The meteoric rise of no-code AI platforms has enabled companies of all sizes and verticals to benefit from powerful technologies that were previously available only to large organizations. In this post, we will discuss the obstacles to developing traditional ML algorithms, how no-code AI platforms address said challenges, and why this will be a key trend for businesses in 2021.
Forbes cites how 83% of companies rank AI in their strategic business plans - but the biggest limitation is the lack of ML development talent. The next few years of further democratization of this technology will be a notable trend to watch, as companies address the expenses tied to training and hiring the highest level of developers.
What are the issues with traditional AI methods?
In the past, most companies' adoption of AI ended on PowerPoint slides pitching a new form of data management. And for good reason - creating an effective model that does everything according to plan is difficult and resource-intensive.
In 2021, AI technology is no longer just a competitive advantage but it has become crucial for businesses of all sizes. The sheer amount of data being uploaded to the internet is growing exponentially as web technology becomes more accessible and the number of global daily users rises daily. For instance, Anshul Pandey from Tech Radar notes how digital text data, which has seen an exponential increase in uploads through the past decade, continues to experience 55-65% annual growth.
The need to intelligently address this influx of data is increasing at the same pace but humans simply do not have enough processing power or available labor hours to keep up with the speed. How do we even begin to delve into this vast ocean of information?
For the most advanced organizations, machine learning has become the "golden ticket" technology to leverage human labor intelligently: You develop a model, set your parameters, and watch it work its magic.
Unfortunately, things are never that simple in practicality. The development process can be challenging, expensive, and most of all, time-consuming.
Developing high-performance machine learning systems used to call for people with ample experience in software development as well as specific understanding of machine learning and cloud technology.
As the demand for developers has grown, the available supply has become more scarce, and naturally, higher-priced. Finding and hiring a top ML engineer is guaranteed to eat up time and resources, and a cheap hire won't be able to address such issues in a satisfactory manner. Hence PowerPoint.
What is no-code AI?
As you will expect, we are reaching the turning point. No-code software has taken command of large spaces in web development (Wix, Webflow, Wordpress), automation (Integromat, Zapier, Tonkean), and databases (Airtable), and it is now expanding into artificial intelligence.
The term no-code was originally popularized in the web-development sphere by platforms like Wix and Webflow. Their value proposition was the ability to create deployable websites in hours without a single line of code.
Similar in spirit, No-code AI takes this trend a step further and allows you to train, test, and deploy machine learning models that used to require a software engineer. This allows users to perform a variety of tasks, from automating a host of manual processes to creating forecasts and analyses without having to write code.
AI for non-technical users
Unlike traditional programming methods, no-code AI platforms tend to have intuitive and aesthetically pleasing user interfaces and abstract away many of the decisions one might take.
Contrary to some people’s beliefs, doing so doesn’t necessarily lead to poor performance – quite the opposite: Many of the steps that are involved when building an AI system are repetitive and have little impact on how well the model is able to predict something, such as which upload server to choose.
For business analysts and leaders, underwriters, product and risk managers, or sales professionals, this means you can build models quickly and efficiently while technical teams have more time to focus on other high-value development tasks.
Earlier this year, we have mapped out the no-code AI landscape and identified five advantages that users typically report: Accessibility, usability, speed (or time to value), quality, and scalability. If you want to dive deeper, have a look at the article we wrote about this subject.
No-code machine learning is still an emerging field that is quickly establishing itself as a necessity for startups and established businesses alike. We predict that the no-code approach will become dominant when it comes to practical uses of intelligent systems.
The research firm Gartner recently reported that “AI-enabled tools will generate $2.9 trillion in business value" in 2021 alone. Just because they say so it doesn't mean that you need to jump on the train as well. But through speaking to hundreds of businesses of different sizes, we learned that almost all of them could gain a sizeable edge by enhancing their automation efforts through the use of AI.