Fireside chat recap: beyond ChatGPT for document processing

Companies are increasing their investments in AI due to the influence of ChatGPT. In this Fireside Chat, three industry experts led an in-depth discussion on the potential impact of AI on how we work and operate enterprises.

The panelists discussed the need to process unstructured data using enterprise-ready AI models, such as intelligent document processing (IDP), vs. popular generative AI tools known to compromise data security.

Jun 21, 2023 by Craig Woolard

The image shows, from left to right, Automation Hero's founder, Stefan Groschupf, VP of Global Sales, Mark Stripp, and Chief Product Officer, Maximilian Michel who led an in-depth discussion on the accuracy, truthfulness, and security of popular LLM chatbots and more.

If you are exploring enterprise-ready automation technologies for unstructured data, you will find this fireside chat a must-watch. The three participants from Automation Hero were:

You can read the full bios of the speakers at the end of the article, but in the meantime, here’s a quick summary recapping the discussion between these three industry veterans:

  • Automation Hero CEO Stefan Groschupf emphasized the increasing importance and availability of AI tools for businesses. The panelists discussed the widespread global use of artificial intelligence and highlighted recent advancements in AI models like Generative Pre-trained Transformers (commonly known as GPT). 
  • They also explored the applications of AI in document processing and decision-making, highlighting the value of technology that can read and process documents with above-human accuracy, including the ability to “unlock” the business intelligence stuck in contracts, historical data, and more to improve business performance. 
  • Concerns are raised about truthfulness and security with popular pre-trained large language models (LLMs), such as ChatGPT. The panelists stressed the need for organizations to adopt enterprise-ready AI tools quickly to stay competitive.

Watch the full recording below for more details.

Below, we captured the best questions that stood out and highlighted the key insights from the webinar:

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What are the potential economic impacts of AI?

Now that we can overcome nearly all of the obstacles that plagued the Big Data era through the revolutions in transformer technology, the time for large language models has arrived. According to Mark Stripp, ChatGPT, the fastest-growing app of all time, and Nvidia’s emergence as a trillion-dollar company are signs that AI is having its iPhone moment.  

“ChatGPT, the fastest-growing app of all time, and Nvidia’s emergence as a trillion-dollar company are signs that AI is having its iPhone moment.” 

Automation Hero CEO Stefan Groschupf set the scene by weighing in on AI’s potential business impacts over the next 20 years. According to a well known University of Oxford study, AI could eliminate 47% of US jobs over the next 20 years.

“47% of US jobs could be eliminated by AI over the next 20 years.”

On this, Stefan elaborated further, noting the potential impact AI will have on an organization’s ability to remain competitive in the future:

“I do really think that we are in an incredible time right now, and the landscape in how we do business will be disrupted within the next few years. I think that very, very quickly, the world will be divided by those that are using AI tools and those who don’t.”

Stefan highlighted the potential opportunities AI can bring to the table. According to the same Oxford University study: 

“77% of global companies are either using or exploring AI in their businesses, and this is only going to accelerate.”

Stefan also called attention to the wide adoption of AI tools since ChatGPT’s 2022 introduction, noting that more business leaders are increasingly recognizing the importance of AI as a business strategy:

“45% of executives reported that the publicity of ChatGPT has prompted them to increase AI investments.”

Every time a new disruptive technology emerges, there are those who thrive and those who are disrupted. On this topic, Stefan reminded viewers of what happened when electricity eventually replaced the steam engine. By 1920, electricity had already replaced steam as the major source of automotive power, and in 1929, electric motors represented almost 78% of the total capacity for automotive machinery. Stefan emphasized the importance for business leaders to act by incorporating enterprise-ready AI technology now or be left behind:

“I really think that we really need to rethink how we run companies. The intelligent  enterprise is a thing that needs to be achieved very quickly because, right now, AI is a competitive advantage, but very quickly, AI will be a competitive disadvantage to those who do not adopt it early enough…If you don’t have your AI story, your AI platform, and your AI use case together, you will be disrupted.

What is the business value of technology that reads documents?

The panelists emphasized the value of technology that can read and process documents. When addressing this question, Stefan cited research conducted by the IDC, raising an interesting data point about the intelligence trapped inside business documents: 

“By 2025, worldwide data will exceed 175 zettabytes. With most of this information locked inside emails, texts, PDFs, and scanned documents, it poses a real barrier to automation and digital transformation.”  

