Like many people, I like good advice. But sometimes, I need help to get some work done.
The next Rev of AI (Agentiic AI) will take us from suggesting to getting the job done. This will allow businesses to take advantage of it for a transformative leap.
But what to jump to? How is the transformation?
Agent AI can reduce the cost of customer support by 25-50%, while greatly improving quality and customer satisfaction as it goes beyond simple task execution. It also solves complex workflows and customer interactions independently. For example, when applied to customer support, agents not only respond to queries, but also address queries in an all-round way from beginning to end, reducing human intervention and increasing efficiency.
Like all new technologies, adopting proxy AI presents challenges. A company must have a verifiable workflow, deep knowledge, and a strong knowledge base where agent AI can draw. Like generating AI, data privacy and security issues require companies to understand the large language models (LLMs) they utilize and how information is stored and passed on.
However, the correct adoption of smart automation can ensure success. To get the most out of the way, a company will need to do three things:
- Start at the right location
- Balanced Agent AI has human expertise
- Mining agent expertise network
Although it’s early, it’s our collaboration with customers in various industries to integrate agent AI into their workflows and operations.
Don’t start small – start smart
Perhaps counterintuitively, the best starting point is to use with your most use cases. Isn’t that a risk? If done incorrectly. In fact, while starting with a small batch use case may reduce the risk, it is actually Increase There is no risk of seeing enough impact to justify the investment.
Offering the maximum potential return on investment (ROI) starting with high-volume use cases allows companies to quickly achieve significant impact, maximize efficiency improvements, and demonstrate the clear value of using AI agents.
How do you mitigate the risk of getting started too much? Initially only implements agents with only 1% of the maximum use case volume. This approach allows you to identify and resolve potential issues as you prepare for a wider automation.
For a retail company, this could mean automating “Where is my order?” or returning to the processing workflow. In addition to monitoring the goods on the company’s fulfillment network, AI agents can also verify the identity of customers, check real-time status and update customers – even offer options if the order is unexpectedly delayed.
For returns, the agent can check the company’s return policy, collect customer information about the return, recommend the next step and complete the appropriate related tasks, such as printing the return label, arranging the pickup, issuing a refund, etc. Return agents can also focus on abuse patterns, and if guarantees are guaranteed, adjust their decisions and next steps.
After the company deploys the AI agent on a sample portion of a large-capacity workflow, it must monitor workflow activity to determine where it may need to adjust. When the agent runs smoothly, the company can expand the amount it uses until it finally processes the entire workflow.
Of course, not all tasks and workflows can be automated with Agentic AI. In fact, connecting human experts to the overall operation of AI agents will produce the best results.
Balance AI with human expertise
When a company looks at the workflow and processes of its automated candidates, it will find examples that are best suited for human supervision or direct action. Proxy AI is an incredible, powerful innovation, but it has limitations.
Especially three:
AI agents, like their LLM, have no general intelligence at the moment. They play their best role in narrow, well-defined areas. So while humans may learn how to perform specific tasks and abstractions from these knowledge principles and then apply them to different, irrelevant tasks, AI can’t at the moment.
Then, there are some workflows with extremely complex decision matrixes that require a lot of experience and experience-based judgments. For example, a retail company may need content for direct marketing activities. Agents can handle this and execute campaigns.
But want to reexamine the brand’s expression and commitment in multiple markets? The agent will not be able to complete the task. It requires a deep understanding of market trends, brand perception, cultural differences across markets, and insight into how brands evoke emotions.
Finally, the workflow often depends on “messy” human communication and emotional nuances, which require obvious human elements, such as human compassion, for example, the best. Think about customer service issues involving angry clients or medical interactions, where the patient’s emotional or mental state can be dangerous.
But I’m not describing the binary decision-making process: handing it over to an AI agent; everything else goes to humans. In fact, the hybrid model works best.
Although it needs to be clearly described between AI and human roles, AI should expand its capabilities and leverage its expertise at hand, even if tasks need to be handled by human experts.
In general, companies should use proxy AI to conduct transactions, repeatable tasks, and leverage human expertise to conduct high-risk interactions, emotionally complex scenarios, and situations that require nuanced judgment. A $50 warranty claim may be fully automated, while a $5,000 claim will likely benefit from human emotional intelligence and brand-sensitive handling.
Click on the proxy network
Perhaps most importantly, don’t try to sneak into the proxy AI solo. Establish a network of expert partners. Emerging proxy AI platforms can provide technology across the digital and voice channels. System integrators and consultants who understand the customer’s operating environment can train agent models to meet specific customer needs and then integrate them into the company’s operations.
Integrating these models into enterprise systems requires deep expertise in complex workflows and industry-specific challenges. It also needs to be where or where human interaction is most needed, so proxy AI is a boon for worker and team productivity.
Agent AI provides businesses with a powerful way to increase efficiency, enhance customer experience and drive innovation. But success does not mean rushing in. It’s about making smart, wise choices: start at the right place, apply human/AI models and leverage the right network.
Because as the world of AI changes so rapidly, you can’t go alone.
