S2 Ep 1: Twelve Data & AI Predictions for 2026 (The Data Literacy Show)

Welcome to the Data Literacy Show, the podcast that helps organizations build, measure, and level up their data and AI literacy. I'm Ally Torbin, the senior data literacy advocate here at Data Literacy. — And hi Ally, I'm Ben Jones, co-founder and CEO of Data Literacy, where we help people learn the language of data and AI through tailored training and assessments. We also do a lot of consulting work and things like that. We're really happy to have you uh joining this show, the first of the new year, 2026. We're — sticking with our monthly cadence here. Maybe we'll add in a few more per month. We'll see. We got some plans. But today, — we're looking back at our 2025. And what we're going to do is we're going to share 12 lessons from last year's 12 podcast episodes. And hey, we've taken the time to turn those into 12 predictions for what's next in the world of data and AI literacy in 2026. — Yeah, this back this past year we've covered so much. We talked about data storytelling. We talked about AI agents and now we want to look forward into 2026 and think about based on what we learned, what we think leaders and you know other practitioners what they should be watching for in the coming year. So let's get into it. Let's do prediction number one, Ben. — Okay, number one out of the gate. So, starting, I guess, with measuring things. Measurement is going to matter more than ever. So, we had this in episode one of last year, how organizations can measure progress in data and AI literacy. You know, we worked with a lot of clients last year to do just that. So, we didn't just talk about it, we did it. Now, we're talking about objective skill and knowledge assessments. We're talking about subjective assessments, maturity models, measuring, tracking progress. So our prediction for this coming year is that hey if you're an organization that is succeeding in investing in measuring early not as an afterthought then that's going to result in success because you know if you can't measure progress you can't really manage or improve it. It's hard to know whether you're moving in the right direction. So you can expect to see a lot of different kinds of assessments quantitative and qualitative that's going to become standard practice in data and AI literacy programs here in the coming year. — Right. And we have talked about it before too, how people are using AI tools without their managers even knowing, right? So it is hard for them to know, well, what does my team know about AI? Maybe we should assess first. — Yeah, exactly. Let's give them a little battery of questions. Sort of see where they are right now. See what gaps there are in perhaps what they know. and maybe also sort of collectively aggregate that to get a team score or an organization score and use that as a basis for comparison in 6 months, in 12 months, in 18 months to see if some of our training initiatives went in the right direction. — Right. Okay. Prediction number two. — Okay, over to you. — Thinking, — yeah, — people's skills are going to outweigh technical skills in some ways. So in episode two that was called the three most overlooked traits of data literacy. We talked about traits that we see uh that are more like non-technical side of data literacy that are really important like curiosity, critical thinking, data communication. And I think in this coming year, organizations are going to start valuing this these human skills um even more so or at least on par as the tools and the technical fluency that they're they always value. Um, I think that the people who can communicate, question each other, debate, I think those teams are going to outperform teams that are just looking at tools because if you cut out the human part and we're all just like really leaning into all these AI tools, we learn that we lose the very important part of judgment, adding context, like shared understanding of what this all means. We lose that. So I think people are going to we've been rushing to the AI tools and I think we're going to realize ah there's a lot of people skills that we have to make sure we are prioritizing still. — Yeah. I think people are even going to crave it. They're going to really want something genuine. They're going to want something that they know is authentic. And so the person that can take AI outputs and then combine them with their own genuine thoughts and ideas and find a way to get the best of both worlds so that you know those interactions still feel human. I mean that's something I think we all really need and we don't want everything to be automated and to feel like it came and copy pasted right out of a chatbot. So — we've seen a lot of that. We've all seen it on LinkedIn. we're seeing in our emails. And so it's sort of to the point now where it's time for us to and we'll see the pendulum swing back to some maybe even some pure human interactions

