Ep 700: Celebrating Everyday AI’s 700th Episode

Resources:

Join the discussion: Got something to say? Let us know on LinkedIn and network with other AI leaders


Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup

Connect with Jordan Wilson: LinkedIn Profile

Start Here Series in our Inner Circle Community: Join for free access

7 Ways AI Is Changing Work Structure—Beyond the Hype

Business leaders focusing on digital transformation are now witnessing a clear trend: AI is no longer simply a “tool” to add to existing processes. Instead, the transcript demonstrates that AI is actively reshaping organizational structure, workflow optimization, and critical skills adoption. This isn’t a theoretical future—these are observable shifts with quantifiable impacts on operational agility, cost structures, and workforce deployment.

1. AI-First Mindset: Redefining Application Ecosystems

The transcript specifically notes that enterprise workflows are shifting away from traditional applications toward front-end large language model (LLM) operating systems. For example, nearly 78% of professionals now bring their preferred AI tools to work, regardless of official policy. Application connectors are being phased out in favor of embedded apps and interactive AI—such as Anthropic’s Claude Cowork and OpenAI’s latest front-end integrations—causing rapid fragmentation in legacy software ecosystems. Leaders must reconsider digital policy and support modular, secure, and flexible environments for varying AI utilities.

2. Flattening Organizational Hierarchy—The AI Impact on Management

Specific research cited indicates Gartner’s prediction: 20% of organizations will use AI to flatten hierarchy and cut half of all middle management roles, with U.S. employers already advertising 42% fewer such positions. The rationale is straightforward—middle management’s primary value was often the translation of information between senior leadership and front-line staff. With AI agents offering real-time, actionable data synthesis and agentic traceability, much of this function can be systematized. Organizations are seeing streamlined communication, accelerated approvals, and increased autonomy for both senior and junior personnel.

3. AI-Enabled Business Operations: The True Source of ROI

Contrary to popular belief, value from AI isn’t about plugging in the “right” model or chasing the best tool. The transcript highlights that 80% of AI’s enterprise ROI comes from redesigning operational processes, not technology selection. The chasm between high-performing organizations and those struggling is not in tool access, but in the methodical redesign of workflows, permissions, and decision protocols. Enterprise decision-makers should focus their investments on digital change management and operational retraining, rather than a never-ending assessment of emerging AI vendors.

4. Agentic Bottlenecks: Scaling Up Without Human Bottlenecks

As enterprise deployments scale, AI agents are now outnumbering humans in production environments. Project management platform ClickUp runs over 3,200 AI agents alongside just 1,300 staff—a near 2:1 ratio. The bottleneck is no longer in human labor, but in digital permissions and oversight. The core business issue is not “how quickly can we add more agents,” but “how efficiently can we route, approve, and monitor autonomous decision-making at scale?”

5. Unstructured Data as Corporate Goldmine

Meetings, voice calls, and free-form discussions—once considered operational noise—are being aggressively pipelined into structured, actionable information via advanced transcription and analysis models. The transcript references the AI meeting transcription market projected to grow almost 10x in the next decade. Enterprises leveraging voice-native models, like Modulate’s Velma, aren’t just archiving—they’re building robust knowledge graphs from live human expertise, improving retention of institutional knowledge and enabling faster onboarding.

6. Contextualization: From Information Retrieval to Actionable Answers

In legacy digital operations, knowledge workers spent the majority of their time navigating disparate web portals and systems. Now, the transcript reveals that workflows are being managed near-completely inside AI agent platforms with integrated context from dynamic business sources—drives, emails, calendars, and even live meetings. The transition is from “internet search” to “LLM-based workflow orchestration,” where deliverables and answers, not raw information or isolated documents, drive daily business value.

7. Context Engineering Over Prompt Engineering

Prompt engineering is rapidly being replaced by context engineering. Modern thinking models—across platforms like Gemini, Claude, and ChatGPT—don't require intricate prompt hacks but instead demand robust, up-to-date business context. Gartner is already telling leaders that “prompt engineering is not enough”; reliable enterprise outcomes require the deliberate packaging of first-party knowledge, business logic, and domain rules. This is a core skill in keeping LLM outputs accurate, relevant, and differentiated from competitors using the same model base.

10 AI Workflows Driving Measurable ROI in Business

Business value is realized in repeatable, measurable workflows. The transcript identifies granular workflows delivering tangible returns:

• Meeting Discussion to Actionable Tasks: Systems like Microsoft Copilot inside Teams can auto-translate spoken meeting points into assigned tasks and follow-ups, saving untold hours in project management and post-meeting coordination.

• Inbox Triage Automation: Direct integration of LLMs with Gmail, Outlook, and other platforms dramatically accelerates email prioritization and response drafting. Cited research notes Microsoft Copilot users each saving 30 minutes per week, per employee.

• Sales Call Objection Handling: By running call transcripts through custom GPTs or AI projects, sales teams perform instantaneous objection analysis and generate tailored follow-up, increasing close rates while reducing manual CRM bottlenecks.

• Proposal and RFP Drafting Grounded in Proprietary Data: Human-augmented LLMs can draft first-pass proposals rooted in company-specific documents, reducing the immense time and resource drain of proposal cycles.

• Research Synthesis into Executive-Ready Memos: Deep research runs (on company data, not just the web) can now summarize hundreds of pages of context—synthesizing industry trends, competitive analysis, or internal performance into succinct, board-ready briefs.

• Customer Support Agent Assist: AI can triage, draft, categorize, and escalate customer service tickets, shrinking response times from hours to near real-time and offloading up to 80% of transactional decisions.

• Finance Variance Narratives: Instead of raw Excel tables, AI platforms generate clear, cited explanations for variance and financial movement, aligning both executive and board reporting with narrative-driven data.

• Policy & Compliance Q&A: AI projects locked to internal-only data eliminate hallucinations and unleash instant, citation-backed answers to complex HR and compliance queries.

• Automated Content Repurposing: Podcast episodes, webinars, and documents are automatically transformed into new media formats, supporting dozens or hundreds of derivative content outputs with zero incremental resource overhead.

• Scheduled Personalized Research: LLMs run recurring research tasks on evolving business data—monitoring competitors, news, or financials—delivering real-time synthesized insights without human intervention.

10 AI Skills Critical for 2026-Ready Business Professionals

AI’s acceleration is leaving behind those clinging to static technical skills and legacy processes. Evidence from the transcript outlines the must-have capabilities for organizational competitiveness:

1. Change Leadership: Successful teams are discarding once-effective playbooks in favor of continual operational reboot.

2. AI Skepticism and Verification: Reliance on outputs is dangerous—critical thinking and result verification are required to battle “skill atrophy.”

