Future of artificial intelligence in the ERP

The future of artificial intelligence (AI) and truly ‘smart’ computing has been a major topic of fascination. This is perhaps from Stephen Hawking’s comments last month that AI has the potential to ‘supersede’ humanity – but is also almost certainly tied to the swelling number of recent innovation in AI technologies.

It is impossible for a single human to digest the constant deluge of new information, let alone understand it in the context of existing knowledge. But cognitive systems such as Watson can do just that. This is because they have capacity to digest millions of pages of literature and journal articles and constantly build upon their knowledge base. This, in addition to the supreme computational abilities of computers, creates an opportunity for professionals to augment their expertise and decision making by using these systems as a tool. For example, a cognitive system may indicate confidence intervals as to the likelihood of certain diagnosis.

Predictive business intelligence is on its way of just passing into the next phase of maturity for BI software. Business Performance Management has evolved to predicting what your next move is based if you feed it the correct information. ”Smart systems” that “learn” from past performance can predict the future. This can be compared to artificial intelligence software where it learns from repetition and past performance.

Now imagine “Smart ERP systems.” These systems might predict proactive, pre-emptive actions based on conditions that you set. An example of this may be if an inventory level falls below a certain level the system automatically reorders which exists today as min/max levels if they are set. What if these systems start to predict business process flows with alerts modeled after an existing workflow? It sets up similar alerts, calculates probability of completion, calculates risk of the operation, compares the alert and sign-off hierarchy to existing workflows/processes, and even compares stock levels and calculates stock levels to complete, and time to complete the preset tasks. These are starting to become a slow reality in enterprise system software.

ERP has been stated as the new Information System (ISs) paradigm and ERP success models draw upon information systems success evaluation patterns. The most famous model that emerges from IS successful research projects is the Delone and McLean model (D&M model). Delone and McLean introduced their framework as a review of 180 empirical studies in 1992. This model consists of six major surrogates, including ‘‘system quality’’, ‘‘information quality’’, ‘‘use’’, ‘‘user satisfaction’’, ‘‘individual impact’’ and ‘‘organizational impact’’.

The technologies of SOA, integration, collaboration, and business performance metrics are working in unison with some agility throughout the ERP system.

“Predictive ERP,” the next wave of software evolution will definitely take its cue from the business intelligence and demand planning software that exists today. The term Predictive ERP refers to systems that ascertain, calculate,e and predict certain repetitive behaviours that mimic existing business processes and conditions. This can stem all the way from suggesting to apply a software patch all the way to desiging a workflow with alerts, event management, and sign-offs by supervisors. This level of intelligence should help organizations gain more ROI in their technology investment, utilize more functionality, gain operational efficiencies, and maximize profit margins by process and SKU.

As ERP and all enterprise business software evolves and changes, it’s an exciting time to see what the right software can unleash in your company. Will “Predictive ERP” systems be sold the same way they are now? How will support be handled? Will ROI be measured much differently from today’s technology investments? How long will this take to get there to this level? H ow will technology advance to support this new predictive architecture? How will implementation be differ from today? Will it be more difficult or easier than today’s systems? Will this transfer to cloud computing? How fast? These are all questions that organizations and vendors will need to answer and collaborate on getting there to make this a quicker reality. The use of the BI can even give directions to users by understanding workflows and may even be able to help in configuration.

Another example of this system is the smart refrigerator that is connected to the internet tells you that you should pick up the short items such as milk or eggs (which may be set on a min/max level) and places the order for you through your connected credit card and will wait for you to confirm the delivery time - directly to your door.

For enterprise software, big data and BI have made incredible advances lately, but we still may be a while away from systems that configure themselves at the enterprise level. Vendors have however come a far way in the design of their interfaces and business agility such as the BLINC architecture that allows customers to configure their systems themselves according to changing business requirements. We will see where the next level of next-generation ERP technology takes us and what it will be capable of doing.

Overall, the public facing applications of artificial intelligence and cognitive systems are still in their infancy. While it remains to be seen exactly how they will impact our daily lives, it is impossible to deny their potential to shape our future.

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