Lesson 4: Not all Roads lead to AI

Regardless of your stance on AI and whether it is a bubble, an opportunity, a savior or our potential doom, the reality is that it is here to stay. What hopefully will change, though, is how we talk about it. Remember how, during the Pandemic, all other diseases just seemed to take a break? All of a sudden, we could only get infected with COVID-19; Have a cough? COVID! Have a sneeze? COVID! Have a seat? COV- I mean CHAIR!

You get the idea.

Well, it is my impression that we have reached a similar point today in the realm of technology, where everything somehow falls under the umbrella of AI. Which brings us to today’s lesson: AI. It is NOT always the answer.

AI needs careful consideration

It is to be expected that nowadays we all gravitate with our problems towards an "AI-powered" solution. It is indeed everywhere. But taking an innocent look at the variety of AI-powered offerings today, it becomes immediately clear that this is a buyer’s market. Therefore, buyer beware. AI, by its complex nature, requires significantly more consideration before purchasing than other software. It is not a one-size-fits-all solution. It is definitely not plug-and-play!

Sidebar: If you hear someone say that they have a plug-and-play or out-of-the-box or insert-hypenated-words solution, hang up. Run. Avoid at all costs. You are staring down 12 months of billable work to get off the ground and a cool €500,000 minimum investment.

It’s the difference between going to the store to buy groceries versus deciding to start your own vegetable garden in the backyard. It is not just a matter of convenience and short-term need. It is a long-tail decision that requires careful, thoughtful consideration. Put it this way: AI is not a purchase. It’s a commitment.

Clarify your long-term ambition

So before moving any further, clarify your vision (read: long-term ambitions). What exactly do you aim to achieve in the long-term? How do you want the market to perceive your company? How is the market changing and likely to continue to change in your space? Will AI help you achieve that vision? Have you considered any alternatives?

For hints on the vision aspect of this, check out Lesson 1 again.

Define a clear purpose for your AI

Once you are clear on your destination, consider that AI is a complex and expensive commitment, so you need to minimize risks associated with it. Here is how you can get started and protect yourself from bad decisions. Before any resources or time flows into this, consider its purpose thoroughly. Put simply: what do you want it to do? Here are some examples to help you start out (and examples of when maybe AI is not exactly what you are looking for):

Production: We want to optimize processes within our manufacturing to reduce costs without affecting our product quality.

AI may seem like an obvious answer here, but there are other options to consider. Robotic Process Automation (RPA) has been used over the last decade or so effectively in optimizing and automating processes, particularly in well-defined, repetitive production and operational processes.

People: We want to streamline our recruitment process by implementing filtering systems for CVs and cover letters so only those that fulfil our criteria get through to a human recruiter.

While this may in fact not seem like a big deal, consider this: a large consultancy can actually spend an approximate €25,000 per potential recruit (this includes software costs, labor costs for recruiters, interviewers, travel costs for applicants, and so on). That is a significant investment into an uncertain new hire. An AI to help filter applicants could optimize this process and reduce costs significantly.

Portfolio: We want to constantly optimize our product portfolio based on live sales performance across our categories and market trends to ensure we are investing in products best suited for commercial success both today and tomorrow.

Let's break it down to better understand this complex scenario. We can start with portfolio strategy (likely best done by a dedicated team of experts). Add an element of market research (potential here for some AI-powered research and reporting). Finally, there is an aspect here of continuous improvement, which has AI written all over it. Ultimately, consider how the human and AI “resources” need to collaborate: all work done by your experts needs to be planned out and executed in a clear, structured fashion so that, by the end of it all, their outputs can become inputs for an AI learning model to ensure they no longer need to constantly supervise the portfolio and the market, but a learning AI can do that moving forward.

I know, this may seem a bit overkill for a blog post, but it is important to understand these nuances. Primarily so people stop asking me “can't we just use an AI here”? Sure, let me just check my desk drawer in which I keep semi-sentient robots. I am sure one will do. NEXT!

Audit your current architecture and IT environment; and adapt it as needed

Now that you know why you need the AI and you have a fairly good idea of what your vision for this tool will be, it’s time for some introspection: are your current IT architecture and databases suitable for your ambition?

An AI needs, above all, a database to learn from (a STRUCTURED database). That involves having your data architecture audited and, where needed, improved to fit the needs of your learning model.

While you are at it, also consider WHERE you will store that database. In the Cloud? Or will you set up your own servers? Where will you store these? How many will you need? How will you ensure their safety (both physical and in terms of cybersecurity)? Do you have in-house experts to manage these?

Long story short, it all starts and ends with data. If yours is not up to scratch, your AI will not have the environment it needs to get off the ground.

Map out in detail what jobs need to be done

Since your AI is “employed” to do specific tasks, start mapping those out similarly to when you hire for a new role in your company. Think through what the role (the AI) needs to accomplish: their responsibilities, day-to-day jobs to be done, etc. Do this at a very granular level, mapped out as a structured workflow. This helps down the road when programming your AI (or choosing an AI tool) by outlining clearly what it needs to be able to do, step by step.

Develop the model (or customize a purchased AI software)

In case you were keeping track: this is the point in the process where you can say “let’s get an AI” and actually mean it, because the one thing left to do is to go ahead and develop your model. This means either purchasing an AI tool that emulates the workflow you have outlined previously or developing one on your own. The tools are in place, the garden is prepared, the sun is shining, you are wearing your best farming clothes, it is time to get your hands dirty.

This is also the point where you will most likely need some outside support. But fret not, the steps you have taken so far will ensure that your partner in development will have a clear path forward and that you are aligned on what exactly needs to be done. It will hopefully minimize any misunderstandings along the way, manage expectations, and reduce the risk of needing to go back on your work and pivot your development. Hey, you can even go a bit crazy and be Agile in your development! Remember that can of worms? I do… I can never forget it.