Like many, I really like good advice. But sometimes, I want help to get something done.
The subsequent rev of AI — agentic AI — will move us from advice to getting stuff done. It should enable businesses that harness it to take a transformative step forward.
But leap to what? And transform how?
Agentic AI can reduce the fee of customer support by 25-50% while dramatically improving quality and customer satisfaction since it goes beyond sure bet execution. It might probably also autonomously resolve complex workflows and customer interactions. When applied to customer support, for instance, agents don’t just reply to queries but comprehensively resolve inquiries from start to complete, reducing human intervention and increasing efficiency.
As with all recent technologies, adopting agentic AI presents challenges. An organization should have its workflows well-documented and deeply understood and possess a sturdy knowledge base on which the agentic AI can draw. And just as with generative AI, data privacy and security concerns require corporations to know the massive language models (LLMs) they tap into and the way information is stored and passed by them.
Nevertheless, the proper adoption strategy for intelligent automation can ensure success. To reap essentially the most advantages, corporations might want to do three things:
- Start in the proper place
- Balance agentic AI with human expertise
- Tap right into a network of agentic expertise
While it’s still early days, here’s what we’re learning as we work with clients in various industries to integrate agentic AI into their workflows and operations.
Don’t start small — start smart
Perhaps counterintuitively, the most effective place to begin is along with your highest-volume use cases. Isn’t that dangerous? Not if done properly. In actual fact, although starting with low-volume use cases might appear to cut back risk, it actually the danger of not seeing sufficient impact to justify the investment.
Starting with high-volume use cases offers the best potential return on investment (ROI), enabling an organization to quickly realize significant impact, maximize efficiency gains, and show the clear value of using AI agents.
How do you mitigate the danger of starting too big? By initially implementing the agents with just 1% of the most important use case volumes. This approach means that you can discover and fix potential issues while preparing for broader automation.
For a retail company, this might mean automating “where’s my order?” or return-processing workflows. Along with monitoring shipments across the corporate’s success network, an AI agent could confirm a customer’s identity, check real-time status and update the client — even offer options if the order has been unexpectedly delayed.
For returns, an agent could check the corporate’s return policies, gather customer information in regards to the return, suggest next steps, and complete appropriate associated tasks, like printing a return label, scheduling a pickup, issuing a refund, etc. The return agent could also look ahead to patterns of abuse and, if warranted, adjust its decisions and next steps accordingly.
After an organization deploys an AI agent on a sample portion of a high-volume workflow, it must monitor workflow activity to discover where it would need adjustments. When the agent functions easily, the corporate can expand its use in pre-defined amounts until it will definitely handles all the workflow volume.
In fact, not all tasks and workflows lend themselves to total automation with agentic AI. in reality, keeping human experts connected to the general workings of AI agents will yield the most effective results.
Balance AI with human expertise
As an organization examines its workflows and processes for automation candidates, it’ll find instances best suited to human oversight or direct motion. Agentic AI is an incredible, highly capable innovation, nevertheless it has limitations.
Three particularly:
AI agents, just like the LLMs that support them, don’t currently possess general intelligence. They function best in narrow, well-defined areas. So, while humans might learn learn how to perform a selected task and abstract from that knowledge principles they then apply to different, unrelated tasks, AI currently cannot.
Then, there are workflows with extremely complex decision matrices that demand significant experience and experience-based judgment. For instance, a retail company might need content for an easy marketing campaign. An agent can handle that — and execute the campaign.
But wish to revisit a brand’s expression and promise across multiple markets? An agent wouldn’t be as much as the duty. It could require insight into market trends, brand perception, cultural differences across markets, and insight into how brands evoke emotions.
Finally, workflows depending on typically “messy” human communication and emotional nuance that require distinctly human elements equivalent to compassion best remain with humans. Consider customer support issues involving irate customers or healthcare interactions where a patient’s emotional or mental state could also be in danger.
But I’m not describing a binary decision process: give this to the AI agents; every part else goes to humans. In practice, a hybrid model works best.
While there must be a transparent delineation between AI and human roles, even when tasks must be handled by human experts, AI should still be readily available to increase their abilities and benefit from their expertise.
Generally speaking, corporations should use agentic AI for transactional, repeatable tasks and tap human expertise for high-stakes interactions, emotionally complex scenarios, and situations requiring nuanced judgment. A $50 warranty claim is likely to be fully automated, while a $5,000 claim would almost certainly profit from human emotional intelligence and brand-sensitive handling.
Tap into an agentic network
Perhaps most vital, don’t attempt to dive into agentic AI solo. Establish a network of expert partners. Emerging agentic AI platforms can supply the technology across digital and voice channels. A systems integrator and advisor that understands customer operating environments can train agentic models for specific customer needs after which integrate them into an organization’s operations.
Integrating these models into enterprise systems requires deep expertise in complex workflows and industry-specific challenges. It also requires an intricate understanding of workflow decision points and where human interaction is most needed – or useful, in order that agentic AI is a boon to employees and team productivity.
Agentic AI offers businesses a strong solution to improve efficiency, enhance customer experiences, and drive innovation. But success isn’t about rushing in. It’s about making smart, informed selections: Starting in the proper place, applying a hybrid human/AI model, and tapping into the proper network.
Because with the world of AI changing so quickly, you possibly can’t afford to go it alone.
