of what, Salesforce's agent force offers. I'm like, well, maybe that 10% is now what, is covered in this new IBM Watson and Salesforce collaboration. I don't know. Right? And Ty will, you know, answer that story.

Jordan Wilson [00:36:09]:
But, you know, this one kinda struck me as interesting because I'm like, you know, good on IBM for being able to pull off, this level of agentic collaboration with Salesforce. But I'm also like, okay. Is this gonna potentially take away from, you know, Salesforce's agent force? I guess in the end, right, you still have to be a Salesforce customer in order to integrate it within, IBM watsonx, but I was like, maybe this takes a little bit away, from the agent force platform. But like I said, I think time will tell on that one. Alright. One or two quick other ones here, as we, start to wrap up today's show. So the other another big announcement with, you know, when we talk about enterprise, I mean, Oracle. So now watsonx Orchestrate, is coming to Oracle Cloud infrastructure for enterprise customers.

Jordan Wilson [00:36:59]:
AI agents can operate seamlessly across Oracle and non Oracle business, applications. Some of the initial use cases, at least right now, are focusing on HR use cases, with plans to expand to other departments. So this partnership right now gives organizations way more flexibility in where they can actually deploy their AI workloads as part of IBM's hybrid cloud strategy. So this collaboration with major cloud providers, supports clients who wanna run AI where their data, resides and, resides. And that's that's pretty that's pretty big. Right? Because, you know, a lot of companies, they have, you know, huge data investments. And so you might need this, you know, kind of official, official layer of collaboration between, you know, in this instance, IBM and Oracle. Let's get tiny.

Jordan Wilson [00:37:55]:
We talked about some big announcements. Let's get tiny. Granite four point o. Alright. So this is a new preview, of IBM's small, small version of their, language model, Granite. So right now, I believe the latest full model, I think we're at the 3.3, version of Granite. So this is a preview. So we should see as with most large language model, anytime you go up from, like, you know, a three three, three four, three five to a four, you know, it's usually a pretty significant step in terms of capabilities, performance.

Jordan Wilson [00:38:34]:
So, you know, pretty pretty big here. So we don't have the full thing. Unfortunately, I was, you know, hoping we might see the full, Granite four series, but we have the four tiny preview. So what this is, it is a small 7,000,000,000 parameter model, from IBM in their Granite series that's more efficient and performs similar, IBM says, to larger models. It also dramatically reduces computing requirements, making AI more affordable to deploy. And it's open source. Right? So the open source release encourages community innovation and customization. And here's the thing that I really liked about Granite four point o, tiny.

Jordan Wilson [00:39:14]:
It can run on a consumer grade GPU, which is funny because there's always arguments and companies always say, right, like, oh, this can run on a consumer grade GPU, but this one actually can't. Right? Because sometimes people say, like, consumer grade GPUs, and they're talking about, like, you know, like a $2,000, NVIDIA chip, this can run off, like, a $350 GPU. Right. So for like, for the most part, especially, if you have a newer, you you know, PC, this is one that you probably would even have to go and upgrade, your GPU to go ahead and run this model locally. So that's extremely, extremely exciting. And like I said, this is part of the granite, four series that should be, released later this summer. So we will be covering that when, the new the the the full family, but at least, right now, there is the granite four o tiny preview. You can access it in hugging face.

Jordan Wilson [00:40:16]:
IBM does say like, hey. This is a preview, right? So this is not something that they're necessarily recommending that you, you know, integrate across enterprise because it is a preview. But if your company, if your organization, is using the granite models now and you're finding a ton of value, which I know a lot of companies are, it's it's anytime you get a preview, even if it is one of the smaller models, I think it's always important to start experimenting, with what's new. Right? Running side by side comparisons, seeing how this may or may not, change your workload, how it might make it better, some things you might have to, improve upon, and honestly, just the new capabilities. Right? Because, presumably, we're gonna have a lot more capabilities, in the Granite four o family of models. Alright. Let's talk about, last but not least, the watsonx data intelligence. Alright.