3. Multi-Model Fluency: Experts master strengths and limitations across multiple LLMs (Gemini, Claude, ChatGPT) and flex between them for ROI.

4. Process Thinking: Obsessing over new tool launches is wasteful; value comes from modular workflow design and smooth AI integration.

5. Context Engineering: Results are only as valuable as the quality of business context—model selection is secondary.

6. Crisp Input Writing: Clear, unambiguous instructions raise response quality and eliminate ambiguity.

7. Continuous Learning & Adaptability: The most relevant skill is the ability to shift and re-learn, as even ‘core’ processes are being rewritten monthly.

8. Automation Fundamentals: Routine use of scheduled LLM tasks, connectors, and background automations maximizes efficiency.

9. Human-AI Collaboration: Treat AI as a proactive digital teammate; studies highlight a 60% productivity gain in hybrid human-AI teams.

10. Decision-Driven Communication: The emphasis is now on actionable outputs, not volume of exchanged information.

Conclusion: Strategy for Business Value in an AI-Driven Market

The contemporary business edge is built on smart workflow transformation, targeted skill development, and deliberate operational redesign. The difference between adoption and stagnation is not access to the latest LLM, but mastery of configuration, oversight, and synthesis at scale. As business environments shift, so must the mindset and the day-to-day cognitive toolkit—grounded always in the specifics of operational reality.

For in-depth actionable frameworks and examples, refer to the detailed workflow breakdowns and recommendations highlighted in this analysis. The transition to AI-optimized business management is under way—those who design and measure for these shifts will capture the clearest competitive and financial gains.

Topics Covered in This Episode:

1. Seven Ways AI Is Reshaping Work
2. AI-First Operating Systems in Workplaces
3. Flattening Corporate Hierarchies With AI
4. Redesigning Operations for AI ROI
5. AI Agents Outnumbering Human Workers
6. AI Meeting Transcription as Data Pipeline
7. First-Party Company Reasoning With AI
8. Knowledge Workers Shift From Internet to LLMs
9. Context Engineering vs. Prompt Engineering
10. Ten AI Workflows Delivering Business ROI
11. Meeting Transcription to Task Automation
12. AI-Powered Inbox Triage and Email Automation
13. Sales Call Analysis and Automated Follow-Up
14. AI-Driven Proposal and RFP Drafting
15. Deep Research Workflows With LLMs
16. Customer Support AI Agent Assist
17. Finance Reports Automated by AI
18. AI Compliance and Policy Q&A Workflows
19. Automated Content Repurposing With AI
20. Scheduled Personalized AI Research Workflows
21. Ten AI Skills Every Professional Needs
22. Change Leadership for AI Integration
23. Critical Thinking and AI Verification
24. Multimodal AI Model Fluency
25. AI Process Thinking & Workflow Redesign
26. Mastering Context Engineering Techniques
27. Writing Effective AI Model Inputs
28. Continuous Learning and Change Adaptability
29. Automation Basics & Task Scheduling in AI
30. Human-AI Collaboration for Productivity
31. Actionable AI Communication for Decision-Making

Episode Transcript 

Jordan Wilson [00:00:18]:
Most AI tools that analyze sales or customer support calls turn those conversations into a text based transcript, but text only transcripts miss most of the value, and Modulate fixes that. Modulate's new Velma voice native AI model and their ELM technology actually understand what's happening on those calls. It picks up the valuable tone, timing, emotion, and intent that all AI transcription tools can't provide. So whether it's for sales, customer support, or voice agents, Modulate's new Velma model helps you capitalize on what text only AI tools miss. Demand more from your AI today with Modulate at modulate.ai. For almost three years, I've spent almost every single weekday talking to the smartest people in AI, reading about the latest AI advancements, and putting it all into practice and hopefully helping some of you along the way. And I feel that over the years, I've picked up a few helpful tips along the way. I mean, that's what this podcast is all about.

Jordan Wilson [00:01:24]:
But I know sometimes those best practices, tips, and tricks are spread out all over the place. So once in a while, we do these kind of special milestone episodes that I think are a nice, huge knowledge dump and maybe even a gem for those of you staring AI implementation in the face with a lot of questions. So today, we're doing one of those milestone episodes as we celebrate everyday AI's seven hundredth episode. Don't worry. I'm not gonna rattle off 700 facts and stats. Don't worry. Ain't nobody got time for that, but we're gonna be doing the seven times 10 times 10 route and going over the seven ways AI is re reshaping how we work, the 10 AI workflows that actually deliver ROI today, and 10 AI skills every professional needs to know in 2026. Alright.

Jordan Wilson [00:02:15]:
I'm excited for today's episode. I hope you are too. Thank you for joining me, and let's get into it. Welcome to Everyday AI. If you are new here, my name is Jordan Wilson, and this thing's for you. It's an unedited, unscripted daily livestream podcast and free daily newsletter, helping everyday business leaders like you and me keep up with the nonstop AI advancements, how to make sense of it, cut the BS, and just grow our companies and our careers. So if that's what you're trying to do, awesome. You're in the right place.

Jordan Wilson [00:02:42]:
It starts here, but stick to the next level. Make sure to go to our website at youreverydayai.com. Sign up for the free daily newsletter. We're gonna be recapping not just today's show, but also all of the daily AI news that you need to know. So it's one of those special episodes, but we actually did another pretty good one on our six hundredth episode. So if you missed that one, hit rewind a little bit and go check out episode 600. We did the six, AI myths you should stop believing, the 10 AI systems you must learn, and 10 AI trends you can't afford to ignore. So make sure you go check out episode 600 if you missed that.

Jordan Wilson [00:03:20]:
But let's get over and talk about episode 700. That is today's. And, live stream audience, I'm curious. Out of 700 episodes, how many do you think you've listened to? Five, ten, 700? Let me know. I'm curious. But like I said, we're gonna be running down these things in the three categories today. Episode 600, I think, was kind of the map, and this is the game plan. Alright.

Jordan Wilson [00:03:44]:
So, maybe if you're listening on the podcast, hit pause. Maybe go listen to episode six hundred first if you missed it, and then come back and, hit play on this, and let's get into it. So let's first talk about the seven ways that AI is shaping work. So I'm letting you know these aren't predictions. These are already happening. And I think that the traditional corporate hierarchy was designed for manual knowledge work. It was made for smart humans and domain experts to be able to go synthesize and personalize information and to create value for businesses, and I think that that design is becoming a little obsolete. So let's get into these seven ways.

Jordan Wilson [00:04:28]:
So way number one that AI is reshaping how we work, AI first. Right? This is an easy one. You've heard me talk about, AI operating systems. This is an example of that. But right now, you aren't using apps. Right? And you're gonna be using apps even less. Right? Another good example, this changes all the time. Right? So a couple of weeks ago, OpenAI kind of got rid of connectors, and they moved everything to apps.