that don't involve any AI outputs. You know, heaven forbid. — Weird, — right? I know. Why would you do that? Okay, so prediction number three, a crystal ball here. — Um, well, this is just where we are, right? AI fluency is going to become table stakes, a minimum expectation. We ran an episode last year number three. What is artificial intelligence? Just covering the basics. You know, what is it? What is it not? How does it all work? AI is such a huge umbrella. So, you know, people might have wrong impressions about it and they might believe some myths. So, we're in a place where we need to understand and we need people on our teams in our organizations to understand what it is. So, you know, that's really to me going to define what a modern organization is all about. Everyone on the team has at least a baseline AI fluency level and they can really kind of speak to that. And so, you know, whether you're a decision maker or an individual contributor, you've got to be able to understand all of this. And you know you don't need to be an expert in it or have a PhD in it but you do need to have that basic concept and understanding about deep learning machine learning even these large language models what they are how they work some of the pitfalls those things are so important so AI fluency it's important at a societal level we can talk about that later but of course for organizations as well it's super important — yeah I think having a basic understanding of how this all works and how the models were trained just on a very basic level, it does help you understand where could this kind of go wrong as I'm using it. — Yeah, — a good one. — Absolutely. — All right, prediction number four. I am thinking that testing your data story is going to become just standard practice. — So in episode four, we it was called create better data stories by testing early and often. And we emphasize that you should be iteratively testing your data stories. You want to get feedback early on, in the middle, at the end to make sure that it's really relevant to the person that you're speaking to. So, in the coming year, I think that we're going to start leaning on AI a little bit more to help us create those data stories, which I think is great, but I think that you also need to be testing it on other humans. So your co-workers and your stakeholders as well to really be making sure what AI helped you create is actually meaningful to the people you're going to be presenting it to. So I think we should have been testing our data stories all along, but I think using AI is going to make it more and more important. So we should be doing more of that in 2026. — Yeah. I mean so many people again, they're just going to copy and paste and just run with whatever the AI output is and then they're going to start seeing that fails. And so they're going to ask themselves, wait a minute, what do I need to do to go back and make sure that what I'm getting here is going in the right direction and is going to resonate land with the audience. I think your uh framework uh around data storytelling for impact is a perfect way to kind of go about that in almost an agile fashion and to figure out how AI can help fuel that and you know speed it up. But at the same time, you're still in charge, you know, of making sure if this is going to land with your own human audience. That's critical. — Yep. Exactly. — Okay. Number five. So, I'm a framework person. I really believe in them. I love them. I've been building tool agnostic frameworks for a few years now. So, I really think that these analysis frameworks are going to drive better outcomes when it comes to crunching the numbers. So, you know, going back to last year in May, episode 5, how to analyze data with the wisdom framework. The wisdom framework is kind of the anchor of our level two course which is about working with raw data tables. I think that that's going to be key. People are going to need to learn how to apply some of these frameworks to prompts to AI skills and agentic frameworks that then ensure or help increase the possibility that the way they along with the AI tools they're using are going through the data analysis process in a sound and accurate way that incorporates all of what we've learned over the last decade or two manually interacting with data, right? So if you have a good framework, let's say you're going to explore a data set for the first time, that's called data profiling. What's your approach? What is your process? What steps? What questions do you ask? If you have that framework, I think that that's going to be something that gives you a huge advantage in the coming year as you use AI tools and you point them in the direction of good practices that help you get the job done in a way that doesn't lead you down the wrong path. So that's my prediction number five. If you've got a good framework, you're going to get better outcomes. — Yeah, it's just like the data storytelling framework. We got the data analytics framework. Both of them work

