How can AI help us redesign work instead of simply making it faster?

In this solo episode of the Happier at Work podcast, Aoife O'Brien explores how artificial intelligence could change the way work gets done. Rather than focusing only on automation and efficiency, she asks a more fundamental question: what work needs to happen at all, and where does it create value? Drawing on her experience in market research, she reimagines the traditional client brief as a focused conversation about the decision that needs to be made, with AI providing access to information and people bringing context, judgment, and the right questions.

In This Episode, You’ll Discover:

• Why redesigning work requires more than making existing tasks faster.
• How AI can help teams access information and support faster decision-making.
• The human value of context, probing questions, judgment, and problem diagnosis.
• The importance of asking whether work is producing real value for the client, team, department, or organisation.

Related Topics Covered:

Human Capability, Meetings at Work, Decision-making

Connect with Aoife O’Brien | Host of Happier at Work®:

• Website
• LinkedIn
• YouTube

Related Episodes & Resources:

Episode 251: How I use AI in My Business and Personal life with Aoife O’Brien

Episode 275: AI Won’t Take your Job, but It Will Redefine It

Episode 312: Maximising AI Productivity with Rebecca Hinds

About Happier at Work®

Happier at Work® is the podcast for business leaders who want to create meaningful, human-centric workplaces. Hosted by Aoife O’Brien, the show explores leadership, career clarity, imposter syndrome, workplace culture, and employee engagement — helping you and your team thrive.

If you enjoy podcasts like WorkLife with Adam Grant, The Happiness Lab, or Squiggly Careers, you’ll love Happier at Work®.

Editing by Amanda Fitzgerald.

Website: https://happieratwork.ie LinkedIn: https://www.linkedin.com/in/aoifemobrien/ YouTube: https://www.youtube.com/@HappierAtWorkHQ

Most conversations about AI tend to focus on how we can automate or how can we make things faster, but I think there's a fundamental question that we're missing here and an opportunity as I see it. Now, this is not a fully formed idea, Just yet. These are my thoughts about changes that we can make to how work actually gets done. I'm thinking that that there's a bigger question we need to be asking about the design of work. This is the Happier at Work podcast. I'm your host, Aoife O'Brien, and today's episode is about how AI can help us redesign the future of work and the kind of work that we do. Information is something that's readily available. And so it gives us the opportunity to redesign how we actually think about work.

When you think about the access to information that we have now, the information even contained within an organization should be a lot more accessible. I'll talk about that in a second, but it's no longer something that's at a premium when we have access to all of this information. So it gives us the opportunity to rethink how we actually do work. Now, If I think back to my corporate days, a lot of that work centered around getting a client brief. So we would either receive a written brief and kind of go through it, accept it as is, but most of the time we kind of push back and try and understand really what's at the crux of what they're trying to get to. Quite a, usually also quite a lengthy brief. And, you know, my market research friends, don't get too annoyed, but I'm gonna skip a few steps here. But essentially what we would do is review the brief, interrogate the data that we had access to, put together a presentation, and deliver a presentation to a client.

Sometimes we were delivering reports instead of presentations, but most of the time that was a stand-up presentation. Now, as I understand it, since the pandemic, a lot of those presentations have moved online. People are dialing in from home, things like that. But it also got me thinking of how we can rethink just that process because it's my own experience. And maybe you can think of how this applies to your experience as well. So instead of having a brief, we have a meeting, we have access to all of the information. The information is at our fingertips. And what we're really doing in the meeting is having a discussion about a decision that needs to be made.

And I think this was one of the hardest things working in that industry was oftentimes the clients were very prescriptive in telling us what they wanted, but they didn't necessarily say the decision that they were trying to make. And if we can surface that decision through a conversation, and again, this is where the humans come in. This is where it's useful to have the context. It's useful to know the probing questions to ask someone. If we come back to this idea that it's not a 2-week, 4-week process where we get a brief, we go away, and maybe we haven't properly answered the question sufficiently so that they can make a decision. it actually comes back down to, we're sitting in a room, we have access to the data already. We have the human context from the client. We have the human context from the researchers and we interrogate the data with a view to making a decision based on what that data is.