Jordan Wilson [00:04:55]:
In the last couple of days, right, we've seen, Anthropic's Claude cowork really take off, less than checking my watch here, twenty four hours ago. Anthropic also, released their version of interactive apps. So everything is really moving to front end large language models, and you've heard me talk about this, a lot. But right now, even if you aren't doing this, your employees probably are. Right. A recent study showed that 78%, 78% of professionals now bring their preferred AI tools to work regardless of company policy. So if you are a decision maker right now and you're like, oh, no. Well, we just use our version.

Jordan Wilson [00:05:34]:
Right? Whatever AI tool. No. People are using whatever version of AI that they feel comfortable with. And a lot of times, many different versions, different models. And, I think that if you are a decision maker at your company and you haven't already, kind of turn that switch, it's time to turn that switch. Right? Work is AI first. The second way that AI is shaping how we work and this is not fun to talk about. Right? The traditional organizational structure is flattening fast, and I think this is happening in two ways, but the way that I wanna talk about here is I think middle management over the next five years is really gonna get flattened out.

Jordan Wilson [00:06:14]:
Alright? And I'm not saying that to, you know, if if you are in middle management or aspiring to be in middle management, I'm not saying that to dampen, your career aspirations. That's not what I'm trying to do here. I'm looking at the stats and the facts and the writing on the wall, and this is what what's happening. So, Gartner predicted that by this year, 20% of organizations are going to use AI to flatten structure and eliminate half of middle management. And US employers right now are advertising 42% fewer middle management positions. Well, this is a stat from 2025. And I think by 2026, it's actually gonna be worse. Right? So the number of middle management positions open, opening up are going down drastically, and research shows that organization well, that's what they're using AI for.

Jordan Wilson [00:07:01]:
Because whether we want to admit this or not, right, I've been in middle management myself for middle, many years. In some instances, it's not needed. And it is kind of it can be kind of this unneeded bureaucracy. And I think that a lot of the work that middle management does, it is synthesizing and personalizing communication two ways. Right? From maybe frontline workers who are hands on keyboard doing the work, your entry level workers, and then, you know, upper management, senior management, your your your c suite workers. Right? A lot of times, middle management is kind of organizing the chaos on both ends. Right? But a lot of it is just sometimes project management and synthesizing and personalizing information or putting out fires. And I think that, as we see improved agentic traceability, observability with your everyday large language model systems, bringing in all of, you know, companies, data into, large language models where you can see everything happening before your eyes.

Jordan Wilson [00:08:02]:
I think eventually, we are gonna see a decreased need, for middle management positions. Number three. Yeah. People think that AI implementation is a tool problem, but it is a operations problem. And we're gonna talk about this more in a little bit, but 80% of the value that companies get from AI is just redesigning how they work. It's not choosing the right tool. It's not choosing the right model. It is changing the way that you work.

Jordan Wilson [00:08:32]:
And I think that that's one of the reasons why, and it's shocking to me. This is probably if I had to pick one thing in 2026 that I am most shocked about. Right? Two of them would maybe be, one would be just the default agentic nature of today's models and how they can think and reason, like a human and just their level of intelligence, out of the box. But number two is just the adoption gap is mind boggling to me. Right? That you still have large enterprise organizations that haven't fully leveraged AI. Maybe because of the bureaucracy, because of the yellow tape, because of, lack of of training, because of lack of education. Right? But those who have and those who have not, that gap is so wide, and it's, something that is continuing to surprise me every single day. But that is changing how we work.

Jordan Wilson [00:09:26]:
Number four, the bottlenecks. Right. This is, really impacting the flows. And I think that 2026 marks a tipping point where AI agents are significantly starting to outnumber humans in enterprise environments with exponentially more permissions. Right. I actually saw the, piece of software that I've used there, the unicorn company, called ClickUp. So they do project management. And their CEO said yesterday, I believe, that they have more than 3,200 AI agents working alongside 1,300 humans.

Jordan Wilson [00:10:07]:
So, humans are almost outnumbered two to one, and I'm sure that that number will, grow. Now, obviously, they're selling an AI agent product. So, you know, you would, expect them to put that kind of messaging out there. But I've talked to many people both on the record on this show and off the record who have confirmed as much that they have more AI agents, working in their organization than humans. And a lot of times, the only reason that that number of, you know, two x the number of agents, five x, 10 x, it's the bottleneck of permissions. And that is actually becoming a huge roadblock and a huge area of focus. So, yes, on the back end, you know, we have to talk about observability, traceability, all of those things. Right? Expert driven loops.

Jordan Wilson [00:10:51]:
But on the front end, it's actually changing how we work because it's causing, I think, agentic progress to slow down. So the fifth thing that is changing how we work, well, unstructured data like meetings is becoming a huge data pipeline. So studies show that AI meeting transcription market is projected to grow, up to $30,000,000,000 by 2034, about 10 x of what it is, nearly 10 x, about nine x of what it is right now. And, well, you might be thinking, why? And shouldn't we be with AI? Shouldn't we be doing fewer and fewer meetings? I've always said absolutely not. I think we actually need more meetings, but we need more meetings in a very smart and intelligent way because I think that the a lot of the work that the majority of knowledge workers so if you sit in front of a computer, you know, use the Internet and create business value, which is what most of us do. A lot of the work that we've been doing from, you know, 2020 to 2025. Now AI agents, if they have the right scaffolding, can do way better than us. Right? So you might be thinking then, okay.

Jordan Wilson [00:12:00]:
Well, then what do humans do? And unfortunately well, fortunately unfortunately, depending on how you view it, it is more human and social interaction. But I think what we have to do and think about how AI is changing how we work, it is recording and turning all of that into first party company gold. Right? So I've been talking about this for a long time. I swear I swear it's it's going to be a, multi $100,000,000,000 industry as soon as people figure it out. Right? Someone please steal this idea. Let me know how it goes. Give me 1% equity or something like that. Right? But, transformer models, right, they they really benefited from having RAG, retrieval log meta generation.

Jordan Wilson [00:12:40]:
But for the most part, the how companies have fed these models, right, whether they built their own, just, you you know, on the back end, fine tuning models via, you know, OpenAI, and for that Google's API, etcetera, right, is they bring in their structured data. Right? And that has helped these, quote, unquote, old school transformer models be better and be useful, and create business value. Where I think it's headed, and this is a little longer because unstructured data is harder to, monetize than structured data sometimes. Right? But I think that's where we're headed. It is the company's decision making process. And, obviously, we have the silver tsunami. Right? We are losing, just millions of baby boomers who have this institutional knowledge that some of it may die. Right? So I think that a big, focus on how AI is changing how we work is, well, recording these meetings and transcribing and getting a better idea of the human expertise that separates your company from everyone else.

