The Future of AI in business analytics is very promising. It is an inevitable conversation every entrepreneur needs to have today. As reported by a Forbes survey, 56% of businesses are already investing in AI integration for business process optimization. This of course, also includes the business analytics side of things. In business, as with anything else, practice is the only thing that makes you perfect. Business analytics is the metaphorical performance gauge in this context. The only way to know how, to improve is through analysis. You won’t know something is wrong until you assess it. For the longest time, business analysts have been the drivers of change in businesses. So much so that they are even contracted at a premium. All because the savings they can create are worth every penny. Now, however, an entirely new variable is changing the game: AI. AI in business analytics is a massive leap in progress for business process optimization. Business analysts are no longer stuck manually collecting and organizing data to analyze. While automation technology has been a great boon, AI is a different ball game altogether. The difficulty lies in implementation. AI is a highly specialized technology and must be handled with great care. While it offers much for business analytics purposes, there is also a lot that can go wrong. Let’s talk about some of the ways everyone else is leveraging AI, and how it affects outcomes. As the saying goes, don’t knock it till you try it. Or till you learn more about it online at the very least. One of the biggest contributions of AI in business analytics is predictive analysis. The fact that today, machines can make reasonably accurate future predictions, is a landmark in human progress. With AI, business analysts can process data and generate incredibly accurate predictions for the future. This can include things like future business financial performance, for example. Such metrics are invaluable for decision-making. A business analyst can work best when they have an idea of what to expect. Budgets can be adjusted based on predictive data to time optimizations during high cash flow seasons. With predictive analysis, while not entirely accurate, business analysts can draw a clear road map of optimization goals. Instead of waiting to be able to implement optimizations, a clear plan with priorities becomes viable. Then, business activities can be pivoted to try and achieve optimization goals. The savings from each optimization milestone contribute to the next. As for the future of AI in business analytics, 83% of businesses have already begun implementation. VentureBeat additionally reports that 44% of these businesses have incorporated AI fully into the strategy room. Suffice it to say, the future of AI in business is looking bright and sunny so far. The most difficult part of business analytics is collecting and organizing the required data. Businesses are data-generating monsters, with terabytes of data regularly being generated. All of this data is simply too cumbersome to make use of properly. A good portion of a business analyst’s time is spent trying to sort out good data from bad. Then, this data needs to further be categorized and analyzed to derive business performance insights. As much as 44% of weekly work time is wasted sifting through data according to a report by ZDNet. All of that wasted time can be spent doing productive analysis and developing implementation strategies. Business analysts are there to tell you where things are going wrong and how to improve them.So instead of wasting everyone’s time, why not turn to AI for help? Sure, there are risks involved in this. For one, there is simply so much data that bad data will inevitably get mixed in. There are also ethical considerations to be made on how an AI is using the data fed into it. Security concerns on whether the data in the AI database is secure are another major risk factor. There is much that can go wrong when trusting a machine with sensitive information. This is where the beauty of technology shines through. While trusting AI with everything is probably not the best idea, it is also not a reason to disregard it. Business analysts can still leverage AI to great results while staying on the safe side. For example, feeding only marketing and public financial analytics to AI is a pretty safe move. This data is not as sensitive and for lack of a better word, is expendable. In the unlikely event that there is a data leak, sensitive data like customer information is far from the scandal. By selectively leveraging superior data processing abilities, you can have your cake and eat it too. In the future, if you are more confident in AI’s abilities and security, you can transition to a full implementation. AI-driven automation is an incredible tool for business analysts. Even outside of data processing, tons of business processes can simply be automated. Data entry, report generation, pattern identification, behavioral analysis, and so many more activities can be addressed with AI. For example, businesses can feed customer reviews and interactions into AI. The AI can then analyze the text in bulk and generate an empirical report on customer response. Similarly, say that there is a pattern in the sales of a certain product at certain times of the year. AI can sniff out any repeating patterns and behaviors in the market and suggest possible causes. All of this work is doable normally, but would it not be easier to have an AI do the basic data management and report production? Then, business analysts can chisel that rough data into a clear and smooth performance picture. Why make people work more than they need to?Then there is the aspect of error mitigation. So many bad decisions find their roots in incomplete or incorrect data. One number mistyped can completely change strategy. With AI, a lot of these human errors are easily eliminated. Not to say that AI is impervious to making mistakes. More often than not, however, AI will be able to recognize and correct itself. Relatively speaking, AI will make far fewer mistakes than regular people, and that’s the takeaway. As with any new technology, there are upsides and downsides to AI. There is also an equally rational argument out there for why AI should be regarded with skepticism. Expertise Accelerated provides an excellent nuanced look at what AI can do and what is purely hype and myth.Our purpose here is not to decide who is right or wrong. Instead, we mean to open discussion on integrating AI in business analytics. What benefits it can offer, and what are its limitations. Ultimately, it is your business and decision to use AI or hold off. What we offer is a glimpse at the potential of AI in business analytics. With luck, this discussion should help empower business analysts and entrepreneurs to make an informed business decision. How AI in Business Analytics is Reshaping the Business LandscapePredictive Data Analysis
Unparalleled Data Processing Ability
The Role of AI in Business Analytics
Eliminating Human Error and Time Waste with AI Automation
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