Original article was published by Massimiliano Costacurta on Artificial Intelligence on Medium
Internet is the best place in the world to turn your suspicions into nightmares: suspecting your partner is cheating on you? Every Facebook Like will confirm it; Feeling a little dizzy? Dr. Google will immediately diagnose how many days you have left to live; Wondering if the Earth really might be flat? Well, you get the idea… Shifting to your workplace:
if you think your company should adopt AI, the web will serve you crucial “insight” on the imminence of your bankruptcy if you don’t act immediately.
Your initial enthusiasm into researching AI will soon be bogged down and paralyzed by concepts such as clustering, deep learning, random forests, SVM, LIME, SHAPS and other strange acronyms.
As you wade into the waters of AI frameworks, you’ll inadvertently find you’re out of your depth, trying to figure out why your AI searches produce results on Machine Learning (ML).
What differentiates AI from ML?
To clearly understand the difference between AI and ML, I personally like John McCarthy’s definition of AI, as it is very simple:
“AI involves machines that can perform tasks that are characteristic of human intelligence.”
Such tasks include things like understanding natural language, identifying objects in images, recognizing sounds and playing complex strategy games. I find this definition very powerful as it does not put any stress on the underlying technology. It basically tells us that AI is a glorified version of good-old process automation, which now includes human-centric processes, which weren’t possible just a decade ago.
ML, at its core, is nothing but one of the many technologies used to achieve AI. Disruptive, innovative, sexy… but still just a technology. If we don’t untangle this difference, we will find ourselves asking the mother of all incorrect questions: “what problems can I solve with ML?”.
This question unconsciously traps you into searching for the right problems for the technology at your disposal, which is never a good business approach. As the famous psychologist Abraham Maslow once stated: “if all you have is a hammer, everything looks like a nail”.
The problem is that your company might not need a nail at all.
Don’t get me wrong, I’m not saying you should never ask yourself this question, I’m simply saying that this should not be a part of any AI-strategy conversation. It’s a headache your Data Science team will be more than happy to take on.
How to NOT fail
After years of experience in complex supply chain automation, I’ve seen projects fail for reasons ranging from using the wrong technology, to manipulating dirty data or lack of team cooperation. While addressing these problems is clearly important, not understanding the logic behind decisions and overlooking their impact on the business are by far the most lethal mistakes.
Focus on the decisions you want to automate, not on the technology.
Decisions are the natural outcome of any learning process; we learn things to better react to the situations we face and to avoid our previous errors. At the end of the day, introducing AI in your company is nothing but allowing machines to transform your data into decisions. That’s why, in every successful project, we have always started with the end in mind, focusing on the output we wanted to create and asking ourselves: what decisions are we trying to automate? by how much and when do we want to improve the decision-making process we are looking at?
It’s all about Decision Automation
It is interesting how focusing on understanding decision logic led us to become increasingly detached from technological conversations. The term AI basically disappeared and was replaced with “Decision Automation”, which, while not a new concept, isolates the final outcome and its scope of work: enhancing the quality of our decisions and removing humans from the part of the underlying process which does not require judgment, creativity or control.
Let AI do (only) what it can do better than you.
Focusing on the decisions can greatly help us to build a simple framework that can better identify and tackle our next AI-project.
Start by asking the right questions
Some of the questions we might want to ask ourselves are:
Are we looking at operational or strategic decisions?
Operational decisions happen daily and repeatedly and are often boring and unedifying for the people in charge; they rely on well-defined rules or logic and are therefore the perfect candidates for automation. For example, saving time while reducing inefficiencies should be the focus of our attention when replenishing distribution centers, identifying fraudulent claims or non-performing loans. On the other hand, strategic decisions such as “should I make this investment?” or “should I partner with this company?” require unstructured insight, which quite simply cannot be automated, but only, as defined by Gartner, “augmented” by using the right technology.
What is the impact of wrong decisions?
Being able to shape the effect of wrong decision making, both in terms of lost money and people affected, is essential when prioritizing the tasks you want to automate. Experiencing recurrent out-of-stocks or overstocks might tempt you to jump into optimizing your replenishment process, while a large part of the problem might be due to an incorrect setup of your master data, which is affecting not only distribution, but also production, transportation and forecasting (yes, true story).
Can we decide fast enough to modify events in due course?
While the quality of our decisions might be good enough, the process involved to reach these results may be excessively taxing on the business. For example, most business-critical activities in the supply chain are still done manually or semi-manually and are consequently lacking in flexibility and resilience.
Do we know the logic behind the decisions? Do we know how and why something happens?
Here (and only here), we talk about technologies. If the answer is yes, technology can provide businesses with support through RPAs and rule automation for simpler tasks, and low-to-no code ETL tools and optimizers for the more challenging ones. If the answer is no, but there is an underlying logic, then ML can dig it out from the data. An example is customer churn analysis, as it is impossible to predict upfront what drives customers to leave, but that information is probably hidden inside the data.
Being able to provide quantitative answers, such as the number of decisions involved, the inherent cost of wrong decisions, or the man/hours needed to support the process, should be the gateway to automation investments.
These answers help us build compelling cases to convince management of its value and serve us as leading indicators, whether we are ready or not to invest in innovation and automation.