really well with AI, but you also have to make sure that you're um keeping the human side of it for it to be really uh useful. — Yep. — Okay. Prediction number six. I am thinking that data storytelling patterns, we're going to finally be recognizing those patterns more and then we able to reuse them. So in episode six, it was called what the best data stories have in common and we talked about some of the traits that we were seeing across all data stories. So having a really clear message really aligned with a specific audience and the it was mostly talking about the impact to humans. Why does this data actually matter to humans? And so in 2026, I think that as we do use AI more to help us with data storytelling, we are going to realize that we have some data storytelling patterns that we can reuse specifically maybe for certain situations or certain industries or when you're speaking to a specific audience. And it'll help you kind of build this library of narrative templates for your real world scenarios that you can reuse over and over again. you know that they work and it will save you time. — I love it. Yeah. I mean, I really love the idea of, you know, story types and story patterns. — I think that they can be shortcuts to getting your audience to follow along with you. — Yeah. — And understanding some of the kinds of templates you can apply in certain situations is a huge benefit. — You're not starting from necessarily from scratch from a blank page. You have kind of a an overall kind of structure that you know a certain type of data story will fit into and you know people who are really good at that I think will excel. — Yeah. If it works use it over and over. — That's right. And you know exactly like you know take your own data story and fit it into that template. It's not going to be a cookie cutter thing where it just drops right in. You're going to need to massage it. You're going to have some nuances about it that aren't really like common perhaps or by the book let's say. But, you know, applying some creativity while also leveraging some of what we know just works well, you know, um that that's going to be key. Okay, number seven is a topic that I really think is critical. Okay, so just to set this topic up, I noticed right before I came on to record this that Amazon laid 16,000 people off today. I simply couldn't believe it. That's a staggering number of people. I know I haven't read the article, but um I think that is true. I'll I need to double check that, but I couldn't believe it, you know. So, the fact of the matter is, you know, we've been seeing these tech layoffs over and over again. And so, that what does that mean? That means some really, really talented people — are out of work, are looking for work. So, my prediction number seven for 2026 is that data freelancers and consultants are going to become strategic partners. We touched on this, of course, last year. We had an episode where we just really focused on what's a better way to hire data freelancers and consultants. You know, we even started up our own little um upscale Upwork, let's call it, at dataf freelancers. com. And we talked about how you can tap into some of the amazing data and AI expertise that's out there in ways that doesn't require you to get a whole new job requisition approved and opened through all of the different levels of you know review within your company. So with all of these amazing people on the job market looking for jobs, they're going to be doing some freelancing work I believe here and there. So, it's a great time in 2026 for organizations to put them to work while they do their job search. Not just to fill gaps, but to really kind of integrate them into more strategic kinds of conversations, planning out what's next. I mean, would you take an Amazon data expert to give you some perspective about what you can do differently? You know, you probably would ask them a few questions, right? And you'd really benefit from perhaps that conversation. So, I think organizations that are learning to treat freelancers as great sources of input and even potentially partners that they work with on an ongoing basis, you know, not just as a little one-off consulting gig here or there. That's going to be huge. The talent is out there. The willingness is out there. I've seen it. I've talked to a lot of people that are wondering whether maybe doing freelancing full-time is the way to go since it has been tough, you know, to benefit uh to to um to depend rather on um you know, full-time employment, right? So, — the economy is in a little bit of a weird state. I think it makes it a great time for people to lean into freelancing and organizations that learn how to tap into that, they're going to benefit. So, that's my prediction number seven. Yeah, I think some organizations probably still feel like hiring a freelancer is like just uh hiring a extra pair of