And, you know, this is just something I was thinking of this morning when I was doing research for this episode. And I'm not sure if that's actually what's happening right now, But for me, it is certainly an opportunity to work that way in the future. Where AI can help us is accessing data from lots of different databases, hopefully more up-to-date, more current as well. And where the human plays a role is providing the context, knowing what questions to ask, knowing how to interrogate, but also bringing context from other categories from other markets and where the client plays a role is really clearly defining what decision is it that we're trying to make here. And then together we work to try and solve that issue. And the result, rather than weeks of analysis and maybe not fully answering the question, but the result is a meeting. Maybe we've had it over an hour or 2 hours, but it results in a decision that that could have previously taken 2 to 4 weeks to actually make. And then it's up to the client to implement that decision, obviously.

But just thinking about this in quite a different way, and I'm using my own context to be able to shed some light, maybe you can apply that to your context as well. Now, there is an important thing to raise here. And if I think of my career, so I had a 20-year career doing this and learned from the bottom up what good analysis looks like and all, you know, made all of the mistakes to learn what it is that I do. But if AI is doing this, then what are the considerations for people coming into that industry? And how are they going to know and recognize and understand the context and understand the kinds of questions? It's a very different way of thinking about work. And I know this challenge applies to so many different industries. I see the headlines about you know, people getting rid of that bottom layer in organizations, and therefore we don't have that entry level anymore of people to really understand the context, to do the grunt work, to then be able to graduate or qualify to do that higher level work. I don't have an answer to that, but I would love to know what questions you have. I think one of the biggest reframes that we could have in this situation is is just the idea of whether the work you're doing actually produces value.

Now, I did talk about AI and how we're using AI in a previous episode with Rebecca Hines. Definitely go and check that out. But I also talked about the idea of impact and value with Dr. Shirley Kavanagh. And it's such an important question. Like, we use this term in entrepreneurship of moving the dial. I think that's, it's kind of, it's very jargony, I think, in my opinion, but it's really thinking about the work that I'm producing. Is that actually giving value? And you can think about that like, what objectives do you have to meet? And is the work meeting those objectives? That's kind of a simple way to really break it down.

But even thinking beyond that, how does this impact on the department, the team, the organization, whatever level you want to think about it at? Thinking about, you know, what is the purpose of this work? And is it going to have a benefit for a client, for someone internally? And if it's not adding value, why is it that we're actually producing it? Or why are we doing it to begin with? Now, this is something I struggle with in my own business because I find myself getting caught up in busywork that I think needs to be done, but actually doesn't have any, you know, it doesn't produce the outcome that I'm trying to achieve. It may make things easier longer term. So I'm trying to think of how do I balance those longer-term projects that are not producing immediate value with the things that are producing immediate value. And if I think about it, for me, when I think about that, it's sales, it's revenue, it's bringing money into the business. Because if I don't have money coming in, then I just have an expensive hobby that I'm doing, that I'm making a podcast and I'm talking to people about work, but I'm not actually bringing any money in to support my business. For the longer-term growth. One thing I do want to point out here is the idea, and we used to talk about this in market research, it's shit in, shit out. If you want a less jarring version of that, maybe it's junk in, junk out.

And I've seen people post about that on LinkedIn as well, especially when it comes to AI, because AI is only as good as the information that it has access to. I have been doing a project, um, for the podcast. So I want to move the podcast beyond, I release an episode and then 2 weeks later it's part of an archive and people may find it if they go searching specifically, but it may be harder to surface, especially since I have over 300 episodes to get through. So I've been working on this project to turn it into a body of work that people can interrogate, that you can use some sort of a chat functionality, ask it pressing question related to work and surface an answer that will help you in what you're doing day to day. That answer could be a full podcast episode. It could be a clip from an episode. It could be a video, but also it could be a tool that helps you to get to the heart of the question. This is what I'm building.

And what I found was it was a very manual process to get all of the previous transcripts because I didn't have transcripts for every single episode going back to episode number 1. So getting transcripts for all of those episodes and then making sure that the transcripts were named correctly, that they were okay in terms of speaker allocation and the words that were used and that things weren't changed. So I won't bore you with all of the details, but suffice to say, It was hard to get to that level. And then I asked it to produce like a high-level summary across each episode. And even getting to that was quite hard. So there were inconsistencies across different episodes. It was doing it in batches. They weren't all the same.

