While every business today needs the capabilities to deploy artificial intelligence (AI) to withstand the speed of change and disruption, not every business is able to act on that opportunity. Data science talent shortages and high costs are huge barriers between organizations and AI-driven innovation, especially small and mid-size companies that typically don’t have the resources to deploy powerful technologies like AI and machine learning (ML).
However, no-code AI is emerging as a solution to help propel AI adoption forward. By making application development more simple, fast, accessible, and affordable, no-code AI levels the AI playing field for organizations of all shapes and sizes. No-code AI enables organizations to build AI and ML models without the need for costly, specialized engineering or data science expertise, or in-depth coding knowledge.
According to Gartner, 65 percent of all app development is going to be low-code/no-code by 2024. Organizations can use no-code AI to leverage the benefits of their data and deploy AI models that drive specific business outcomes and optimize operations.
Benefits of No-code AI
Typically, no-code AI is available via a platform that can be integrated into an organization’s existing technology stack and begin being used almost immediately. They’re designed to be user-friendly through features like custom dashboards and drag-and-drop interfaces that enable users to import data for model training, re-training or improvement. The data is automatically classified and normalized, and model selection and training is automated based on the provided data and the type of prediction that’s desired.
Some benefits of no-code AI include:
- More affordable than customizable AI solutions as there’s less data scientists required to develop ML models.
- Though it’s not a fully customizable solution, no-code AI is adaptable to suit various business needs.
- Intuitive for non-technical users, such as product managers or sales teams, to instill ML capabilities into customer service applications or CRM systems for example, to drive new competitive opportunities.
- Data can be transformed into actionable insights in minutes instead of weeks or longer.
- Speeds up development time compared to custom AI solutions, which involves writing code, data cleansing, data classification, data structuring, and training and debugging models.
No-code AI isn’t a replacement for data scientists and ML engineers — many no-code solutions may still require some technical proficiency for more sophisticated application development. This is because more complex applications will still need a deeper comprehension of workflows, algorithms, and user experience (UX) design, to name just a few.
Instead, much of the value for an organization is the empowerment for citizen developers to quickly build an app that can resolve certain pain points - not to build mission-critical applications. No-code AI also augments professional developers by speeding up their workflows and providing them with agile tools for more experimentation.
No-Code AI Use Cases
One of the biggest drivers for no-code AI adoption is that it isn’t limited to any specific use case. In many instances, it boils down to identifying the best project and platform for their needs. This extends to how well the solution will fit into the business ecosystem, if the organization can truly benefit from a no-code solution vs a completely custom AI solution, and if the specific tool aligns with business needs.
With that in mind, there’s some interesting no-code AI use cases across data-rich sectors:
No-code AI can be used by financial services teams to optimize processes, manage financial risk, and improve the customer experience. For example, automating customer onboarding and loan application approval based on specific customer risk levels and criteria, for example. frees up underwriters to make faster, better decisions rather than manually sorting through customer applications - also saving money and time.
Organizations can use no-code AI tools to better align their marketing campaigns with customer demand and make more informed decisions regarding customer segmentation. For example, a model can be created that identifies patterns in images, text or audio and analyzes sales transcripts and notes in addition to marketing data to reduce churn, or create targeted social media ads.
No-code AI can help accelerate digital transformation for healthcare organizations by equipping healthcare administrators and staff with tools that help them to optimize billing systems or revenue cycle management, for example, via chatbots, robotic process automation (RPA) or mobile solutions.
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Powering Digital Agendas With No-Code AI
No-code AI remains an emerging field, but is quickly growing as organizations look to push technological advancement and digital strategies forward without the complexity that comes with traditional AI solutions.
Visit Simplilearn for more resources about the future progression of no-code AI and how it impacts the data science community.