The Data Daily

Council Post: When The Rise Of AI Meets The Ease Of No-Code

Council Post: 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.

It’s not just limited to these insights, one of our first clients after we publicly launched Obviously AI is a Cookie Company that uses AI to predict how many cookies to bake to avoid waste and save money and streamline inventory management.

Rather than being limited to tech companies operating out of Silicon Valley, now those small businesses that are the bedrock of the economy can use no-code AI to cut costs, maximize profits and operate more efficiently.

Getting some companies to see these opportunities doesn’t come without its challenges. We regularly convince organizations that they already have the tools to become AI-driven. Previously they thought machine learning model development, training, deployment, monitoring and performance needed large-scale human capital and specific expertise along the end-to-end workflow.

Likewise, another challenge for organizations is that they must have a data collection and storage method. Many companies want to implement AI but don’t currently have these measures in place. This in itself requires a time investment or capital investment. Whilst no-code AI is very cost-effective to implement, these data collection methods may be expensive. For example, IoT devices like sensors that collect equipment, submeter, and environmental data are costly to implement and automate. This data should ideally be converted to a centralized platform to ensure synergies between different components; once this is in place, the benefits snowball.

The Future Of AI Analytics And No-Code

As the AI analytics field continues to mature, the next logical step will be prescriptive analytics—using those predictions to make recommendations and take action.

You can already use tools like Zapier to automatically take actions based on predictive analytics. For example, if AI predicts a customer is likely to churn, you can automatically add them into a specific email sequence.

However, in the near future, AI will be able to choose the best actions and tailor them to get the best possible results. Going back to our email sequence example, by seeing which responses get the best results, AI would then be able to use what it has learned to tailor your future responses.

Rather than just collecting data, visualizing it or using it to predict events, the next generation will allow you to create a fully functioning live data ecosystem, constantly updating and adjusting in real-time based on the latest data.

Having a no-code data ecosystem will bring about several benefits. The speed at which they are deployed results in the process being cost-effective. For minimal upfront costs, we are seeing companies optimize and automate workflows and processes that were previously enterprise-level.

When that happens, it’ll be a game changer for everyone.

Opening Up AI Analytics For Everyone

Rather than being solely reserved for the most technically minded scientists and software engineers and large enterprises, no-code solutions mean AI analytics can now be used by anyone to improve their business.

Just as no-code solutions like Webflow have changed web design, these new tools are changing the way we think about machine learning and democratizing data science so everyone can use it.

Now every company can be an AI company.

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