Jordan Wilson [00:13:37]:
Because people thought using LLMs would be a differentiator. It's not. People thought, you know, connecting their, structured data to LLMs is a differentiator. It may be for another year, but the real long term advantage is your first company, first party company reasoning, and bringing that into large language models. Number six, on our seven ways AI is reshaping work. Well, information is getting repackaged into answers. What do I mean by that? Well, knowledge workers, we're gonna stop for the most part using the Internet. And I know that might sound weird.

Jordan Wilson [00:14:12]:
Right? But I would say if we looked at, you know, 2025, maybe, let's just say, let's say that you went from 90%, Internet, 10% chatbot in 2024. And then in 2025, you were fifty fifty. Right? I think it's gonna inverse. I think the smartest, in most AI native people are gonna have only 10% on the Internet and 90% inside of large language models. To me, there's not really a reason for the most part, to use the Internet anymore. Right? Because just about anything can be done inside of these large language models, these front end AI operating systems. We're seeing them, you know, now bring in, data from every single, you know, website that you would use. Right? You you know, let alone the, you know, the model context protocol servers being able to bring in anything, but even just by default, these direct integrations that work inside of Google Gemini, that work, inside of Claude's anthropic or sorry, anthropic's Claude in that that work inside of OpenAI's chat g p t.

Jordan Wilson [00:15:17]:
Right? It's bringing all of your dynamic data to you so you don't have to move. Right? So, I do think that is a big thing that's gonna change how we work. And then number seven, no surprise here, but context engineering is replacing prompt engineering. I think that thinking models do the basics of what twenty twenty twenty two, twenty twenty two's version of prompt engineering did. Right? We did the, you know, the chain of thought prompting and, you know, think step by step. Right? And there's all these prompt engineering techniques, that I think, you know, individuals really leaned into heavily and thought, oh, this is the future of work, and there's gonna be all these prompt engineering roles. FYI, I never said that because I didn't believe it, because I I knew and I started to see the trend that, hey. Once these models get smarter, what we've been doing in prompt engineering is no longer relevant.

Jordan Wilson [00:16:06]:
Right? That's why, even in our quote, unquote prompt engineering course, before context engineering was a thing, we've been teaching it. Right? We've been teaching it with our refined queue. And if anyone's taken that, and you can take it for free, by the way. Here, I'll give you the secret. If you haven't already, go to starthere.com. Alright? And you, or sorry, the starthereseries.com. Starthereseries.com. You know, that takes you to our new start here series, but you can also get, free access to our prime prompt polish prompt engineering course.

Jordan Wilson [00:16:36]:
But we've been teaching context engineering since before it was a thing. So in our refined queue, go check out, f and I of that refined queue. It's an acronym, and you'll see exactly what I mean. But, Gardner is even telling leaders right now that prompt engineering is not enough, and context engineering is how teams get reliable results at scale. So yeah. What the human does to get the most out of the model, I think, is gonna become less and less important. I think a really clear illustration of this is like mid journey. Right? So if you ever used mid journey in AI image, generator, you know, one of the most popular early on, it's like you almost had to speak as mid journey language to it.

Jordan Wilson [00:17:16]:
And if you spoke in natural language, stuff just, like, didn't work. Right? And kind of prompt engineering has changed a little bit as well. If you talk to models from 2022 and 2023 in a certain way and you had a certain prompting technique, you've got way better results than everyone else. Now it's not the case because these models, well, they're smarter than us. Alright? So now it's all about bringing your business context, your personal context before the model gets to work. Alright. Let's get into section two. So we went over the seven ways that AI is reshaping how we work.

Jordan Wilson [00:17:49]:
Now let's get into the 10 AI workflows that actually deliver ROI. And, yes, I am gonna be going a little fast through these. Don't worry. What I'll probably do because I actually had a bunch of things that didn't, make the list, but I really wanted to, and I had to make some tough decisions. So if you repost this, I'll just share my entire, notes file, obviously, in a very well put together interactive website. So if you want, you you know, all the details, I'm not sharing everything that I have on screen and all the things that didn't hit, make the cut, just make sure to go repost this episode on LinkedIn, and I'll send it all to you. So let's get to section two, the 10 AI workflows that actually deliver ROI. So let me just tell you a little secret about ROI.

Jordan Wilson [00:18:34]:
Companies aren't getting it. Right? If you believed sorry. I'm gonna be a little harsh here. If you believed that MIT piece of marketing that they call the study, that 95% of, Gen AI pilots failed and didn't provide ROI, that that means that you didn't take the time to read it. Right? Because that was based on 52 informal conversations, which is not an actual study. Right? It was a piece of marketing. They were selling something. So I think people have this wrong, viewpoint when it comes to getting ROI on generative AI.

Jordan Wilson [00:19:05]:
But I'll tell you this. If you're using generative AI, you are a 100% getting ROI. There's literally no way around it. That's like saying, like, if you take a cross country flight that you're not saving time than if you were to walk. Alright? It's the same thing. A generative AI and large language models goes at the speed of hyper jets breaking the sound barrier versus walking or skipping on one foot backwards. So anyone out there that's like, oh, well, we can't prove ROI. Well, that means you have a human measurement problem more than anything else.

Jordan Wilson [00:19:39]:
Alright. So all of these workflows, I think, are just dead simple. And I think one of the biggest mistakes that companies make is they try to over engineer their, their AI their AI implementations. Right? They they try to make it this this grandiose, you you know, huge undertaking when it's like, no. Like, keep it simple, stupid. Right? Kiss. And I think, you know, the the the boring stuff, the unsexy stuff, that's where you're gonna get the biggest ROI. And the biggest ROI, you're probably not gonna know about it because employees are just pocketing it.

Jordan Wilson [00:20:12]:
Right? I think especially in, remote or work from home capacities, right, employees are sometimes completely automating their job. Right? We talked about the, you you know, bring your own AI. In 2023, I called it second computer AI. Yeah. This is rampant. Everyone's doing it. So that's where your ROI is getting. So I can almost guarantee, and please don't do this.

Jordan Wilson [00:20:35]:
But if you were to actually, like, look over the shoulder or put cameras on all your employees or, you know, use screen monitoring, please don't do that. That's terrible. But if companies actually did that and did it at scale, for, employees that have been properly trained on ROI, they'd be straight up shocked at the amount that they're getting. Alright? And to go, well, it is Tuesday, so maybe I'll go on a little rant here. I don't hate it. I don't hate that employees are pocketing, their saved time because and a lot of this too in larger organizations, it's corporate greed. Right? You have these, huge enterprise companies that are showing record profits, and they're still just laying people off in mass. Like, they're like, they're not making money.

