Business Processes that AI can take over!!
Avenir is at the forefront of developing AI based business solutions to automate and streamline business processes. We highlight and emphasize on understanding the current workforce problems and how a digital workforce can alleviate these issues. When approaching projects, ask your teams, “How can we improve the day-to-day processes to make your life easier?”
Hence the emphasis on automating most manual and time consuming efforts with RPA ands AI. Processes –Where your staff feel the most burdened. Consider the menial, time-consuming jobs that could be taking your team away from being creative, and driving new ideas or projects. When it comes to picking pilot processes to automate, business leaders will need a deep understanding of the needs of every department. Each team will be concerned with its own challenges, but the best RPA opportunities might increase efficiency across multiple departments. So aligning each division’s problems, needs and goals is a good approach to finding the best solution company-wide.
Typically, processes that are ready for RPA takeover can be defined by:
• High volume transactions or keyboard activities
• Being workflow-enabled
• Being error-prone due to manual entry
• Being speed-sensitive with the possibility of causing delay to other processes
• Using more than one system at a time (or requiring dual-data entry)
• Requiring actions such as searching, collating, updating, matching
• Needing irregular labour (bots can be scaled up or down to align with resource needs)
Today, call centers and large administrative offices use RPA. To achieve more widespread adoption, RPA needs to become smarter. The promise at Avenir is that with the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques, more complex and less defined tasks can be supported. Humans learn by doing and learn from a coach. The goal is that RPA tools learn in the same way. For example, by observing human problem resolving capabilities RPA tools can adapt and handle non-standard cases.
In addition, the interplay between RPA agents and humans is interesting. When a case turns out to be exceptional, the RPA agent may handover the case to a human. By observing the human handling complex cases, the RPA system can learn. There is also an obvious link with process mining. For example, RPA vendor UiPath and process mining vendor Avenir collaborate to automatically visualize and select processes with the highest automation potential, and subsequently, build, test, and deploy RPA agents driven by the discovered process models. Other vendors report similar use cases. Process discovery can be used to learn processes ‘‘by example’’ and process fragments that are suitable for RPA can be detected subsequently. Conformance checking can be used to check for deviations, predict problems, and signal handovers from agents to humans. The uptake of RPA and AI has become a hot topic in most businesses today which introduces many interesting aspects. Some of them are not new, but addressing them has become more urgent