3 Ways No-Code is Democratizing AI
July 26, 2022

Loren Goodman
InRule Technology

We have become accustomed to so many different technologies in our lives today. Most share a common history — there was a democratizing force that brought it from an abstract, unattainable thing we only heard about to something we can no longer live without. Take the computer — for decades, computers were larger than refrigerators and were only accessible to the largest companies or nations. Today though, most of the world carries one in their pocket. Mass-marketed desktop computers balanced cost and operational restraints with measured capabilities tailored to the needs of an individual; they were relatively easy to use, and one did not need to know how they worked to get value from them. They provided immense value for its users.

Looking at the world of emerging technologies, artificial intelligence (AI) appears to be the next technology on the cusp of breaking into the mainstream, thanks to the development and growing proliferation of no-code AI. A code-free technology that enables non-AI experts to implement and test their ideas without any need for a data scientist, no-code AI will prove to be a democratizing force within the AI industry enabling greater accessibility and use by businesses outside of the Fortune 100.

For businesses to remain competitive, they must invest time, money and resources into their AI capabilities. Luckily, with no-code AI, much like the desktop PC, there are varying levels of investment accessible to all instead of just those that can leap the historical barriers to entry. There is no need to establish a data-science practice, and there is no need to learn complex technologies.

To reap value, you need data and a system of automation; no-code AI brings the automation, and most organizations are already overflowing with data. A 2020 Salesforce report, which surveyed 100 global IT and engineering leaders, found that company departments utilizing workflow automation reduced time spent on manual processes by 66% and reduced cost by 46% compared to year-over-year outcomes. By streamlining manual processes through no-code AI models, businesses can scale faster while reducing the consumption of human time and increasing the accuracy of predictions.

Here are three key ways no-code AI is democratizing the technology:

1. No-Code AI Removes the Need for A High Degree of Training

It is no secret that a labor shortage exists for highly technical and skilled employees. Companies across all industries have difficulty finding and retaining qualified data scientists who can develop and create AI/ML models. This skills gap isn't going away anytime soon. No-code AI workflows empower citizen data scientists, including business analysts and other subject matter experts, to create, train and deploy powerful machine learning models. Another benefit of no-code AI is the relatively flat learning curve, allowing it to be quickly taught, replicated, and scaled across departments. This enables employers to combat the skills gap within their workforce by incorporating no-code tools training into employee development and onboarding.

2. Quicker Model Creation and Faster ROI

For companies lucky enough to have a data scientist on their staff, no-code AI enables them to work faster and more efficiently than before. No-code AI provides data scientists with automation and reusability that allows quick delivery of prototypes and iterative improvements using model explainability, revealing areas of model weakness. By employing no-code AI, data scientists can easily pinpoint problem areas within models, allowing managers to address any issues before they manifest into greater dilemmas quickly. Optimizing these processes frees up valuable bandwidth for data scientists to focus on more specialized research and projects within the company.

3. Better Explainability + Bias Detection Are Baked In

The Silicon Valley ethos of “move fast and break things” is gone. After years of eroding trust by big tech, consumers and businesses want to ensure the tech they invest in follows the “first do no harm” principle. Because no-code AI is created with the goal of enabling non-data scientists to harness the power of AI, it needs to be embedded with a higher level of explainability and bias detection to reduce the risk of human error.

No-code AI has proven to be a simple yet powerful solution aiming to bring AI to the masses. By democratizing AI with improved accessibility, businesses, regardless of size, will be able to compete more aggressively with AI in their toolbox.

Loren Goodman is Co-Founder and CTO of InRule Technology
Share this

Industry News

November 20, 2024

Spectro Cloud completed a $75 million Series C funding round led by Growth Equity at Goldman Sachs Alternatives with participation from existing Spectro Cloud investors.

November 20, 2024

The Cloud Native Computing Foundation® (CNCF®), which builds sustainable ecosystems for cloud native software, has announced significant momentum around cloud native training and certifications with the addition of three new project-centric certifications and a series of new Platform Engineering-specific certifications:

November 20, 2024

Red Hat announced the latest version of Red Hat OpenShift AI, its artificial intelligence (AI) and machine learning (ML) platform built on Red Hat OpenShift that enables enterprises to create and deliver AI-enabled applications at scale across the hybrid cloud.

November 20, 2024

Salesforce announced agentic lifecycle management tools to automate Agentforce testing, prototype agents in secure Sandbox environments, and transparently manage usage at scale.

November 19, 2024

OpenText™ unveiled Cloud Editions (CE) 24.4, presenting a suite of transformative advancements in Business Cloud, AI, and Technology to empower the future of AI-driven knowledge work.

November 19, 2024

Red Hat announced new capabilities and enhancements for Red Hat Developer Hub, Red Hat’s enterprise-grade developer portal based on the Backstage project.

November 19, 2024

Pegasystems announced the availability of new AI-driven legacy discovery capabilities in Pega GenAI Blueprint™ to accelerate the daunting task of modernizing legacy systems that hold organizations back.

November 19, 2024

Tricentis launched enhanced cloud capabilities for its flagship solution, Tricentis Tosca, bringing enterprise-ready end-to-end test automation to the cloud.

November 19, 2024

Rafay Systems announced new platform advancements that help enterprises and GPU cloud providers deliver developer-friendly consumption workflows for GPU infrastructure.

November 19, 2024

Apiiro introduced Code-to-Runtime, a new capability using Apiiro’s deep code analysis (DCA) technology to map software architecture and trace all types of software components including APIs, open source software (OSS), and containers to code owners while enriching it with business impact.

November 19, 2024

Zesty announced the launch of Kompass, its automated Kubernetes optimization platform.

November 18, 2024

MacStadium announced the launch of Orka Engine, the latest addition to its Orka product line.

November 18, 2024

Elastic announced its AI ecosystem to help enterprise developers accelerate building and deploying their Retrieval Augmented Generation (RAG) applications.

Read the full news on APMdigest

November 18, 2024

Red Hat introduced new capabilities and enhancements for Red Hat OpenShift, a hybrid cloud application platform powered by Kubernetes, as well as the technology preview of Red Hat OpenShift Lightspeed.

November 18, 2024

Traefik Labs announced API Sandbox as a Service to streamline and accelerate mock API development, and Traefik Proxy v3.2.