As organizations globally accelerate AI adoption, the focus shifts from merely implementing AI to ensuring it drives tangible returns on the often massive investment. But an ROI on AI is not just about deploying the technology; it hinges on the explainability of AI decisions. This post explores why explainability is the key to unlocking AI’s ROI. We’ll also take a peek behind the curtain of data.world’s AI Context Engine™ as a pivotal tool for explainability.
Beyond the AI trend
Implementing AI is often seen as a strategic move to stay competitive. But the real justification shouldn’t come from jumping on the bandwagon. It should come from the specific, long-term value that AI adds—increased efficiency, cost reduction, enhanced customer experiences, and new revenue opportunities, to name a few.
As a business, you need to build a robust case for AI that goes beyond following trends. You need to focus on measurable outcomes.
The crucial role of explainability in AI ROI
Explainability in AI refers to the ability of AI systems to describe their processes and decisions in a way that’s understandable to humans. “Explainability” is the context behind an LLM response. It’s basically akin to a teacher asking a student to “show their work” on a long and complicated math assignment. This path to the response – “explainability” – is crucial for several reasons:
Trust: Stakeholders are more likely to trust and adopt AI solutions that provide clear, understandable explanations for their decisions
Compliance: In many sectors, being able to audit and explain AI decisions is not just beneficial but legally required
Error reduction: Explainable AI helps identify errors and biases in AI models, leading to better decision-making and more accurate outputs
Increased time-to-value: Immediately getting all teams on the same page to reduce confusion on business terms and key metrics, meaning product development and customer initiatives see value faster
An explainability use case
One of our customers had a lot of questions around their cruise ship business. They had questions like, “What is the longest cruise that we offer in 2025?”, “Which ship can carry the most passengers?”, and “What are all the ports that a given ship will visit in 2025?”
Different teams might have different interpretations of what “longest cruise” means. Is it the longest distance? Is it the longest period of time? People accessing this data need a clear business understanding of what a passenger is, what is a length of stay, what a port is, where that data is. We solved that problem for them with their existing data catalog platform. This is the direction that data catalogs should be headed.
What’s more valuable - chatting with data in general, or chatting with your data? Chatting with your own organization’s data means you have explainability baked in. Then, you can link this data to explainable AI outcomes, ensuring that the contributions of AI are visible, attributable, and giving you a serious return on your investment in AI infrastructure.
Challenges of predicting AI ROI
Forecasting the ROI of AI projects involves uncertainties related to data quality, integration complexities, and the evolving nature of AI technologies. These challenges make it crucial to start with a solid foundation of organized and understandable data—a foundation provided by explainable AI solutions.
Organized data alone is not enough. Organizations should use a data catalog, to organize data across disparate datasets and enhance explainability. This structured and clear dataset is crucial for AI systems to develop insights that are accurate, relevant, and easy to understand for all users.
Explainability and context
data.world’s AI Context Engine™ integrates these principles by combining a sophisticated data catalog with advanced AI capabilities, offering:
Natural language processing: Makes complex data queries accessible to non-technical users
Traceable and governable AI outputs: Every AI decision is documented, explainable, and compliant with regulatory standards
Enhanced decision-making: Better, faster business decisions that directly contribute to ROI
Final thoughts…
The path to realizing substantial ROI from AI investments rests on explainability. Without it, organizations risk misinterpretations and slow time-to-value with AI investments. Through solutions like the AI Context Engine™, businesses can ensure their AI deployments are both profitable and transparent.
Learn more about how the the AI Context Engine™ can make your AI investments worthwhile with explainability.
