AI has been a hot topic for at least a decade yet. Yet, there exists obstacles in the way that can inhibit businesses adoption. Cost is one such obstacle, for implementing AI technologies and expertise can be an expensive investment.
No-code solutions can be an alternative to traditional AI technologies by helping to democratize AI through making it widely and easily available at a low cost.
In that light, in this research we will go over what no-code AI is, why it’s important to businesses, what are it’s benefits, before rounding the article off by discussing the differences between AutoML and no-code AI.
What is no-code AI?
No-code AI, also called codeless AI, is a category in the AI landscape that aims to democratize AI. No-code AI means using a no-code development platform with a visual, code-free, and often drag-and-drop interface to deploy AI and machine learning models. A wide range of tools provide no code AI capabilities, including dedicated no code AI tools, as well as some automation tools (e.g. some RPA software providers) that include integrated AI capabilities in a no-code user interface.
No code AI enables non-technical users to quickly classify, analyze data and easily build accurate models to make predictions.
Why is no-code AI important for businesses?
No-code AI makes it feasible to build AI models, without necessarily having trained personnel. That is beneficial for businesses because currently, there is just not enough data science talent out there.
According to Forbes, 83% of businesses say AI is a strategic priority for their businesses today. That sentiment has been encapsulated by an increase in demand for AI talent. A 2020 LinkedIn report shows that in the US, for instance, demand for the position of “Artificial Intelligence” grew by 74% in the preceding four years.
Yet, there is not enough data science talent. And currently, technology and financial service companies are currently absorbing 60% of AI talent, which forces smaller companies to rely on citizen data scientists for leveraging AI use cases.
Building AI models (i.e. training ML models) requires time, effort, and experience. No-code AI reduces the time to build AI models to minutes enabling companies to easily adopt machine learning models in their processes.
According to Google Trends, although the interest in no-code AI has started to increase, it is still much lower than the number of people interested in learning ML or AutoML (Figure 1). No-code AI solutions have not yet replaced data scientists. This is still an emerging field. Increasing the maturity and flexibility of existing solutions and widespread integrations will drive more adoption.
What are the benefits of no-code AI solutions?
No code AI solutions reduce entry barriers for individuals and businesses to start experimenting with AI and machine learning. These solutions help businesses adopt AI models quickly and at a low cost, enabling their domain experts to benefit from the latest technology.
It combine business experience with AI
Data science is still an emerging field and most data scientists have less business experience than domain experts. According to a data science survey conducted by data science competition platform, Kaggle which is a crowdsourcing solution for AI projects, the most common age of respondents is 24 and the median is 30. Thanks to no-code solutions, business users can leverage their domain-specific experience and quickly build AI solutions.
Building custom AI solutions requires writing code, cleaning data, categorizing, structuring data, training, and debugging the model. These take even longer for those who are not familiar with data science. Studies claim that low code/no-code solutions have the potential to reduce the development time up by 90%.
It’s low cost
One of the most obvious benefits of automation and no code technologies is savings. Companies need fewer data scientists when they can have their business users build machine learning models.
It helps data scientists focus
For businesses that already have a data science team, requests of other employees shift the data science team’s focus to easy-to-solve tasks. No-code solutions minimize these distracting requests since they enable business users to tackle such requests themselves.
What is the difference between AutoML and no code AI?
Although AutoML and no-code AI might sound similar (in that they both enable non-technical employees to quickly develop AI solutions), they are slightly different from one another:
- AutoML solutions are focused on empowering data scientists to be more efficient. They provide transparency on the whole machine learning pipeline which increases complexity but also allows data scientists to refine how models are built.
- No code AI solutions are focused on helping non-technical users build ML models without getting into the details of every step in the process of building an ML model. This makes them easy to use but harder to customize.
For more on no-code AI
To learn more about no-code AI, read:
- 32 Low Code/ No Code Statistics from Reputable Sources
- Low/No Code Development: How It Works & Use Cases
- Low/No-Code RPA Software: Benefits & Best Practices
Moreover, if you believe your business would benefit from adopting a no-code/low-code solution, feel free to check out our sortable and transparent list of top no code AI vendors.
And if you still have questions on no-code AI vendors, don’t hesitate to contact us:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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