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This is just some announcement which people will want to pay attention to! Learn More

Does your business really need AI?

Investing in new solutions for your business is a huge decision. You want to be sure that you’re choosing the right tools that will work for your unique enterprise, helping you achieve specific goals, saving you time, and giving you a reasonable ROI. You also want to make sure that your solutions are scalable, so that they can continue enabling you as your business grows and changes over time. So if you already invest in business intelligence solutions and other data analytics and exploration tools, do you really need to invest in artificial intelligence tools, too?

First let’s look at what existing business intelligence (BI) tools do for an enterprise. 

BI has been critical to most businesses for many years now. Technically, some forms of BI have been around for 150 years or more: merely using a spreadsheet to track performance can be considered a form of BI! But business intelligence as we know it today—the solutions like reporting, dashboards, and advanced visualization that most enterprises have grown accustomed to—have been in regular use for between ten and twenty years at most companies. That’s long enough for a lot of organizations to stop noticing how little value their BI solutions might be delivering, despite the fact that BI is often one of their biggest investments

The promise that BI solutions offer tends to be similar across companies and solutions: the aggregation and visualization of data will help your organization make better business decisions. 

But there are some problems with this ubiquitous promise. The first is an issue familiar to workers at many levels in all kinds of businesses: humans become inundated with the massive numbers of reports that their BI generates. There is so much data that simple visualization is no longer enough to make it helpful. Companies are sometimes forced to hire multiple employees simply to tackle the interpretation of reports, wasting time and resources. 

The second problem with BI is that it only provides high-level ideas for enterprises. It still leaves the decision-making—even the day-to-day minutia—to humans who could be spending their time more productively. The purpose of BI is to speed up the process of making large-scale business decisions by making more data available for decision-makers. Because it isn’t tuned to the level of human intelligence, BI cannot actually make decisions for you, not even tiny ones that seem like they should be automatic. 

This is why businesses do need AI: it can solve both of the problems mentioned above. Unlike BI, AI drills deeper into your business and actually makes small, daily decisions for the enterprise based on fully analyzed, complete data sets that are being constantly updated and monitored. AI can eliminate the need for humans to deal with reports and analyze massive data sets that might be changing each day or even multiple times per day. AI can analyze data at a scale and rate that humans can’t, and automatically make decisions that propel you towards your business objectives, leaving humans free to spend their time working towards the more ambitious and complex goals that AI can’t handle. 

AI isn’t failsafe, of course. Many AI projects fail for a variety of reasons, most commonly:

  • Data isn’t adequately structured before use
  • Business challenges aren’t translated into workable data science problems
  • Biases aren’t eliminated from AI solutions

CrowdANALYTIX has managed to solve these AI issues so that AI can reach its full potential, often working in conjunction with older BI solutions. By using a specific solution for data structuring, deep-diving into the business to fully understand its unique challenges, and leveraging the power of crowdsourcing to do away with bias, CrowdANALYTIX stands apart from other AI providers. 

By adding AI to existing BI, your business can move from working with large-scale overviews of data to working with granular, detailed data that’s always up-to-date, and even start relying on AI to make faster decisions on a small scale, freeing up human resources and reducing the time it takes to reach important goals. 

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