The use of generative AI at work jumped from 34% in 2024 to 62% in 2026, according to the Hays Guide 2026. In Brazil, this advancement also scaled up: in January 2026, 12.3 million people were already using AI tools in professional activities, a 340% increase compared to 2024, as reported by IBGE.
This progress shifts the focus of comparison. The question of “the best generative AI” is no longer a generic brand dispute but depends on benchmarks per use case: text, prompt creation, design, customer service, and execution of more complex workflows. In 2026, available data shows a market less centered on a single winner and more oriented by operational specialization.
The Market Has Moved Beyond the Experimental Phase
Accelerated adoption is quantitatively based. The Hays Guide 2026 shows that professional use of generative AI platforms doubled in two years, from 34% to 62%.
The same data points to a broader organizational shift:
- 52% of organizations promote the use of generative AI in 2026, up from 27% two years prior
- 67% of organizations identify productivity and efficiency as the main benefit
- 52% cite idea generation and creativity
- 49% mention support for data analysis
- Among workers, 64% highlight productivity and efficiency
- 48% mention idea generation and creativity
- 39% point to support for data analysis
There is, however, a significant mismatch. Only 27% of workers claim to have received specific AI training, although 90% express interest, also according to the Hays Guide 2026.
This data matters because benchmarks without operational context produce an incomplete picture. A tool may perform well in comparative testing and still generate little impact if the company lacks sufficient processes, training, and integration to absorb the gains.
The economic dimension helps explain why this debate has gained priority. McKinsey estimates that generative AI could add between 0.1 and 0.6 percentage points to annual labor productivity growth by 2040, depending on adoption and time reallocation.
In General-Purpose Benchmarks, ChatGPT and Claude Appear in Different Positions
When the focus is general productivity, ChatGPT remains a practical benchmark. In TechTudo comparisons, it appears as a competent generalist tool for creating prompts and resumes.
In the resume test, the publication describes ChatGPT as “the most practical solution” for beginner users, with “direct and competent” text, though “slightly more generic” in some cases. In the prompt comparison, the same publication treats it as a generalist option that serves everyday use well.
Operationally, this positions ChatGPT as an efficient initial layer for broad text production tasks. It tends to work well when the priority is speed, accessibility, and coverage of multiple uses within a single interface.
Claude, on the other hand, is better positioned in the specific benchmark for prompt creation. According to TechTudo, “Claude performed best in the test.” The publication also describes it as a generalist tool “capable of even generating video.”
The practical takeaway is clear:
- ChatGPT: strong in generalist use, text productivity, and support for tasks like resumes and prompts
- Claude: better performance in the cited prompt creation test, with a generalist profile and greater multimodal versatility according to the source
This does not authorize an absolute conclusion about “the best model” in all scenarios. It does, however, authorize a more useful conclusion for businesses: in 2026, available benchmarks favor choices by task, not by brand reputation.
In Image and Design, Comparison Depends More on Workflow Than Model
In the visual field, available sources point less to a direct dispute between models and more to the incorporation of AI into already established platforms.
Adobe Express offers image generation with Firefly, background removal, and automatic resizing. The free version includes these features, but with monthly limits for AI functionalities, as well as restrictions on some templates, fonts, and exports.
Figma, described as the industry standard for UI/UX, integrated AI functionalities in 2026. This move is relevant because it shifts the comparison: instead of just evaluating which model generates the best isolated output, it becomes important which tool best fits the real workflow of design, prototyping, and collaboration.
There is another important signal outside the traditional corporate productivity environment. In March 2026, Nvidia announced DLSS 5 with a focus on realistic AI-generated visuals. The announcement reinforces that visual benchmarks also advance on specific fronts, with their own performance criteria.
For marketing, product, and design teams, this suggests three objective interpretations:
- visual benchmarks cannot be reduced to text or prompts
- integration into the workflow weighs as much as generation quality
- tools with embedded AI gain traction because they reduce operational friction
Customer Service Already Shows Operational Result Benchmarks
While text and design still focus on tool comparisons, customer service already offers a benchmark closer to what operational leaders need to measure: process effectiveness.
