Artificial intelligence (AI) has the potential to revolutionize the way we live, work, and interact with the world. However, the benefits of AI are not always straightforward to everyone in your organization and adoption of these practices can be a challenge. While AI has the potential to transform numerous fields, it often requires a specialized skill-set to manage – one that only data scientists or similar roles might have.

However, there are new efforts to make AI simpler to use across organizations. Accessible AI refers to creating AI systems that are easy to use and understand and are accessible to a diverse group of users who may not have specific training or expertise that would be required otherwise.

In this article, we will explore three building blocks of accessible AI to help your organization gain momentum and break down the barriers of these practices.

Building block 1: AI-ready no-code or low-code cloud platform

One of the biggest challenges in creating accessible AI is the complexity of the technology and the need for niche roles, such as data scientists, to operate it. Most AI systems require specialized knowledge and expertise to develop, deploy, and maintain. This can create a barrier to entry for many users, especially those with limited technical expertise.

An AI-ready no-code or low-code cloud platform can help address this challenge. These platforms provide a place to prep data, then leverage pre-built AI models that can be configured, trained, and integrated into applications without requiring heavy coding. Many options even offer intuitive visual interfaces that make it easy to design, train, and deploy AI models.

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By providing a low barrier to entry, these platforms enable a wide range of users from marketing and media teams to benefit from AI. For example, people with visual impairments can use AI-powered assistive technologies that enable them to access digital content and interact with the world in new ways.

Building block 2: AI-trained multidisciplinary ecosystem

Creating accessible AI requires a multidisciplinary approach for data observability, training, and explaining models for use in broader data and decisioning operations. This means that data scientists, software engineers, UX designers, and domain experts need to work together to ensure that the data used to train the AI system is inclusive of the population it serves. They must also make sure that the AI model is transparent and explainable to the end users so they can understand how the model arrived at its decisions.

In addition, the team needs to have a process in place for continuous monitoring and improvement of the model’s accuracy and fairness. This requires the creation of an AI-trained ecosystem that includes team members who can validate and make the models accurate, annotate the outputs, and help with active learning. It also requires a team of people who can monitor, explain, and interpret the results of these models, ensuring that the AI system aligns with the organization’s values and ethical standards.

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Building block 3: Integrated AI ops

Deploying and maintaining AI systems can be complex and challenging. This is particularly true for organizations with limited technical expertise. However, it is essential that AI systems are deployed and maintained properly to ensure that they are accessible and meet user needs.

An integrated AI ops approach can help address this challenge. This combines AI operations (AI ops) with traditional IT operations (IT ops) to create a unified approach to deploying and maintaining AI systems. This approach includes a range of tools and techniques for automating AI operations, monitoring performance, and detecting and resolving issues.

By integrating AI ops with IT ops, organizations can ensure that their AI systems are deployed and maintained properly, which is critical to building systems that are accessible and meet the needs of all users.

What this looks like in action

A retailer needed to create a model to onboard 700,000 product SKUs across three product categories with a six-person team that had no data science knowledge. The team made this happen in a mere nine weeks as opposed to previous 30-week engagements by building the models, validating the outputs, and retraining it to not only onboard the product, but create an informative listing of the product. This gave shoppers all the details needed to make a knowledgeable purchase and improved productivity by 70%!

Creating accessible AI requires a multifaceted approach. An AI-ready no-code or low-code cloud platform, an AI-trained multidisciplinary ecosystem, and an integrated AI ops approach can help ensure that AI systems are accessible and meet the needs of all users. By making AI more accessible, we can unlock its full potential and create a more inclusive future for any team looking to get started.