hands for this short amount of time for this small project. But I think if you start like you said thinking of them as partners with really specific expertise that you can tap into really quickly that can level you up really fast. — So don't forget about that. — Okay, prediction number eight. I am thinking AI will make charts faster but not smarter or better. — So in episode 8 it was called three chart swaps to make your graphics more effective and we talked about how there are some simple things that you can do when you're trying to communicate with data making a chart that will make your uh your data relationship a little bit more um obvious to your reader. So — AI tools are getting really great at making charts just, you know, one thing I did recently was I had an image of it was a picture literally a picture of a table and um it was for pickle ball data and I put I just I didn't want to have to hand jam this uh that data into an Excel file then make the chart myself. So I was like oh I'll just upload it to Chat GPT and ask it to make this uh diverging bar chart for me. and it made it really fast. But there were still like, you know, that last mile of tweaks that I needed to do in order to make it really clear. Like I didn't label things correctly. It flipped the axes in a way that didn't quite make sense. Um the colors were very off and um it just wasn't as communicative as it should have been. So AI is a really great tool to make charts fast, but you do really need to make sure that you still can communicate really well with them. Your data communication expertise is going to be much more important in the future because anybody is going to be able to just output a chart really quick, but you can stand apart and do better work if you're focusing on increasing your data communication expertise. — Yeah. It's almost like the AI makes a good chart prototype and then from there — it's up to you to put the data annotation layer in there to put the fit and finish — and that can take some time and you know that can make all the difference. So — yeah or if you don't know what chart type to use it can help you brainstorm but it also might pick something that doesn't work well. So you still like it just doesn't take the expertise out of data communication. It is a helpful tool but don't forget about the expertise in 2026. — Yeah. All the best data experts, data visualization experts I know um you know are taking what's coming out and doing a lot of work to — Yeah. — put it in the right shape. Yeah. And even some cases just starting over, you know, — right? Yeah. Sometimes you're like, you're not even close. I can do it faster myself. — Yeah, that's right. You know, someone who's really good at it might not even benefit from that prototype to uh to come out with natural language. — Number nine. Prediction number nine. I think this one has been happening. it's been building. We've seen it for a number of years now. And that is that, you know, when it comes to training, if it's tailored, that's really going to be critical. So, a tailored training modality will become critical. And content, we talked a bit about that last year, which training style is best for your team. Lots of different pros and cons of the different approaches there, right? You could put together cohorts. You could do asynchronous or on demand. You can do some tailored and customized or even maybe blend all of that. I think in this year going forward, especially with the big focus on personalization that comes in with AI, I think this idea of one-sizefits-all training is just really going to no longer cut it. you know, people and teams, they're going to start to demand training that meets them where they are based on their role, based on their scenario, based on even potentially their own data, directly tied to their own business outcomes. And you know, that I think is going to be something that, you know, we just kind of have entered a world now where everything is sort of personalized around us. And so it'll feel almost just like it'll feel weird and wrong to be in a kind of one-sizefits-all training where everybody's learning the same thing that has nothing to do with who they are. You know, that's really not going to be something people are okay with. I think uh in 2026 and beyond. — Yeah. I think as things get more efficient, people's expectations for wasting time — becomes really low. — I don't feel why am I wasting my time with this? — Yeah. Exactly. Okay, prediction number 10. I'm thinking that AI strategy is going to become a broader boardroom topic, not just something that the tech team needs to worry about. So, in episode 10, it was practical steps for a strong AI strategy. And we had that cool graphic where it was a visual metaphor, you know, showing connecting the AI strategy purpose, the people, the process, the data, the culture. That was the wind blowing through the building.

