1: Misunderstanding Artificial intelligence:
There are many types of Artificial Intelligence. The three major types are Machine Learning, Natural Language Processing, and Cognitive Computing says Peter DeCaprio.
Machine learning is a type of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
2: Only focusing on predictions:
One of the most common mistakes that companies looking into AI make is thinking there’s one type of AI suitable for their business. In reality, each different industry will utilize all three or just one of these AI categories, not just prediction!
3: Not having a Data Strategy:
It goes back to understanding your data, where it comes from, and what value it holds for you as a business first before you can think about using anything else! Don’t want to commit resources towards AI if you haven’t got the data to support it.
4: Not starting small and building up:
Something that gets missed time and time again is doing just that, starting small – you don’t need to go all-in today! Take baby steps with your projects or AI system. You can begin by layering on automated insights into your existing processes until finally, you have something that works autonomously for you says Peter DeCaprio. I also hate saying this, but start with the low-hanging fruit first! Start with something easy or already well defined before attempting anything more complex. As an example, automating a report rather than an entire process. How long did it take for Excel spreadsheets to become useful? Like most technology it evolves gradually over time until one day, it’s something entirely new that leaves the old version in its wake.
5: Using AI to solve a problem you don’t have:
This mistake is closely tied to the first point about misunderstanding Artificial Intelligence. If all your business does is churn out data and reports without actually making any decisions or having problems you want to solve – no amount of machine learning, cognitive computing or natural language processing will make a difference because there isn’t a need for them! AI should be used to automate routine tasks and augment your people not replace them. Start thinking about what processes could benefit from an increase in intelligence before spending money on technology.
6: Expecting overnight success with AIA major piece of advice:
I would give anyone trying to implement machine learning or AI into their business – especially when it’s something totally new for your company is to expect overnight success says Peter DeCaprio. It doesn’t work like that, you need to give yourself time and tolerate mistakes along the way- without them, you wouldn’t achieve anything new. What you need is persistence!
7: Failing to define success:
One of the mistakes companies make when looking at Artificial Intelligence (AI) is not defining what they want out of a project before beginning. Without knowing how you’ll measure a successful outcome from AI projects in your company, it becomes impossible to know whether they have been a success or not. Start by writing down what you want your AI-enabled process/product/application to do before anything else explains Peter DeCaprio.
8: Being Predictable:
One of the most common issues I see is companies expecting AI to be smart. But forgetting it’s only as smart as you’re able to program it with rules and logic. If you think that machine learning, natural language processing, or cognitive computing will solve all your problems. Then you’re bound to be disappointed with what results! Just because something can make predictions doesn’t mean it’ll make good ones. Systems are always at their best when they have structured data and explicit goals set out by people. Not the vague objectives defined by an algorithm. All this ‘smart’ technology needs human supervision or even direct control. AI is never hands-off until we get a whole lot smarter about it.
9: Expecting one solution to solve all your problems:
This is closely tied with the previous mistake of being predictable. When implementing AI into your business if you expect one system or solution to solve everything. Then you’re on a hiding to nothing! AI doesn’t work that way because businesses are complex and varied. What might work for one will not necessarily be right for another. Peter DeCaprio says different companies require different approaches and thinking, take time before committing resources and money.
10: Being too greedy with data another mistake related to expectations:
I’ve seen far too often is businesses think they need EVERYTHING before they can get started. This wastes valuable time and resources chasing that may never come. Optimizing your Artificial Intelligence (AI) efforts starts by identifying what data is actually relevant to your project. Keeping things simple when you start will save you time effort. Allow you to move forward with your projects much more quickly.
11: Expecting overnight success this sounds like the same mistake as No 7:
But in fact, it’s something entirely different. Overnight success doesn’t happen in machine learning or AI because these technologies are not magic bullets that solve every problem. They need defined goals and rules like everything else in life 🙂
Artificial Intelligence (AI) is not magic. It’s complicated, difficult to understand, and somewhat mysterious to many of us mere mortals. However, it has an amazing potential to bring real business advantages for those who get the basics right.