like the hype Artificial intelligence continues to gain strength, companies of all sizes and from all sectors are investing in this technology.
According to a Forbes Advisor survey, more than half of companies use artificial intelligence tools to improve and refine business operations and help with cybersecurity and fraud management. Fewer are leveraging AI for more specific applications, such as customer relationship management, inventory management, and content production, but its popularity is growing. Some companies are even using AI for recruiting and talent search.
Unfortunately, organizations are learning that heeding the adage “garbage in, garbage out” has never been more important. As AI implementations become more frequent (and more complex), companies are finding that the quality and quantity of data used by the AI system is directly responsible for the type of results they achieve.
This may seem obvious, but many companies base their data storage strategies on the end of the month or the end of the year. In a world where a generative AI application is expected to deliver results in real time, this immediately sets the organization up for failure. The company’s AI data strategy should be to deliver the data to be used for AI to any downstream repository from any operating system in time so that the AI engine not only “analyzes” the data and executes predictions, but also also implement them on time. to make difference or change the results of a process in flight.
Garbage in garbage out
All aspects of AI depend on massive data sets, so it makes sense that the biggest risk to AI lies in bad data. Poor quality data will not only lead to a poor result, but will also train the model incorrectly for all future calculations and predictions. If a company includes unstructured, non-standard, and incomplete data in its AI models, the results will be completely unusable at worst, or incorrect at best.
The way an AI model is designed is obviously vital to system performance, but the model depends on data to complete its tasks. The more diverse and complete the data, the better the AI can perform.
With its ability to process and analyze massive data sets in real time, AI should enable businesses to transcend the traditional limitations of data analysis. Instead, organizations are facing new challenges in their rush to jump on the AI bandwagon. Even the concept of real-time data collection can become a challenge, as it takes time for data to reach the server where it is stored and processed, and that small delay is often not accounted for in AI models.
Paying attention to privacy
Data privacy is another aspect that many companies do not consider when starting their AI journeys. AI systems collect and use a large amount of data to learn, but while most data can be collected from intentional sources, such as when customers provide their personal information, a large amount is also collected from unintentional sources, such as when customers provide their personal information. where AI collects data. without individuals realizing it.
Since AI can unintentionally collect our personal data without us realizing it, and since data can end up being used in ways that are not always expected, regulations (and the ability of organizations to comply with them) are struggling to keep up. . For example, some call centers have begun using AI-powered speech recognition to help identify a customer’s mood and better tailor their interactions with them. If they call in a bad mood, the AI can identify the fact that they are angry or frustrated, allowing the customer service agent to start the conversation in a more peaceful manner than with someone who has been identified as being in a good mood.
Therefore, it is vital that we find a balance between personalization and privacy. The ability of AI to generate deepfakes adds an additional layer to this problem. Companies must evaluate the full privacy implications of how they use AI, taking a close look at how data is used, stored and accessed. Privacy laws like Popia and GDPR restrict the collection and use of customer data, requiring careful handling of information to ensure privacy, but while AI systems can start out complying, there are hundreds of different ways where data privacy may become an issue as AI learns and evolves
As we continue to advance our AI implementations, understanding these dynamics will be key to harnessing the true transformative power of the technology. People tend to believe that AI is a magic wand that will solve all our data quality and trust problems, but the role of AI is to find insights from good quality data, not necessarily to try to fix data management processes. decades-old data. From predictive analytics to anomaly detection, AI algorithms can uncover patterns that human analysts might miss, allowing businesses to make proactive, data-driven decisions, but the first step is recognizing the fact that The organization must start with a data strategy, not a strategy. AI strategy.
