What are AI agents?
AI agents are software programs designed to perform a specific task. They’re built to interact with their environment to collect and use it to work towards a predetermined goal.
AI agents are considered autonomous because you don’t necessarily need to direct or supervise them. Unlike traditional software, which is fairly static unless you’re pushing buttons telling it to create text or shift an image, AI agents can take information and make logical decisions.
What can AI agents do?
AI agents can do almost anything that includes at least one of their programmed functions. These functions are task- and skill-based and include:
- making decisions within an AI framework or model
- using natural language processing (NLP) to respond to input from a human user
- executing tasks based on automated triggers (e.g. order more inventory once a tracked item drops below par level)
- solving problems within their own technological framework (e.g. auto-respond to an IT hiccup or react to a flaw within software design)
- personalizing based on customer data (e.g. decide which push notifications to send a gamer based on app play)
Note that agents can’t do any of this unless you give them a framework to exist within. Chatbots are a good example of this. In the scenario below, a customer might click on the website and want to know when their order is going to arrive.
If you’ve programmed your agent correctly, the process will go something like this:
- Customer clicks on the customer service chatbot and either types in their question or clicks on prepared options that include a button for “Where is my order?”
- The AI agent responds with a request for an identifier, such as an order number or telephone number.
- The customer provides the requested information, and the agent runs it against internal files to pull up the tracking information.
- The AI agent then offers several follow-up possibilities based on the original question, such as “Track a different order” or “Speak with a live agent.”
Each of these interactions must be pre-programmed so the AI agent understands what its job is, what information it needs, what it may do with that information and what the customer might want next.
Examples of AI agents
Chatbots and automated doctor’s appointment reminders are very visible examples of ways AI agents are already impacting your daily life.
But the possibilities for AI agents in the workplace can be much more varied. We’re limited only by our imagination and ability to program that all-important framework. If you have an idea, an AI agent could potentially complete it for you.
AI agents can:
- be your virtual assistant
- screen resumes to help speed up recruiting and onboarding of new employees
- personalize recommendations, such as telling an upcoming hotel guest about a special event on-site or the updated spa menu
- collect customer data to create buyer personas built on actual preferences and purchasing habits
- increase efficiency in project management by spearheading task allocation according to team members’ skills and availability and tracking progress
- streamline supply chain management, including automating the ordering and document generation processes
- improve content output, including boosting SEO and brand visibility and ideating topics for a content strategy outline
- analyze sales funnels to identify performance bottlenecks
- conduct real-time cash management and financial models incorporating internal spending, accounting for market trends and acknowledging customer insights
The future of work: AI or human?
The future of Earth’s workplaces probably won’t be entirely AI or entirely human. Instead, the reality will likely fall somewhere in between, with hybrid workforces coming together to offer the best of human insight and technological abilities.
Even now, there are a couple of ways AI, machine learning and human expertise combine forces:
- Human in the loop (HITL): This human-led approach involves live people taking a direct role in creating and training AI agents. Frequent check-in points gauge the agent’s efficacy and check for issues, such as missing data affecting performance or possible points of discrimination in the algorithm.
- Human on the loop (HOTL): With HOTL, humans are still involved, but they take a more “set-it-and-temporarily-forget-it” approach. Humans are the managers, creating the AI system but then letting it operate autonomously unless there’s a need to step in and halt an action due to risk, a chance of circumstance or another urgent scenario.
HITL is more hands-on, which can seem advantageous if you’re newer to AI technology in the workplace. Humans play a vital role in the closed feedback loop, so your AI isn’t as automated and autonomous as it could be.
That’s a necessary compromise in some HITL models, such as medical diagnosis systems (AI models review an MRI, but a doctor also interprets the data later) and self-driving cars.
HOTL is often the most efficient route if you’re trying to expand your company’s bandwidth and productivity. You create the AI model to test and, if successful, it will run itself. You still have control if you need to interrupt an AI agent’s action, but you aren’t required to be present to initiate every important step.
Consider reserving HOTL agents for situations that aren’t as high-stakes, such as a customer service chatbot or an online forum moderation program.
Who should invest in the AI workforce?
Most businesses, large and small, can stand to benefit from some level of AI assistance. The ability to automate routine and repetitive tasks frees your team from dedicating a portion of their day to low-input jobs. Instead, they can reallocate their time and energy to tasks that require attention to detail and creativity.
For that reason, AI can be looked at as a way to augment the workforce, not replace it. You can do more with the same number of people than doing the same amount with a smaller team.
The AI workforce, powered by autonomous AI agents, may transform businesses by automating tasks, enhancing decision making and personalizing customer experiences. By integrating AI with human expertise through approaches like HITL and HOTL, organizations can boost productivity and efficiency. Embracing this hybrid model can allow businesses of all sizes to augment their workforce.
