The Increasing Demand For AI
Explainability Frameworks

Artificial Intelligence (AI) has been growing in popularity for many years now, to the point where it has become a daily ‘norm’ in businesses across the globe.

This new form of technology is not only making businesses work faster but it is having a range of knock-on benefits for employees and end customers too.

However, as AI is becoming more widely used, it’s also becoming increasingly more complex and requires a range of new rules and regulations to govern its usage. These enhanced control over the use and management of AI are known as AI explainability frameworks.

What is AI?

Artificial Intelligence, commonly shortened to AI, is a branch of computer science that focuses on creating smart machines that can make decisions and perform tasks that would have previously needed some human intervention.

AI has come a long way since it started to be used, now companies across the globe rely on AI, machine learning (ML), and Big Data to make their operations smoother and more efficient.

How Is AI Used in Business?

AI can be used in business to enhance the existing functions of your teams, but it can also be used to identify opportunities for technological growth – which could be the missing piece in your company’s growth aspirations.

This type of technology can usually provide benefits in three categories, process automation, cognitive insight, and cognitive engagement.

  • Process automation: Automated digital and physical tasks like back-office admin and financial activities that AI can take on, freeing up the time of human employees to work on more meaningful and important tasks.
  • Cognitive insight: Where machines use patterns and algorithms when interpreting data which can then be used by sales/marketing teams to make more informed decisions.
  • Cognitive engagement: Helps the business, employees, and customers communicate with each other easily through natural language processing chatbots, intelligent agents, and machine learning.

Why Is AI Explainability the Next Big Thing?

Because AI is expanding at such a fast rate, many regulators, consumers, and stakeholders are starting to pay more attention to how it’s working and the knock-on impacts this type of technology may have.

This is why AI explainability (XAI) is starting to become a priority for many companies that are looking to implement new technology solutions into their existing workflows.

Through XAI, businesses can quickly and clearly communicate the how’s and why’s of AI to their employees and customers to ensure there is no distrust or lack of understanding around how the new platforms will be working.

It can also help to bridge the gaps between different groups of people within a business. For example, developers who are interested in how models work and if they meet functional requirements have very different needs from business managers who are concerned with regulatory compliance and larger business goals.

By using a solid AI explainability framework, you can bridge the gap between your top executive and developers by implementing a strategy for everyone to understand how the AI works and how it relates to their specific job role.

Ultimately, the need for AI to be more understandable and accessible is becoming increasingly important in all industries. Even in industries like finance where AI is already being used on a widespread level, there still needs to be an ongoing strategy as their technology continues to grow and adapt.

It’s up to all employees to understand AI and be committed to ensuring that it follows all the necessary regulations and is used in a way that benefits the business in the long run.

How to Make Your AI Explainability Framework a Success

Introducing a new AI explainability framework in your business can often be a hard task when your employees don’t know much about AI, or if you’ve experienced a lot of pushback with new technology in the past.

However, if you spend the time to understand the needs of your business and choose AI platforms that complement existing processes, implementation can be a largely smooth process.

If you’re thinking about implementing new AI technologies into your business, it’s important to consider some of the following points before you start rolling everything out:

  • Have a good understanding of the types of data that you are currently working with, how this data is processed, and how it’s stored
  • Speak to your employees and conduct assessments of the needs and requirements of new AI technologies. What teams could benefit from the use of AI the most and how will the implementation of new technologies affect different teams; everyday tasks
  • Fully understand the system that you wish to implement, if you have a good idea of how it’s going to work, it will much easier for you to explain this to your team
  • Know the sector and domain that your new AI is going to be working in and make sure that it will be relevant