As we discussed previously, AI and machine learning do a great job in helping to manage and enhance images (i.e. automatic corrections, subject grouping, facial/object recognition, etc.).The features offered by AI-backed software are getting better and can tackle more problems at a staggering speed. Many product personalization solutions use it to enrich user experiences. In situations where images are already available for analysis, such as in archival solutions like Google Photo or mobile apps where photos are already on a single device, AI provides extraordinary value in cataloging user image collections in meaningful and useful ways. In some cases, they can even use that cataloging to propose products with limited design options.
The limited design option is the core issue here. AI is an excellent tool to extract a meaningful subset of images from a larger collection and can help to guess a sequence when there is a common context such as a wedding or a photo shoot. However, AI is not used to create a quality design or to make the best use out of the images.
Why shouldn’t we use AI for product design too?
To answer this question, let’s compare artificial intelligence versus “normal” software development by experienced developers. Both approaches provide a framework of tools to potentially allow for building designs, but neither knows anything about design. Artificial intelligence, more specifically machine learning, can figure out how to do something based on large data sets and success criteria. Building meaningful data and tweaking what is considered a “successful design” is extremely costly and time-consuming. Where AI shines is in scenarios where it’s non-trivial to codify what constitutes a good implementation. In the case of designing a book, however, we can codify what constitutes good design decisions. Safe and normalized margins, as well as aesthetics and content-driven layout rules, can be provided by design experts and implemented for a fraction of the cost, while still providing variety. Sure, the black theme with one photo per page works, but to keep feeding new, interesting, and relevant content to users is still much more cost-effective with traditional tools.
But in the end, we don’t have to choose between artificial intelligence and traditional software development. Both can be used in building great solutions. For example, we can use today’s artificial intelligence strengths to feed traditional algorithms with meaningful information, such as whether a photo has a higher pertinence than another, without necessarily having it determine where that photo should be on a page.
Using artificial intelligence to its full potential today is currently restricted by its total cost of ownership. It is a challenge to economically connect all the required building blocks to make the most out of all this data. Licensing costs, computing requirements, poorer performance, and a decrease to the bottom line revenue are key barriers to large-scale adoption.
A down-to-earth question that we should ask ourselves as an industry is whether all of those features that artificial intelligence bring are actually needed? In theory, having photos grouped by colors, discarding images based on quality, and using advanced metadata to make design decisions are great and make for amazing presentations, but will they actually drive the sale? What will make the customer excited, and will they even understand what’s going on? Should we educate end-users on what AI can bring to them, or should we just make things simpler for them?
We still have to make the case for individual artificial intelligence features, so that investing in them at this point in time can generate revenue and improve the user experience today. At the speed by which artificial intelligence innovations happen, we can expect the “financially viable” possibilities to continue growing. We can also expect more data on what works and what doesn’t so that investments can be justified.
Let’s also explore why aesthetics of AI-proposed projects are important and pose as a key barrier to widespread adoption. Imagine that a current AI system had to create a photobook from your photos. Would you let it choose which photos will make it (or not) without looking at them as well? Would you trust it to guess if this is for a gift, a wedding ceremony, or a simple family souvenir? Would you settle for only black pages, and centered images? Would you like to choose how much you want to pay for that product and the level of quality of the paper, binding, and so on? And, perhaps most importantly, do you believe those answers could be the same for everyone?
Fully automated product building requires pre-determined discrimination algorithms, which can either be based on “what usually works” or on carefully selected input from the user. So, can AI actually choose a logical aesthetic sequence without some contextual instructions from the consumer? Well, yes, as long as you’re okay with a generic book. And even then, making a generic book with traditional software development will be much cheaper and easier to improve as you learn about your specific market.
AI can aid in managing large data sets and making automated decisions, like triaging images and understanding what’s in an image, but again, at a high cost and with additional delays from the user’s point of view because of the required computer workload, especially in the context of an e-commerce store. Analyzing a large set of uploaded photos to regroup, categorize, and put into a pleasing sequence on websites can easily add a few dollars per created book, regardless of whether it was ordered or not. Is the additional revenue worth the financial risk? Consider the following:
- When shoppers enter a site and upload large amounts of pictures for their projects, yet on average 20% end up not purchasing, the business will incur computing costs that are not all offset by sales.
- Analyzing voluminous loads of photos can take several minutes. Consumers perceive this “crunch” as a significant production lag. Today’s speed-driven consumers don’t accept delays, even when warranted by complex analysis, and these delays affect conversion rates.
- The engineering cost of stringing and initiating these AI building blocks to create auto-suggested layouts is very expensive. Since the resources required are constantly changing, knowledge, technology, and future engineering requirements are scarce and pricey.
While Mediaclip is constantly appraising AI and other progressive technologies, we found it is more flexible and cost-efficient to write algorithms that perform intelligent tasks like regrouping photos, generating pleasing product themes, optimizing book layout scenarios, and interestingly placing one or many images while respecting their image content ratio on each individual page/surface, all at a reasonable price and with the support of expert design, while retaining the variety consumers are expecting.
