Want to know why AI reliability matters so much? Imagine this: a financial institution deploys an AI-powered loan approval system. Initially, it seems to work flawlessly, streamlining processes and speeding up approvals.

However, over time, inconsistencies begin to surface.

• Some applicants are unfairly denied
• Others receive approvals based on user-input false data
• Still others receive an approval that is later rescinded when human review reveals it should not have happened.

How do you think this impacts executive commitment to AI transformation? Consumer loyalty to the bank? The bottom line? The system, conceived as a way to enhance efficiency and customer experience, now breeds lost revenue, increased financial risk, distrust and potential legal liabilities.

This cautionary tale illustrates the critical need for reliability excellence in AI.

Preventing Big Problems with AI Reliability

Problems like this are all too real and they aren’t confined to the financial services sector. Here are just a few real-world examples of companies that suffered significant problems as a result of unreliable AI solutions:

• An Air Canada customer service chatbot provided a passenger with incorrect information regarding bereavement travel discounts.This led to a negative customer experience, massive negative publicity, and a court ruling against the airline.
• McDonald's AI drive-through experiment resulted in humorous yet problematic scenarios where the AI misinterpreted orders, such as excessively increasing the quantity of items, leading to the termination of the pilot program.
• Perhaps more concerning was the case of IBM Watson for Oncology, which provided inaccurate and potentially unsafe cancer treatment recommendations due to its training on a limited set of hypothetical patient data rather than comprehensive real-world cases.

While the buzz around artificial intelligence is undeniable, actually making it happen can also instill fear in business leaders who read “disaster stories” like those above. Amidst the excitement, a nagging question persists: can we deploy AI solutions that embody the reliability and dependability that are critical to achieving our larger goals for AI transformation?

A Winning Framework for Delivering AI Reliability

Naturally, there is a lot of specific technical knowledge required to deliver an effective AI solution. We focus on a business model that combines human experts and agentic AI to enable you to capitalize on AI across your organization without having to build a large internal AI and data science team. However, as a decision-maker, it’s important for you to have a conceptual understanding of AI best practices so you can ensure that the solutions you deploy deliver maximum value to your organization. This post will explain those key concepts in language designed for the generalist executive.

The RapidCanvas Reliable AI Framework

1) Grounding

AI solutions must be anchored in verifiable reality. Think of it as ensuring your AI isn't living in a fantasy world. We've all heard stories of AI "hallucinations," where models invent facts or confidently assert falsehoods. This can lead to serious errors and erode user trust.

Grounding AI involves three core elements:

• Rigorous training with high-quality datasets: Data used by the model must be controlled and accurate. Sounds simple, but there are ultiple very public examples about AI solutions making up information. Just as problematic and fraught with potential risks are solutions built on data that are themselves unreliable. Most of us know the adage, you can prove anything with statistics. Grounded AI is always based on quality data pertinent to your specific industry.
• Continuous fact verification: Ongoing validation of the data used by your AI solutions ensures the AI accuracy necessary to create confidence and trust. It ensures truthfulness. If accuracy problems arise in the data, it’s important to catch them before they drive bad business decisions.
• Clear attribution to reliable sources. Attribution ensures that we can trace data to its source and validate its veracity. This not only ensures AI reaches correct conclusions but that users are assured that the conclusions are correct. Similarly, reliable sources help avoid biased data.

Companies that deliver Grounded AI are far more likely to gain the transformation benefits we all want for our businesses. Here are some examples of companies that did get it right:

• Carrefour Taiwan's AI Sommelier assists customers in selecting wines by leveraging a vast, grounded wine database 46.
• Etsy utilizes AI training to optimize its search recommendations, effectively grounding the AI in product and user data 46.
• ING Bank has developed a generative AI chatbot for its employees, grounded in the bank's internal knowledge base to enhance self-service capabilities 46.
• Scotiabank uses AI to power its chatbot, grounded in the specific financial data of the user financial data to offer more personal and predictive experiences.

2) Explainability

Explainable Artificial Intelligence (XAI) us all about ensuring that the people who use the solution understand and trust the results and outputs. This is crucial for building confidence when crafting system design and deploying AI models into production environments. By providing insights into how AI arrives at its conclusions, XAI delivers transparency.

XAI is also essential to ensure legal and regulatory compliance in AI system design, especially in highly regulated industries like finance and healthcare and in regions with privacy regulations like the EU. Your AI development methodology should be established with explainability by design.

Here are three examples that underscore the value of Explainability:

• Healthcare, XAI is used to explain diagnoses derived from medical images, such as highlighting specific areas on an X-ray that indicate a potential condition and outlining the reasoning behind predictions of patient outcomes.
• Financial Services: XAI is critical for explaining the rationale behind loan approvals and denials, providing transparency in credit scoring processes, and identifying factors that trigger fraud alerts.
• Human Resources: XAI ensures fairness and transparency in recruitment processes by revealing the factors considered in screening candidates. It can demonstrate that a hiring decision is the correct answer based on company needs.

