Summer vacation is here. For our family, this means a trip to the summer house. After arrival, we
concluded that we have too much stuff inside the house, but also an empty wall. We are in Sweden, so
the solution, can of course be found at IKEA. The nearest IKEA store is a long boat ride away, so
let’s go e-shopping. I know roughly what I would like to buy, so it shouldn’t be difficult,
right? In the end, I was able to get a bookcase, but the path was riddled with artificial
stupidity – missed opportunities where only slightly more intelligent systems would have smoothened
the shopping process, providing value to both customers and to IKEA.

(Clarification added in hindsight: The term “artificial stupidity” refers only to misbehaving
machines, as in the examples below, and is not a statement about any human decision making. I place
no judgement on the actions of any individual or team. The purpose of this post is merely to shift
focus when discussing data-driven products.)

IKEA, I apologise for using you as an example here. It so happens that I am fond of the company and
your products. I buy most of my furniture from you, so it is merely one example of interaction with
a supplier that was born before the internet, and you happened to provide a suitable narrative. The
scenarios below are typical, and representative of my other private service suppliers as well:
Skandiabanken, Volvo, Bilia, Trygg-Hansa, etc. I chose to share this story, since it illustrates the
discrepancy between the current focus on AI and data science, and what challenges traditional
companies need to overcome first.

In each of the artificial stupidity cases mentioned below, the missing functionality is not complex,
nor does it involve any AI, machine learning, large scale stream processing or other complicated
technical solution. What was missing was either data that needs to propagate from one internal
system to another, or information that is easy to compute with simple means. In this article, IKEA
receives a bashing, which is completely unfair; they are far better than their direct competitors,
and the missed opportunities described below are typical to most mature Scandinavian
companies. Meanwhile, the same companies announce new AI strategies and fiercely compete with each
other to hire data scientists and machine learning engineers. But these new hires are unlikely to be
effectively used unless they arrive in a company that already masters the foundations of data
processing, which Monica Rogati eloquently described in theAI hierarchy of

My search for a bookcase starts, where I navigate to the
bookcase section, in the hope to get an overview of the different series. I find instead a listing
of all 257 bookcase products, including accessories, such as hinges and extra shelves. The full
listing not give me an overview, however, and I do not find anything useful.

I went to the Swedish site, but some screenshots are from the UK site for the benefit of non-Scandinavian readers.

Looking for bookcases

I went to the Swedish site, but some screenshots are from the UK site for the benefit of non-Scandinavian readers.

An impatient customer
could have given up at this stage, but I persist and pursue other paths, as described below. I am
probably not alone trying this path, giving up, and moving on to another strategy, or leaving the
site completely. There are ways to address lost customers, such as user journey mapping – analysing
the paths that users take during their interactions with a site, looking for common anomalous
patterns. It requires the capability to collect user behavioural data, connecting the data points to
aggregations of user journeys, and present it in a actionable format. It involves multiple systems
and teams, and getting data to flow between them. It does not, however, require any advanced
computations or machine learning.

I move on to a source of product information that I have known since I was a child. The IKEA
catalogue is a masterpiece of 20th century product marketing, and a mere glance will make anyone
enchanted and filled with desire to furnish a home. I cannot see IKEA’s full bookcase offering here,
but I get inspired and find something similar to what I want – the classic series “Ivar”, which is
suitably rustique for a 1970’s Scandinavian summer house. The catalogue does not contain an Ivar
model of appropriate size, so I try clicking in order to get to the Ivar web pages. But the
catalogue is essentially a series of jpegs embedded in a web page, without any hyperlinks – another
example of data not properly flowing from one system to another. I am convinced that the team
creating the web catalogue would have been able to add hyperlinks, if they only had access to product
identifiers from the team in control over the catalogue contents, as well as a reliable map from
product identifiers to URLs.

I go back to the web product directory. I enter “Ivar” in the search box, and get 101 results back,
in what seems to me like an arbitrary order. There are no bookcases in the first screen of items,
mostly accessories and matching chairs.

The search result page gives me the option to specify sort order, which is “best match” by
default. I ask for the items to be sorted by descending price, and find the large Ivar variants that
I am looking for. Sorting by price worked in my case, since I was looking for one of the largest
bookcase combinations. Had I been looking for one of the more common bookcases, however, I would
have spent more time searching. In that case, it would have been more helpful if the search results
had been sorted by popularity. But in order to measure and rank results by popularity, data needs to
flow from the web and checkout systems, where it can be collected, to the search indexing and
ranking system.

Personalisation is a popular subject these days, and recommendation systems have become common as
well as complex. This use case might seem like a good candidate for some personalisation
AI. Furniture are infrequently bought items, however, so it is difficult to obtain a good user
profile. Imagine the clickbait that some heavily personalised popular applications would present if
they knew you were looking for bookcases:

  • Amazon: “You must be a bookcase collector! I will suggest bookcases for the next three months.”
  • Instagram: “50 crazy ways to pimp your bookcase.”
  • Facebook: “Provocative racist bookcases that will make you angry.”
  • Youtube: “Spectacular failures while putting up a bookcase.”

For furniture shopping, simple data-driven ranking would serve us better than a complex and fancy

I put Ivar in my virtual basket, and log in to IKEAs “My profile”, where personal information,
such as home address and IKEA Family bonus club number, is already stored. I proceed to check
whether Ivar is in stock (“I lager” in Swedish), since I would prefer to pick it up at a store in
order to quickly get my hands on it. Since I have saved my home address to my profile, and I make
most of my IKEA shopping at the nearest IKEA store, “Stockholm Barkarby”, I expect that store to be
selected as default. But this data is not propagated to the web shop, so I have to select the store
that I am interested in.

There is one item in stock at the store, great. I proceed to checkout, where I need to fill in my
zip code (“postnummer”), even though it is already present in my profile. But it is not readily
available to the team and the system that handles the checkout.

I select “pickup at store”, and the page requests me to

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