The Future of Enterprise Software—AI, LLMs, ERP, and the Road Ahead

Think about the days when horses were used everywhere for travel and transport. When cars first appeared, people doubted them and felt unsure about their use. But slowly, cars changed everything—our lives, our businesses, and the economy. Today, we are again at a similar inflection point. The rise of Generative AI and Large Language Models (LLMs) is changing how companies use important software like ERP (Enterprise Resource Planning). Understanding this change is not just helpful—it is necessary for businesses today.

As Paul Von Hindenburg said "When staring down the barrel of a revolution, it's better to undertake it than to undergo it"

ERPs and other enterprise softwares are certain to be affected

ERP software has always been important but often difficult to use. AI and LLMs will change this completely. Imagine being able to talk to your ERP software naturally, asking questions or getting reports without complicated steps. AI/LLM-driven ERP systems will  quickly study large amounts of data and provide accurate predictions and suggestions for better decisions. Such a system will be able to also customize the software to fit exactly how you work, making everyday tasks easier and faster. AI and LLMs will not just improve ERPs, they will evolve it. Businesses will become smarter, quicker and more competitive with a smaller headcount.

Adoption of AI/LLMs in ERPs have their challenges

Using AI and LLM enabled systems means handling huge amounts of sensitive financial information. At the moment, there are legit concerns and worries about data security and meeting legal requirements. An AI/LLM powered system will not be immune to repeating biases present in historical data, or sometimes  will give incorrect information confidently, "AI hallucinations." How will such a hallucination be caught? Businesses may find it difficult to trust AI if the decision-making process isn't clear. There is the ever present elephant in the room, Jobs. AI will take over tasks that people do, causing worry about job loss and the need to retrain staff. Then there is the question of the mode of deployment. On premise? On cloud? Where will the compute power come from and how much will it cost? We don't have a clear answer as of now. One thing is for certain, the high costs and technical complexities of integrating AI will stop some companies from adopting it in the beginning for sure but not all of them. 

We are at an inflection point, let there be no doubt

Change often feels scary, but history teaches us that resistance to new technology doesn't last forever. Just like how cars eventually replaced horses and created new types of jobs and industries, AI/LLM driven enterprise software driven changes also offer new opportunities. Companies that adapted to changes in the past often succeeded by embracing new ways of working and growing their businesses.

There may not yet be clear on exactly when or how to fully use AI in ERP, but it's important to start preparing. Educate your teams about AI and its possible uses. Keep an eye on what others are doing—learn from their successes and challenges. Test AI integration in small, controlled projects to build familiarity without high risk. Also, strengthen your data management, security measures, and technical systems early to be ready for future AI adoption.  

It's time to upskill your staff and start looking at ways to employ generative AI and LLMs in our business processes today beyond just drafting emails and messages. 

What will adoption look like?

Not all companies adopt new technologies at the same time. Some quickly start using new tech and are called Innovators. Early adopters see early success and then strategically use new technologies. The Early Majority are careful businesses that wait until technology is proven beneficial. The Late Majority are cautious, waiting until a technology is fully established and widely used. Finally, Laggards are very careful and only adopt new technologies when absolutely necessary.

Businesses will have to figure out where they fall on the technology adoption life cycle and respond accordingly.
 

What do you think?

Are you cautious, eager, or somewhere in between when it comes to adopting AI? Share your thoughts and experiences. Together, we can better understand and manage the future.