How AI-driven hyperautomation can improve business efficiency

People are excited about AI and hyperautomation, and not without reason. People are excited about the potential of AI to automate enterprise tasks and involve the complexity of human thought and behavior.

#AI technology promotes the development of enterprises to achieve ultra-high automation, just like the development of self-driving cars. Tesla drives people to their destinations on demand, and Waymo roams the streets of San Francisco and Phoenix without a driver. This demonstrates the huge potential of self-driving technology, but there is still a lot of work that needs to be done on the road to full autonomy. Before realizing full autonomous driving, we need to solve many challenges and problems, including improving the safety, reliability and adaptability of the system to ensure that it can operate normally in various complex environments. At the same time, we also need to develop a more complete legal and regulatory framework to ensure that the promotion and application of autonomous driving technology can meet legal and ethical

challenges including incomplete data map versions, different and changing Road conditions, driving culture, obstacles, and many other variables mean the system won't work on all roads, cities, and locations, nor in larger, congested cities, and, in all cases, it will still require human supervision.

The same goes for enterprise automation, some automation exists, but to have effective hyperautomation in an enterprise, a lot has to happen first. Specifically: a "learning phase" to ensure automation adapts to the enterprise's challenges, which includes thousands of processes in every type of system, each with nuanced policies and different teams embedded in how tasks are completed knowledge.

Using artificial intelligence to carefully learn business processes and apply the right learning methods, it is possible to speed up complex enterprise processes through hyper-automation.

Customer Support

Customer support is a people-intensive enterprise process that can benefit from AI-driven hyperautomation. Deloitte research shows that 80% of contact centers are considering or have already participated in the AI ​​deployment process.

18 months ago, the customer support/service world changed with the advent of GenAI. Chatbots are now fundamentally more effective at solving problems and cheaper to run and implement than ever before. So as all the existing customer service platform providers - Salesforce, Zendesk, ServiceNow, etc. - add GenAI to their core platform capabilities, their bots will become exponentially more useful and powerful because they are based on those systems data and learn from it.

However, what about all the things you can’t deviate from? Those who still need a broker! For out-and-out customer support, the opportunity for hyperautomation is greater. By definition, every customer transaction is a one-off and the risk is high - because it's not simple enough to automate!

For example, a customer support engineer handling product shipping issues needs to navigate various systems - internal and external "stacks" and tools (e.g., ServiceNow, Salesforce, SAP, Oracle ERP, shipping tools, and homegrown applications) - and make decisions based on a large number of contexts. The automated fulfillment process may be the same in the U.S. and Germany, with one (critical) exception: choosing a different local fulfillment partner.

Similar high-volume, high-risk functions that require cognitive abilities include claims processing, medical revenue operations, provider onboarding and more back-office functions.

Automate the process: Build a learning machine

By using AI to observe and learn from an agent’s actual workflow at scale, models specific to the agent environment can be efficiently created and trained, Enable them to anticipate and respond accordingly.

By anchoring the AI ​​model in problems solved by humans, the model will continuously learn from real-life workflows, rather than generative, morphing models derived from statistical suggestions rather than logic, which will help You achieve your best.

In short, this new "learning machine" has three prerequisites:

1. Work deeply

The deeper you can perform workflow analysis , the better you can define an individual workflow. Not all workflows are created equally, even if they run the same process. High-value step- and time-saving opportunities may be hidden within individual workflows or in obscure combinations of steps.

2. Listen to your data

By drilling down into the process at each workflow level, you can identify subtle differences in execution, helping you determine the best running state for your modeling, based on Optimize with actual data and logic - don't make any assumptions.

3. Train carefully and listen to your examples

The model will be the most powerful if you train it with many different users in different scenarios. Unlike RPA, there is no one-size-fits-all approach. Just like you would have many different cars driving on the road and map it out when creating our self-driving car on top of it, you would need many different agent training models to make sure things are correct and accurate.

For example, assume two agents are working in an execution operation. In terms of getting to a solution, one agent performed the process significantly faster than most others, and the other agent worked much slower, using more steps and systems in a longer workflow.

It is easy to think that the FAST agent is automatically "correct" and declares his workflow to be optimal for your AI model. However, on a deeper analysis, the FAST agent reveals a lot about what's going on in the backend. Case reopened (because of errors in the way he solved these problems), in contrast, the "slower" second agent had a stable 100% resolution.

Alternatively, you might have two "identical" agents working side by side to complete a task, however, one of them might have access to additional systems than her second-tier partner (because she is the first layers), their workflows may overlap, but understanding the nuances is critical to properly automating the process. Does the automation layer require additional access to this system? Why does only layer 2 have access and should the flow aspect be rethought?

Deflect and Beyond

There is no doubt that AI will enable more Business functions are shifting away from humans toward robots and other smarter autonomous technologies, so expect more departures from GenAI and its successors.

The next big win for AI will be automating processes for lengthy transactions that involve multiple systems and many physical steps for real-time agents that must keep up with an increasingly highly automated business to satisfy customers. , financial, regulatory and board expectations. AI-driven learning “machines” based on workflow analytics and other perspectives can help close enterprise application gaps as quickly as possible.