by Hemraj Sadhnani, on Sep 19, 2024 6:48:30 PM
Key takeaways from the blog
- AI Assistants are the smallest intelligent unit workforce in Agentic Process Automation or APA.
- They automate complex business requirements in contextual frameworks, improving speed.
- They simplify process complexities and take the shortest path in task automation.
In agile business environments, a break in the automation workflow for human intervention is a showstopper. Bots that execute along the rule-based automation workflows follow pre-defined business rules while halting in exceptional scenarios requiring human intervention. However, bot-driven workflows are inadequate where process speed is paramount. As businesses continue to operate in complex environments, they must harness the power of Artificial Intelligence and AI Assistants to automate business processes seamlessly to improve speed and productivity.
Why do you require AI Assistants?
Decisions and actions must be agile in dynamic business environments and automated workflows. Rule-based automation breaks the flow where human decision is required, increasing the turnaround time. AI Assistants are your intelligent colleagues, who go beyond the repetitive task automation that bots perform. With AI at the core, these intelligent AI Assistants are adept at making contextual decisions by weighing the gains and trade-offs, a feat unthinkable for bots that run on pre-programmed business rules. Technically referred to as AI-driven Agentic Process Automation or APA, it dynamically oversees workflows for contextual decision-making while adapting to and orchestrating the changing work conditions. In popular parlance, APA is RPA with high levels of contextual intelligence.
For example, a mid-school tennis ball vending machine needs replenishment from the nearest depot when the stock falls to 5 units. However, the school is due to remain closed for the summer vacation. The AI Assistant cross-checks the school’s online timetable and reschedules the replenishment order after the school reopens. AI Assistants are adept at taking automated goal-oriented actions in agile environments like an intelligent human assistant would. They are more human-like intelligent counterparts of bots that operate and respond by understanding the scenario using fuzzy logic.
What are AI Assistants?
AI Assistants or Copilots are intelligent and autonomous workers, similar to your intelligent and experienced colleagues, who perform assigned tasks in a contextual framework but without human intervention. They leverage enterprise data and use Large Language Models (LLMs) to deliver personalized experiences to end users and customers.
AI Assistants intelligently engage with customers by using their past business interaction history, directly influencing customer satisfaction, conversion, and loyalty. With the evolution of Generative AI and APA technologies, personalized connectors and components for enterprise applications, and Large Action Models (LAMs), the AI Assistants are evolving rapidly to suit business situations on the ground.
For example, AI Assistants analyze dynamic product demand in the market, identify the right product pricing, and quote it to the customer at run-time during a conversation. AI Assistants continuously analyze the enterprise data to decide the next best action to support the defined goals.
How do AI Assistants address crucial decision-making in multi-lingual scenarios?
AI Assistants powered by Deep Learning and Advanced AI algorithms work on text translation by identifying the business intent and the context. Being trained on LLMs to identify nuances even while working with different language sensibilities, AI Assistants make the right decisions at each workflow node. They are adept at better decision-making similar to how your intelligent human colleague would exhibit contextual awareness that transcends mere language translation.
For example, an AI Assistant on the website of an international hotel in India quickly deciphers the question posed by a person in Germany in his or her native language and makes intelligent offers and cuisine suggestions that suit a German national’s palette. They display transparent behavior that is explainable and weeds out bias. With the rapidly evolving technology, AI Assistants are adept at human-like critical decision-making in different business environments and multi-lingual scenarios.
Advantage of AI Assistants in Multi-Lingual Environments
- Hyperautomation: AI Assistants trained on LLMs and adapted to autonomous task handling, build synergies in multi-lingual workspace environments. They handle complex business requirements in contextual frameworks and make accurate decisions in changing work scenarios while being explainable and transparent.
For example, take the case of an AI Assistant at a hotel in Dubai assisting a frequent traveller enroute Singapore from the Americas along a stopover journey at Dubai. It understands the traveller’s preferences through the traveller’s credit card and portal booking history and awards transit hotel rates at a discounted price along with their preferred cuisine by retracing the traveller’s preferences. - Shorter response times: APA significantly reduces turnaround time. They select the optimum path for executing transactions irrespective of human presence. To reiterate the frequent traveller’s example, the AI Assistant responds within seconds as it leverages historical information to assist the traveller in the present context.
- Cost savings: They generate significant cost savings due to shorter response times without requiring back-and-forth human interventions involving different executives or departments. To recall the mid-school example, the AI Assistant smartly executes critical decisions such as postponing the replenishment without having to refer human managers at critically busier times such as closing for summer vacation, thereby saving human time and effort.
- Simplified automation: AI Assistants simplify complex scenarios through automation and take the shortest path to automate tasks irrespective of the type of natural language used. For example, the AI Assistant quickly offers the required information to a Japanese traveller irrespective of the language used while communicating by recalling historical data. Similarly, the AI Assistant on a car booking app proactively suggests smaller cars for shorter distances and SUVs only for longer travel distances irrespective of the natural language used in communication.
- Higher NPS scores: APA influences customer satisfaction, conversion, and loyalty. It results in higher customer advocacy. To refer the frequent traveller’s example, the mere fact that he or she does not have to list down his preferences all over again as the AI Agent recalls the traveller’s credit card and portal history and issues bonus points by making reference to the earlier travel makes the traveller elated making him or her opt the same stopover hotel during the next visit.
Simply put
AI Assistants autonomously perform tasks in a contextual framework without human intervention. Powered by GenAI, APA, LAMs, LLMs, enterprise data, and personalized connectors and components, the AI Assistants are adept at agile decisions in complex business scenarios and multi-lingual environments, just like your intelligent colleagues. They act on the principle of next best action to support pre-defined business goals. AI Assistants thereby simplify process automation and improve process agility and efficiency.
