Fatih Nayebi

Before building any AI, ask whether you have a brain problem or a muscle problem.

So whatever we have a problem that we want to solve, we look at it from if it's a brain problem, intelligence problem, or is it just a muscle or just application or process problem?

Gen AI generates. It doesn't predict. Retailers still need both.

The prescriptive and predictive models are going to be still very important because the gen AI, of course, is not there to predict things, but more so to generate things.

Agile won't take care of your data practices for you.

Many of us, we come from the engineering background with application development experience or data engineering type of background. And sometimes we neglect the importance of the data itself. If we do agile development, agile by itself is not going to take care of the practices around the data. So you need to infuse that.

Not every AI project should reach production, and that's normal.

We need to experiment things. We need to try things. And not everything that we do is going to go to production because sometimes we find out we don't have the data or the expectations are far from what we're getting.

The most useful AI hire may be the person who can say what AI can't do.

To ensure that you have the right approach, you need people that they can bridge the gap between the technicality and the business side of it so that they can transparently explain what can be done and what cannot be done.

A hackathon idea: talk to a shoe website instead of typing keywords.

So the key idea was to make the search purely conversational and trend-aware. So instead of just you go to website and do some keyword search, you could talk to the website and then explain what are the type of products that you're looking for.

Generative AI doesn't understand physics, which is a problem when designing a store.

So, one of the challenges with Gen.ai today is it's not physically aware. And there are going to be physics-oriented machine learning models that they can understand physicality, dimensions, volumes, the capacities, and so forth to be able to match things accordingly.

Data good enough for daily decisions is often not good enough for machine learning.

The data sometimes is not managed accordingly to become a data for AI. So, in most of the cases, you have the current data that is well managed and has a high quality to do day-to-day decisions or maybe near, you know, past, near future type of decisions. But once you want to make machine learning models, you want to have many years of data that are very quality.

Tell customers how, why and what they get in return for their data.

One thing for sure is you want to be transparent. You want to showcase how you collect the data, why you collect the data, and what would be for the client in return.

Machine learning for retail doesn't need to know who you are.

For machine learning, once you have this established, you don't need ID type of information, right? You want to have a data that is at the aggregate level, can be used for modeling. So, you want the data to be anonymized such that it does not include any identifiable information and then use that for the modeling.

AI may become the interface itself between people and computers.

I see AI to become a UI between the human and the computer, like from the human computer interaction point of view. To simplify many things, we've been doing programming or we've been trying to create all these user interfaces, always to be able to make computers work for us.

Fine-tuning, then a homemade RAG, then OpenAI's API: one retailer's assistant journey.

We needed to implement first the fine-tuning, but then later on to implement, you know, with pine cone, a RAG from the scratch. And then finally we moved to the OpenAI's assistance API, and now that's behind the scene.

Four layers of metrics to know whether an AI product pays off.

So, at the end of the day, whatever you do, you want this to have an impact on the bottom line, either through profit or we want to measure the ROI for each of the products that we build. But then, to be able to get there, you always have a sort of operational metric.

In retail, the AI that matters most is the unglamorous kind: forecasting and markdowns.

The examples I provided to you, like revenue growth management approaches are like, it's just the bread and the butter in the retail. And these are the ones that are the most impactful ones.