Oracle Debuts AI Agents to Automate Supply Chain Tasks

Oracle has added role-based artificial intelligence (AI) agents to its cloud supply chain and manufacturing suite.

The new offering is designed to automate routine tasks and allow companies to devote more time to strategic supply chain initiatives, according to a Thursday (Jan. 30) press release.

“Supply chain professionals often spend several hours every week on administrative tasks, such as data analysis, policy reviews, and order processing, which takes a significant toll on productivity and resources,” Chris Leone, executive vice president of applications development at Oracle, said in the release.

“Our new AI agents for supply chain management help ease the administrative burden by streamlining workflows and automating routine tasks to enable greater accuracy and efficiency, smarter decision-making, and ultimately, a more agile and responsive supply chain.”

According to the release, the agents can help procurement professionals create, process and fulfill purchase requisitions with more speed and accuracy.

“For example, the agent can provide insight into procurement policies, share product recommendations, and identify specific information needed to complete a purchase requisition,” Oracle said.

The agents can also help manufacturing and production operators determine if operations align with procedural guidelines and safety standards, while also helping “suppliers streamline access to company-specific policies and guidelines to improve productivity.”

PYMNTS examined the role AI agents play in automation earlier this week, contrasting them with the duties handled by robotic process automation, or RPA, which is similar to “a highly skilled but inflexible assembly line of workers.”

An AI agent, meanwhile is software that also automates tasks, while also functioning more like an adaptable knowledge worker. Rather than just following ordained tasks under set rules, it can understand context, make decisions and tweak its approach as circumstances change. Importantly, it can autonomously carry out tasks on behalf of users.

“When RPA faces a problem that doesn’t match its template, it needs to be reprogrammed for changes in process,” PYMNTS wrote.

“When an AI agent faces the same situation, it can reason through the problem and come up with a fitting response. It can learn and adjust to new situations. RPA uses structured inputs and logic; AI agents use unstructured data and reasoning.”

For instance, an RPA-powered chatbot on a website can handle pre-programmed responses for the most commonly asked questions by customers. But deviate from those questions, and the chatbot provides incorrect or irrelevant answers. An AI agent trained on GPT-4, on the other hand, would have a more natural response, and could better answer the customer because it can understand the question and dynamically find the answer.

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