Artificial Intelligence (AI) is moving beyond simply producing information. The more it advances from providing answers to executing and coordinating processes, the more the discussion shifts from technology to AI management.
Over the past few years, this technology has generated a rare combination of enthusiasm, rapid adoption, and high expectations, and there were good reasons for this. Accessing information has become faster; producing texts, analyses, code, and reports now takes a fraction of the time. Activities previously limited to specialists have become accessible to a much larger number of people.
Artificial Intelligence has delivered these results, but as investment grows, a sense of frustration is also beginning to emerge. If AI is capable of doing so much, why don’t organizational results always improve at the same rate?
Perhaps this is because we are discovering a fundamental difference between increasing the capacity of a person or an activity and increasing the capacity of the organization as a whole.
From Answers to Coordination: The Importance of Artificial Intelligence Governance
The use of AI in companies is expanding. This does not necessarily represent a maturity scale, but rather a process of learning and use, with an increasingly broad impact on work. As a result, the main ways AI is used can be represented as follows: Answer → Produce → Act → Coordinate.
In this structure, each element corresponds to the following:
Answer: Using AI to access knowledge, interpret information, and support decisions.
Produce: Using this technology to create analyses, documents, reports, code, plans, or recommendations.
In these first two forms of use, much of the benefit is reflected directly in individual productivity.
Act: This changes the nature of AI use. AI begins to execute activities, access systems, transfer data, initiate workflows, verify rules, and make certain decisions.
Coordinate: This expands its reach even further. People, systems, automations, and different agents begin to participate together in the same workflow.
It is precisely during this transition that technology begins to fall out of step with AI management.
Automating an Activity Does Not Mean Transforming a Process
An individual department can achieve excellent results using Artificial Intelligence. For example, Procurement can analyze suppliers more quickly. Finance can accelerate reconciliations. Human Resources can automate support services. Quality can analyze documents or nonconformities.
But organizations do not operate only within departments. One activity triggers another. One decision affects another department. Process management spans systems, rules, documents, controls, approvals, and responsibilities.
Therefore, individual productivity does not necessarily mean end-to-end performance. Similarly, there are other characteristics of organizations that need to be considered in Artificial Intelligence governance:
- Access to knowledge does not mean organizational capability;
- Technological capability does not mean business capability;
- Automating silos does not necessarily eliminate silos.
In certain situations, this technology simply makes them operate faster. Perhaps this is one of the reasons for the current concern: this technology has advanced faster than many organizations’ ability to learn how to use it in a coordinated manner.
Technology applied to poorly defined processes does not necessarily correct organizational disorder and may even amplify its complexity and speed.
See also: Intelligent Processes: The New Challenge in Operational Management
The Question Is Changing
For a long time, much of digital transformation began with questions such as “Which technology should we use?”, “Which platform should we adopt?” or “Where can we apply Artificial Intelligence?”. These questions remain relevant, but they are no longer sufficient.
Other questions are beginning to gain importance, such as:
- Is the process ready to use this technology?
- Is the data reliable?
- Are responsibilities clear?
- Are decision-making rules defined?
- Are exceptions known?
- Are AI controls and risks linked to the process?
- Is it clear what can be automated and what still requires human intervention?
These questions become even more relevant when we decide that AI should act. While Artificial Intelligence answers a question or produces a document, there is usually a person between the recommendation and its execution.
When AI, as an Intelligent Agent, begins to act directly within processes, that distance decreases. And then new questions arise, including:
- Who can make decisions?
- How far does autonomy extend?
- When is human approval still mandatory?
- What information can be accessed?
- How should an exception be handled?
- Who is responsible for the outcome?
- How can a decision be explained afterward?
For example, when different agents begin to participate in the same workflow, these questions are no longer just technology problems. They become AI governance problems.
And the greater the capacity for action and coordination granted to the technology, the greater the organization’s management and governance capabilities must be.
The Challenge Now Is to Coordinate and Manage AI
Perhaps the next stage of Artificial Intelligence will not be determined solely by more sophisticated models, which will certainly continue to evolve. In fact, organizations are already beginning to face another challenge: coordinating and measuring their actual capacity. In other words, what people, processes, systems, process automations, and agents are capable of accomplishing together.
This requires looking beyond departments; it requires understanding end-to-end processes; and it requires connecting decisions, risks, controls, data, and responsibilities. Furthermore, it requires resisting the temptation, or the illusion, of believing that extraordinary technology will automatically compensate for fragmented processes, departmental silos, conflicts, or unclear operating models.
Therefore, perhaps the next question is not:
“What else will AI be capable of doing?”
But rather:
“How will the organization coordinate everything that people and technology are already capable of doing?”
Because when AI only provides answers, technology can be at the center of the discussion. When it begins to act and coordinate processes, the problem changes. The new challenge becomes Artificial Intelligence governance.
FAQ – Frequently Asked Questions
It is the ability to coordinate and manage the use of AI within an organization, considering processes, systems, automations, agents, decisions, risks, controls, data, and responsibilities.
Because AI is moving beyond simply producing information and is beginning to execute activities, access systems, transfer data, initiate workflows, verify rules, and make certain decisions. As the technology’s capacity to act increases, so does the need for management and governance.
There are four main forms: Answer, Produce, Act, and Coordinate. Answer involves accessing knowledge, interpreting information, and supporting decisions. Produce includes creating analyses, documents, reports, code, plans, and recommendations. Act involves executing activities and interacting directly with systems and processes. Coordinate expands this scope by integrating people, systems, automations, and different agents into the same workflow.
No. Automating an activity can increase productivity within a department, but it does not necessarily mean improving end-to-end process performance. Processes span departments, systems, rules, documents, controls, approvals, and responsibilities.
Technology does not necessarily correct organizational disorder. According to the article, applying it to poorly defined processes may even amplify complexity and increase its speed.
It is important to assess whether the process is ready, whether the data is reliable, whether responsibilities are clear, whether decision-making rules are defined, whether exceptions are known, and whether risks and controls are linked to the process. It is also necessary to define what can be automated and what still requires human intervention.
The distance between an AI recommendation and its execution decreases. As a result, clearer definitions become necessary regarding who can make decisions, the limits of autonomy, when human approval is mandatory, what information can be accessed, how exceptions should be handled, and who is responsible for the outcome.
The greater the capacity for action and coordination granted to the technology, the greater the organization’s management and governance capabilities must be. This is especially relevant when different agents begin to participate in the same workflow.
When AI only provides answers or produces a recommendation, there is usually a person between the recommendation and its execution. As AI begins to act directly, it becomes necessary to establish when human approval remains mandatory and which decisions can be made autonomously.
Because gains achieved individually or within departments do not guarantee improvements in end-to-end processes. One activity can trigger others, affect different departments, and depend on systems, rules, controls, approvals, and responsibilities.
The challenge becomes coordinating and measuring the organization’s actual capacity, considering what people, processes, systems, automations, and agents can accomplish together.
The discussion is moving beyond focusing solely on which technologies to use or where to apply AI and is beginning to consider how the organization will coordinate everything that people and technology are already capable of doing.