Jordan Wilson [00:21:18]:
So I can't necessarily blame the everyday employee that is using AI, pocketing the save time, but that's where your ROI is, FYI. Alright. I got that rant out of the way. It is Tuesday. Thanks for allowing me to do that. So, let's get into 10 AI workflows that actually deliver ROI. Alright. So number one, it's meeting discussion to tasks.

Jordan Wilson [00:21:40]:
Right? This is huge. I think one example, if you've properly enabled Microsoft Copilot in teams, and can use fabric to bring in all of your data, that's enormous. Right? And and and studies have shown, that, I think it's for every, dollar that companies invest in Gen AI. If you measure it out correctly, they get $3.70 on a return. And I think one of the reasons is this, going from meeting transcriptions to decisions. That is one of the easiest things to do. And you can, you know, whether you're using Google Meet, Zoom, you know, Teams, etcetera, if you have everything set up in permissions, this is usually automatically done. So this is, you know, I'm not saying this is replacing the traditional role of project managers, but it's really helping cut down the amount of time that people should be spending doing meeting follow-up back and forth, emails.

Jordan Wilson [00:22:34]:
Right? It's it's archaic looking at it now, but that's the probably one of the biggest, ROIs. Number two, inbox triage. This is huge. So, yes, make sure you talk with your company about, you know, proper data sensitivity, all that good stuff. Right? But, I mean, chat g b t, Claude, especially, I think those two, and obviously, Google, if you are a Gmail organization. Right? But the the the major three as well as actually, Copilot, even Copilot online. They have connectors for the other competitors, which I think is great. Right? So as an example, even if you're using Copilot, the online version, you can connect, your, you know, Google Workspace.

Jordan Wilson [00:23:14]:
If you're using, you know, Chat GPT, you can connect your Outlook, you know, etcetera. But this is huge. This is one thing I use AI for, not the most, but probably the most because I can't keep up with my inbox because I get spammed all the time. But I'm just like, hey. What are the 10 emails, you you know, having a task, that's automatically triage triage is my emails. Like, hey. What are the 10 most important emails that I missed? The 10 most important emails I need to follow-up on, and then give me suggested replies based on the context that you know about me. Right? Make sure that you share the right context with the large language model, but, I mean, this is huge.

Jordan Wilson [00:23:51]:
In in a study, Microsoft three sixty five Copilot users saw save an average of thirty minutes per week just on emails. For me, it's way more than that. Especially, I get on these, like, super long threads that are, like, 60 emails deep, and I forget things because I have, like, 20 of those going. So I'm just constantly if I'm being honest, I'm constantly using, like, whisper transcribe, and talking to, you know, Claude or Chat GPT and just being like, yo. Like, catch me up on this email. I'm lost. I already forgot. What do what do they need from me? What do I need from them? Bullet point it.

Jordan Wilson [00:24:26]:
Help me, you know, help me with the draft. I go in there, finesse it and send. Alright. Number three, ten AI workflows that actually deliver ROI sales call to objections to tailored follow-up. This is amazing. Right? And I think this is I've shared this multiple times on different of our AI at work on Wednesday shows. You know, creating different GPTs or projects that by default with some custom instructions, you just dump a transcript in there, and then they are automatically gonna, you know, go through objection handling. You know, go through making a little, you you know, project management piece of software or disposable daily, dump of, you know, priorities based on a meeting transcript.

Jordan Wilson [00:25:08]:
Right? This is another big one. And I don't know. Maybe in our community, in our free community, the, the inner circle. Maybe I should start, you know, sharing these. So yeah. Let me know. Inner circle people, let me know if I should. So that's number three.

Jordan Wilson [00:25:21]:
Number four, proposal and RFP first draft grounded in your documents. That's the important thing, though. You know, you have to ground it in your source of truth. But, I remember back in the day having to work on, you know, RFPs working in a nonprofit, they were so dang time consuming because you couldn't just use, like, a copy and paste draft really because so so much of it had to be personalized, and there's always so many different, you know, requirements. But, ultimately, it was using your company's knowledge and in a modular fashion. And that's what large language models are great at. So, whether you're doing proposals, RFPs, write first drafts, that's a huge time savings. But again, make sure that you ground it using context engineering best practices in your data.

Jordan Wilson [00:26:08]:
So you're not just getting a bunch of, generic hallucinations. Alright. Number five. On the 10 AI workflows that actually deliver ROI, research brief to executive memo on a weekly schedule. I like this one using different deep research tools. Alright. I'm not gonna go through, you you know, how they work in each one, but you can use deep research, which is highly accurate. Right? Because it usually takes anywhere from five to thirty minutes to go through and do one of these runs.

Jordan Wilson [00:26:38]:
But a lot of people overlook the fact that in Gemini, Claude, and OpenAI's chat, you can do deep research just with your connected data. So just with your email inbox, just with your calendar, just with your, you know, drive storage. Again, assuming that you have the permission to connect those things. That's huge. Right? So if you have a huge meeting, a huge presentation, right, maybe you do something quarterly, you know, that you have multiple meetings with multiple teams, and you have to put together maybe something for internal, stakeholders, external partners, etcetera. And it's a big part of what you do. Well, running these deep research, on your document is is, a tremendous time saver and talk about ROI. Right? And let alone the fact of just how answer engines now are replacing traditional browsing, even the ability to do deep research, but personalized browsing based on your context and your data.

Jordan Wilson [00:27:31]:
Again, just a straight up silly, ROI. Alright. We got more, but before we do take a very quick break for a word from our partners. Fraudsters used to need fake documents or stolen credentials to scam a business. Now they just need a few clicks to get your CEO to say anything they want. Voice deepfakes rose more than 680% last year. And most fraud detection systems only flag suspicious transactions after the money is already gone. They're not actually listening to the call where the scam is happening.

Jordan Wilson [00:28:04]:
Modulate is. Modulate's new Velma model analyzes live conversations for the signals that give fraudsters away. Stress patterns that don't match the story they're telling, urgency that sounds performed instead of real, voices that are synthetic instead of human, all detected for your business in real time, not in a report days later. Modulate's Velma model was trained on twenty one billion hours of real audio and is trusted by Fortune 500 companies. It outperforms voice models from leading AI labs, and it's a 100 times more cost effective. So go see Velma catch what your current tools miss at modulate.ai. All right. Let's keep it rolling.

