The accelerated adoption of low-code will change the organization of teamsDuring a pandemic, many organizations are using low code platforms to quickly build and deploy new applications. This experience will lead to acceptance by most development stores for low code tools and more. Expect new hybrid teams to emerge as business users and professional developers build applications alongside low-code tools built on cloud platforms.
Artificial Intelligence will drive more development automationMore than a third of developers are predicted to use machine learning in 2022 to automate development activities. Teams will use machine learning models to make test automation smarter, and natural language processing will be used to review test cases and eliminate duplicates, and identify gaps in test coverage. New breakthroughs such as GPT-3 will spark a raging debate: Will AI replace and reduce demand for enterprise developers, or increase their day-to-day activities and free them up to solve business problems and increase the amount of software delivered?
If you combine these two predictions ablove, you get the third. The world will need AI applications on a low code or no coding platform. And here you can speculate, find advantages and limitations. To begin with, interest in codeless AI platforms is growing all the time. A growing number of startups and big tech firms are now offering "easy-to-use" ML platforms.
Why is using AI platforms without code beneficial?This is interesting for both small and large corporations. After all, such developments are less expensive and help to get out of the crisis faster. Indeed, in most cases, many projects remain unfulfilled due to a lack of data, investments, and management's interest. Let's talk about the benefits of using AI platforms without code:
- Reduce the workload of data scientists so that they can focus on larger and more complex projects.
- Accelerate the development of specific projects. For example, to automate some internal workflows: analysis of churn rates, product cost of living, dynamic pricing, analysis of contract data.
- AI without code is still a growing market. Most of the players seem to position themselves primarily on the typology of technologies (NLP, computer vision, etc.) or specific use cases (CRM Management, ...) In the near future, it will be possible to see complete tools that will make it possible to cover almost all uses, thus avoiding the need to invest in multiple tools and benefit from the knowledge.
- Knowledge benefit refers to the development of online educational programs. They will train product managers to use at least one of the AI tools without code and be knowledgeable about managing the dataset.
- Allows you to implement a client blocking strategy. This is when a customer is so dependent on a supplier of products and services that they cannot switch to another supplier without significant switching costs.
What risks should be assessed when implementing AI without code?There are several risks that you need to consider when choosing no-code AI, among them flexibility, maintenance, dependencies on professionals, and more. Let's talk more about these risks.
The balance between ease of use and flexibility.
You need to make sure that the required scalability of your project can be achieved using the AI platform without code. Agree that it is impossible to have a scalable solution using non-AI platforms for "complex" use cases. In addition, the contractual relationship (data ownership) must be carefully scrutinized in terms of cost and reversibility.
If a vendor of AI without code can guarantee scalability (perhaps in some use cases), then the overall licensing costs need to be estimated at scale to ensure long-term sustainability. The worst thing that can happen is to create PoC using a quick and dirty approach and then move on to production, trying to scale that same PoC.
The degree of personalization.
Some solutions without code lack the ability to fine-tune and design various parameters. Another disadvantage is associated with the increased danger of creating biased algorithms.
The need for precise internal processes.
No code AI tools are beneficial, but they require specific controls. Otherwise, it will lead to Shadow IT. This is when applications are managed and used without the knowledge of the enterprise IT department.
Dependence on experienced professionals.
If you want to scale your project, then you can't do without experienced technical specialists. The success of an ML project depends largely on the ability to collect, manage and maintain a dataset. However, this is usually the work of the Data Scientist. «Senior» will find it difficult to do this job.
Many other risks can be cited: deployment, security, integration with legacy systems, etc. However, the usefulness of non-code AI platforms is not a myth.
This leads to the conclusion that the success of a machine learning-driven project with a no-code solution will largely depend on your functional needs. If the needs are very specific, the right balance needs to be struck between speed of implementation and expectations for functionality. Determine from the beginning what your goal is. If you just need to check the relevance of an idea or build a long-term application, it is best to quickly create a one-off PoC using non-AI code solutions, if possible. Otherwise, it is better to use the traditional ML approach and build a stabilized and maintainable version.
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