As a digital product team, we meet with a wide range of companies and help them create their fascinating products. From strategy development to testing and measuring the performance of the finished product, we are their trusted partners and as a result, we have a slightly deeper insight into the AI application challenges that SMEs face than individual companies. Based on our experience over the past ~1 year, we have gathered a few aspects that we believe all digital product managers should be aware of. Or at least those who want to implement AI solutions in their next digital product and don't have endless resources to do so.
The list is not comprehensive, we elaborate on the topic further in our latest white paper, “The AI Innovation Guidebook – Mastering AI Adoption in Digital Product Development.”
So, these 3+1 points are meant as inspiration for now!
Be honest: is your product idea validated?
Amidst today's hype, in many cases, companies are including AI elements in their products for the sake of AI. If AI alone were a salvation, there would be no problem with this hurry, but having an AI-driven digital product does not guarantee success. It can be dangerous if, in the euphoria of implementation, we skip steps that have already been proven to reduce the risks of digital product development. For example, the use of methodologies to assess the potential market value of a product, such as the lean startup framework, design thinking, etc.
Revenue only comes from serving validated customer needs, and of course, being effective in that with AI is a plus. Artificial intelligence and machine learning can and will bring innovation in many areas. It can be the vehicle for a technological revolution that will ripple through whole industries, but only if it can create value beyond itself.
Correctly assess the league you play in
Unfortunately, the rules are not the same for SMEs as for large companies. AI is currently an investment for everyone, but this type of development is only a small part of the portfolio of large companies. In contrast, the costs of AI development are much more burdensome for SMEs, as they represent a much larger share of their revenues. The strength of SMEs lies in the fact that they can be much closer to their customers than big corporations, so they can have expertise in niche areas. However, this means that they should be careful about developing core technology, and rather customize it to their needs.
Do you know who will actually deliver it?
AI is a resource-intensive field - whether you consider the technology requirements, expertise, data, or time. Before embarking on AI development, it is important to ask yourself whether you have successfully completed smaller data science / development projects, as it is not easy to leapfrog in this area. AI projects require researchers, data scientists, and engineers who understand advanced concepts such as neural networks, deep learning architectures, and language modeling techniques. Therefore delivery in an AI project will be a complex task for your company’s HR, legal, and accounting departments as well.
+1. Look even further
AI is not the only technology area with high hopes, other branches of applied science are also on an upward trajectory, many of them at an earlier point. Which of these will take off next is a matter of speculation, but there are many promising developments in robotics, climate tech, quantum computing, biotech, to name a few. It's worth looking at the whole tech landscape from time to time, because behind all the hype lies decades of continuous development.
If you want to take your product to the next level with AI integration and you want to do it in the most efficient way, then you should definitely read our latest white paper, about how you can master AI adoption in digital product development, or contact the Bobcats Coding team directly!
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