IDC, “Intelligent Document Processing Demonstrates How AI Can Make a Practical Difference to Business Right Now”

The panel also discussed how the ability to “unlock” the hidden value in contracts and historical data can improve executive decision-making and business performance. However, without the help of enterprise-ready AI, the data we need to process is growing rapidly, causing significant challenges for organizations worldwide: 

“The data we need to process is only getting bigger…the problem really here…is that 80 percent of your enterprise data is unstructured, and that is the massive treasure chest.”

Enterprise-ready AI that understands documents at the semantic level is needed to automate critical document-centric processes. Stefan explained how enterprise-ready intelligent document processing (IDP) technology helps organizations realize the full potential of their data:

“If you think about all your business intelligence [assets]…your ERP investments, your ETL data integration data, and your warehouse investments…all of that is only scratching the surface. You didn’t even get to the core yet because that’s only 20 percent structured data. So the big question is, really, how can you quickly turn these [unstructured data] assets that you have into structured data, but then what kind of business use cases are unlocked around that?”

What exactly is generative AI?

Automation Hero’s Chief Product Officer, Max Michel, gave a high-level definition of generative AI. 

“Generative AI is basically the technology behind the tools that everybody is talking about right now, so the Midjourneys and the ChatGPTs that everybody wants to use. It takes an input, for instance, a text or a prompt…and then it generates the output, so it generates a new image…or an explanation, or a poem, or whatever you ask it to do. The special thing is…it can also do things in different styles, so you can ask it for a portrait to be very photorealistic or very abstract, and that’s basically up to the user…but there’s always an input, and then an output is generated based on what the AI has learned before.

Max also explained how generative AI tools, which generate outputs based on inputs, contrast with discriminative AI, which provides specific answers based on given parameters:

“There’s also this other way to work with AI, and this would be the discriminative AI approach. With discriminative AI, you basically give some guidelines, so it can only group these few specific classifications, or it can only give you the answer based on the input that you gave it…this is really what you want to do in an enterprise setting…so discriminative AI is safer, but where I see the real power of generative AI is in creative work, of course.”

What is a large language model? 

Max explained that language models are a way to make languages computable for machines to understand. On the topic of human and computer interactions, he says:

“In general, technology is really good at stuff that we are bad at, like multiplying big numbers, but technology is really bad at things that are easy for us, such as talking, picking up a glass of water, and stuff like that. This is really difficult for a robotic arm to do, so basically, all of the AI researchers are really focusing on closing this gap.

To close the gap so that technology can become even better at performing tasks that humans traditionally excel at, Max explained the concept of large language models further:

“Large language models are the bigger version of it, so if the language model has over one billion parameters, we call it a ‘large language model.’ It can learn not only the syntax and grammar of language but also the semantic relationship between words, and especially the very large language models can go further than that and learn concepts about the world and how to work with longer inputs, so yeah, it’s basically a tool to make language computable.”

What does “GPT” stand for?

For the uninitiated, Mark Stripp asked Max to explain what “GPT” in ChatGPT means:

Generative Pre-trained Transformers. Transformers are basically the technology for an AI to keep context over a long string of words. That was a big challenge before. Generative AI models that generate output based on text input and pre-trainers meant that ChatGPT was pre-trained on web-scale data to learn concepts about the world.”

Stefan provided an interesting metaphor on the topic, describing AI research as “simulating the human brain”:

The human brain has neurons that fire when they get inputs and we’re just trying to find a mathematical representation for that…there are a lot of learnings that come from the AI research for understanding how the human brain works, but technically we are trying to simulate mathematically how the brain works.”

What are AI “hallucinations”? 

Mark Stripp asked Max to explain the concept of AI hallucinations to the audience: 

“When we think about hallucinations in general, it’s always your brain or your neural network creating a different output than the real world, so there’s a difference between the real world and [the output] you’re seeing, so that’s how I would define hallucination.”

What is the issue with generative AI for documents?

“I think it’s really interesting and creative work if the AI can come up with really creative things and hallucinate really great images and stuff like that, but for an enterprise setting where you want to know the truth about what’s actually inside your document, you don’t want the AI to make things up. This is a big danger that I saw very early on with generative text summarization models and stuff like that.”

Max described his own research into generative AI models and their limitations: 

“For instance, I was working with a legal text generator that was trained on court orders to generate summaries of court orders, and I just tried it out on contracts, and it was always trying to create a victim and somebody who did something wrong, even though it was just a contract. Of course, really big generative AI’s have some guardrails about that, but there’s still the danger that the generative AI can hallucinate such information on top of what is actually in the document or in the input that you give it.

What is the relationship between automation and AI, specifically with documents? 