Jordan Wilson [00:28:49]:
So in the 10 AI workflows that actually deliver ROI number six, customer support agent assist. So to draft categorize, and go on to your next step. So as an example, you know, using Microsoft Copilot, to, you know, go into your knowledge base, get recommendations on how to reply on customer support. That's an easy one. Right? I've helped companies in the past, right, before generative AI, but, you know, to work with, you know, kind of online, chat bots and, you know, having customer support have to go through and manually look in their knowledge base and find the right answer and customize it. It's something that AI is honestly just a lot faster at right now. Danfoss, actually said that they automated 80% of transactional decisions, with AI taking the response time from 42 to near real time. So, there you go.

Jordan Wilson [00:29:44]:
That should tell you everything you need to know. Alright. Number seven, finance variance explanations that are board ready. So this is a great example as, for another use case. So having executive teams, you know, increasingly wants, the narrative layer, not raw tables, so AI can accelerate kind of that first draft significantly. So organizations report 50 to 70% reduction in data preparation efforts after they implement AI assisted engineering platforms. So, being able to create narratives out of a lot of boring structured data like finance reports and being able to present that, in a digestible way, to executive teams is huge. Right? There's obviously, people whose entire job this is.

Jordan Wilson [00:30:39]:
Right? They're working with the finance department, and they need to get, you know, marketing on board. They need to sell this, to the c suite, etcetera. Right? So being able to use AI and lean into AI for that is a huge ROI. Number eight, policy and compliance q and a with internal only answers. So this is something I think is great. That's great for something like Claude projects, ChachiPT projects, or even notebook l n. Right? Because when you're talking about policy and compliance, you don't always need the creativity and the brainstorming prowess of a, you know, of a Claude Opus 4.5 or a Gemini three pro as an example. Maybe you can just use something that is rooted in your organization's data like notebook l m, especially when you're talking about policy and compliance.

Jordan Wilson [00:31:23]:
But this directly reduces hallucination risk by forcing your answers to stay inside approved documents only. This is always one of those, kind of demos whenever I go out, you know, every once in a while, I'll go do some some keynotes and some in person workshops, when when I have a a little bit of time, which isn't a lot. This is one that always really shocks people. Right? You know, I I'll I'll get copies of their old, compliance, policies, and then to just be able to show people that, oh, you can ask questions of a thousand pages of documents at once and get cited, sourced answers that aren't hallucinated, you know, especially HR departments, legal departments, you know, that it's to to to see the look on people's faces, it's it's kinda wild. Alright. Number nine, content repurposing. You already know this, right? But this is great and something I practice all the time. Right? So as soon as I'm done with this podcast, you know, someone from our team's gonna upload this into a program, and it's automatically gonna transcribe the podcast, and it can spit it out in hundreds of different, formats.

Jordan Wilson [00:32:31]:
Right? We have prepackaged prompts that this goes to, and then that can send it via Zapier to anything. Right? So, if I really wanted to, I could make a 100 videos, a 100 websites, a 100 different, you know, graphical animations just from this without really doing anything, without putting any work in. Right? So content repurposing, it is so good now. It is on autopilot. I think especially, since we've seen models, like, Google Gemini's nano banana 2.5, nano banana two, nano banana pro two, chat GPT's GPT image 1.5. Right. And then being able to work with those systems on the API and just having it do all automatically. Right? A lot of my previous career was kind of manual content repurposing.

Jordan Wilson [00:33:19]:
And where we're at now, I mean, it's you can't even really measure the return on investment because the capabilities are kind of endless. Alright. And then number 10, scheduleized personalized, scheduled personalized research. This is one that I use all the time like chat GPT tasks. I have a handful of tasks that get run every day, but they're usually just personalized research. And then I make sure that each day it's not looking at the previous day's information. So if you are someone that has to constantly, stay abreast of of industry, movements, competitors, you you know, just news that impacts. Right? If you're in finance, if you're in, spaces that change often, I don't know, crisis communication.

Jordan Wilson [00:34:05]:
Right? There's so many different, spaces that change often. Using scheduled tasks to go out and personalized research and personalized report for you, is huge. Right? And this is separate than, you know, triaging your email and all those things. This is just synthesizing and personalizing up to date information by default. Alright. Trying to go fast here. Alright. I I I was promising myself.

Jordan Wilson [00:34:26]:
I'm like, alright. Even though I'm gonna go seven ten ten, this isn't gonna be a a fifty minute podcast. Alright. So, we're gonna try to go through these next 10 in ten minutes. Let's see if I can do it. Alright. So let's talk section three, and this is the 10 AI skills every professional needs now. Alright.

Jordan Wilson [00:34:44]:
I'm cutting it to you straight. Again, this isn't these aren't just hot takes for me. These are through conversations with hundreds of really smart people that are putting this to work in enterprises, small businesses, AI start ups, etcetera. But the AI skill gap that I'm talking about here is not what you think. Because if you think about, oh, AI skills that I need to know, oh, I need to learn. You know? I don't I need to learn JSON. I need to learn Python. No.

Jordan Wilson [00:35:09]:
You don't. Alright. Let's talk about the 10 AI skills that are gonna keep you 2026 ready. Number one, change leadership. Huge. Old winning playbooks are expiring quickly. Companies are finding this out the hard way. Those that especially were slow to adopt to AI.

Jordan Wilson [00:35:26]:
They said, well, we've been profitable year over year. Right? We're we're leading our category. We're crushing competitors in twenty twenty four, twenty twenty five. We don't need to adopt to AI. Yeah. Those companies are gonna slowly lose their spot atop food chain. I think teams with the biggest wins in 2026 are those that have already thrown away successful playbooks. And I think this has to do with just change management and looking at work completely differently.

Jordan Wilson [00:35:54]:
Alright. I could talk about this for hours, but I'm not. But I think that, right, especially if you're mid career right now, we've been able to hang our hat on, this, you know, input output, equilibrium. Right? If we if we input, hard work, effort, industry top notch skill sets, we're gonna on the output, we're gonna receive something the same way. It's still the the truth, but what you have to slide in there, is AI native, which is hard. Right? Because it's changing every single day. But you can't do what you were doing ten years ago every single day and expect it to pay off. It's not.

Jordan Wilson [00:36:38]:
Because, again, ten years ago as an example, let me do the math. Yeah. I was working marketing, at a nonprofit. Right? Working a lot with Nike and Jordan brand and doing these activations, but a lot of what I was doing is content repurposing. If I was doing the same, even though I was putting out just insane day to day effort, high quality work, if I was doing it the exact same today as I was ten years ago, I'm getting lapped. I'm not even on the track. Right? So you have to completely throw away successful playbooks. The second skill is the ability to not trust AI at all and to go through the proper verification process.

