The power of artificial intelligence (AI) has been in various businesses and applications. We’ve seen it over the past decade in examples such as financial trading, data security, marketing personalization, and recommendations. Those virtual assistants like Amazon Echo can perform basic tasks and they keep getting better.
Understanding AI requires extensive technical skills and programming. Python, Java, C++, Prolog, and Lisp are among the major AI programming languages used to build these applications. With a shortage of employees who this specialized knowledge, it is difficult to integrate these huge AI projects.
What is no-code?
Software design systems that use no-code frameworks enable non-technical people to execute software without writing a line of code. No-code tools generally have a user-friendly interface and have drag-and-drop capabilities.
The visual development interface of these programming platforms thus allows non-technical users to build applications. The drag and drop components allow users to create a full app with no previous coding experience required.
What is no-code AI?
No-code AI can be thought of as a subsect of these no-code platforms. It helps companies that use AI to perform a variety of activities and build AI models to drive those specific business goals. For example, a company could use this technology to perform data classification and analysis.
Typically, no-code AI comes in the form of a custom-developed platform or model. The company can then integrate this platform into its current technology stack. Companies can start using it right away after this process.
A custom desktop or a drag-and-drop interface is often included in the features to make the process more straightforward. The idea behind the implementation is to ensure the design is as usable as possible.
How low code is Different from No-Code
Since programming languages have been around, the desire for low or no-code platforms has existed. Finding technical talent or training people to code is challenging. Low-code production systems in their present form have been around for around a decade. Examples include 4th or 5th generation high-level programming languages like Python and SQL.
Low-code development uses a rapid application or high-productivity development methods. There is an option to use code or scripting with these tools. The approaches to automating or completing application development activities vary by platform.
These platforms may have drag-and-drop editors, component assembly, code generation, and other features to make development easier. Professional developers are typically the market for these low-code platforms. However, junior developers and technical business users can navigate these platforms.
Here is a comparison of low code versus no-code:
Requires a developer or someone with previous coding experience to build and connect apps.
The core functionality is automatic however there is some coding that’s necessary to complete the application’s development.
Software developers that wish to build applications faster are the primary targets for low-code platforms.
Low-code systems are open systems. That means customers will have to test its application if an update occurs.
No prior coding experience is necessary; Virtually anyone can build apps with light training.
No coding skills are necessary to complete the application’s development.
Business users are the target for no-code platforms.
No-code systems are closed systems. Customers do not have to test if an update occurs.
What are the benefits of no-code AI?
Through no-code AI, the use of AI and machine learning is more accessible to many different companies. The technology was created to help democratize these important functions. According to Garner Survey, employing AI has grown 270% from 2014 to 2019. Before that timeframe, AI implementation and deployment were only reported by 10% of respondents.
Enabling AI and machine learning requires companies to seek out extremely technical talent that there are simply not enough of. The benefits of using a no-code AI platform are important for any company that wishes to implement these technologies and doesn’t have the workforce to make it happen. Other benefits are discussed below.
No-code AI will never be fully custom software, but it can be adjusted to fit certain business demands. When a company has a specific need, the platforms and integrable modules can be adapted accordingly. As a result, you can use it for a variety of different purposes in most cases.
Enables Companies to become Data-Drive (without a Data team)
Many companies that wish to be data-driven, don’t have the data science team to realize this goal. It’s also difficult to scale a data science team or know what types of data science tools are available. No-code AI gives companies like this a viable alternative that can provide results in far less time.
Reduce Costs, Improve Profits
Using no-code AI, companies can leverage it to find areas where they can cut costs. For example, they can run a simulation that looks at historical data to make these predictions. It can also be used to increase your profit opportunities.
For example, you could use historical pricing data to take a dynamic pricing model. A no-code AI platform could help your company predict how much a customer will pay for your product/service at a certain time. This type of pricing model is used by airlines.
Less expensive than a Custom AI Solution
The cost of implementing a fully custom AI solution is high. That may deter some companies from adopting this technology. Others will find it to be a less stressful process and like the fact that you don’t need to onboard an AI team to implement.
Expedites Processes to Save Time
Repetitive activities like contact validation, form filling, and invoicing are often processes that AI is used to automate. Companies can complete these types of processes faster. This increases workplace productivity and allows employees to have more time to focus on other tasks.
Can be used for Business Intelligence
Humans simply can’t analyze the same amount of data as a computer. AI solutions don’t have limitations. In addition, they can enable smarter decisions by delivering valuable insights.
Create and Scale Products
Personalization, content, and product curation are among the demands of today’s customers. To satisfy this, companies need to have data input and output to appeal to their customer’s needs and create products.
Using no-code AI, companies can create machine learning systems to make predictions to make better decisions about their products, reduce the speed to market, improve UX, and more. It is more scalable to deliver unique experiences when you have access to this type of technology.