3) Consistency

Consistency is about ensuring AI performs predictably and dependably over time, even as it scales to handle increasing workloads. Inconsistent AI can lead to inappropriate system functionality and erratic behavior and fluctuating performance, making it difficult for businesses to rely on it for mission-critical tasks. Maintaining consistency requires robust model design, comprehensive testing, and continuous monitoring.

A great example of the importance of consistency comes from the ecommerce space. Imagine an e-commerce platform using AI to personalize product recommendations. If the recommendations become less accurate over time, customers will fail to consider them and may even move to buying from a competitor that can deliver more value.

Another comes from the arena of predictive maintenance. If you deploy an AI solution to help prevent manufacturing or service failures, inconsistent AI can cause more problems than it solves. You could be engaging in unnecessary maintenance activities or failing to detect indications of potential catastrophic failure.

4) Privacy and Security

Almost every business collects, processes, and leverages vast amounts of customer information – much of it extremely private in nature. Business also have their own methodologies and competitive advantages – the intellectual property (IP) that helps them deliver extraordinary value. Protecting this data is essential for both compliance and customer trust.

Private AI and secure AI ensures that data remains confidential. Techniques like data encryption and anonymization further enhance security and are critical for AI reliability. Here’s how that comes into play in a couple of highly regulated industries:

• Financial Services: Private and secure AI could help identify potential fraud without compromising the personal information and security of the company.
• Healthcare: Private AI could enable a company to leverage AI for diagnosis while remaining compliant with HIPAA and other patient privacy recommendations.

5) Benchmarking and Currency

One of the most important attributes of a strong AI solution is continuous improvement. The AI landscape is constantly evolving, with new models and techniques emerging regularly. Benchmarking involves systematically evaluating and comparing AI models to ensure they utilize state-of-the-art technology.

Great AI solutions incorporate continuous learning and retraining to maintain and enhance AI model accuracy rates and effectiveness over time. This ensures that AI systems adapt to changing data patterns and remain relevant.

Let me provide just a couple of examples of why continuous learning and improvement are so important:

Competitive benchmarking: AI applications are increasingly a key lament of a company’s competitive advantage. Companies that stay abreast of the latest advances and developments in AI, and reflect those in their tools, maintain and enhance their competitive advantage. Those that don’t update and improve their solutions can quickly fall behind and lose their edge.

Natural language processing models are constantly improving as systems ingest more data, find more language patterns and connections, and adapt to changing consumer usage. By constantly enhancing natural language capabilities, you can keep customer-facing experiences relevant and make them progressively easier to use. Similarly, AI applications that are used by “laypeople” within your company also need constant learning.

Recommendations engines are greatly enhanced when they reflect the latest shopping patterns and preferences of consumers. In ecommerce, current AI can leverage insights on “hot” items to deliver more appealing upsell and cross sell suggestions.

AI Reliability Conclusions

The buck stops with you. As a leader, you are responsible for ensuring that your company gains maximum benefit and ROI from AI initiatives. You are also responsible for ensuring that your AI initiatives don’t bring inaccurate information, reputational damage, and legal risk.

If you’re reading this post, you’re probably not a data scientist. Your expertise is in your respective function and industry. By understanding AI reliability and the framework for delivering it, you can help guide your company’s AI development decisions with greater confidence.

At RapidCanvas, we understand the critical importance of building trust in AI. Our methodology is deeply rooted in the five pillars of reliable AI.

• We prioritize grounded AI by ensuring our models are trained on high-quality datasets and continuously verified.
• We embrace explainable AI, providing transparency into AI decision-making.
• We achieve AI consistency is achieved through rigorous testing and monitoring
• AI privacy and security are paramount in our data handling practices.
• Finally, we remain committed to continuous improvement and benchmarking our models and staying abreast of the latest AI advancements.

We recognize that businesses need AI solutions they can depend on. That's why RapidCanvas goes beyond simply providing cutting-edge technology; we deliver AI with integrity. We believe that AI system trustworthiness is the cornerstone of successful AI adoption, and we're dedicated to helping businesses bridge the AI trust gap.

Get Expert Perspective for Your Company

Our team would be delighted to meet with you to discuss your AI needs and the right way to develop and deploy transformational AI solutions. Our “human experts + Agentic AI model” provides the best and fastest approach to delivering the AI-powered solutions you need to drive measurable and meaningful ROI gains. Contact us for a consultation today.

You can also read our AI Terms Crib Sheet for Non-Data Scientists.