AI Pitfalls: Two Reasons Why Your AI Technology Doesn’t Feel So Intelligent

Editor’s Note: This post was originally published in December 2023. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

Artificial intelligence (AI) is changing our lives in ways we never could have imagined just a short time ago. For years, virtual assistants like Siri and Alexa have provided helpful reminders and performed tasks for us. Self-driving cars fill our roadways and parking lots. And GenAI solutions like ChatGPT, Claude, and Gemini, are all the rage now, providing extraordinary capabilities for doing research, analysis, summarization, content creation, and much more.

AI delivers value in many ways. But many times, it also fails to deliver on its promises. Faulty results can trigger a string of negative effects, ranging from bad decisions and business disruptions to reputational harm and legal challenges. 

The good news is that there are ways to overcome these challenges. To avoid AI pitfalls, organizations must embrace two fundamental things: clean data (the input data AI is trained on using machine learning models, and data used in context in RAG use cases) and human intelligence. Together, businesses can realize the true potential of AI and use it to work smarter and achieve unparalleled success. 

Reason 1: Your Data is Junky

Clean data is essential for any successful AI application. And it makes sense. When the data used to train the AI models is incomplete, inconsistent, or irrelevant, the results will follow suit.  In addition, when poor quality data is used as context for an AI application request, the model will work and write badly.  Think of it like giving an intern the wrong information about a customer, and then asking them to write the customer an email.

Unfortunately, junky data is a pervasive problem. And that junky data comes with a price. Bad data can cost organizations millions per year. And while I believe that financial losses are reason enough to prioritize improving data quality, if you need another reason, consider the cost of irrefutable harm to your brand reputation. 

Fixing poor data quality is not a simple task. And when companies rush to operationalize their data before fixing the underlying quality issues, it becomes even more difficult to fix and degrades organizational trust in that data. But just as AI is revolutionizing how we live and work, it can also play a critical role in improving data quality. Outdated, rules-based approaches to data cleansing simply cannot keep pace with the ever-growing volume and variety of data that AI models require. That’s why many organizations are turning to AI-native MDM to help them quickly and efficiently identify missing data and spot inconsistencies. 

Using advanced AI and machine learning models, AI-native MDM standardizes, validates, and enriches data from a myriad of sources, unifying it into a cohesive format that is consumable by users. The result is validated, trustworthy data that helps you increase operational efficiency, deliver exceptional customer experiences, uncover hidden revenue opportunities, and safeguard your business from unforeseen risks. 

Reason 2: AI Needs Human Intelligence

Cleaning up your data is a good first step in improving AI success. But better data alone is not enough. That’s why humans continue to play a critical role when it comes to validating AI success. From concerns about the lack of transparency in the learning models to questions about bias, discrimination, and ethics, many people still don’t trust the insights AI provides. But when you combine AI with human intelligence, you foster greater trust in the data. It’s a critical step in the MDM journey, and one you shouldn’t overlook.

Humans have the innate ability to provide contextual understanding to the data that’s based on their personal experiences and inherent knowledge - that’s something a machine alone can’t do. Humans can apply intuition and emotional intelligence as well, adding depth and clarity to decision-making. And through critical evaluation, they can provide feedback when they spot something that looks off. 

The key, however, is making it easy for humans to provide feedback and for the AI models to learn from it. AI-native MDM employs a closed-loop process and user-friendly interfaces that  make it easy for cross-functional stakeholders to share feedback that improves the quality of the data and, therefore, the quality of the models themselves. Humans can also highlight results that appear off - either because they are inaccurate, biased, or unethical - safeguarding the business from potential harm. 

Help Your Business Run Better

Capturing the full potential of your data requires a new, AI-first approach that accelerates the mastering of data, while still acknowledging the critical role human expertise plays to ensure the data remains trustworthy and high quality. That’s where AI-native MDM comes into play, providing value and benefits that rules-based MDM simply cannot deliver. With AI-based data mastering, you can overcome the limitations of traditional MDM solutions by providing the flexibility to adapt to the needs of modern, data-driven businesses. Through the combination of AI’s efficiency and scalability with business context and human expertise, AI-native MDM enables companies like yours to (finally!) deliver data everyone can trust.

AI-driven innovation is the path to future business success. Ignore it, and you’re left behind. But embracing AI comes with its fair share of challenges. The secret lies in improving data quality and harnessing the intelligence and valuable feedback from people across your business. That way, not only do you instill confidence in your data, but you also build trust in the power of your AI, enabling your business to work smarter and exceed its goals. 

To learn how Tamr can support your MDM journey, please download our latest ebook.