Technology is expected to continue changing and getting better. App building technology is particularly focused to grow faster as a result of the increased need for mobile applications with improved user interface (UI) and artificial intelligence (AI). Graphical programming is therefore inevitable as the platforms make app development more seamless and less costly. This is where low code development comes in as leverage especially for the workforce with lesser competency levels in IT. 

Are you afraid that AI might take your job? Make sure you are the one who is building it.

STAY RELEVANT IN THE RISING AI INDUSTRY! ūüĖĖ

A revolution in coding, low-code has shaken the coding industry and made hand coding more obsolete. In fact, it is easier for non-technical developers without IT skills to build applications using interactive visual interfaces. So low code has therefore reduced demand for skilled IT experts and reduced the cost of coding in the long run. Creatio offers more support to low-code development platforms with more benefits to businesses. 

In addition, the drag-and-drop solutions, graphical user interfaces together with the other applicable user-friendly tools make low-code a platform for citizen users. However, it may not be one of the best app-building tools without integrating with AI. This is why businesses need to combine low code with AI for optimum achievement. In this article, we discuss how you can combine AI with low code for your business.

AI gives businesses enhanced technology and innovation making it the first choice for many C-level executives. One very important impact of AI on app building is it makes it easier for users to build applications through question-and-answer models. This is achieved when users answer questions and their answers are picked and used by AI in improving key user experience by taking into consideration user needs.

1. Identify Key Challenges

Modern businesses face a bunch of challenges. Particularly, how to manage customer interactions and their relationship with the business. Research shows that more than 60% of customers will not be happy and may not give business to companies who give them a bad experience on the first deal. Artificial Intelligence machine will be able to get your business out of this loop by automating feedback mechanisms so that teams can easily identify and correct processes that lead to bad customer experiences. 

Whether you want to get data entered or services suggested, AI will get you through without overburdening any team member. And, if you have so much scam coming in through the marketing emails, AI using will make your experience sharable. Besides, manual work is difficult. If your team is currently struggling with opportunity identification in large volumes of data, it is the right time to automate the selection process by integrating AI in the process. 

It is however a key provision that key challenges in business should be highlighted before integrating AI with low code. Besides, it highly improves the ability of the application to make a quick observation and suggest solutions. 

2. Assess Your Readiness

Once your company identifies the right problems that you need AI to solve, it is the right time to assess how ready your business is to build, manage as well as support AI-related solutions. A large number of machine-based learning services are already made available by some of the major cloud providers. You will be able to access tools that help you build custom models together with other on-demand services that support speech and image recognition. Besides, low-code platforms also give you access to in-house building systems that support app development through GPUs and ML-focused software package frameworks. 

Decision Tree

In order to be able to integrate AI efficiently, you will need large volumes of data to be able to train your AI models. Whether it is sensor data, the Internet of Things, or customer interactions. Besides, the data you have apart from bearing the quantity mark has to be quality data too. 

It is therefore very important for a business to check whether they are ready for AI or not. This is because the reasons behind data collection which include intent and what to use it for are key questions every business has to answer before implementing AI technology. 

Several companies already use data cleaning and collection pipelines. In fact, many businesses report having integrated data incorporated and data analytics in their data management systems. 

3. Map Existing And Missing Skills

Once you have identified your problems and identified the level of data needs, the next step is to identify the potential of your organization to implement AI by assessing skill levels. It has been revealed that a number of business managers are not so sure how to offer the right skills to staff that is directly affected by disruptions coming as a result of AI especially as roles get different shapes in the company. 

Decision Tree

It is recommended that you map out available skills and those that the business will need in the future before trying to build new skills ‚Äď including harnessing partner organizations, recruitment, and internal skills programs. Of course, they may not be the only skills your business will need in the change-over but includes skills your employees will need when their jobs change due to the integration of AI-related technologies.¬†Skills bank and the understanding of employee level of understanding of the AI technology are therefore key considerations before integrating AI.¬†

4. Nurture A Culture That Allows Employees To Experiment And Evaluate AI

Experimenting with AI is one key step in assessing the general understanding and acceptance of Ai by employees amid changing roles. However, it is recommended that businesses use the right methods to pick the most relevant projects for the experiments. Besides, teams should be encouraged to start small before scaling up. It makes it easier to get feedback, build confidence and learn lessons. 

5. Bias And Ethics

Remember, machine-learning systems will only get as good as the data you train them on. If your data is wrong or biased, the machines will only learn it the flawed way. Blind spots around race, socioeconomics, gender, or ethnicity should therefore be eliminated to ensure your AI technology is sensitive to them in the outcomes we get from the low-code platforms. Combining AI and low code in your business gives you a big advantage over the competition. See how you can do this and get your business on its foot with better advantages. 

Decision Tree

6. Business Process Management

Depending on the type of work your business does, AI in business supports the workflow processes to ensure efficiency in production lines and customer interactions. Besides, AI-enabled BPM platforms can easily identify processes or activities that need to be automated fully or at least partially. This is because every business is unique and automation needs depend on the type of processes involved.

7. Customer Experience

This is one of the most important business areas which need AI recognition especially when building apps with low code technology. Low-code AI should therefore be built to enable the customer to manage their time by being able to get the right answers promptly without waiting too much. 

8. Personalization

Low-code AI helps businesses to analyze collected data. By following the actions of prospects together with the feedback they leave on the website, it collects the right data to be able to respond or present offers that customers are more likely to buy. 

Thanks for reading!

Rubik's Code

Rubik's Code

Building Smart Apps

Rubik’s Code is a boutique data science and software service company with more than 10 years of experience in Machine Learning, Artificial Intelligence & Software development. Check out the services we provide.

Eager to learn how to build¬†Deep Learning¬†systems using Tensorflow 2 and Python? Get our ‚ÄėDeep Learning for Programmers‚Äė ebook¬†here!

Read our blog posts here.

Ultimate Guide to Machine Learning with Python

This bundle of e-books is specially crafted for beginners.

Everything from Python basics to the deployment of Machine Learning algorithms to production in one place.

Become a Machine Learning Superhero TODAY!