In recent years, the quantity of digital text data has grown exponentially and continues to grow at 55-65% each year (IDC). From social media posts to customer transactions, online communities, surveys, reviews, chats, emails, and more, businesses across industries face the challenge of monitoring various sources and extracting the most relevant data.
Artificial intelligence (AI) and machine learning (ML) help businesses sort through unstructured data more accurately. However, implementing traditional AI and ML require additional manpower and subject matter expertise and can be time consuming and costly. With the advent of new technologies and growth in data, businesses that can extract information and create actionable insights quickly and at scale will have the most leverage in a competitive landscape.
Traditional AI and automation
The terms Artificial Intelligence and Automation are often used interchangeably. They allow businesses and teams to operate more efficiently and effectively. However, both are extremely complex on two different levels. Automation is the application of technology, programs, robotics, or processes to produce goods or services and achieve outcomes with minimal human assistance. On the other hand, AI is a science and engineering process that makes it possible for machines to learn from experience, adjust to new inputs and real-time data, and perform tasks at a human level or higher.
Traditional implementation of AI requires heavy technical skills and programming. Java, Python, Lisp, Prolog, and C++ are major AI programming languages used for AI to satisfy different needs and to develop and design different applications for business processes. For a typical business end user, implementing AI is impossible with the technical expertise and knowledge required to build out AI processes. The no-code movement is making it possible to see broader movement towards AI implementation now.
What is no-code AI?
The rise of the no-code movement has enabled businesses across all industries to reevaluate their technical processes and needs. Organizations can easily implement agile development strategies using no-code tools, while achieving similar and sometimes even better results and increasing productivity. No code tools are most commonly known for web and app development but can also develop and build AI and ML models. No-code allows users to transform business processes by quickly developing new solutions to meet customer needs and has attracted many financial services firms to adopt no-code AI into their workflows.
AI is used within the financial services industry to streamline and optimize processes ranging from monitoring credit risk, building quantitative trading algorithms, managing financial risks, providing better client experiences, and more. Before the rise of no-code AI, risk managers, underwriters, lenders, asset managers, and business analysts relied on their data scientists and IT teams to model automated processes for them. However, creating and implementing a single automated solution can take months or even years, due to the time-consuming processes of writing code, cleaning data, categorizing, and structuring data.
No-code AI provides financial services teams more efficient solutions to the time-consuming, manual processes of data research, extraction, and analysis. AI-powered tools can now run on a pre-developed backend and flexible front-end user interface, which means that financial firms can become nimbler, make faster and better decisions, and save time and money, while implementing AI solutions that match their business needs.
In other words, no-code is democratizing AI so that business analysts and leaders, underwriters, and product and risk managers, can create their own models, quickly and efficiently, bypassing the IT bottleneck. Data scientists are then free to work on highly sophisticated projects, and business users can be far more efficient. No-code AI takes the complex technical and coding skills out of the traditional methods, enabling anyone to build out AI models.
Three things to consider with no-code AI
While business users are now familiar with the concept of AI and machine learning, they are not technologists who can write code to create new use cases for AI. For financial services to reap the benefits AI can bring to efficiency and ROI, they need to empower business users to take the lead. A no-code, artificial intelligence work process enables users to focus on maximizing results, instead of executing manual processes.
Through simple commands and an easy-to-understand user interface, business users can quickly reap the benefits of artificial intelligence and automation without time delays, manpower restrictions, and a huge learning curve. No-code AI changes the game of the financial services industry by improving efficiency and ROI and freeing up technology teams’ times. Companies that are quick to adopt this new method will gain a competitive advantage.
Not all companies are fit for this new technology though. Organizations interested in no-code solutions must determine whether their company is a good fit. Those that already have many manual processes, a structured team of data scientists, and are looking to scale rapidly may not want to spend time in restructuring to implement no-code AI. Additionally, companies with large teams of advanced technical experts who are used to actual coding and are expecting to reconfigure, and tweak code may feel that migrating to a no-code platform isn’t a good fit for their organization.
As AI is increasingly making its impact on our world and businesses it’s important to make it as business and user friendly as other disruptive and innovative technologies today. Like email, Excel spreadsheets, and high-speed internet, AI is poised to change the way the world does business. With no-code AI, business end users can create new solutions without having to code, improving business efficiencies, productivity, ROI, and customer retention.