In this episode of the Process and Automation Podcast, hosts Sascha and Arno discuss a series of data‑driven observations about how AI and automation are reshaping the workplace. They begin by outlining how robotic process automation (RPA) combined with AI—often called intelligent automation—delivers strong productivity gains across clerical, customer‑service, accounting and manufacturing roles, and how hyper‑automation platforms can orchestrate end‑to‑end processes such as banking customer‑lifecycle management, supply‑chain optimization and insurance underwriting. They stress that AI adoption is becoming a strategic imperative: a recent industry survey shows 44 % of companies plan to invest in AI this year, and firms that lag risk losing relevance. The hosts highlight ChatGPT’s record‑breaking uptake (over 100 million users) as evidence of massive appetite for generative AI, and point to emerging integrations (e.g., Azure + OpenAI, Wolfram Alpha) that will add computational capabilities beyond text generation. Economic implications are explored, citing Goldman Sachs forecasts that up to 80 % of work could be automated, potentially adding 7 % to global GDP, while the World Economic Forum projects roughly 97 million new jobs by 2025. They also note less obvious AI applications, such as supporting sustainability, ESG, and governance goals. Finally, they warn about data‑privacy and security risks—citing incidents of IP leakage in AI tools—and advise careful vendor selection and a platform‑centric approach to hyper‑automation. The episode concludes with a call for listener engagement and a reminder to subscribe.
Chapters
Hello everyone, welcome back to another episode of the Process and Automation Podcast with the Automation Guys. So yeah, with AI and automation is more acknowledged by the, the wider audience, we thought it would be great to share a few facts about the future of work, especially when it comes to the accelerated adoption of, of, of AI and automation. So all the newer technologies. So it's not just around that, but in general there's lots of, yeah, use going forward. So yeah, while we can't really predict everything that's, that's coming when it comes to AI and automation, but We, we, we know there are some really cool trends and stats that give us a really good idea where things are headed. And, you know, of course, lately everyone's just, everyone is really excited about conversational AI, generative AI, and everyone really wants to experiment with that. And yeah, and to unlock the, the great benefits when it comes to all the automation tech. So today we will go through, yeah, a couple of facts about AI and automation and how that does impact the future of work. And Arno, I think you have one ready to go.
Yeah, I'll kick off. Thank you, Sascha. So in terms of productivity, we are seeing that the gains will continue with automation, actually progression, or with the progression of automation. So again, you know, businesses have got, you know, a significant opportunity ahead of them to automate, you know, the process-related task and to see very strong gains in their future efficiency and also productivity. And again, with, with the right technology in place, many mundane and routine type tasks can be offloaded and handed over to digital workers using robotic process automation or RPA and artificial intelligence. And of course, this frees up employees to do higher value work. That requires generally human judgment. And again, this applies to a variety of jobs from sort of clerical workers to customer service reps, accounting specialists, accounting technicians. And I think if you look at the manufacturing industry, they have been long a proponent of automation, and, you know, they've, they've reaped significant gains from the introduction of, let's say, like industrial automation using robots on factory floors. So if you look at this, plus the fact that intelligent automation, which of course refers to the combination of using artificial, artificial intelligence and robotic process automation makes automation more powerful out there because companies can use the two tools in almost like a tandem to enhance their operational efficiency. So, you know, if I look at it, you know, to drive really significant business value, you know, companies can go one step further than just using kind of intelligent automation and, you know, use a platform for process automation, also called hyperautomation, which kind of unified multiple technologies together to help the enterprise sort of automate complex end-to-end processes. You know, any examples of this, you know, we talk a lot about use cases, but that's sort of, you know, managing customer lifecycle in banking, optimizing supply chain operations, speeding up insurance underwriting. So again, you know, we're seeing that the productivity gains will in future continue to, you know, move on with these automation technologies.