Use Cases for AI
It is harder to think of a business process that can’t be changed or completed by artificial intelligence. You can train AI to do almost anything that has a procedural method. AI will also do these processes better than a human.
The businesses processes that make the most sense for AI to overtake are processes that are:
- High volume
Activities like order processing and data handling are still handled by humans for many companies. These types of processes (rule-based) are the first that artificial intelligence will complete. Let’s talk about the different use cases we’re seeing AI technologies being used now.
Personal assistants including Google Home and Amazon Eco are AI-powered virtual assistants that can perform basic tasks. These personal assistants are getting even smarter, able to handle more functions and daily activities.
There is a lot of data that is required to make email campaigns successful. That is why we’re seeing an influx of large email service providers incorporating AI functions into their products. Connecting the data with AI will improve email marketing campaigns and answer key questions like:
- What are the keywords that have had the best results?
- Which headlines garner the highest open rates?
- What characteristics of email messages have generated the highest engagement levels?
Automated and AI-based chatbots have improved customer service for many companies. The majority of the chatbots used for customer service are rules-based. In the future, they will be AI-powered and may even have voice capabilities.
A large chunk of the work that accountants do today is daily, repetitive, and rule-based activities. This type of work can easily be automated by using AI. The work that accountants do today will most likely completely change when AI technology continues its disruption into the financial sector.
From generating leads to spitting out personalized marketing messages, AI can be used to improve every step of the sales process. AI features are being built into CRM software by major providers. This capability allows companies to predict customer needs and buying processes better.
Comparing competitors and producing detailed reports can be performed by AI-based applications to do research for companies. This will help them with launching more successful products and services as these AI tools become more mature.
Where no-code AI is trending
As you can see, AI is changing and improving processes across many applications for companies. The desire to have no-code AI seems obvious as it removes the requirement for skilled engineers to build out solutions.
Some of the players currently out there include NoCode, Zeroqode, and MakerPad. It is still a new, growing market with plenty of prospects for the future. What is happening in this space for companies that exist within it is that they’re positioning themselves in technologies.
For example, instead of choosing a specific use case like CRM or business apps, they’re voice recognition or NLP technologies. The essence of a no-code AI solution is that it is usable by non-technical people. That means that the average worker with no coding experience can pick up and use the tool.
We’ll cover a few of these no-code AI platforms now.
This Google product is an independent app that allows users to build models for Sight (Vision and Video Intelligence), Language (NLP & Translation), and structured data (Tables) functions. AutoML is available through the cloud.
MonkeyLearn is an all-in-one text analysis and data visualization studio. It can use unstructured text-based data to get keywords, intent, sentiment, etc. Among its features is that it will automatically tag business data, visualize actionable trends and insights, and simplify the process for text classification and extraction.
Another tool by tool, Teachable Machine allows you to create models that can classify sound, body postures, and images. Its UI is a simple and easy drag-and-drop structure. Your machine can learn by living in your browser, creating a data set with your webcam, and more.
This no-code AI technology allows you to classify images, texts, and documents. Users can train custom models using their specific data for use cases. That means that businesses of any size can use it. Users can keep full control by creating custom models and flows that include an option to keep a human-in-the-loop.
This platform for computer vision and NLP builds high-quality training datasets. It offers advanced tooling and QA, ML and automation features, offline access, integrated annotation services, and more. Text, image, and video annotation, data curation, automation, and quality management are among its many features.
Implications of No Code AI
If you’re a company that’s considering using a no-code AI platform to improve your business processes, there are things you should keep in mind.
For example, the customization options are somewhat restricted at this point in time. As mentioned, most of the providers in this space are focused on technologies versus use cases. This lack of customization restricts the functionality of the platform to handle specific problems.
If you work in an industry where security is of the highest importance, some of these no-code AI platforms may not work. Some of these platforms do not have solid design access protocols to securely process data. Therefore, before your company adopts a no-code AI platform, it must complete due diligence to understand how the data is processed.
While we’ve seen no-code (and low-code) AI platforms grow in popularity, they aren’t quite viewed as practical as traditional ML approaches. There’s a lack of trust as a result. The variety of resources and libraries about ML and computer vision is greater than what’s available for no-code AI platforms. One can argue that this will change as no-code AI platforms and technologies increase in availability.
How to Get Involved with No Code AI
No-code AI platforms are increasingly becoming popular and useful for a variety of applications. However, they will not replace traditional ML and computer vision. So if your company is considering adopting no-code AI, it’s important to determine specifically how the solution will fit into your business goals. In some cases, you may realize that a fully custom AI would be the right solution.
Another key factor is finding the right vendor that meets your needs. The growing market for no-code platforms means that many offerings are available. Some vendors will work better than others for your specific goals. A great approach is to see if the vendor will allow you to try out its platform first before onboarding the solution to your systems. You’ll get a better sense of how it works and can determine whether it will work for you.
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