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

Artificial intelligence (AI) is changing our lives in ways we never could have imagined just a few short years ago. Self-driving cars fill our roadways and parking lots. Virtual assistants like Siri and Alexa answer our questions, provide personalized recommendations, and remind us of tasks. And large language models (LLMs) like ChatGPT (OpenAI), (Facebook AI), Bert (Google) and Roberta, are gaining momentum - and they’re gaining it fast.

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 training data (the input data AI is trained on using machine learning models) 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. 

Unfortunately, junky data is a pervasive problem. And that junky data comes with a price. Bad data can cost organizations up to 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 bad data continues to overwhelm our systems, it becomes increasingly difficult to keep it clean. But just as AI is revolutionizing how we live and work, it also plays 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 Outdated, rules-based approaches to data cleansing simply cannot keep pace with the ever-growing volume and variety of data that AI models require.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 data products to help them quickly and efficiently identify missing data and spot inconsistencies. 

Using advanced AI and machine learning models, data products standardize, validate, and enrich 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. 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. Data products employ 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

Data products are getting a lot hype. But If you ask three people, you’ll likely hear three different definitions. So, let me set the record straight. Simply put, data products are the best version of your data. And yes, it’s really that simple. 

Said differently, data products are a consumption-ready set of high-quality, trustworthy, and accessible data that people across an organization can use to solve business challenges. They are comprehensive, clean, curated, continuously-updated data sets aligned to key entities, such as customers, suppliers, or patients, that humans and machines can consume broadly and securely across an enterprise. Data products, powered by AI-driven efficiency with human oversight, provide a critical way to collect and manage data, guaranteeing its quality, reliability, and trustworthiness. 

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 data products can help you improve data quality, please book a demo.