The growth of artificial intelligence hasn't happened overnight, but it does come with a lot of responsibility for business leaders. It's your responsibility to apply artificial intelligence applications and solutions in a way that is smart, ethical and economical.
Every single new application of technology brings its own challenges, and artificial intelligence is no different. You have a lot of decisions to make when it comes to implementing AI into your business, but choosing not to implement it at all is going to leave your business in the dust. With every other enterprise opting for better technology, you don't want your business to be left behind because you couldn't choose between GPU vs CPU. You need to constantly monitor artificial intelligence use in business and in light of this, here are some of the things that you should consider when implementing or even considering implementing AI into your business.
Consider cases that can deliver value quickly. Your return on investment is important, but your speed is actually more critical. One of the most common mistakes that many businesses make when selecting the first business challenge to solve with AI is choosing one that creates the biggest profit. That's not how this works. In fact, what you need to be doing is choosing the.Problem that is generating more value. Of course, your overall ROI will play a role in your decision making, but the cost of the solution should never exceed the value of its benefit.
Don't let it run wild. It seems like an obvious thing, but it bears repeating that you should always involve your business and stay connected with business throughout the life cycle of an initiative. If you decide to implement a new artificial intelligence program, then you need to make sure that you are watching like a hawk to see how it affects your business and people in it. There will be trade offs and decisions that will be needed along the way but you need to make sure that you are watching the implementation of this AI first and foremost.
Analyse your data. Any savings that your business may make by using artificial intelligence can be lost very quickly by the costs that are incurred. As artificial intelligence's success is so heavily dependent on data, you need to make sure that the data is both trustworthy and of a high quality. As such, data needs to be fed to AI in real time as the old data can generate insight that no longer has value.
Start small. When you're implementing new AI practices, it's important that you start small so that you get it right the first time. You're never going to have every program that you implement right immediately, but you can learn better when you go slowly and start small. Failure, when it does happen, must happen in fast order to learn what to correct. The stakes are high when it comes to artificial intelligence, so start small and don't go too big because you could end up wrecking your business for an experiment.
Measure your outcomes. IT projects are different from AI projects. AI is mostly using the quest to understand something we don't know. So it's important that you measure your outcomes and realise that you won't know what your output will be ahead of time.
