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

While we step into the age of autonomous transformation, AI agents transform how companies work and create value. But with hundreds of suppliers who claim to offer ‘AI agents’, how do we cut the hype and we understand what these systems can really achieve and, more importantly, how to use them?

The answer is more complicated than making a list of tasks that can be automated and test or an AI agent who can reach tasks against benchmarks. A jet can move faster than a car, but it is the wrong choice for a trip to the supermarket.

Why we should not try to replace our work with AI agents

Every organization creates a certain number of value for their customers, partners and employees.

This amount is a fraction of the total addressable value creation (that is, the total amount of value that the organization can create that would be welcomed by its customers, partners and employees).

If every employee leaves the working day with a long list of tasks for the next day and a different list of tasks to completely depriorize items that would have created value if they could have been given priority, is an imbalance of value, time and effort, so that value remains on the table.

The simplest place to start with AI agents, looks at the work that has already been done and the value that is created. This makes the initial mental maths simple, because you can map the value that already exists and analyze opportunities to create the same value faster or more reliable.

There is nothing wrong with this exercise as a phase in a transformation process, but where most organizations and AI initiatives failed Consider only How AI can apply to value that is already being made. This limits their focus and investments in the narrow overlapping sliver in the Venn diagram below, leaving the majority of the addressable value on the table.

People and machines have inherently different strengths and weaknesses. Organizations that work together with their business, technology and industrial partners will surpass those who only focus on one value of value and endlessly pursue a greater degree of automation without increasing the total value output.

Insight into AI Agent -possibilities via the Spar -Mraimwerk

To help explain how AI agents work, we have created what we call the Spar -Framework: Sense, Plan, Act and Reflection. This framework reflects how people achieve our own goals and offers a natural way to understand how AI agents work.

Detection: Just as we use our senses to collect information around the world around us, AI agents collect signals from their environment. They follow triggers, collect relevant information and monitor their operational context.

Planning: As soon as an agent has collected signals about his environment, it is not only in the implementation. Just like people who consider their options before they act, AI agents are developed to process available information in the context of their objectives and rules to make informed decisions about achieving their goals.

Act: The possibility of taking concrete action, AI agents eliminates simple analytical systems. They can coordinate multiple tools and systems to perform tasks, check their actions in real time and make adjustments to stay on course.

Reflective: Perhaps the most advanced possibilities are learning from experience. Advanced AI agents can evaluate their performance, analyze results and refine their approaches based on what works best – creating a continuous improvement cycle.

What makes AI agents powerful is how these four options work together in an integrated cycle, creating a system that can strive for complex goals with increasing refinement.

This exploratory capacity can be contrasted with existing processes that have been optimized several times through digital transformation. Their reinvestment can yield small profit in the short term, but investigating new methods for creating value and making new markets can result in exponential growth.

5 steps to build your AI agent strategy

Most technologists, consultants and managers follow a traditional approach to the introduction of AI (good for a failure percentage of 87%):

1. Make a list of problems;

or

1. Investigate your data;
2. Choose a set of potential use cases;
3. Analyzing user cases for return on investment (ROI), feasibility, costs, timeline;
4. Choose a subset of use cases and invest in implementation.

This approach may seem defensible because it is generally conceived as the best practice, but the data shows that it does not work. It is time for a new approach.

1. Map the total addressable value creation that your organization can offer to your customers and partners, given your core competencies and the regulatory and geopolitical circumstances of the market.
2. Assess the current value creation of your organization.
3. Choose the top five most valuable and market options for your organization to create new value.
4. Analyze for ROI, feasibility, costs and timeline to engineer AI agent solutions (repeat steps 3 and 4 if necessary).
5. Choose a subset of value seekers and invest in implementation.

Create new value with AI

The journey to the age of autonomous transformation (with more autonomous systems that continuously create value) is not a sprint – it is a strategic progression, building organizational capacity in addition to technological progress. By initially identifying value and growing ambitions, you position your organization to thrive in the AI ​​agents era.

Brian Evergreen is the author of Autonomous transformation: create a more human future in the era of artificial intelligence

Pascal Bornet is the author of Agentic artificial intelligence: AI agents use to reinvent things, work and life

Evergreen and Bornet give a new online course about AI agents with Cassie Kozyrkov: Agentic artificial intelligence for leaders