Jordan Wilson [00:41:10]:
So this transforms unstructured data like documents and email into valuable AI inputs. So, yeah, some big improvements here on their data platform. IBM says that this improves AI response accuracy, this new updates to the platform by 40% compared to conventional methods and content aware storage automatically processes and indexes, information for AI use. This is one thing that really also stood out to me, in, Arvind's keynote yesterday. So the CEO and chairman, Arvind Krishna, what stood out is, you know, he said right now, 9999% of businesses data isn't being used, within AI systems. Right? Which at first kind of shocked me because I'm like, wait, that's a lot of data that, you know, companies really need to spend more time, bringing into large language models. If you wanna be an AI first or an AI native organization, you really have to work hard to bring that 99% of data, whatever that is, whether it's structured data, unstructured data, your company's knowledge. Right? It's a long list of things that are not currently inside large language models.

Jordan Wilson [00:42:25]:
But that kinda struck me as also, like, shocking, but also, like, okay. Duh. That makes sense. Right? Think of even right now, I think so much of the data that we're just bringing into large language models is just it's it's it's predicated just on tasks that we need to complete, right? Unless you already are using, right, something like IBM watsonx and, you you know, have hooked up all of your major data sources, you you you know, your your data warehouses, data lakes, whatever. Right? But otherwise and for I would say even for a lot of enterprise companies, they're really just connecting their data on a need to have basis. So there's I think there's so much room for growth. Right? Because using AI is not a separator. Using AI is not a moat, in for your organization.

Jordan Wilson [00:43:12]:
Bare minimum, you have to start bringing in, you know, more of that 99%, to really stay competitive, I think, in 2026 and beyond. So this is pretty cool, this new, watsonx data intelligence, and just how it's able to, better bring in unstructured content and make it usable within their platform. Right? So think, you you know, unstructured. Right? So structured, unstructured. Right? The easiest thing is I say structured data. Right? If you're not a technical person. Structured data is something that would limit a database. Right? It's something that can be categorized.

Jordan Wilson [00:43:46]:
It's something that could be in a, you know, in a CRM. Right? There's a drop down list or a spreadsheet. Right? Unstructured data is everything else. Right? I think one of the biggest gold mines of unstructured data is how your company makes decisions. So decision making process, all of this domain expertise that lives in people's heads, all your meeting transcripts. Right? I know everyone, especially, I think, you you know, since we've had the the hybrid in work from home era, it's like it seems like there's more and more meetings. Meetings are gold. Right? And being able to get the transcripts and being able to get the in straights, insights, from your team, from your leaders, that's huge.

Jordan Wilson [00:44:26]:
So, this watsonx data intelligence, great, great advancements from IBM Watson. Alright. That's a wrap, for at least for our more nontechnical audience, our everyday business leaders, what came out of IBM Think 2025 so far. Alright. We're gonna have more, throughout probably, a little bit of this week and next. I know there's a lot going on. I'm, like I said, about to go head down right now, for another keynote presentation here at the conference. So, hey, I hope this was helpful.

Jordan Wilson [00:44:59]:
Let me know if there is anything else that you wanna know if your company is, using IBM watsonx. If you have questions, if, you know, because I know anytime there's new, there's new releases, there's also so many questions like, oh, this is great. How do I get access? How do we use it? What's the difference between, you know, feature a and feature b? If you have those questions, reach out to me. Right? That's the thing. You know, I'm I'm lucky enough to have, you you know, good connections at a lot of these big companies and, you know, maybe, you know, you have a rep at at some of these companies, maybe you don't. But I'd love, hearing from you all what questions you have or, you know, maybe what's exciting you most about, what we just went over in today's show. I hope it was helpful. If so, if you're listening on the podcast, please subscribe and leave us, leave us a rating.

Jordan Wilson [00:45:42]:
If this was helpful, click that repost button. You probably have some coworkers, some colleagues, some people you know I, using IBM Watson, you know, their AI tools. So let them know about it. Thank you for tuning in. Please go to youreverydayai.com. Sign up for the free daily newsletter. We'll see you back tomorrow and every day for more everyday AI. Thanks, y'all.