The Real Challenge Begins After the Demo

Most enterprises think that picking the correct model of AI is the most important step towards modernization of their ERP. Although AI demonstrations usually make the management quite happy, the projects tend to stall once the enterprises try to scale past the initial demonstration stage.

According to an AI Journal study, however, the problem usually lies not in the AI technology itself, but in the fact that enterprises fail to consider how AI will fit within the existing ERP, the business processes, and the overall enterprise activity. A successful demonstration only means that AI can do the job – it doesn’t mean that it will be able to do it throughout the enterprise.

Integration Matters More Than AI Performance

Businesses usually concentrate on developing robust AI models, lowering computer expenses, and integration of the applications. All of this is important, yet not everything.

The article says that what really counts is setting up the proper working environment for the AI system. It is necessary to make sure that the AI system knows all the rules of the business process, its workflow, approvals and other peculiarities of the company's functioning. Without this working environment, AI will be nothing more than an isolated technology.

It turns out that businesses ignoring this issue face the problem of poor performance of the AI system when it comes to the real business environment with different departments and massive amounts of real data.

Why AI Pilots Lose Momentum

One of the greatest blunders that companies commit is approaching AI adoption as a technological exercise rather than a business transformation exercise.

While a proof-of-concept exercise takes place in an organized setting with clean data and chosen scenarios, when the company tries to apply the same model to its finance, procurement, supply chain, customer service, and other ERP processes, unexpected problems crop up. The lack of systems, inconsistent processes, and contextual data makes AI ineffective.

It is evident from the above article that there is a need for companies to have business intelligence in order to provide accurate results from AI adoption.

Building an AI-Ready ERP Environment

Instead of focusing solely on what the AI is capable of doing, enterprises must get ready to have their ERP systems be capable of making smart decisions.

To achieve this, it is necessary to have well-defined business processes, define ownership, maintain quality data, and provide AI with the context necessary for making decisions. Today’s ERP systems must transform into intelligent execution systems whereby AI will function in tandem with other business operations.

For such enterprises, moving from pilots to full implementation becomes easier and ensures productivity improvements.

Looking Beyond the AI Demo

A good AI showcase is just the first step of the way forward. The key to long-term success lies in the degree to which AI is able to become a part of the company’s ERP system, business processes, and overall governance structure.

According to the AI Journal, it can be concluded that the emphasis should not be made on choosing high-quality AI models but rather on establishing the necessary base for them to work effectively.