Oh yeah, thank you. So you just, you mentioned their use cases and yeah, indeed we do plenty of use case episodes here on the podcast. So to our listeners out there, if you have some very specific, use cases in your mind and you like to get our view on it and maybe want us to maybe talk about certain industries or departments and want to know more about use cases, then yeah, send us a message on theautomationguys.net or via all our other channels. And then yeah, we will pick that up and we will be very keen on doing these use case episodes, very popular Lots of feedback we had so far. Yeah, thanks a lot. And yeah, let's, let's go back into the facts of future of work. The next one which stands out is businesses will either use AI or fall behind. And yeah, yeah, it is, it is that dramatic. Yeah, it is really of huge importance for businesses to, to make that priority, make the integration of AI and automation into the operations a priority. And they are, according to a recent industry survey, a significant 44% of companies have plans to invest in AI this year. So, and then it will be a huge increase from, from that, I guess, next year as well. Yeah, it is really, really clear that or it's not, not clear, but evident. So, so you see, you see really good examples already out there, case studies that, that these organizations who are making the step into the AI, that these organizations gain a notable competitive advantage really over those who are slow to, to really get started. Was that one? Yeah, so those businesses in a couple of years, they probably do not exist anymore. So the potential of AI is really vast. We also talk about the different, different AI topics here, enabling companies to automate pretty much every task across these different use cases. You touched on the customer experience there, Arno. Yeah, so the tasks to enhance the customer experience and customer service, for example, simplifying billing, the invoice processing, bringing intelligent document processing. So really, so all those kind of areas, yeah, and to stay ahead, organizations must make that a priority and they have to explore all sorts of ways to seamlessly incorporate AI and have to explore all the automation technologies which are available. It's very easy to use these days and very easy to get started. So And they have to explore how that all fits into existing processes and workflows. And everyone who is doing that and making the strategic approach, yeah, they will position themselves to harness this superpower of AI and make them really a leading-edge business out there and very competitive.
Yeah. And of course, the third fact here is the fact that ChatGPT broke all adoption records. You know, we look at sort of the likes of Facebook, for example, and, you know, onboarding kind of people and, you know, important sort of data points about the importance of emerging AI. And ChatGPT is a good example. Is, is really the, the speed of the record adoption upon each, you know, sort of release, post-release. Um, it sort of reached over 100 million users and just—
wow, that's incredible—
which is just like, you know, incredible compared to the sort of previous waves we've seen. And it kind of shows us, um, it's not just people that's, that's that's got an appetite for AI. They actually purposefully going out there and sort of seeking it and wanting it in their life. And I think it's one good example of, you know, how companies and employees will continue to use artificial intelligence, you know, as part of their workflows, you know, from kind of drafting emails to generating ideas to generating websites. So, you know, you talked a bit earlier about generative AI. This is just a massive step because it just means what else is around the corner, right? I think for ChatGPT, it's sort of just the start. It's one example of it, and all it does, it just takes content and it, you know, presents it back to you with ideas. If you You know, it's effectively a language model. If you actually bring in computational models in that, which is, by the way, not part of ChatGPT yet. Wolfram Alpha is a sort of computational algorithm that integrates with, or it's got sort of capability to start integrating with ChatGPT. You know, that's going to be a whole new sort of chapter in AI where it's not just about generating content, but it's actually representing systems and models and ways of thinking and computational algorithms within easily consumable AI. So it is just a kind of incredible, you know, sort of welcome to the world of AI. Here's ChatGPT, but there's lots more to come, right?
And I think it also goes, or it's also very important that this will be properly integrated, isn't it? And really, this is what the big challenge is still for enterprises. How can they actually really use it for some proper end-to-end process automation? Sure, it's very, very clever, but then and needs to be put into the, into the right context of that enterprise. Yeah, and I guess, and that's already happening, isn't it, with Microsoft Azure and the OpenAI integration. So you can put that even into your secure enterprise context and use the power of OpenAI and ChatGPT and all the other models. And then it gets really, really exciting. Currently, sure, 100 million users Creating emails, that's great, and doing images and that kind of— it's very, very fascinating what you can do with it. But yeah, the next big step is to really, yeah, get that properly integrated and allow all the systems to interact with it. I know some people are very afraid about this, but I think it will give businesses really that competitive edge.
So yeah, I'm sorry, one step. Once the computational element comes into play, then that will be sort of again a leap forward because, like I said, it's not just about generating content based on human knowledge at this point in time. It's more computational, which means that it's gonna be things that can be computed that we don't know exist yet. And I think that's when the whole kind of paradigm shift will happen, where you can start to truly model a business, perhaps a business's digital twin, in a way that it can understand it and you can interrogate it and you can run scenarios past it. So I think it's a fascinating topic and I think that it's something to look out for. And, you know, we are touching today on the sort of the facts of future work, but I very much think that that is the future where, you know, the computational element of kind of AI will really drive forward more optimization within every walk of life, not just business, but anything we want to streamline, everything we want to, you know, make better, anything we want to kind of optimize, you know, which is an exciting future because I think that that just allows people to sort of be more inquisitive in the way they do stuff, be more inquisitive in sort of the options they choose to do different things. And You know, it will just take automation kind of like not even to the next level, but to the sort of the next front, right? The next frontier.
