Lack of business alignment in AI projects
The AI project you’re about to start needs to align with your overall business. It’s not a separate part that lives its own life. A data-driven company should make the business/tech alignment its priority. Adoption of any technology should be done with a strategy, and with the wider business goals in mind. So… what can go wrong this time?
Scenario 1: I want to be the next Amazon!
Don’t just copy the giants. Copying the exact solutions that Amazon uses won’t guarantee that you’ll reach the same level of success. You won’t become Amazon’s number 1 competitor simply because you implement the same technologies.
It’s important to remember that each business is different. Even the “smallest” differences matter. And often, these differences are not even that small. Your business model may be similar to that of Amazon in terms of online retail. So yes, you can use a variety of AI-powered solutions to drive results for your business. Like Amazon, you can implement recommender systems, dynamic pricing, or many variations of specialized predictive models. But what you want to predict – what you implement – is chosen based on your business goals, your needs, the available data, your budget… There’s a whole variety of factors influencing the selection of the appropriate AI tech that will support your business.
Scenario 2: So I want to build a model doing X
When organizations say they want to deploy a given model, there are 2 possibilities:
- They did their homework and they know that this model will bring the results they need.
- They heard or read about this solution and want to experience the benefits it promises, but they lack AI know-how to have a full understanding of such solution.
In the first case, it’s all good. The match between an actual business goal and the right technology is the way to go. However, in the latter case, problems may arise. For example, if I have an e-commerce store, I may be tempted to implement a recommender system. I know that many other companies, no matter what they sell, do that, so it seems like a reasonable idea. However, I am not aware of the limitations and challenges posed by such solutions, and because of that, I also don’t know whether I have all the right data to make this model work. So I assume it will work for me because it works for company ABC. And this way, we go back to scenario 1.
Scenario 3: I really don’t know what to do with AI
Believe it or not, but there still are organizations that want to implement AI just for the sake of having AI. This is not the best place to start, but it’s not hopeless. In such a case, the AI experts’ job is to discuss the business needs, problems, and processes to identify potential use cases. When implementing AI, it really is crucial that there is a good match between the business and the technology – and not just in a problem-solution way, but also between the domain specialists (people who will be using the model in their everyday work) and the data science team (who rock at building models but may be unaware of the exact needs it has to be adjusted to).
Scenario 4: Reinventing the wheel
Not every AI project has to be built, even if it’s a good idea. The truth is that many of the tools you want to use have probably been implemented in the services you may already be using, e.g. a CRM. So if what you’re planning to build is basically the same concept that is easily available in a SaaS format, you have to consider all the pros and cons. It’s important to note here that off-the-shelf solutions are often not flexible enough to provide results similar to those brought by custom made models. There are certain limitations to these off-the-shelf products, and as long as it works for you, it’s fine. Again: if you’re not certain which way to go with AI, get advice from an AI consultant, a data science team, or an AI development agency. Making these decisions can be difficult, it’s good to ask questions.