From AI agent hype to practicality: Why enterprises must consider fit over flash

When we enter the era of independent transformation, Artificial Intelligence Agent It is changing the way businesses operate and create value. But, with hundreds of vendors claiming to offer “AI agents,” how do we cut the hype and understand what these systems can really do, and more importantly, how should we use them?

This answer is more complex than creating a list of tasks that can be automated and testing whether AI agents can implement these tasks through benchmarks. The jets move faster than the car, but it’s the wrong choice for traveling to the grocery store.

Why shouldn’t we try to replace our work with AI agents

Each organization creates a certain amount of value for its customers, partners, and employees.

This amount is part of the total addressable value creation (i.e., the total amount of value that an organization is able to create its customers, partners, and employees welcome).

If each employee leaves the workday and has a long to-do list the next day and another to-do list completely deprives items that are completely deprived of value, then these items will be given priority, which will be an imbalance of value, time and energy, while the value on the table leaves value behind.

The easiest place Artificial Intelligence Agent Viewing the work done and the value created. This makes initial psychological math easy because you can plot the value that already exists and analyze opportunities to create the same value faster or more reliably.

There is nothing wrong with this exercise being a stage in the transformation process, but where most organizations and AI programs fail Consider only How AI is applied to value that has been created. This narrows their focus and investments to the narrow overlapping SLIVER in the Venn diagram below, thus making most of the addressable value on the table.

Humans and machines inherently have different advantages and disadvantages. Organizations that work with their business, technology and industry partners will outperform those that focus only on one value and pursue greater automation endlessly without increasing the total value output.

Understanding AI proxy functions through SPAR framework

Help explain how Artificial Intelligence Agent WorkWe created what is called SPAR framework: senses, planning, action and reflection. The framework reflects how humans achieve our own goals and provides a natural way to understand how AI agents work.

induction: Just as we use our senses to collect information about the world around us, AI agents collect signals from their environment. They track triggers, collect relevant information and monitor their operating environment.

planning: Once the agent collects signals about its environment, it will not only get stuck in execution. Just as humans consider their own choices before acting, AI agents process available information in the context of their goals and rules to make informed decisions about achieving their goals.

Performance: The ability to take concrete roles to distinguish AI agents from simple analytical systems. They can coordinate multiple tools and systems to perform tasks, monitor their operations in real time, and make adjustments to keep the course.

Reflection: Perhaps the most complex ability is learning from experience. Advanced AI agents can evaluate their performance based on the most efficient method, analyze their results and refine their methods – creating a continuous cycle of improvement.

What makes AI agents powerful is how these four functions work together in the integration cycle to create a system that can pursue complex goals through increasingly complex systems.

This exploration capability can be compared with existing processes that have been optimized multiple times by digital transformation. Their reshaping may bring in small amounts of short-term gains, but exploring new ways to create value and create new markets may result in exponential growth.

5 Steps to Establish Your AI Agent Policy

When AI was introduced, most technicians, consultants, and business leaders followed the traditional approach (87% of failures):

1. Create a list of questions;

or

1. Check your data;
2. Select a set of potential use cases;
3. Analyze use cases for return on investment (ROI), feasibility, cost, timeline;
4. Select a subset of use cases and invest in execution.

This approach seems to be justified, as it is often considered a best practice, but the data suggests it doesn’t work. It’s time to take a new approach.

1. Given your core capabilities and the regulatory and geopolitical state of the market, your organization can provide addressable total value creation to customers and partners.
2. Evaluate the current value creation of your organization.
3. Choose the five most valuable and most valuable marketing opportunities for your organization to create new value.
4. Analyze the ROI, feasibility, cost and timeline of the engineer’s AI agent solution (repeat steps 3 and 4 if necessary).
5. Select a subset of value cases and invest in execution.

Create new values ​​with AI

The journey of the era of autonomous transformation (with more autonomous systems creating value continuously) is not a sprint – it is a strategic advancement, and while technological advancement, it builds organizational capabilities. By initially identifying value and growth ambitions in a methodical way, you will enable organizations to thrive in the age of AI agents.

Brian Evergreen is Independent Transformation: Creating a More Human Future in the Age of Artificial Intelligence

Pascal Bornet is Agent Artificial Intelligence: Reshaping business, work and life with AI agents

Evergreen and Bornet are teaching new online courses with Cassie Kozyrkov’s AI agent: Leader’s Agent Artificial Intelligence