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While we are fully entering the era of autonomous transformation, AI agents transform the way companies work and create value. But with hundreds of suppliers pretending to offer “AI agents”, how to cut media threshing and understand what these systems can really do and, more importantly, how to use them?
The answer is more complicated than the creation of a list of tasks that could be automated and test if an AI agent can perform these tasks against references. A jet can move faster than a car, but it’s the right choice for a grocery trip.
Why should we not try to replace our work with AI agents
Each organization creates a certain value for its customers, partners and employees.
This amount is a fraction of the total creation of addressable value (that is to say the total amount of the value that the organization is capable of creating which would be welcomed by its customers, partners and employees).
If each employee leaves the working day with a long list of tasks for the next day and another list of tasks to prioritize completely – elements that would have created value if he could have been prioritized – there is an imbalance of value, time and efforts, leaving the value on the table.
The easiest place to start with AI agents is to look at the work already done and the value created. This makes the initial mental mathematics easy, because you can map the value that already exists and analyze opportunities to create the same value faster or more reliably.
There is nothing wrong with this exercise as a phase of a process of transformation, but where most organizations and initiatives of IA fail is in only considering How AI can apply to the already created value. This narrows their concentration and their investments on the narrow ribbon that overlap in the Venn diagram below, leaving the majority of the addressable value on the table.
Humans and machines intrinsically different strengths and weaknesses. Organizations that reinvent in collaboration with their companies, technology and industry partners will have to surpass those who simply focus on a value body and constantly pursue higher automation degrees without increasing total value production.
Understand the capacities of AI agents through the Spar framework
To explain how AI agents work, we have created what we call the Spar frame: meaning, plan, act and reflect. This framework reflects how humans achieve our own objectives and provides a natural means of understanding how AI agents work.
Detection: Just as we use our senses to collect information about the world around us, AI agents collect signals from their environment. They follow the triggers, collect relevant information and monitor their operating context.
Planning: Once an agent has collected signals on his environment, he does not just move to execution. As humans considering their options before acting, AI agents are developed to process the information available in the context of their objectives and rules to make informed decisions on achieving their objectives.
Interim: The ability to take concrete actions distinguishes AI agents from simple analytical systems. They can coordinate several tools and systems to perform tasks, monitor their actions in real time and make adjustments to stay on course.
Thoughtful: The most sophisticated capacity may be to learn from experience. Advanced AI agents can assess their performance, analyze the results and refine their approaches according to what works best – creating a continuous improvement cycle.
What makes AI agents powerful is how these four capacities work together in an integrated cycle, creating a system that can pursue complex objectives with increasing sophistication.
This exploratory capacity can be contrasted with existing processes that have already been optimized several times by digital transformation. Their reinvention could produce small short -term gains, but the exploration of new methods of creating value and manufacturing new markets could produce exponential growth.
5 steps to build your AI agent strategy
Most technologists, consultants and business leaders follow a traditional approach during the introduction of AI (representing a failure rate of 87%):
- Create a list of problems;
Or
- Examine your data;
- Choose a set of potential use cases;
- Analyze use cases for return on investment (king), feasibility, cost, calendar;
- Choose a use of use cases and invest in execution.
This approach may seem defensible because it is generally understood as the best practice, but the data shows that it does not work. It is time for a new approach.
- Map the creation of total addressable value that your organization could provide to your customers and partners given your basic skills and regulatory and geopolitical conditions on the market.
- Evaluate the current value creation of your organization.
- Choose the five most precious and most precious opportunities for your organization to create new value.
- Analyze the king, the feasibility, the cost and the calendar to design AI agent solutions (repeat steps 3 and 4 if necessary).
- Choose a value case subset and invest in execution.
Creation of a new value with AI
Traveling in the era of autonomous transformation (with more autonomous systems creating continuous value) is not a sprint – it is a strategic progression, strengthening organizational capacity at the same time with technological advancement. By initially identifying the value and increasing the ambitions methodically, you will position your organization to prosper in the era of AI agents.
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: operating AI agents to reinvent business, work and life
Evergreen and Bornet teach a new online course on AI agents with Kozyrkov Cassie: Agentic artificial intelligence for managers
