AI Can Process Your Data 100 Times Faster Than Your Team. Here Are 3 Ways to Turn Your Data Into a Real Competitive Advantage.

Key Takeaways

• Most C-suite executives admit their companies aren’t extracting real value from AI.
• Keeping humans in the loop isn’t a workaround for AI’s limitations.
• Founders seeing success with market trend reports aren’t reading category-level data — they’re drilling into behavioral signals specific to their own customer base, like a high-value segment quietly reducing purchase frequency.

Entrepreneurs turn to data to help guide their decision-making. Now, AI is giving them the chance to dig deeper into the information they gather, helping them make competitive gains faster.

What makes AI such a helpful tool to unlock faster growth? Two answers: AI systems can process millions of data points quickly and identify even the slightest relationships among them. Although humans can do likewise, they can’t do it at the speed or scale of AI. In fact, some reports suggest that an AI system’s processing time is 100 times that of a comparable human team. And other research indicates that AI tools are powerful for long-term tasks, not just one-off functions.

In other words, AI-driven tools could help companies gain a meaningful edge over competitors. Yet many founders (and their direct reports) still haven’t figured out how to use AI to leverage their way to the top. In fact, the majority of C-suite executives in one survey admitted that their companies weren’t seeing the potential value from AI.

This isn’t an AI problem, though. It’s a human problem: many founders and their teams aren’t taking the steps that could best help them use AI to get the most from their data. However, there are a few steps they can take to begin seeing more benefits from AI and the data it uncovers.

Identify and remove all data silos

Many businesses have a lineup of data silos. Each silo contains a wealth of information, but the information isn’t shared with other silos. Consequently, valuable insights from cross-comparing data across those silos can be difficult to identify.

AI software and tools can help break down those silos, turning locked data into usable data. For example, many industrial organizations have turned to highly customized DeepAuto.ai to centralize complex data across their systems and then deploy AI agents that can extract valuable insights from the shared data. Because the AI agents can work both independently and collaboratively, they can help identify opportunities and gaps in workflows more quickly. Once known, any uncovered strengths and weaknesses can be appropriately handled by team members.

How can teams find their data silos in the first place? Salesforce recommends creating a comprehensive organizational data map based on survey analysis that’s obtained from team members across departments. Salesforce offers a Data 360 solution designed to bring data together from disparate, siloed sources in a generalized way, rather than being customized by industry or company.

The mapping process itself may reveal gaps, such as sales teams sitting on customer churn signals that product teams never see, or finance holding cost data that could significantly influence a marketing budget.

Just completing the exercise requires cross-departmental conversations that many organizations may have postponed, and these conversations alone can lead to meaningful operational changes.

Avoid going on autopilot and keep humans in the mix

AI can help find data, but it hasn’t reached the point where it can make decisions the way humans can. Human oversight remains important for data analysis and reporting, helping organizations get more value from AI-derived data.

Some companies are also exploring the use of generative AI to assist, particularly in customer service roles and processes. Although humans can (and should) engage with customers, they can resolve issues faster when generative AI chatbots help in the background. The chatbots can fetch information and give human agents insights to offer practical solutions to customers.

The point is that humans shouldn’t necessarily be excluded from the equation. It can be preferable to keep humans in charge to provide guidance, ask questions, and ensure that AI and data applications are grounded in real-world needs.

This is particularly the case if the AI reveals an anomaly or an outlier in the data, the sort of event that a human might view as a temporary shock to the market rather than a trend worth pursuing.

If founders are ready to treat AI’s output as the final answer rather than a starting point, they could face costly pivots based on incomplete context.

Prioritize the creation and analysis of market trend reports

Many companies can use their own and others’ data to identify emerging trends. While AI-based reporting isn’t a crystal ball, it can be a useful way to forecast industry trends.

For instance, Faraday’s AI platform can provide context on customer behavior. Its predictive capabilities can provide insights into potential customer needs and actions based on a mix of historical and likely behaviors derived from data points.

Of course, leaders in most areas of a business can benefit from identifying market trends before they become widespread.

One way to stay ahead is to pull regular trending reports for different areas of their business. (This can be made much simpler with off-the-shelf tools like Microsoft’s Power BI, which can provide quick visualizations of real-time data once it’s programmed.)

So, what separates the founders who act on these reports from those who file them away? Specificity.

A big trend report showing that customer spending is slowing down is mildly interesting. A report showing that your highest lifetime-value customer cohort is buying 18% less often over the last 90 days is actionable.

Founders creating a reporting cadence must ensure they push their team to focus on behavioral signals rather than just category trends. The specific, actionable insights will uncover where the real competitive intelligence is.

Companies and their teams have been reviewing solutions to the challenge of how to best use their own data for some time. Yet most haven’t been able to make the most of the data at their fingertips. They can uncover more information from data connections that might have gone unused in the past.