Illumia, a company within the Waiken ILW holding, reports serving over 10 million people in Latin America with generative AI conversational solutions. For clients like DIRECTV Latin America and SKY Brasil, their AI agents achieved 80% effectiveness in complex transactions and reduced call recurrence by 20%.
These numbers shift the focus of comparison. Instead of just asking which model writes better, the discussion becomes which architecture delivers less rework, fewer bottlenecks, and higher first-contact resolution.
Daniel Figueirido, CEO of Illumia, describes this evolution: “Generative AI is not just about responding; it's about reasoning and adapting in real-time to solve real problems. With Accenture and Eleven Labs, we created agents that not only perceive the emotional context of each interaction but also resolve issues autonomously, with the closeness of a human conversation.”
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André Barreira, director of Accenture Latin America, adds: “At Accenture, we support the integration of advanced Generative Artificial Intelligence capabilities, conversational experience design, and technological scalability, with a clear focus on humanizing every interaction. These types of solutions demonstrate how AI can combine operational efficiency with empathy, tangibly transforming the relationship between companies and their customers.”
For businesses, this is a decisive point. The most relevant benchmark is not always that of the most popular interface, but that of the operation that reduces recurrence, automates complex steps, and improves service indicators.
The Next Comparison Is Already Shifting from Isolated GenAI to Agentic AI
Beyond tool comparisons, 2026 also marks an architectural shift. Part of the debate is already moving from isolated generative AI to agents capable of executing more complete workflows.
Maurício Fernandes, CEO of Dedalus, summarizes this transition directly: “Yes, generative AI is no longer the frontier of technology. If you have already implemented solutions with GenAI, or if you haven't even had time, well, it's time to understand what's happening. [...] There is a belief that environments with agentic AI will be the new architecture for complex systems to solve equally complex challenges in a way that seems simple, in natural language, with superior autonomy and efficiency.”
This movement does not invalidate current benchmarks for ChatGPT, Claude, Adobe Express, or Figma. It changes the decision criteria for companies with more mature operations.
Instead of just asking “which model responds best?”, the evaluation now includes questions such as:
- which tool integrates best with existing processes
- where AI measurably reduces operational bottlenecks
- which workflows require custom automation, not just interface use
- where governance and training still limit real gains
Adoption data reinforces that this transition is not marginal. Brazil already has 12.3 million professionals using AI at work, and Brazilian Generation Z ranks second globally in generative AI adoption among young people aged 16 to 24. According to the Digital 2026 report by Manochi, 70.1% of this group uses ChatGPT monthly.
In other words: the usage base is growing rapidly, but the maturity of application still varies greatly between individual consumption, personal productivity, and intelligence integrated into company processes.
So, What Is the Best Generative AI in 2026?
The most precise answer, based on available sources, is this: the best generative AI in 2026 depends on the benchmark that matters for your use case.
If the focus is an objective summary of the comparison, it is as follows:
- For generalist text productivity: ChatGPT appears as a competent and practical tool, even for beginners
- For prompt creation: Claude performed best in the test cited by TechTudo
- For operational design and visual production: Adobe Express and Figma gain relevance due to AI integration into the creative workflow
- For customer service and journey automation: the results reported by Illumia show a more concrete operational benchmark, with 80% effectiveness in complex transactions and a 20% reduction in call recurrence
- For more complex environments: the discussion is beginning to shift towards agentic AI and more autonomous architectures
For leaders in operations, IT, and digital transformation, this is the central distinction. Generalist tools solve part of the problem. Consistent gains in productivity and operational quality depend on integration, process design, and tailored use.
What Comes Next
The next market milestones point to five areas of focus:
- evolution of AI agents with greater autonomy to execute complete workflows
- advancement of AI adoption frameworks at scale in companies
- pressure for training, given the mismatch between use and education
- greater weight of governance and ethical criteria in implementation
- continued incorporation of AI into established work and creation platforms
The best generative AI in 2026 is no longer necessarily the one that impresses most in a demonstration. It is the one that delivers measurable productivity, reduces bottlenecks, and fits into operations with integrated intelligence. For businesses, this comparison has become less about isolated tools and more about real results. To delve deeper, see our article on AI diagnostics for businesses.
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