Jordan Wilson [00:37:16]:
So a new Gartner study predicted that 50% of organizations will require AI free skills assessments by this year due to critical thinking atrophy. Right? I I I struggle with this all the time if I'm being if I'm being honest. This is why I read chain of thought so, like chain of thought summaries all day, because I need it to stay on top of my skill sets. I think sometimes the more that we hand things off blindly to AI, and we don't verify or trust anything, not only does that increase the risk of hallucinations, right, which I think are becoming less and less of a factor, as the models get better. But what it actually does is the the atrophy. Right? Just our our human abilities and our human skill sets, if we're not practicing them, if we're not riding the bike on a daily basis, even if the bike looks different, the bike is actually, you know, a jetpack. Well, you still gotta ride it, right, to get your repetitions in. Alright.

Jordan Wilson [00:38:16]:
Skill set number three that you need is you have to be fluent in in multi multi modality. And I'm not saying, oh, you need to understand, you know, text and image in chat g b t. No. I'm saying you need to understand multiple models. Right? You need to practice, kind of the concept of being modular. Right? And being able to modularly, solve your company's problems. And this is why instantly, if I ever see anyone on on social media or otherwise, right, say, oh, I canceled my, you know, Gemini subscription, and I'm only using Chat GPT, or I'm I canceled my Chat GPT subscription, and I'm only using Claude. Alright? If I'm being honest, those people are probably not gonna make it.

Jordan Wilson [00:39:01]:
And here's why. If you really wanna see if someone knows what they're talking about, ask them what's the best model. And if they have a very simple and very definitive if they're talking, like, in black and white, and they're like, oh, Gemini the best. Hands down. Right? Claude, Chad Gbitti, garbage. No. Right? The the answer is very hard. If you ask me, and if and if you say tell me the whole truth, I'm like, well, you better have four hours, because I'm gonna tell you the pros and the cons of every seal every single different type of work.

Jordan Wilson [00:39:33]:
I'm gonna tell you the difference in, you know, GPT five two pro, versus, you know, GPT five, two thinking, and how sometimes I still use GPT 4.1. And I'm gonna tell you a little bit, on the on the pros and the cons of using, Gemini three Pro in AI studio versus using it, on the Gemini chat versus using it in the business version of Gemini. Right? You have to understand, the different models and the pros and the cons. It's it's no longer, you know, something where it's like, okay. I'm fluent in one, you know, in one model or in one system. Right? For the most part, even though I do think, yes, organizations need to pick an AI operating system of choice. I've said that. But and you need to move all your day to day processes in there, but that doesn't mean that you should exclusively be using those.

Jordan Wilson [00:40:21]:
Right? Maybe 80% of the time, but the other 20% of the time, a different model is probably gonna be two x, three x faster, and two x, three x better. Alright. So getting back to ROI, and you have to be able to, measure what matters. You gotta be you gotta know those things. There is not one model. I don't care Gemini three Pro, GPT five, two pro Opus 4.5. There's no one model that is the best in everything. All right.

Jordan Wilson [00:40:48]:
So if your organization, or even if you personally are wearing a lot of different hats, it's not it's not always the best practice to just stick with one model. Alright. The fourth skill set that you need is process thinking. So redesigning your workflows is always gonna be obsessing over AI tools. Another, kind of common mistake that I see people make is straight up obsessing over all the different AI tools. It's something I don't do. Right? Yeah. We put the top, you know, three AI tools of the day in the newsletter, but I don't use them.

Jordan Wilson [00:41:27]:
Right? Like, people are always shocked at yes. I've I I have used thousands of AI tools over the past four or five years, and I I continue to try out new ones that are, you know, that are trending. But I'm not making them part of my day to day workflow. For the most part, you know, it's certain things. Like I said, I'm working modularly as well, but, you know, let's just say I do 80% of, you know, my day to day work inside of one of the the AI, models and then the, you know, 10% in the second, 10% in the third, and I'm not using all these other tools. Right? A lot of people are like, oh, I I use these 15, you know, tools just for AI writing. And I'm like, okay. Don't.

Jordan Wilson [00:42:12]:
Right? Like, the amount of duct tape, that shiny, shiny AI object syndrome creates takes away from your ROI. Right? So you like, if I'm being honest, you have to try to ignore a lot of those shiny tools even though they look really cool and say no. What are those boring time consuming things that we can do right now in our AI operating system? Number five, context engineering. Right? Give AI what it needs to be right. Alright. Like I said, the the the combination of the model is getting better and the ability to kind of have, like, one click mini rag, in these tools, hallucinations are I'm not saying that they're a thing of the past, but they're not really a huge concern. And it makes it so easy, especially using things like, projects in chat GBT or Claude, Google Gems, etcetera. Right? It makes it so easy to bring your company's context with these connectors, with these apps.

Jordan Wilson [00:43:14]:
It's a couple clicks. Yes. You still have to verify outputs. You have to understand the chain of thought. You have to be able to scope and test and measure. Right? But once you do those things and you do them continually, right, so you have to that's an ongoing process. But after that, it's context engineering. It's making sure the model has the right information and then using the right model and the right mode.

Jordan Wilson [00:43:36]:
Alright. The next skill, writing crisp inputs. Yes. Even the best of of context engineering, the best models, if you're giving just half hearted inputs, your responses aren't going to be that good. Right? I always say use more words. The the the tired old example that I use all the time, right right now, large language models don't understand words. Right? Even though I communicate or you communicate or we communicate, with large language models with words, they don't know them. They convert our words into tokens, then they think and produce in tokens that is converted back to words.

Jordan Wilson [00:44:15]:
And the dumbest example that I give is there's seven different ways. The single the single word just, j u s t, There's seven different ways that that can be tokenized or seven different meanings, that a large language model might look at that word. So you have to understand just like when you were learning to read or maybe learning a new language, how important context is to a single word. Now think of, you know, these things that are able to process thousands of tokens per second. Think of how important the surrounding context, in your words, let alone your your context engineering, but just your words, you have to be extremely clear. And this is why I read chain of thought so often because I catch I catch it all the time myself falling short. I'm like, oh, I I use this three word phrase, and I probably should have written out two full sentences, right, to explain that a little better. That's another reason why I'm using a lot more voice dictation now cause it's well, it's faster, but sometimes, I can explain things a little bit better speaking them than I would if I type them.

Jordan Wilson [00:45:16]:
Because if I type them, sometimes I overthink and oversimplify. Because as a former journalist, I'm used to writing tight, right, and cutting the fat. So just FYI, writing crisp inputs is a huge, and one of our 10 AI skill sets that every professional needs now. Number seven, continuous learning and adaptability. You know, it's it's sometimes called change fitness. So change fitness is now a career requirement. It used to be that you could really master a skill set and ride it out for five, ten, fifteen, twenty years. Right? I think, for for the most part, right, post I think post Internet maybe changed it slowly, and but it probably took a decade for that to reveal itself, the same thing with social media.

