Getting AI-ready by Overcoming AI Trust Issues in 3 Easy Steps

In today’s rapidly evolving technological landscape, businesses are increasingly turning to artificial intelligence (AI) to gain valuable insights, improve decision-making processes, and drive innovation. As companies look to increase productivity and efficiency, better serve and target prospective and current customers, and create new business ideas, the explosion in the adoption of AI comes as no surprise.

PwC predicted that AI will contribute up to $15.7 trillion to the global economy in 2030 – more than the current output of China and India combined. But while artificial intelligence is expected to maintain its spot as a company-wide business priority for the foreseeable future, the sobering reality is that many organizations are not prepared with AI-ready data. In fact, in 2023 only 4% of organizations reported their data to be “AI-ready,” according to Gartner.

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Alongside this we’re increasingly seeing the unintended outcomes that businesses face when they rush into AI without the proper preparations in place. Last year alone we saw AI failures ranging from AI-written briefs containing fake citations, to renowned consulting firms implicated in non-existent scandals. Additionally, we’re seeing that many companies are lacking diverse representation in their data, resulting in AI being fueled by potentially biased datasets. While we like to think of data as being something that’s factual, or even impartial, the truth is that human biases can create data biases too. This is creating a variety of real-world issues – from impaired facial recognition software that less accurately identifies women and people of color, to inequities in healthcare provision, and more.

In each of these cases, the model that produced the results had inadequate training and inference data for the intended purpose, leading to skewed and flawed outputs – underscoring the need for AI-powered by trusted data.

Ultimately, AI outputs are only as trustworthy, or as meaningful, as the data fueling them.

So, for AI success, businesses must ensure the data powering AI models has integrity – meaning that it needs to have maximum accuracy, consistency, and context. Below I outline the three key steps companies should take to overcome data trust issues and help drive AI-readiness.

Solving AI Challenges with Data Integrity

The benefits of AI are plentiful, from leveraging AI-powered assistants to improve customer experiences to utilizing AI-powered workflows that streamline operations, and more. While accelerating innovation and improving productivity and efficiency are top use cases for leveraging artificial intelligence, these technologies don’t solve data management issues on their own. Diligent, ongoing management of the data fueling the AI is critical to ensure the data’s accuracy and relevancy and to catch any errors or biases in the training data, thus preventing any incorrect, or misinformed, AI outputs.

With this in mind, there are three crucial steps businesses can take to overcome the most commonly seen data challenges and ensure that their data is AI-ready:

Ensure Access to Critical and Relevant Datasets

Many companies embarking on AI programs are finding that they lack a holistic view of all their datasets. This is experienced when data is siloed in a variety of locations across different systems, and isn’t easily accessible in the cloud environment where AI is being managed. As a result, AI models get trained on datasets that are partial, for example to a specific geography or customer demographics, and outputs can become biased and unreliable. To address this and help produce reliable and trustworthy results, organizations must integrate their data and train AI models with all relevant critical data on-premises, in the cloud, and in hybrid environments – including complex data residing on mainframes.

Build Data Accuracy and Consistency

Even if a company has access to all critical datasets, the data itself could still be inaccurate or inconsistent. To prevent this, business leaders must invest in building a comprehensive data quality and governance strategy to make sure the data being used is accurate, consistent, and ready for use. For trusted AI outcomes, your data needs to meet rigorous quality metrics; It should be accurate, complete, validly structured, standardized, and free of duplicates. High-integrity data also needs to be timely and governed using a robust framework.

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Such strategies can also involve proactive data observability tools that use machine learning (ML) techniques to monitor the health of data pipelines, quickly identifying and addressing any anomalies in the datasets that may cause issues downstream if they were left unaddressed.

Unlock Greater Power from AI with Data Context

To produce AI outputs and recommendations that are contextualized, nuanced, and relevant, business leaders need to consider adding third-party data and geospatial insights. Adding context to datasets such as points of interest, demographics, or risk insights is critical to achieving data integrity, and therefore maximizing the accuracy and relevance of AI outputs.

For instance, when using AI tools to output natural catastrophe insights or predictive modeling, adding detailed geospatial points such as address information or environmental risk factors helps business leaders achieve more informed results and uncover patterns in the data that may not otherwise have been visible.

The Outcome? Reliable and Trustworthy AI-Powered Insights

It seems clear that for the foreseeable, we can expect the AI market to continue to grow at an exponential rate, with a seemingly limitless number of artificial intelligence capabilities and services available to those who want to leverage them. As such, the AI topic is one that is only expected to continue dominating C-Suite discussions as business leaders focus on improving efficiency and productivity across their organizations.

But in order to reap the full benefits of AI and avoid unwanted outcomes, businesses must first resolve trust issues with the data fueling their AI models. By prioritizing a robust data integrity strategy that helps build foundations of accurate, consistent, and contextual data, business leaders can drive AI initiatives that are high-performance, reliable, and produce quality outputs.

Ultimately, AI’s possibilities are as vast as the data it learns from, and this means that prioritizing data integrity isn’t just beneficial for today’s business leaders – it’s a necessity.

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