How Artificial Intelligence Is Transforming the World’s Largest Industry
How are agencies today calculating the impact of AI on project profitability?
59,4%
of agencies still do not measure how AI affects the profitability of their projects.
The adoption of Artificial Intelligence among agencies in Latin America is accelerating rapidly, but it faces a critical challenge in strategic management: measurement. According to the report, 59.4% of agencies still do not measure the real impact of AI on the profitability of their projects.
6 out of 10
agencies do not measure the impact of AI
While AI tools are actively used for tasks such as copywriting and design, this lack of financial visibility creates a disconnect between operational efficiency and business performance.
In fact, more than half of industry leaders (56.3%) state that AI has not generated visible changes in their profitability — or they simply don’t know whether it has.
This happens because most agencies remain tied to traditional metrics (such as time tracking), while new costs — like token consumption and decentralized licenses — go unnoticed or silently erode profit margins.
The conclusion is clear: AI is only scalable if it’s measurable. Without an infrastructure that centralizes usage and measures real return on investment, the technology risks becoming just another operating expense, rather than the profitability lever it promises to be.
In which areas of the agency are AI-powered tools already being used?
16,3%
Copywriting
15,3%
Graphic Design
Artificial Intelligence is already present in many agencies, but its implementation is still mostly tactical. According to the COR AI Report, the areas with the highest adoption are copywriting (16.3%) and graphic design (15.3%).
In other words, AI is being used to solve urgent needs: generating copy, visual variations, or creative assets faster. But it’s not yet being used to redesign the operation itself.
This pattern reveals a clear phenomenon: AI is still perceived as a productivity tool, not as a transformation lever. Instead of being integrated as a structural part of the operational workflow, it’s used at specific moments within the creative process. This prevents agencies from capturing its true potential — which goes far beyond automating deliverables. AI has the ability to optimize decision-making, analyze data in real time, and even reinvent entire business models.
The risk is clear. If agencies limit AI usage to peripheral tasks, they’ll continue operating under the same exhausted model — just faster. The real opportunity lies beyond production: designing a hybrid system where humans and agents work together from the very beginning of each project. Well-used AI isn’t just about efficiency. It’s about structural redesign.
Does your agency have policies in place to regulate AI usage?
54,8%
do NOT have clear policies
More than half of agencies (54.8%) admit they do not have clear guidelines for the use of AI.
This creates an increasingly critical operational and reputational risk: most agencies are uploading sensitive data to cloud-based tools without traceability, without data sanitization, and without knowing which models that data is training.
The enthusiasm for adopting AI has moved faster than the ability to establish rules. And in an industry where trust is everything, that gap can be fatal.
From who owns the generated content to how client data is protected, the lack of protocols exposes agencies to legal risks, output errors, and reputational damage.
Implementing AI without rules is like driving without brakes. AI must not only be efficient — it must be secure, ethical, and traceable. Agencies that understand this in time will be able to scale in a controlled, sustainable, and effective way.
How integrated is AI into your agency’s daily processes?
Only 17,1%
integrate it on a daily basis
Only 17.1% of agencies have AI integrated into their daily operations. The vast majority (63.4%) use it at a “medium” level — meaning in certain teams or for specific tasks. This reveals a structural fragmentation: AI is advancing, but it’s not scaling.
The issue isn’t the tool — it’s the lack of a systematic process. When each designer or copywriter uses their own account, without centralized governance, shared metrics, or a clear connection to the business, what looks like efficiency often turns into chaos. Data gets lost, licenses are duplicated, and the real impact can’t be measured.
AI cannot be left in the hands of individual enthusiasts. To create real value, it must be integrated across the entire workflow, with visibility, clear rules, and shared metrics. Only then does it stop being an isolated experiment and become scalable infrastructure.
Do you believe AI is changing the way your agency generates revenue?
9 out of 10
leaders anticipate the change
90% of leaders acknowledge that AI is already impacting — or will soon impact — the way their agencies generate revenue.
However, that shift hasn’t yet translated into widespread action. Only 34.4% say their business model is already changing.
The hourly billing model is starting to break down. When tasks that used to take days can now be completed in minutes by an agent, how do you justify that fee? This mismatch between operational efficiency and pricing forces agencies to rethink their models: pricing based on value delivered, impact, or results.
AI doesn’t just change how we work — it changes how we charge. Agencies that understand this early will lead the transformation. Those that continue billing purely based on time spent will quietly lose margin without even realizing it.
What challenges or issues have you faced when using AI?
28%
Team training
The biggest obstacle to AI adoption isn’t technical — it’s human. 28% of leaders say their main challenge is training their teams, followed by fear of replacement (13.4%) and the difficulty of integrating AI with existing systems.
These insights reveal that resistance to change is largely cultural. In many agencies, talent still sees AI as a threat rather than an ally. This perception slows down strategic adoption and creates a gap between available technical capabilities and real-world usage.
