Implementing the latest and greatest artificial intelligence (AI) tool may seem like a smart decision. Knowing that customers are looking for real-time, 24/7 support and that intelligent machine learning can make teams more efficient, some business leaders may be quick to implement without thinking through the decision. But as many will recognize, moving quickly isn’t always going to lead to a triumphant first-place victory. Instead, industries that move fast may be overlooking key components of their decision-making process, skipping essential steps in the hopes that everything will work out in the end.
This guess-and-hope strategy, however, is going to cut it in today’s oversaturated, highly competitive marketplace. Customers will see right through a business that is quick on its feet, but doesn’t follow through. The same goes for employees who recognize that AI models with insufficient data will just churn out more insufficient data, not fix the issue at hand.
Here’s why data fixes will outpace AI chasing.
Treating Data as a Product Will Lead to Better Results
Having a bunch of data may seem like a good problem, and yet it may just cause more internal chaos and lead to swayed decisions. Data overload can create excessive noise, hindering the actual ability to identify what is important information and what is superfluous. But when data is the focus, and not the byproduct, everyone is working together to convert raw information into actionable, reliable insights. Teams move from working in silos to utilizing the same shared platforms for consistent, single sources of truth.
This type of work can lead to improved agility and efficiency. Companies can react proactively to new information sources, rather than scrambling when things take a turn for the worse. With everyone focused on the same internal platforms, it also reduces effort. Instead of multiple employees rechecking incoming data and finding the same types of errors, fewer people are QAing.
Lastly, when data becomes a product, there is clear ownership over it. Teams restructure themselves to ensure the right people are checking and looking for patterns and trends. This level of quality control can eventually be taught to the AI program, mimicking human-like thought processes and extrapolating information that is most pertinent to the organization. And having real data with actual meaning can lead to better decision-making all around.
Fixing Foundational Processes Means Extracting Relevant Data
Too much of anything is never a good idea, particularly when it comes to raw data. But adding an AI-powered tool to help clean up the data is only as good as the results you put into it. Instead, companies need to fix foundational processes to obtain reliable, relevant data that can actually make a meaningful difference for their business operations.
One way to do this is to focus on data classification. Effective classification systems ensure data is easy-to-access and even easier to understand. For example, an auto dealership with a master spreadsheet of potential leads to pursue is only helpful if proper contact information is also listed along with their respective interest level. A list of names isn’t going to help a sales team that’s eager to close deals quickly and efficiently.
But with the right classification system and organizational protocol in place, an automotive company can leverage generative AI in automotive tool applications to automatically classify, organize, and analyze a vast amount of data. With this, sales teams will know which leads are worth pursuing and when, as well as other pertinent client information to help clinch the deal.
Thinking About ROI Will Facilitate Better Internal Coordination
In the early days of AI, leaders and executives were quick to bring on new smart tech and tools to stay relevant. And while many of these tools meant well, they may not have been the best source of resources for every industry. AI is not a one-size-fits-all approach, and every industry has best practices and standards to abide by. Important features that are essential for an automotive sales team, for example, may not matter for an over-the-counter cosmetic brand.
Just like too much raw data can be detrimental to a company, too many tools can also be more harmful than they are worth. A marketing team’s tool doesn’t speak with an editorial or sales team’s tools, meaning that everyone is working in isolation rather than collaborating. Because of this, employees are faced with multiple logins, inefficient processes, and workflows that are more convoluted than they need to be.
Leaders who are focused on ROI, or return on investment, need to peel back the layers and get back to the basics. Having a few tools can be beneficial, and many of them can be connected for seamless collaboration. This means departments can once again focus on a common goal, rather than pushing down individual lanes. With this, data is synced and supported in one platform rather than being scattered across dozens of platforms.
Concluding Thoughts
AI isn’t going anywhere. It will continue to be part of businesses’ success stories. However, companies that fail to recognize that more data inputs aren’t always for the best won’t reap the rewards of what many of today’s top AI tools can do. Treating data as a product, fixing foundational processes, and facilitating better internal collaboration will allow for improved internal data quality, operational efficiency, and enhanced decision-making.
