Using AI to Transform Business Processes & Outcomes

Emmanuel Olivier

Artificial Intelligence (AI), the simulation of human intelligence by machines, has been around for longer than some might think: The actual beginnings of this field lie in the 1950s, at Dartmouth College in New Hampshire. Over the decades, the excitement has alternately waxed and waned. What catapulted AI to the absolute highest hype levels, however, was the release of ChatGPT by OpenAI in November 2022. ChatGPT — which stands for Chat Generative Pre-trained Transformer — is a Large Language Model (LLM) that unleashed a new era of usability of AI.

The initial enthusiasm was immense. Speculation about all the problems AI will solve, and what problems it will create, abounded. Almost a year later, the buzz has died down a little, but governments all over the world are considering legislation as to how AI can and should be used responsibly. For private use, AI capabilities are used for entertainment and, not least because of cost, will probably remain so for a while to come. Businesses, meanwhile, are ready to hit the ground running. It is no surprise that Microsoft has embedded a variety of  AI-piloted features in its products.

Although it gets the most attention due to its revolutionary capabilities, ChatGPT is not all that AI can do. There are many different layers, and their targeted and pragmatic use can help organizations in their digital transformation.

Machine learning algorithms are not programmed. Instead, they “learn” from user input to solve specific problems, continuously advancing these learning capabilities. They can, for example, extract relevant data from customer orders sent in a variety of formats: free-text emails, PDFs, images and even, for those still stuck in the 90s, faxes. Sometimes, however, misidentifications occur, and values are not correctly read. This is where human intervention is still required to rectify these errors. The machine learning AI will then remember the correction, and, with the help of historical data analysis, from there on out rapidly predict increasingly correct outcomes.

Deep learning, a subcategory of machine learning, can execute even more advanced tasks. Relying on artificial neural networks that mimic the human brain and on enormous amounts of validated data, deep learning extracts information from multiple data layers, yielding even higher accuracy and optimisation. Ideal business uses for deep learning technology can be found, for example, in email triage for shared inboxes, both on the supplier and customer sides. Questions, invoices and purchase orders can all be filtered out and forwarded to the correct recipient. For the Finance department, deep learning can even pinpoint small changes in payment behaviors, order dynamics and credit ratings. These indicators can have a significant impact on working capital requirements, and thus, the earlier they are detected, the better the business can react accordingly.

And then, of course, there is ChatGPT. In addition to the fun moments it can provide when entering a query,  for example, to generate an article about AI business use-cases in the voice of Yoda, it does have some real-life applications. Customers who email a business with questions such as “Where is my order?” or “When will my reimbursement arrive?” are all living different experiences. While sentiment analysis can identify when a customer might be getting frustrated, ChatGPT can help in generating an appropriate response. This refers to both tone  and content: pulling the correct information from the different systems and then suggesting a fitting answer.  The emphasis here lies on “suggesting.” A human should always remain in control and make necessary adjustments, rather than relying on ChatGPT for fully automated responses. The potential for errors is just too great otherwise.

The hoped-for result would be an efficient workplace that cuts out repetitive and meaningless tasks by using AI, and brings to light hidden financial details and business indicators. These tools need to remain assistive rather than replacing humans, since no matter how intelligent AI is, the human touch just cannot be replaced. Properly used, with an awareness of potential risks, AI technologies should be used to increase well-being, strengthen our democracies, and improve the shared information environment.

Author Bio

Emmanuel Olivier

As Worldwide Chief Operating Officer, Emmanuel Olivier leads Esker's operations worldwide, covering sales, marketing and consulting activities. He also supervises Esker's finances and is in charge of its financial communication and investor relations. Emmanuel joined Esker in 1999 as Chief Financial Officer and was promoted to his current role in 2003.

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