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4 Proven Benefits of Using AI in Data Analysis in Business

The world as we know and see is being completely revolutionized by our current technological era, giving birth to advances like AI (Artificial Intelligence) that’s evolving businesses.

In fact, according to a report from SEM Rush, AI is projected to significantly enhance business value and worker capabilities. “In 2021 alone, the increased usage of AI across businesses is estimated to create over $2 trillion in business value and over 6 billion hours in worker productivity.”

Stats like that is enough to prove just how significant the use of AI has become in business. Companies are now utilizing machine algorithms in identifying various trends & insights in large quantities of data and to make quick decisions.

From predictive analytics & deep learning to image recognition and chatbots, AI along with machine learning is completely changing the way businesses are engaging with customers with increased delivery rates in less time. But this is only the tip of a very large iceberg.

4 Ways AI in Data Analytics Benefits Businesses

It’s quite clear that businesses utilizing AI poses great value and significant benefits to each sector. As such, here are 4 proven benefits of implementing the use of AI in data analysis and what it means for businesses.

1. Efficiency & Productivity Gains

These two have achieved quite the fame and name among enterprises that have implemented the use of AI. And how is that? Well, the way the technology of AI handles tasks is at a much greater pace & scale that no humans can match.

Moreover, the removal of such tasks from the workers’ end helps create a space for the workers to shift their focus on those high-value tasks that technology simply can’t do. Thus, allowing organizations/enterprises to bring down operational costs related to performing mundane and repeatable tasks as those can be easily performed through the use of AI.

Since AI can automate many operational tasks, it also allows business leaders to handle more difficult business issues and decision-making. For instance, data analysis would usually consume hours of human resources but with AI, it is processed in seconds.

There are also better deliveries and immediate ROI due to a shortened timeline as AI can enable shorter developmental cycles and reduces the duration taken to shift from design to commercialization.

2. Limited Human Error

The reality of human fallibility is quite intense. While humans are required to provide context and comprehend certain situations, data science can benefit a whole lot from reduced errors, thus opening up the slots for more accuracy in predictions & data analytics.

By implementing AI, organizations can observe plenty of cutback in human errors along with a stronger attachment to established standards. Moreover, when AI & machine learning are combined with technologies like RPA that automates those rules-based and repetitive tasks, the combo not only accelerates processes and reduces those errors but it can be trained for improvement and to perform broader tasks as well.

For instance, utilizing AI in areas like financial reconciliation is typically known to bring error-free results. Now compare that exact scenario with human employees and the result can be potentially disastrous and may even cost millions of dollars.

3. Greater Business Insight& Improved Monitoring

Companies have been playing the prediction game for a long time by trying to predict consumer interest and market shifts as an attempt to prepare for the future. And so, forecasting has become quite the common practice that businesses often find themselves benefiting from AI.

Through the capability of AI to process over a billion data points within seconds and even go as far as utilizing historical data for future prediction and that too with high accuracy, it all carves a valuable pathway for businesses to prepare more informed decisions.

The capacity of artificial intelligence in consuming and processing tons of data, particularly in real-time allows organizations to implement nearly instantaneous monitoring capabilities with the capacity to alert on issues, suggest action and even launch a response.

For instance, AI can consume the information gathered on factory equipment for detecting problems in those very machines along with predicting the type of maintenance needed, hence, preventing costly & disruptive breakdowns, and let’s not forget the maintenance cost.

These particular monitoring capabilities of AI prove to be equally beneficial in other areas, like in enterprises with cybersecurity operations that analyze and processes large volumes of data.

4. Personalization through AI

Through the use of AI, today, marketers can interact with each customer in a manner of relevancy and personalization. This helps create and maintain a stronger relationship that ultimately results in increased ROI over time.

This is currently used to empower decision-making processes for various marketing purposes. One interesting example is Dynamic Yield, a U.S based Software Company that makes use of advanced machine-learning to develop actionable customer segments, in order to personalize overall customer experience &service.

AI provides marketers with deep personalization on grounds of communication, such as targeting individuals rather than specific groups (as marketers relied on earlier). This works by predicting certain customer behaviors based on previous interaction – meaning marketers deliver content & engagement that’ll most likely convert leads into a sale.

Now, most of us are already familiar with such tailored or personalized recommendations as seen when logging into sites like Netflix or Amazon. However, another great example of personalization via AI is the popular music streaming platform Spotify.

With a massive load of data at their disposal relating to search behaviors, song preferences, and more, the application performs its analysis to grasp instances like music taste, new artist discovery, etc. Spotify then uses AI via its predictive recommendation engine that enables the app to curate those personalized playlists we now know as “Discovery Weekly”.

Final Thoughts

The complexity and extent of data that are currently being produced and utilized by businesses across various sectors exceed the amount human employees can manage by themselves. Therefore, many enterprises continue to adopt this new wave of AI in analytics as a way of tackling data while also improving their processes.

In recent years, the implementation of AI has seen great success, thus, assisting businesses in analyzing data faster, optimizing their operations, and making data teams more productive.

Shirley Stark
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Shirley Stark

Shirley Stark currently working at InfoCleance as a Marketing Team Lead. She Has hands-on experience in B2B marketing and loves to write blogs, tips, reading b2b articles, Creating Business strategies and traveling.

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