Very cool. — I love that. Yeah. So in 2026, we pretty much know the organizations are going to be using AI. I mean like we talked about at the beginning, a lot of your um the people on your team are probably using AI even if you don't know it. So yes, it's not if we're using it, we are using it. Therefore, this is going to be the AI strategy, the broader AI strategy is going to have to be something that all teams are talking about, not just the tech teams. — Yep. How are we using this? How are we not using it? How are we connecting it to our data? What kinds of what shape is that data in? Is it ready? Do we need to do some work to get it ready? Those are things I think that need to leaders need to be involved in those conversations and they need to support it. They need to find a way to — um you know educate themselves enough to participate in that topic which is a challenge. We're all really uh drinking from the fire hose right now in terms of all these kind of new technologies, you know, and trying to apply what works well um and trying to get the most out of them. Speaking of that, okay, number 11. Wow, talk about the buzzword of the year. Last year, I think we all got sick of hearing the word agents and agentic. The thing about this is now this coming year, it's not just going to be a buzzword. Those are going to become really practical tools. Um, you know, we talked about that last year toward the end of the year. We had a whole conversation around what does it mean to use an AI agent? When should we use it? maybe when should we be more careful about using it in what situations etc. So that discussion on AI agents I think was all about helping demystify what it means to create and use an autonomous AI workflow. So I think in this coming year we're going to see AI agents become more practical. Those are going to be almost like co-workers for us. They're going to take on a lot of our repetitive tasks. They're going to start carrying out a lot of workflows and steps without our necessarily kind of need to intervene. Even when it comes to working with data, they can do a lot of that heavy lifting in data prep and transformations. You talked about making charts. And those agents are going to take on more and more of the decision-m capability within some of these processes. And so the question then is how do we leverage these practical tools without them kind of running away with us and so the winners I think are going to be the teams that define you know when and how agents should work and most importantly how humans should interface with all of that right — and so I think that's coming I mean it's already here you know we saw the launch of Claude co-work in the last few weeks um I've been playing around with an interesting tool um called vers that allows you to basically take control of your screen and you know carry forward a number of different uh steps and such. So those yeah those sorts of approaches I think are going to become really common place in this coming year and then you know — there will probably be a lot of mistakes that get made. There probably will be a lot of scenarios where maybe someone wishes they hadn't automated something uh without kind of going through the right due diligence to make sure it's all ready to go. So, I think that we've got to find a way, you know, to make these tools help us and helpful for us and uh and that's critical. — Yeah, it's a pretty uh dense topic and you think, oh, agent agentic, like I can hardly say it. What are what — the word itself is? — So, definitely go back to episode 11 if you want a refresher or kind of a way of thinking about certain tasks that are ripe for using AI agents. Um Ben and I walk through that in a very uh clear way so you can get a good primer on that. Okay, last prediction for 2026. Prediction number 12. — I am thinking that small data literacy programs are going to grow into fullcale change engines in organizations. I was really inspired by episode 12. We had our first guest Neil Richards on the show and the episode was called real lessons from building a data literacy program and he talked about how to grow a sustainable data literacy program. How to get leadership buyin little small wins that you can have um to integrate data literacy into the whole culture in your of your organization. So I think in 2026 people are going to start thinking I'm hoping they start thinking small in terms of the their data literacy programs like let's have a um some sort of prototype or some sort of um way of introducing small things data literacy AI literacy into a small team for example test it out and then see if we can expand it like learn from our small wins and kind of being an early adopter in our organization and show them how we're using data and improving and that's a way that's just going to spread

like wildfire through the organization and make some real change. — Yeah, I think pilots are huge and then the question is how do you then grow from that pilot? How do you take your lessons and learned? how do you incorporate them and you know think about rolling it out and um we've worked with a lot of organizations lately on pilots and I do think you're right that you know it's time to think beyond a pilot — and um and a lot of organizations are doing just that so — yeah and I think across all 12 of the episodes one thing one theme kept coming up again and again is that you know there's lots of cool tools out there and it's all constantly changing but — the human side of data and using AI like how we think, how we communicate, how we make decisions, how we analyze output of AI, it's never going to go away and it's just going to become more and more necessary. We can't get rid of that human part of it. — No. And I think um you're right, it is going to become more and more important as time goes along here, especially now as we go into the year. So, as we head into 2026, I really believe that organizations that are investing in both data and AI skills and the people using them, those are going to be the ones that really rise up. So, thank you again for starting this new year with us and for tuning into our show. Um, we're off and running here with our um with our program for the year, episode or season number two, episode one. So, — we didn't get cancelled. — No, we did not. I know. We got it. Yeah, we got a uh we — That's right. We got reuped for another season. So, they must have done something right. But uh yeah, if you found today's episode to be helpful, share it with someone shaping their data culture and their organization. — Mhm. Yep. And don't forget to sub subscribe to the show so you never miss an episode. It drops right into your queue. All the links we talked about are in the show notes at dataliteracy. com. And we will see you next time. — All right. Bye everyone. Talk soon.