Jordan Wilson [00:45:58]:
So, yes, that's been impacted. But I'll say for the most part, I don't know, since the early two thousands, you've been able to become really good at one skill set, you know, kind of keep up, you know, a little bit of polish. But for the most part, you've had people make entire careers of a quarter century, just being really good at one thing. Right? It's not gonna be the case moving in the future because large language models are gonna be really good at that one thing, and we are going to get domain specific models. So it's no longer, oh, I know something. Right? No. It's how you can apply AI to that thing that you know and use it in the right way. But you have to be able, to shift because right now, the tech is shifting faster than teams can adopt.

Jordan Wilson [00:46:45]:
Right? There's literally I do this every day. I can't keep up. Right? I'm a I'm a small I'm a small company, small business. I do this every day. I can't keep up because I know even since I started recording this podcast, I can guarantee there's some new tech new technique that has just been released. Alright. Number eight, the automation basics. You have to understand it, including scheduling tasks.

Jordan Wilson [00:47:09]:
That's a huge one that I think most people overlook. For whatever reason, you know, Claude, Gemini, and OpenAI just kind of hide them hide them. Well, actually, it's not fully rolled out in, Google Gemini yet. But, you know, scheduling tasks is huge. And this is where I think, you know, it's almost like agent creep. Right? Like, all of a sudden, had a great conversation with one of the the the head of AI, at CloudFlare, and we talked about this. We're like, okay. Well, if you're scheduling a task and it's an agentic model, technically, it's an agent.

Jordan Wilson [00:47:42]:
Right? An agent is going out to do work for you without you even telling it. And I think that, you you have to shift that from saying, like, hey. I use AI when I remember to use AI versus AI just runs constantly, always using the updated and most dynamic data, and it's, you know, memory and personalization of our ongoing conversations. Number nine, the ninth skill set, human AI collaboration. You have to work with it, not around it. Alright. This one might be uncomfortable, but I think you need to treat AI like a junior teammate you manage that's trying to outwork you and take your next promotion. Right? Why do I say that? Well, that's what's happening.

Jordan Wilson [00:48:27]:
Right? And I think it it it does require, a little bit more work than traditionally you've had to put in. Because like I said, again, normally, you you you you dig down. You dig deep on that one skill set. Right? Oh, I'm a great copywriter. Right? So I'm gonna dig deeper, deeper, deeper, deep. Nope. Not anymore. Now if you're a great copywriter, you have to be good at content repurposing.

Jordan Wilson [00:48:48]:
You have to be good at using AI video tools. You have to be good at using, you you know, all of these different things. You can't just dig dig deeper anymore, because if you keep digging and spend your whole career, you'll find at the very bottom of you digging down, oh, that's where all the AI is. They're actually better, and they've been down there. So you have to be able to know, when it's time to collaborate with an AI and not compete with it, and that time is now. Because human AI collaborative teams, study show demonstrates 60% greater productivity than human only teams in research studies. And again, I think you have to think of augmented intelligence. I think right now, most people, their use of AI is just not doing the thing that they should be doing.

Jordan Wilson [00:49:34]:
So let me use an example. Let's say you're data analyst. Right? And if you're using AI to analyze your data and you're just kind of copying and pasting everything. Okay. How long? Right. Again, talking about the skill atrophy, how long until you start to lose some of those data analysis skills? That's why you have to use augmented intelligence. That's why you have to, combine the best of you, the human, with the best of the AI and push each other to make each other better. So again, if you've taken our free prime prompt polish course, you understand that.

Jordan Wilson [00:50:07]:
Alright. And then skill number 10, communication that drives decisions, not just outputs. This is huge. I talk about this all the time. What are the best skill sets I think is not leaving gold at the bottom of your chat when you're done. Right? So whether you're using, again, Gemini Copilot, Claude, Chad GPT, it doesn't matter. What is the action? What is the decision? What is the output? Right. I have to remind myself of this all the time because I am guilty of this as well.

Jordan Wilson [00:50:35]:
I leave so much gold at the bottom of there. Right? It's actually one thing I'm having agents go in and, you know, double check the bottom, you you know, reread my chats and, you know, saying like, hey. Let's let's fly these. Let's make a database of things I need to follow-up on or database of decisions with links. Right? You have to create next steps in actionable value because, you know, I think a lot of times, not that I'm caring about, like, wasting tokens, but I think it was, Sadia Nadella that said we have to stop, you you know, shift from just using tokens to creating value. And I think that's this last, skill set and maybe a good one to leave people with is we have such an unbelievable technology, that is crazily affordable. It is insanely capable. And sometimes we're just looking for that one little thing out of there.

Jordan Wilson [00:51:28]:
But what about everything else? Right? Sometimes I look back and I'm like, okay. I I use this entire chat just to make one decision. But look at everything I left on the table. So I think really developing that that communication skill that drives decisions and not just outputs. Alright. So here's what matters, and I'm gonna leave you with this. Eighty twenty rule flipped on its head a little bit. Right now, I think using the right technology is what is gonna give you or your organization 20% of the of of the value.

Jordan Wilson [00:52:03]:
But redesigning how you work, rebuilding, unlearning, that's the 80%. And I think the winners in 2026 are focusing more of their time on that, more of their time on change management, more of their time on, rethinking about the future of work and unlearning and starting from scratch. And there's nothing wrong with that. That's what we do here on this show every day. So if you are doing that, you're ahead. So I hope that this episode was helpful as we went over these seven ways AI is reshaping how we work, the 10 AI workflows that actually deliver ROI, and 10 AI 10 AI skills every professional needs in 2026. Like I said, if this was helpful, go ahead, repost this. I'll send you, my complete notes in a nice little, interactive Canvas document, that hopefully can be helpful for you as well.

Jordan Wilson [00:52:58]:
And make sure if you haven't already, go to our website at youreverydayai.com. We're gonna be recapping the highlights from today's show as well as a lot more. So thanks for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks y'all. The risk with AI voice agents isn't that they sound too robotic for your company to use. The real risk is that they can sound too confident while saying something completely wrong to your perspective clients or customers. Made up refund policies, promises your company never approved, or discounts that don't even exist.

Jordan Wilson [00:53:33]:
You've gotta give your AI voice agents a trust layer with Modulate. Modulate monitors live voice conversations to flag abuse, false claims, fraud, and user emotions for safer, more empathetic responses. For the guardrail layer you need between your AI agents and your customers, you need Modulate at modulate.ai.