The adoption of IA enters a stage of scale
The next stage is played in the structure of the business
Artificial intelligence is entering a stage in which the main business question goes through the ability to turn technological adoption into economic impact. The advantage begins to depend on how companies reorganize processes, talent, investment and decision-making around the IA.
The adoption of IA enters a stage of scale
Business adoption continues to accelerate. According to AI Index 2026 of Stanford, 88% of the organizations surveyed use IA in at least one function, while the generative IA is already present in at least one business function in 70%. At the same time, the use of IA agents remains at an early stage in most functions.
McKinsey finds a similar signal: almost nine out of ten participants use IA regularly in some function and 44% claim that their organization is carrying the IA on a business scale, compared to 38% of the previous year. In large companies, the proportion is 54%.
The strategic signal is clear: The IA is moving from being a tool used by individuals to becoming a capacity that begins to affect the full architecture of companies.
This movement has a direct consequence for decision makers: the relevant question goes by identifying where the IA can change revenue, costs, productivity, trade speed and decision-making capacity.
Individual productivity needs to become a business result
One of the central market tensions appears between adoption and capture of value.
McKinsey points out that companies are achieving individual productivity benefits while seeking to move them to sustainable financial results. His research suggests that the capture of value requires organizational changes that accompany the incorporation of IA.
Deloitte comes to a convergent conclusion: only 34% of the organizations surveyed claim to be really rethinking their business from the IA.
This places a strategic variable in the foreground:
The productivity generated by IA needs to be connected to specific economic indicators.
For a B2B company, these indicators may include:
- Cost of purchasing customers;
- Trade cycle speed;
- Pipeline conversion;
- Margin per client;
- Response time;
- Operational cost per transaction;
- Recurrence;
- Productivity per employee;
- Launch speed of new services.
Technology is of business relevance when it changes these variables.
IA agents may alter the work structure
The next stage is linked to the AI agents, systems capable of performing tasks and processes with different degrees of autonomy.
McKinsey has a significant growth in the use of agents, especially among large companies: 40% of respondents from companies with incomes above US$One billion states to be climbing IA agents, compared to 27% of the previous year.
Deloitte here identifies one of the main organizational challenges. His research points out that approximately 75% of the executives consulted consider that their operating model will have to change over the next 12 to 18 months to support the expansion of IA.
The change may affect functions such as:
- Commercial: research of prospects, preparation of proposals, monitoring and analysis of opportunities.
- Marketing: content production, segmentation, audience analysis and customization.
- Finance: analysis of information, forecasting and detection of anomalies.
- Operations: planning, documentation, coordination and impact management.
- Technology: software development, testing, documentation and maintenance.
The strategic consequence is relevant: the company begins to need a work architecture where intelligent people and systems share tasks, information and decisions.
The real challenge appears in the processes
Deloitte identifies one of the main barriers to scaling agents: organizations often try to automate processes originally designed for people. His report claims that only 11 per cent of organizations had successfully deployed IA agents in production at the time of their investigation.
This has a direct reading for the business strategy.
A fragmented business process, for example, can be partially automated by IA. Automation can accelerate individual tasks while maintaining the original structure of a process that has multiple friction points.
The opportunity appears when the company first analyses:
- What process generates value;
- Where repetitive work is concentrated;
- What decisions require a human approach;
- What information is available;
- What tasks can be delegated to systems;
- What indicators can measure the result.
This diagnosis makes it possible to define where the IA has a real capacity to generate impact.
Investment in IA increases and also increases the demand for return
Global investment confirms that transformation has a growing economic dimension.
Stanford estimates that global corporate investment in IA more than doubled during 2025. Private investment grew 127.5%, while generative IA concentrated almost half of the private funding related to IA.
This expansion is also increasing expenditure on infrastructure. Companies need computer capacity, storage, data, security and technological architecture to sustain increasing IA loads.
Deloitte warns that the increase in use can exceed the reduction in unit processing costs, creating increasing pressure on infrastructure and technology budget.
This is why a new management variable appears:
The return on investment in IA needs to be assessed at process and economic unity level.
Governance becomes a business variable
The progress of self-governing actors also increases the importance of governance.
Deloitte points out that only one in five companies has a mature model of governance for self-employed actors.
The challenge involves:
- Access to sensitive information;
- Permissions;
- Tracability;
- Security;
- Human monitoring;
- Protection of intellectual property;
- Regulatory compliance;
- Responsibility for automated decisions.
The regulatory discussion is also gaining intensity. In the United States, for example, legislators are driving new security rules and controls for advanced IA systems.
For companies, this makes IA governance a strategic management dimension linked to risk, reputation and operational continuity.
Latin America can capture value from business efficiency
For Latin America, the scenario presents an opportunity especially linked to productivity.
Companies in the region operate in markets where trade efficiency, the availability of specialized talent and the scale can condition growth. The IA can reduce certain information costs, speed up processes and allow relatively small teams to manage more activity.
The strategic point is in the selection of use cases.
Companies can prioritize applications related to:
- Demand generation → identification and prioritization of prospects.
- Sales → pipeline analysis, proposals and follow-up.
- Service → automation of queries and classification of requirements.
- Operations → planning, documentation and coordination.
- Address → information analysis and decision support.
This logic is particularly relevant for B2B companies, where the quality of the business process is directly related to predictability and revenue.
The competitive advantage begins to depend on the reorganisation capacity
The evidence available points to the same direction: the adoption of IA grows rapidly, as organizational transformation advances at a more heterogeneous rate.
McKinsey notes that organizations are moving towards a larger scale of implementation. Deloitte simultaneously identifies a gap between strategic and operational preparation.
For CEOs and business directors, this puts five decisions at the heart of the agenda:
- 1. Prioritize processes with measurable economic impact.
- 2. To define which tasks remain under human responsibility.
- 3. Build a data architecture to scale IA.
- 4. Measure economic return by use.
- 5. Design a commercial and operational structure prepared to work with intelligent systems.
The business transformation led by IA begins to acquire a structural dimension. Technology is a part of change. The organization, processes, talent, investment and indicators determine the ability to capture value.
The strategic agenda for the next 12 to 24 months
For business decision makers, the agenda can be organized around four questions:
Where it generates value the IA.
Identify processes where there is a clear relationship between automation, productivity and economic impact.
What structure the company needs.
Review internal processes, responsibilities, technology and capacities.
What investment each initiative requires.
Measuring infrastructure, talent, integration, security and recurrent costs.
How the result will be measured.
Link each implementation with income, margin, productivity, speed or predictability.
The IA is moving towards a phase in which business transformation capacity begins to be as relevant as technological capacity. Organizations that develop mechanisms to convert smart tools into measurable business improvements will be better prepared to capture the economic value of this new stage.