Responding to Mark’s question about the relationship between AI and automation, Stefan said:

“Automation is the killer use case for AI and Enterprises. I think the AI use cases in the consumer world are very clear, and that’s challenging Google’s dominance in the information retrieval arena. But clearly, the automation use case is the golden goose for AI in enterprises. If we think about how an enterprise works, there’s an opportunity here to really get what every enterprise strives for: economic efficiency with low-cost, high-efficiency rates.”

Stefan described self-driving cars and daily life in Silicon Valley as examples of autonomous business processes: 

“In the Bay Area, San Francisco has given licenses to multiple self-driving car companies. We do see self-driving cars every single day; it’s creepy; nobody’s in there, but they are real. When the error rate is so low that we can truly license them in global cities, it’s not a question of whether we will have self-driving cars or not. So if we have self-driving cars, we will also have self-driving companies or business processes, and we will have them very quickly.” 

When it comes to documents and the need for AI to streamline efficient document-centric business processes, Stefan said:

Companies run on documents. If we’re talking about autonomous companies, companies interact with other entities—either other companies or their customers—through documents. So, if we think about self-driving businesses or business processes, we need to think about how we get there and what it needs.”  

On what companies need, Stefan emphasized that companies need an operating system:

“We need an operating system for autonomous business processes, and we need it really quickly. If you want to peel that onion further, you need an all-tolerant platform that’s highly scalable and self-healing. One that is optimized for AI workloads that are quite different compared to traditional ETL or data warehousing workloads, with different AI capabilities available for less technical users to point and click. The key here is to quickly find technology that is purpose-built for these AI workloads, has these AI capabilities built-in, and allows people to almost curate business processes to get as close—and every day a little closer—to that autonomous business process. Those that do this will significantly outcompete the competition at scale, and with lower costs and higher customer satisfaction.”

Finally, as someone who discusses AI-driven automation technology daily with business leaders worldwide, Mark Stripp offers some concluding thoughts on AI’s business value:

“We’re seeing those impacts coming in from the changing nature of the workforce to some of the bigger trends out there that they need to address to handle productivity and so on. This all stems from the same underlying need: to make better access to information easier so you can do something with it.”

 What should we learn from this AI moment?

Circling back to the original point on the potential economic impacts of AI, Stefan weighed in on a controversial topic with a sense of optimism:

“I’m not sure all jobs will be gone. If we look into the history books, there will be a redistribution of jobs. So there will certainly be a lot of jobs that will change in that sense. So should people on the call be afraid to lose their job? Well, maybe not a job, but maybe the job. The information worker will either scale up very quickly and become a person that is able to use a new generation of tools such as AI, or you might be pushed out or not receive that salary raise that you were looking for. That will be the reality—the reality is always that there will be people who can adapt to the new technology, and they will be more valuable to organizations. It will be people who once said, ‘Ah, who needs computers? I can do this all on a piece of paper much faster than typing it in this computer thing.’ We saw how that went, right?” 

Unlock the intelligence in your documents with our AI-driven automation today

Regardless of the scale of your enterprise or use case, we can help you get there. Here are a few ways to get started:

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Who are the AI experts?

Stefan Groschupf, CEO, Automation Hero

When he started his previous company, Datameer, Stefan Groschupf saw firsthand the challenges financial institutions confronted as they navigated “unstructured” data. As founder and CEO of Automation Hero, Stefan focuses his data analytics experience on processing the diversity of data hiding in unstructured documents faster than ever using the most advanced and complete IDP platform.

Max Michel, Chief Product Officer, Automation Hero

As Chief Product Officer, Max Michel is focused on scaling the product organization by further delivering world-class innovations that address and delight customer needs while aligning with the company’s strategic goals. He also leads research and development in AI, machine learning, and data science. Previously, he was a data scientist at Datameer, where he built smart AI tools for big data analytics and helped the company leverage the power of machine learning to optimize sales operations.

He is a Bauhaus alumnus and holds a master’s degree in computer science and media. He spent four years publishing research on natural language processing and information retrieval.

Mark Stripp, Head of Global Sales, Automation Hero

As Head of Global Sales at Automation Hero, Mark Stripp develops sales teams across all regions and leads our GTM strategy, worldwide. He is passionate about technology and has an eye for strategies that help organizations accelerate their digital transformations. Mark brings nearly two decades of experience in multiple industries across the UK and North America.

Mark started his career at Automation Hero as Regional Sales Director of EMEA following a year of substantial growth and key wins, Mark is now leading the global go-to-market strategy. Prior to Automation Hero, Mark helped strategic clients in multiple sectors achieve their goals and digitally transform through optimizing IoT, business process automation, and integration capabilities.