When The Rise Of AI Meets The Ease Of No-Code
Not too long ago, professional web designers wouldn’t dream of using a no-code website builder—if you didn’t personally write each line of HTML and CSS, could you really call yourself a real designer? Today, many professional web designers have enthusiastically embraced no-code solutions, using them to get more done in less time without sacrificing quality.
Similarly, we’re now seeing advanced artificial intelligence (AI) tools combined with the ease of no-code platforms. These new solutions are changing the way we use data and opening up exciting possibilities for all sorts of businesses.
In the first generation of AI analytics, companies were primarily concerned with collecting and storing data. Database tools like MongoDB, SQL and Redshift were soon developed to help software engineers with this task.
The next step was to look at what we could learn from all this data. In this second generation, data visualization companies like Periscope Data, Mode and Tableau came along to help businesses make sense of the data they were collecting.
Today, we’re in the third generation: predictive analytics. Here, people can use AI to find patterns in their data and predict what’s coming next. However, this latest step in AI analytics has brought new challenges. In those first two generations, users were very technically minded. To help new audiences get the full benefit of predictive analytics, new tools are necessary. As a result, we’ve seen a new category of tools designed to help data analysts use machine learning without writing code.
To be clear, these no-code solutions aren’t about to replace data scientists, no more than calculators replaced accountants. Instead, think of no-code AI as a valuable tool, accelerating what data scientists can do and helping them get results faster, and, better yet, the results are tangible. According to The Digital Banking Report, 64% of respondents (banks and credit unions) ranked the use of AI more important than even improving their customer experience.
Until recently, building and deploying AI models was an expensive and time-consuming process. According to Algorithmia’s 2020 State of Enterprise Machine Learning, putting a trained machine learning model into scaled production takes most companies anywhere from 8 to 90 days. Assuming an optimistic 30-day timeframe and taking into account the average wage for a data scientist, building that model would cost you over $12,500.
The rise of no-code AI tools means all sorts of people can now take advantage of machine learning and use it to see real tangible benefits in their lives and their businesses.
For example, businesses right now can use AI to analyze their existing data and predict:
• Which employees are most likely to quit.
• Which leads are most likely to convert.
• Which customers are most at risk of churn.
• Which transactions could be fraudulent.
• Which applicants are likely to pay back a loan.
• Which ads will generate the highest ROI.
