What many of us assumed about AI and no one bothered to clarify

Illustration Clarifications on Artificial Intelligence

This is what many of us assumed about AI and no one bothered to clarify, it is certainly memorable for a company, the day it decides to undertake its first Artificial Intelligence (AI) initiative.

However, the vast majority of these companies immediately move on to the one step that seems natural: finding, recruiting and hiring the team of experts who will deliver on all those AI-based transformation promises that have been months, or sometimes years, in discussion.

From this point on, the human talent area is entrusted with a mission that seems insurmountable, due to the large number of uncertainties involved in building an elite data team, also known as Data Analytics, or better Data Science, or perhaps Artificial Intelligence.

Or let's be honest, we're not sure what to call it either. The questions that arise range from the simplest to the most far-reaching:

In this post we will talk about what we assumed about AI and nobody bothered to clarify and share some of the most frequent "assumed wrongs" companies make when building their elite teams, which can help adjust expectations to their rightful proportions. And, incidentally, to envisage actions that will increase the likelihood of success. Remember: at Datup, we Transform Data into Savings.
Illustration Business Experts vs Artificial Intelligence Experts

Business Experts vs AI Experts

The first aspect to be clear about is that the arrival of the team of analysts, engineers, data scientists with a laptop on their back is no guarantee of success for the AI initiative. In fact, their probability of success may remain at zero, just as it was before the team was assembled. Here, it is important to keep in mind that no matter how many PhDs, Masters, specialisations or Tensorflow certifications the team has, there will be one essential thing for which their training and experience will be limited: knowing and empathising with the pain or need of the business itself. The real high-value work of the AI team begins where the long work of the business managers ends. It is the senior and middle management who have dissected the frustrations that come with an inaccurate process, with late turnaround times, opportunities that leave money on the table, or a lack of knowledge of the customers they seek to understand and please. For this reason, it is the business leaders who must dictate the strategic direction, where the IA team is and will be the material author, but never the intellectual author. Knowledge and experience are different but complementary skills. At Datup, we help you save time and money in your supply chain using Artificial Intelligence.
Illustration Developing Artificial Intelligence vs Productivising AI

Developing AI vs Productivising AI

One of the greatest ironies I have had the opportunity to witness in multiple AI projects is the close dependency that is created between the data scientist and the data model.

This is because every model that supports a short or long-term initiative involves an initial design stage, followed by development, and finally support. The latter is the most misunderstood stage because 99.9% of models are created to be run more than once, in fact hundreds to thousands of times more.

However, the irony lies in the need for the data scientist to prepare, monitor, tune and finalise the execution. With the mitigating factor that the efficiencies in turnaround times and accuracy, measured in milliseconds and several nines after the point, respectively, are dwarfed by the couple of hours required by the scientist to get the AI model up and running.

This is equivalent to designing a car without an ignition system, where each journey requires the coordinated push of the driver and passengers to start the engine.

This situation stems from the fact that the roles and skills to develop an AI model differ from those profiles and knowledge to make the model a productive application with recurrent use. This gives rise to new concepts such as AutoML or MLOps, which will be the subject of an exclusive post.

Make the most of your data, save time and money in your supply chain using Artificial Intelligence with Datup.

Illustration An Artificial Intelligence model is a one-time effort.

An AI model is a one-time effort.

"AI modelling is a one-time effort". This is not only a common belief, but also a false one.

In an ideal world, AI teams have gained a deep empathy with the strategic business need, enabling them to develop a model that drives performance indicators into the green zones of dashboards.

Next, the model is put into production so that at each new analysis slice, it solves on its own and delivers green results, as it did the first time.

However, in this case, ideal is synonymous with unrealistic. The conditions under which a model performs are too highly variable to assume that the AI system will not require tuning as a continuous activity.

Leaving aside the most obvious reason which is the collection of new data with each run; the adaptation required by the model is mainly due to the ever-changing rules of the business game.

Here again, it is primarily the business experts who are responsible for noticing such adjustments to the AI model, and so the AI team is responsible for feeding the business readings into the model so that the results are consistent with the new context.

From here, it is easy to foresee that this dynamic, instead of tracing a straight line from point A to point B, describes a circular trajectory, a cycle.

If you have any questions or would like to know more about our Artificial Intelligence solutions, please do not hesitate to contact us!

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