Training teams is not optional — it’s the only way to scale. Without proper enablement, AI gets reduced to isolated experiments with no connection to the business. Preparing talent for a new kind of collaboration is essential: one where agents automate repetitive tasks, while people focus on creativity, strategy, and decision-making.
Are you familiar with the concept of “intelligent agents”?
50%
are familiar with the concept, but don’t use it
Half of creative agency leaders are familiar with the concept of intelligent agents (AI that executes tasks within the workflow), but only 12.5% are actively using them. This represents a massive missed opportunity.
Intelligent agents allow agencies to automate entire processes — not just isolated tasks. This is the productivity leap that separates using AI as a patch from turning it into operational infrastructure. It’s not about adding speed; it’s about scaling the model without multiplying costs.
The issue is that many understand the concept, but few know how to implement it. This gap reflects a lack of maturity in AI adoption: awareness exists, but action is missing. Those who manage to orchestrate workflows where humans and agents collaborate within the same system will dominate the next era.
How does your agency currently manage access to and usage of AI tools?
36,6%
Each team chooses its own tools
A critical insight: 36.6% of agencies allow each team to choose its own AI tools. This leads to a fully decentralized tech stack, with severe side effects: duplicated licenses, inefficient resource usage, and a complete loss of traceability.
In addition, another 36.6% operate with only high-level guidelines, but without real enforcement or control. Only a small group works with clear policies or a centralized infrastructure. This shows that most agencies are still operating in an “anarchic mode,” where individuals test, train, and execute independently, disconnected from operations and with no visibility into costs
Lack of centralization is one of the biggest hidden risks. To scale, agencies need to stop operating like isolated islands and start governing AI with structure.
Among the few who measure it, how do they do it?
15.3%
Track hours + tokens
9.4%
Estimate based on time savings
Only 6.3% of agencies have a system that measures AI consumption in real time. The rest rely on imprecise estimates: time savings (9.4%) or a combination of hours + tokens (15.3%). And the majority (59.4%) don’t measure it at all.
This is an alarming insight: how can strategic decisions be made if there’s no clarity on how much AI usage actually costs? Without data, AI becomes a black box — licenses are purchased, usage increases, but no one knows whether it’s generating value or eroding margins.
The real differentiator isn’t using AI — it’s knowing how to measure it. What isn’t measured can’t be improved. Without a measurement system connected to the business, AI won’t become a profitability lever — it will remain an invisible cost.
Does AI always improve margins?
12,5%
reported a decline in profitability
No. In fact, 12.5% of agencies reported a decrease in profitability after implementing AI. Why? Poorly managed license costs, long experimentation periods, and unstructured resource usage. What should drive efficiency ends up eroding margins.
This proves that AI is not a guarantee of profitability. Using it without control can be more expensive than not using it at all — especially when processes aren’t centralized, tools overlap, and focus is lost on real impact.
The problem isn’t AI — it’s disorganized implementation. Scaling without measurement, experimenting without strategy, and automating without traceability all come at a high cost.
Does your agency currently have a centralized platform or workspace where tasks can be assigned to both people and intelligent agents?
40,6%
“No, but we’re exploring it”
Only 25% of agencies have a platform that allows tasks to be assigned to both people and agents. However, 40.6% are actively exploring how to achieve this. There’s clear intent — but infrastructure is still missing.
This shows that agencies already recognize the need to operate in hybrid environments. Yet many are still working with manual or fragmented workflows, which limits true collaboration between humans and AI.
A centralized system that enables task orchestration, result auditing, cost control, and direct connection to profitability metrics is the key to scaling.
What are the biggest benefits AI has brought to your agency so far?
20,2%
Time savings
15,8%
Production capacity
The most frequently cited benefit is time savings on repetitive tasks (20.2%), followed by increased production capacity (15.8%) and improved data analysis (15.8%).
In other words: operational efficiency, not strategic impact.
Less than 1% of respondents mentioned “higher profitability” as the main benefit. This reinforces a core insight from the report: agencies are using AI to produce faster, but they haven’t yet managed to translate that speed into financial results.
The focus remains on velocity, not value. And that gap is what prevents AI from becoming a true competitive advantage.
How are agencies currently calculating the impact of AI usage on project profitability?
In which areas of the agency are AI-powered tools already being used?
Does your agency have policies in place to regulate AI usage?
How integrated is AI into your agency’s day-to-day processes?
Do you believe AI is changing the way your agency generates revenue?
What challenges or issues have you faced when using AI?
Are you familiar with the concept of “intelligent agents”?
How do you currently manage access to and usage of AI tools within your agency?
Among the few who do measure it, how do they do it?
Does AI always improve margins?
Does your agency currently have a centralized platform or workspace?
