In the generative AI era, trust and transparency matter more than ever. Generative AI has revolutionized business processes, but its results are often opaque and unexplainable. Think of the AI tools you likely use every single day. You get an answer, and a majority of the time, you take that answer as fact, which is mostly fine. A hallucination here or there while you are trying to solve a scavenger hunt or plan your vacation isn't likely to have a considerable impact. But for enterprises and highly regulated industries, hallucinations are a big concern. Even though some of the top model builders can achieve sub 2% hallucination rates, many healthcare and financial services companies say that simply isn’t enough.

At Vectara, we have spent years thinking about hallucinations. We understand that they are a significant hurdle to widespread adoption. We also know that the reality of a zero hallucination rate is not likely in the next 2 years. There are workarounds; you can very narrowly define the test set and adjust the prompt language to instruct the system not to try and guess, but even then, you may see some levels of hallucinations. Rolling out to all your RAG use cases would also be very bespoke. You can also keep your RAG answer in check with guardrail models like Vectara’s HHEM model. That, however, is just a component of how Vectara mitigates the risks of hallucinations. The bigger picture is explainable AI. Explainable AI allows for human-in-the-loop correction, while advancements in these models are now starting to move to autonomous hallucination correction.

This blog explores the critical role of explainability, the risks of unexplainable AI, and how Vectara addresses these challenges.

The Business Implications of Explainability

The business implications of explainable AI are profound, particularly when it comes to making confident and informed decisions. If the trends continue, organizations will increasingly rely on AI to guide strategies, optimize processes, and deliver insights. Explainable AI ensures these outputs are reliable and transparent, enabling decision-makers to understand the "what" and the "why" behind AI-generated results. For many companies, this explainability is not a luxury; it's the glue needed to manage the gaps and mitigate the risks of AI hallucinations. This clarity reduces uncertainty and builds trust among stakeholders, fostering a culture of informed decision-making that drives long-term success.

Beyond decision-making, explainable AI offers tangible benefits in regulatory compliance and operational efficiency. By ensuring that AI systems adhere to data privacy and fairness standards, organizations can navigate complex regulatory environments more confidently, avoiding fines or reputational damage. Moreover, observing and addressing potential issues early reduces operational risks and prevents costly mistakes. Combined with the competitive advantage of being seen as a transparent and trustworthy organization, explainable AI mitigates risks and positions businesses to excel in a market that increasingly values accountability and ethical innovation.

Risks of Unexplainable Generative Outputs

Without transparency into how AI systems generate insights, organizations face significant risks that can undermine trust, compliance, and decision-making. This lack of explainability creates vulnerabilities that can harm businesses in profound ways, from losing customer confidence to encountering regulatory penalties or making costly strategic missteps. The damage can be felt deeply and can cascade into the following tricky implications.

Erosion of Trust with Customers and Stakeholders

When AI systems produce results that lack transparency or clarity, users are left questioning the reliability and credibility of these outputs. For example, a generative AI system providing recommendations without verifiable sources can lead to skepticism, undermining user confidence in the system's insights. Over time, this lack of trust can damage customer relationships and diminish the value of AI-driven solutions, making it harder for organizations to maintain a competitive edge.

Complexities with Stitching Together DIY Tools

Delivering generative AI that is wise to your business requires designing a RAG system with access to your specific data sets. This comes with a variety of checkboxes and concerns around data security, privacy, and governance. Many companies believe they can build and manage a RAG system with little overhead and by leveraging open-source innovation. As many companies soon find out, building reliable RAG systems is complex. It requires managing an LLM and vector data stores, model rerankers, and guardrail models to align with better accuracy. Even then, many companies still spend years prototyping a RAG system only to find out that retrieval and accuracy are not at the levels they need for the business. This means that the explainability of results is even more critical. It’s a non-trivial feature to build, especially in a way that enables end users to act on citations.

Regulatory and Compliance Challenges

AI systems that lack transparency may inadvertently produce biased outcomes, violating fairness standards or data privacy regulations. For instance, an organization relying on generative AI might face severe backlash if the system generates discriminatory results or fails to meet compliance requirements. Such incidents can result in hefty fines, reputational damage, and strained relationships with regulators, creating additional hurdles for businesses looking to expand their use of AI technologies.

If you look at this recent Deloitte State of Generative AI Report, you will notice the first 4-6 involve meeting compliance, governance, or putting humans in the loop.

Undermined Decision-Making

Without understanding the rationale behind AI-generated insights, decision-makers are left with limited context, which can lead to suboptimal or even damaging choices. For example, a business leader might base a strategic decision on flawed AI-generated results without realizing the underlying inaccuracies. This wastes valuable resources and reduces confidence in the organization’s ability to leverage AI effectively, impeding future innovation and growth.

How Vectara Meets the Explainability Challenge

Vectara addresses the critical need for explainability in generative AI through powerful features designed to build trust and enhance transparency. A critical foundation is that Vectara provides citations for all generative outputs, ensuring that every piece of information produced by the system is traceable back to its source. This level of transparency not only bolsters user confidence in AI-driven results but also empowers organizations to verify the accuracy and reliability of the information provided.

In addition to citations, Vectara offers advanced observability tools that allow organizations to monitor AI behavior and detect anomalies. These tools provide deep insights into how the model processes data, helping users identify and address biases or errors before they impact end-users. Complementing this is a complete chat history search, which provides seamless access to past interactions for trend and intent analysis. This feature enables businesses to uncover patterns across queries, improving operational efficiency and strategic decision-making.

Conclusion

Explainability in generative AI is no longer a "nice-to-have" feature—it's an absolute necessity in today’s business landscape. The opaque nature of many generative AI systems can erode trust, hinder compliance, and lead to poor decision-making, creating significant risks for organizations. In contrast, transparent, explainable AI empowers businesses to leverage AI confidently, making informed decisions and building stronger relationships with customers and stakeholders.

The risks of unexplainable AI are real, but so are the opportunities for organizations that prioritize transparency. Explainability through features like citations, observability, and chat history analysis enables businesses to mitigate risks, enhance operational efficiency, and gain a competitive edge. Organizations that embrace these principles are better positioned to navigate regulatory requirements and foster trust in their AI solutions.

Vectara stands at the forefront of this shift, offering an enterprise RAG platform designed with explainability at its core. With robust features such as verifiable citations, advanced observability tools, and searchable chat history, Vectara empowers businesses to unlock the full potential of RAG without compromising on trust or transparency. Whether it’s improving decision-making, reducing operational risks, or staying compliant, Vectara provides the tools businesses need to succeed in an increasingly AI-driven world.

By choosing explainable AI, you're not just addressing today’s challenges—you’re building a future of trust and accountability. Partner with Vectara to ensure your AI initiatives are as transparent, ethical, and effective as they need to be in a world where trust is the ultimate currency.