By Dr. Tim Sandle
SCIENCE EDITOR
DIGITAL JOURNAL
April 16, 2026
Image: — © AFP
AI continues to grow and its use is changing the way we interact with computerised systems. Yet our interest in different forms of AI appears to be dropping as output matters more than input.
A new Use.AI survey of 5,800 professionals worldwide suggests that professionals are paying far less attention to which AI model powers their work than many in the industry assume. Here, 61% of professionals say they no longer actively distinguish between individual AI models when completing routine tasks. Furthermore, 55% say they no longer think about which model is completing a task, provided the output meets expectations.
In other words, factors like convenience and workflow efficiency increasingly outweigh model preference in day-to-day usage.
One AI platform is desirable
The findings also show that user prefer to turn to just one platform, rather than seeking to navigate across several. With this, 58% prefer using a single platform that provides access to multiple AI models rather than switching between separate tools.
In this context, 52% report that switching between different AI tools creates unnecessary workflow friction. There is also little attempt to 57% say they value being able to compare outputs from multiple models within one interface.
The data suggests this trend may have significant implications for the broader AI market: If users become increasingly indifferent to model branding, the companies building underlying AI systems may find that technical performance alone is no longer enough to shape user preference, particularly as access to those systems becomes increasingly mediated through third-party platforms and unified interfaces.
Outcomes matter
There is also a shift towards the use of more generalised platforms. This is also picked up by the survey. Only 34% of respondents said they still regularly use standalone AI tools for specialised or technical tasks, indicating that direct engagement with individual models is becoming more concentrated among power users and niche professionals rather than the broader workforce.
According to Ihor Herasymov, Managing Director at Use.AI, the data suggests professionals are increasingly prioritising outcomes over model identity.
In a statement sent to Digital Journal, Herasymov says: “The clearest pattern emerging from the data is that professionals are no longer thinking in terms of individual models; they are thinking in terms of outcomes. What matters increasingly is not whether a task is handled by GPT, Claude, or Gemini, but whether the system delivers the best result quickly, reliably, and within a seamless workflow.”
The trend points to a broader shift in the AI market as model branding becomes less relevant in everyday use and unified AI platforms play a larger role in how professionals interact with generative tools.
In the early stages of mainstream adoption, users often experimented directly with tools such as ChatGPT, Claude, or Gemini, learning the strengths and limitations of each platform individually.
Now, as AI usage becomes increasingly routine, many professionals appear to be moving away from model-by-model experimentation and toward consolidated systems that reduce friction and centralise access.
Hence product differentiation matters mostly to IT companies, and not so much for the business user. AI market may be entering a new phase in which technical competition between model developers remains fierce, but much of that complexity is becoming invisible to the average user.
April 16, 2026
Image: — © AFP
AI continues to grow and its use is changing the way we interact with computerised systems. Yet our interest in different forms of AI appears to be dropping as output matters more than input.
A new Use.AI survey of 5,800 professionals worldwide suggests that professionals are paying far less attention to which AI model powers their work than many in the industry assume. Here, 61% of professionals say they no longer actively distinguish between individual AI models when completing routine tasks. Furthermore, 55% say they no longer think about which model is completing a task, provided the output meets expectations.
In other words, factors like convenience and workflow efficiency increasingly outweigh model preference in day-to-day usage.
One AI platform is desirable
The findings also show that user prefer to turn to just one platform, rather than seeking to navigate across several. With this, 58% prefer using a single platform that provides access to multiple AI models rather than switching between separate tools.
In this context, 52% report that switching between different AI tools creates unnecessary workflow friction. There is also little attempt to 57% say they value being able to compare outputs from multiple models within one interface.
The data suggests this trend may have significant implications for the broader AI market: If users become increasingly indifferent to model branding, the companies building underlying AI systems may find that technical performance alone is no longer enough to shape user preference, particularly as access to those systems becomes increasingly mediated through third-party platforms and unified interfaces.
Outcomes matter
There is also a shift towards the use of more generalised platforms. This is also picked up by the survey. Only 34% of respondents said they still regularly use standalone AI tools for specialised or technical tasks, indicating that direct engagement with individual models is becoming more concentrated among power users and niche professionals rather than the broader workforce.
According to Ihor Herasymov, Managing Director at Use.AI, the data suggests professionals are increasingly prioritising outcomes over model identity.
In a statement sent to Digital Journal, Herasymov says: “The clearest pattern emerging from the data is that professionals are no longer thinking in terms of individual models; they are thinking in terms of outcomes. What matters increasingly is not whether a task is handled by GPT, Claude, or Gemini, but whether the system delivers the best result quickly, reliably, and within a seamless workflow.”
The trend points to a broader shift in the AI market as model branding becomes less relevant in everyday use and unified AI platforms play a larger role in how professionals interact with generative tools.
In the early stages of mainstream adoption, users often experimented directly with tools such as ChatGPT, Claude, or Gemini, learning the strengths and limitations of each platform individually.
Now, as AI usage becomes increasingly routine, many professionals appear to be moving away from model-by-model experimentation and toward consolidated systems that reduce friction and centralise access.
Hence product differentiation matters mostly to IT companies, and not so much for the business user. AI market may be entering a new phase in which technical competition between model developers remains fierce, but much of that complexity is becoming invisible to the average user.
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