What is AI maturity?

When exploring the world of digital transformation, you might stumble across the phrase AI maturity – but if you’re not too familiar with this idea, it might not immediately be clear what this expression means. In our latest blog post, we’re talking about defining this concept, how it impacts data analytics as a whole, and the exciting potential it holds when it comes to reconfiguring future approaches to healthcare.

If you’ve been following this blog for a while, you’ll likely already be familiar with a number of niche concepts within the field of data analytics. That being said, AI maturity is a relatively new area of study that requires a little extra attention and investigation.

As we’ve already covered, there are a number of ethical considerations to be made when implementing a data analytics framework in your organization. It’s important to take note of issues surrounding privacy, security and best practice, and a responsible adoption of this exciting new technology is growing across all sectors. AI-based systems are notably different to non-AI based solutions, and data analytics and AI ethics are key elements to the successful use of a data framework, with one crucial aspect of this being AI maturity.

Essentially, AI maturity is the concept of continually monitoring, training, and evaluating AI-based systems for performance. It’s a wholly necessary stage in the AI implementation process – namely because AI-based systems operate within the realm of probabilities. In order to safely, securely, and responsibly allow an AI-based system to reach its full potential – and enable the organization in question to reap the benefits of this as a result – maintaining the performance, predictability and, most importantly, accuracy AI is paramount in ensuring its successful adoption.

By engaging in continuous observation of AI maturity, organizations can protect themselves and their data against AI that might take a route away from its original purpose or intention. Guarding data against the harms associated with decay and bias are crucial in ensuring the AI technology in question can be trusted: without establishing a concrete sense of reliability, implementing an AI framework is a waste of everyone’s time.

Engaging in consistent and coherent AI maturity observation might seem to be a lengthy and time-consuming process, but the benefits greatly outweigh any potential negative aspects. Businesses which rush to implement AI frameworks without considering the maturity of this concept are likely to face difficulties further down the road – which can range in severity from mildly inconvenient to PR crisis. Ensuring fairness and mitigating bias is essential in ensuring the most basic stage of AI maturity, with training to identify and control this aspect of data analytics crucial to avoiding discriminatory or prejudicial experiences – something of particular importance in the healthcare sector.

It's obvious to see why so many healthcare organizations are eager to implement AI-based frameworks into their day-to-day structures, but it’s important not to rush this process for fear of being left behind. Considering AI maturity is arguably the most important step in digital transformation in the health and patient care sectors, and by taking the time to fully understand how this technology can best function is key in setting realistic and achievable goals, improving healthcare equity, and shaping the future of your business.

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