Many experts herald generative AI (GenAI) as the bridge into a new era of work. Certainly, GenAI will change how employees interact with their systems and accomplish tasks. According to a National Bureau of Economic Research (NBER) study, GenAI integrations contribute to a 14% productivity boost among customer support agents, with low-skilled switzerland whatsapp number data employees receiving the most significant productivity gains. Other professionals in the information economy have seen similar boosts in productivity thanks to GenAI.
Yet Data Quality severely limits the potential of GenAI. AI cannot properly interpret, categorize, or understand data reserves if the hosting organization has no management strategy. Why? Because leading AI tools, including GenAI, subsist on data to contextualize their environment; without the right information to pull from, these advanced technologies are useless. In fact, depending on the use case, AI tools relying on disorganized data can even be detrimental to business outcomes.
To illustrate, let’s return to our earlier example about a bank using GenAI to synthesize its customers’ records. This mid-size bank has one million customers and relies on a manual data management system. An IT leader tasks a new GenAI integration to consolidate duplicative customer data as they look to streamline their organization’s cloud blueprint.