“AI can’t deal with emotion”: We used AI as our photography assistant for a week
AI is already built into most modern cameras, from autofocus tracking to in-camera noise reduction, but with the speed of advances in AI, it’s now possible to use AI before you even pick up the camera.
So-called “agentic AI,” in which AI positions itself as an assistant in daily life, is increasingly popular. Naturally, we were curious to see if its usefulness extended to the life of a photographer, and what we could learn from it about how AI positions itself as a companion in all fields.
In this article, we detailed the results after a week of using AI as a photography assistant from start to finish. AI was used during pre-shoot planning, suggested camera settings on the go, verified AI-based weather forecasts for low-light landscape photography, and was integrated into our post-processing workflow for noise reduction and sharpening.
The results have been mixed, but our experience with AI as a photographer’s assistant has revealed a clear picture of where it truly deserves its place in a photographer’s toolbox alongside the best cameras and lenses – and where a photographer’s eye still matters most.
Using AI for Planning Before Shooting
Last fall we had the idea to take landscape photos on a hill near our house, just a short walk away. If we did it in the right weather and at the right time of day, we knew we could achieve great results. By providing our AI assistant with details such as location, subject, time of day and equipment, we wondered if we could quickly generate an idea of how we might approach the location and what type of images we would look to capture.
First things first: we gave him the coordinates of where we wanted to shoot. First mistake: the location was completely wrong! But with some guidance, we narrowed it down to roughly where we planned to shoot. Our AI assistant then offered suggested viewpoints, likely focal lengths, optimal shooting windows based on sunrise and sunset, and, very helpfully, reminders about access constraints or parking considerations.
While not an overnight scenario, for astrophotography we can see AI proving particularly useful in boiling down complex planning variables into something digestible. If you gave it a location, it would definitely be helpful in telling you where in the night sky and at what angle the object you want to photograph would be located.
One thing that stood out was the speed. Rather than jumping from one app to another, from an image library to a Google search, the AI condensed the information into a single response. It can’t replace specialist scheduling apps, but it worked well as a quick first pass and gave a generalist point of view, offering a way to check whether a shoot was worth attempting before spending more time planning.
As he received prompts over a known area, we could also cross-reference and measure his responses. Fortunately, and reassuringly, most of his conclusions were correct.
Camera settings
Then, the AI was used to recommend camera settings for different shooting scenarios at different times of the day, with different objects in focus: a photo of the city below the hill, an image of the landscape around the hill, etc.
Camera model, lens, subject, and time have all been specified, and we’ve received suggested starting points for ISO, aperture, and shutter speed, with helpful reasoning behind each suggestion. This was one of the most interesting elements of this part of the test: the AI laid out its thinking as part of the results and even managed to point out some specific features of the landscape (such as the limestone roofs in the chosen view), which gave confidence that it knew where we were and what we were asking.
His recommendations were generally sound. He tended to prioritize safe shutter speeds and conservative ISOs, producing usable images right out of the box, but did not think particularly creatively, emphasizing that some experimentation would be necessary for different scenarios. This was a use case under normal conditions, if there was more sun, more clouds or different weather conditions. For beginners, this type of advice could really boost confidence.
Naturally, the difficulties AI faces are in cases that simply cannot be predicted. This left us thinking that AI can suggest technically sound parameters, but it cannot base its responses on intent. It doesn’t know whether you’re willing to accept noise for a decisive moment or sacrifice sharpness for atmosphere, for example: those decisions have to be made on the fly.
Shooting conditions
One of the most promising uses we found for AI was interpreting weather and atmospheric data. Rather than simply presenting forecasts, AI could explain why certain conditions are important.
Starting with a simple prompt (“Can you look at the weather forecast for tomorrow and suggest what we should consider at 11:00”), it resulted in a comprehensive (and, depending on weather apps, accurate) interpretation of the weather, what the prevailing conditions meant about the appearance of the landscape, how it would affect camera settings, and even what we would need to carry in our bag to avoid the effects of passing rain showers. It sounds simple, but we found that this checklist helped us make sure we had everything we needed with us. Sometimes you need someone to point out the obvious.
While testing the AI for night sky work, it summarized satellite images and saw predictions as well as forecasts, which was really helpful. It translated technical measurements into plain language, making it easier to decide whether a marginal forecast was worth it.
It is worth emphasizing, however, that AI cannot really replace specialized tools. Applications dedicated to astronomy and weather services remain more scientifically precise, but also more transparent about uncertainty. AI has a habit of overcoming or mitigating ambiguity, presenting what should be done under different conditions with more confidence than is truly warranted.
Post-processing
Post-processing is the area where AI appears most mature, and many image processing tools now integrate AI into their software for tasks such as noise reduction, sharpening, and spot removal.
In a landscape environment, the greatest advantage was selectivity. AI could aggressively reduce noise in flat areas while preserving detail and texture where it counts. Sharpening algorithms are just as nuanced these days and could significantly improve detail without introducing artifacts.
However, in our experience, it must be recognized that AI can be authoritarian. Images can take on a synthetic look when AI is overused: too smooth, too sharp, and strangely lifeless. If taken correctly, most images require only subtle adjustments in post-processing, and the human eye is still much better than AI at doing this.
Where AI helped and where it failed
After using the AI before, during and after filming over the course of a week, certain patterns became evident. It was excellent in technical optimization, preparation and organization. After some research, with the right background information and prompts, he knew the location we wanted, he knew the weather forecast, he knew what time of day would be best for the photos we were looking for, and he even knew what landscape features we could focus on in a given location. It also helped resolve logistical issues, such as parking and access.
However, the AI doesn’t yet understand why a photographer might break the rules. Sometimes imperfect conditions work. AI can’t manage emotions – it just knows a set of technical “rules”. AI also doesn’t understand local nuances like microclimates and real-world unpredictability, which are exactly the kinds of things photographers want in their photos.
Overall, using AI as a photography assistant for a week didn’t improve our image production, but it made us more efficient. We came to think of it as an office assistant, and it helped us plan and refine technical decisions such as what types of lenses to package. Having a weather-specific gear checklist was also surprisingly helpful.
One thing we found particularly useful was asking our AI assistant to shorten its responses. The AI has a habit of trying to cover all bases and come up with fairly lengthy explanations for things that don’t really need to be decided in advance. This takes some of the magic out of the process, but if you ask him the right questions, he can present the information he provides well.
AI was a tool for asking better questions, not answering them. We found that his responses sometimes included things we hadn’t even thought of, but that’s really the point. If you engage with these answers and ask questions about yourself and your own photographs, it can be very helpful.
If you’re hoping this will replace legwork, real-world experience, and human judgment, then — luckily! – you risk being disappointed.
In accordance with Live Science’s Artificial Intelligence Policy, while the use of AI is the subject of this article, it was not used in the creation or production of the article.
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