And it's still that way. I still need to fix that. So when I went interrogating the data, it would come back and say, well, it's not really consistent and you can't do this. So all of that to say is you need to make sure that the inputs that you have, and I know there are so many organizations that struggle with this, the inputs that you have are fit for purpose for the outcome that you're trying to achieve and the reason that you're able to use AI in the first place. And I suppose specifically, you know, I'm a small business. I know directly where the information comes from, but if you're working in a large organization, like a lot of my clients, then you have to have some sort of governance around that. Like, where is this information actually coming from? Who has access to it? How can they get it? Is it the latest version as well? So being able to answer all of those things, I think is so important and plus so much more. And building on that point of, you know, junk in, junk out, let's say it's not just that the data all has to be right.

AI has to be able to read the most relevant one and maybe it's picking the wrong passage or maybe it's bringing a different version or whatever it might be. So you have to still have that human judgment when it comes to the outputs that AI is actually giving you. Where we bring people in is what decisions actually need to be made here. How are we diagnosing the problem and using that human judgment to know what problem is that we're trying to solve? Putting the context around the problem that we are trying to solve. What have we done before? What has worked? What hasn't worked? What information do we have access to? What information should we be accessing to make this decision? So there's still so much human involvement that needs to happen. What I see so much happening is companies buy an AI tool and then they're looking for a problem to solve through the AI tool that they've bought rather than starting with the problem and then looking for the AI solution. And whether that can actually solve the problem that they have. You can start small with this kind of stuff as well, you know, within the team.

You can start at department level, whatever level feels comfortable and safe for you, but it doesn't have to be an organization-wide, doesn't have to be company-wide thing that you actually do, but just thinking about what does that actually look like. Again, another example from my corporate experience. Or from my current business experience as well is sales. And typically those, the information is divided across multiple different locations. So how do you actually find the information that you need? AI can connect across those systems. I know certainly for me, it's helping with understanding emails, being able to surface, well, what conversations have gone cold or stale or what needs following up? And connecting. I haven't done this yet, but this is on my list to do. But connecting with my CRM, my customer relationship management system as well, to be able to use those things and use both data at the same time to tell me what is it that I need to do without me having to go into each different place individually and look for these things.

I think another important question—I won't say this is an answer, but this is a question—who benefits? From the time that AI saves us. So if we're doing AI right, then we should be saving time. I know that there's so much overwhelm. We have so access to so much more information and much more quickly. So somehow AI has made our lives a lot busier rather than delivering on the promise of making things more efficient and giving us more time back. But a really important question is who gets that time back? If you as an individual are saving time for the company by using AI more efficiently, by doing your work more efficiently, then do you get that time back or are you expected to work the hours that, you know, that are contracted? I think it's a really important question as well. So some of the things to think about, are there parts in your organization, in your team, in your department, where you're just moving information from person to person? And is there an easier way to do that where people can access the information that they need when they need it by having this centralized place? Now, as I mentioned, there needs to be some governance around that. Is it the latest version? Is it the right information? Who has access to it? But just, you know, asking those questions rather than we had a meeting and then the meeting notes go to everyone, but How can we make it accessible so that people are not drowning in information and being sent information that they don't necessarily need? I know this is for me, for me, I get so much information sent to me that I'm like, how can I surface the information that I really need, but at a time when I need it? Which is why I'm doing this big project with the podcast as well, by the way.

So access to information. That you need in the time that you need it, rather than just being sent information that you don't necessarily need in that moment. All of that to say, rethinking work is not about thinking how AI can make work faster. It's about thinking how we reshape what needs to get done at all. Where is the value in what it is that we're doing? And how do we redesign the work? Now I'm doing a much bigger project on thinking about redesigning work more generally in the age of AI for human capability. So we have AI capability, but we also have human capability. That's not what today's episode is about, but it's something worth thinking about. How is AI helping us or how is it getting in the way? As always, would love to hear from you.

You can reach out to me directly on [email protected].