More than sixty years after artificial intelligence was first launched, it is finally being used to streamline and boost business efficiency and productivity.
Today, the building blocks are in place for AI to deliver results. Sensors track products at every step of their life cycle from the shop floor to customer sites. Cloud solutions enable the collection of millions of data points to create the foundation for machine learning. Personal assistants are readily available to simplify and accelerate information retrieval for more informed decision production.
Here are just a few examples of AI is changing ERP.
AI and machine learning can test hundreds of demand forecasting models and possibilities with a new level of precision whilst automatically adjusting to different variables such as new product introductions, supply chain disruptions or sudden changes in demand. Using AI systems, every single part can be tracked from when it’s first manufactured to when it’s assembled and shipped to an end customer.
Walmart cut taking physical inventory from one month to 24 hours by using sophisticated drones that fly through the stockroom, scan products, and check for misplaced items. Using algorithms that learn from experience to optimize logistics, BMW follows apart from the point it was manufactured to when the vehicle is sold—from all of its 31 assembly facilities located in over 15 countries.
Bots can automate repetitive accounting functions including categorizing invoice information into various accounts and even distinguishing between a monthly phone bill and a payment towards a phone purchase. AI can close operations and automate monthly, quarterly and year-end processes, even comparing account balances between various independent systems and verifying statements and reports meant for accuracy. Using machine learning, bots can even learn from different human input to make better judgments and adapt to the behavior patterns of different accounting professionals.
AI solutions can learn from customer service history, enabling chatbots to answer customers’ inquiries more cost-effectively, quickly, and consistently. The quality of customer service can also be improved by integrating real-time data from different customer-facing departments, providing a 360-degree view of the buyer. When the customer requires field service, an AI solution can use knowledge about the required skill set and required parts to assist with the planning and scheduling of service calls.
Talent acquisition software can scan, read, and evaluate applicants and quickly eliminate 75% of them from the recruiting process. AI systems can successfully plan, organize, and coordinate training programs for all staff members. By determining individual affinities and revealing who should get a raise or who might subsist dissatisfied with the life-work balance, AI systems can be proactive and solve the problem of employee churn before it happens.
Currently, sales automation systems are about tracking, reporting, and team productivity. The next wave will be about making individual interaction smarter by using data to determine which content, which answers, and which sales plays are more likely to drive results. AI can be introduced early in the progression by pulling in the oceans of data from outside the organization to help reps build comprehensive profiles of their targets. In toting up customer engagements for inside sales can be fully automated, conducting basic sales triage, such as information gathering, initial qualification and segmentation, and real-time response handling.