Agile experiments are important: you learn best by doing. Therefore, organizations shouldn’t think too much about what the aim of agile experiments is – the most important thing is to experiment, and to do it fast. Moreover, agile experiments shouldn’t be left undone because an organization doesn’t have its own experts. On the contrary: usually it’s much better to outsource agile experiments to a skilled consulting company.
Now especially it’s time to experiment with artificial intelligence (AI) without hesitation. Plenty of experiments were done in the 1950’s to 70’s, again in the 80’s and again now. You will miss out if you don’t act fast.
If you won’t utilize chatbots, organize AI hackathons, or develop AI enhanced mobile applications, you’ll be left behind from your competitors – or at least the grass seems to be greener on the other side of the fence.
Or, should you first focus on your own goals?
Maybe you should first think about your organization’s targets. What are the most important cases that have the most impact, where the workload and level of difficulty are moderate? Those that might not in the end need AI. What’s the actual problem that needs to be solved?
It often feels like it’s more important to show that you’re riding the wave of the trend rather than actually accomplishing something. What used to be normal software development has suddenly become AI.
AI is a good term, especially for consultants. It means nothing and everything at the same time. And, of course, this functionality costs more as it uses AI. Nothing can be guaranteed, of course, and data needs to be in order and accessible. And if legislation forbids putting a good idea into action, oops, what a pity.
AI is as dumb as the data that’s used to train it
Still, companies should, latest now, think about their goals regarding data-driven business, data strategy and AI. Unfortunately, the silo between hype and reality is the widest it’s ever been and investments are easily spent on various technology experiments.
There are several AI and data science professional that have graduated, or will graduate, from Finnish universities. They might not have the full understanding of the reality of the corporate world yet, but on the other hand if you’ve been working for two decades or more and haven’t bothered to learn AI in the level it requires, it’s hard to grasp what it’s really about. That’s why you should hire fresh talent, sit back, and listen. Of course, you can dedicate two hours every Friday afternoon for the topic and hope for the best. This hype too will pass by, and we can focus on the next one.
The AI ship has sailed – but new ones come every day.
But before you act, think about which ship you should take to get to your destination. Also, it does not harm if your data is in order, as AI is as dumb as the data that’s used to train it.
About the Author:
Jyrki Martti, Senior Advisor, is responsible for developing Sofigate’s data analytics concepts. Jyrki is interested in data science and machine learning. And especially how companies, municipalities and other organizations can develop their operations by the means of data analytics and machine learning.
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