Yeah, that's, it's a nice segue into the next, the next fact. AI and automation will positively impact the economy. And that's, that's obviously what we hope as well. It shouldn't, shouldn't negatively impact our living and the economy. So yeah, according to predictions by economists analysts, Goldman Sachs looked at it, approximately 80% of global work has the potential to be automated. That, that doesn't sound too good, well, but in, in various capacities. So, and remarkably, such automation has the potential to contribute a substantial 7% increase in global GDP. So yeah, overall it will add— sure, there will be a change for some roles. That, that, that's always happening, isn't it? So we are, we are all not doing the work we did 100 years ago. So there's, there's a change. So yeah, it will, it will really support the, the, the economic growth going forward. So it's very crucial that we address obviously the concerns around automation and employment. So yeah, in, in fact, the World Economic Forum anticipates that the widespread adoption of AI will foster jobs. Job creation, estimating the generation of approximately 97 million new jobs globally by '25. So with that, we, we see the potential of AI to enhance the workforce and, and, and really fostering that, that synergy between human capabilities and these automation technologies. So yeah, it will have a very big impact on the, on the economy. And, yeah, we probably see that already really, really coming in over the next couple of years.
But I think it's not, not just about the economy. And this brings me on to the fifth point, or fifth fact, is that, you know, not all AI use cases are obvious, and it's not always centered around the boost in efficiency and productivity or enhance decision-making skills within a business environment, for example. You know, there are sort of other applications as well for this technology, you know, and, you know, we look at sort of environmental, social, kind of like governance efforts, you know, AI has actually been put to good use helping companies to reach, for example, sustainability goals for both regulatory reasons and also for good corporate citizenship in general. So we talk a lot about sort of ensuring that your business is profitable, you are as optimized as possible using all of this automation technologies. I think these are sort of the peripheral use cases which are not necessarily talked about, but they are there. And, you know, the application of AI is broad. And I think, you know, it will incorporate, incorporate, you know, some of these elements as well, you know, which, which obviously is beneficial and a kind of positive thing, you know, for everybody.
The last fact we would like to share today is data privacy remains a major concern. Yeah, when we talk about AI and especially around sort of the ChatGPT topic, yeah, we really have to be careful here as well submitting into currently the ChatGPT platform, for example. So obviously everyone warns us about that already, that we shouldn't sort of put IP sort of code. I think there was a recent news article about some Samsung employees, isn't it? They put code in there and apparently, yeah, that, that's retrievable via the platform. So not, not so good. So yeah, companies must really continue to scrutinize the AI usage and the vendors which they do business with. And yeah, with an eye to improve that data privacy. Yeah, it is important that we take a close look at really all the AI tools we use in the company. So the tools nowadays, pretty much every vendor, they throw in with the next release some AI capabilities. So it's obviously very tempting to just get started, but have a close look what, what these AI tools do. Maybe sometimes they connect to the cloud and maybe you want to do more on-prem, but maybe you don't have that on your radar and suddenly you're suddenly hybrid, although you don't want that. Yeah, and additionally, choosing a vendor that has already strong security and data privacy sort of built in the platforms. I think if they are known for that one, they obviously take those things very serious. Yeah, so that's something to look out for. Yeah, data privacy, very important topic. Yeah, so when it comes to AI and automation in general, we talk about that here a lot. It makes sense to not just do little bits and pieces of automation and AI in different solutions. Sure, you can do that, but it's very beneficial to have that platform approach when you think about process automation. Arno touched on, on the hyperautomation early on, to bring all these technologies which are out there—process mining, workflow task automation, workflow automation, AI—all these things together in one platform. So it makes it much easier for everyone if you think about a platform approach. And if you'd like to find out more, what to look out for, we have a couple of episodes on that. And otherwise, yeah, reach out to us. On theautomationguys.net. And yeah, we will be back next week again. And until then, let's automate it. Unfortunately, that's it again with this episode of the Process in Automation Podcast. If you like this episode, please give us a 5-star rating and don't forget to subscribe to this podcast so you don't miss any upcoming episode. We hope you will tune in next time, and until then, let's automate it!
