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Generative AI and Data Intelligence: Redefining What’s Possible

Generative AI and Data Intelligence: Redefining What’s Possible

Feb. 12, 2024
Lumivero
Published: Feb. 12, 2024

Three popular use cases for generative AI

Generative artificial intelligence (AI) is impacting every conceivable type of application, content, and industry, and according to a recent survey by Accenture, 72% of business executives believe that AI will be the most significant business advantage of the future. But how exactly is it influencing data?

As processes grow in complexity, we often find ourselves in the throes of new and emerging technology trends – one of those being generative AI – to help combat that complexity and increase efficiency. But many of us don’t realize the benefits generative AI can bring to our everyday tasks. For example, the data you’re sorting through? It can be analyzed quickly via AI. The sales you’re forecasting for the next five and 10 years? AI can easily forecast and predict the information for you.

Generative AI has countless use cases, and the truth is, you’re probably already using a form of AI and don’t even know it. Let’s take a look at three of the most popular applications of generative AI and data intelligence and how each impacts how we work with data:

  1. Data Analysis and Insights: Generative AI systems can analyze large volumes of data quickly and efficiently – providing valuable insights and finding patterns that might be challenging to identify. This enhances decision-making processes and allows businesses to make impactful data-driven decisions.
  2. Predictive Analytics: Generative AI algorithms can predict future trends and outcomes based on historical data. This is particularly useful in areas such as finance, healthcare, and marketing, where predicting future events can have a significant impact.
  3. Automation of Data Processing: Generative AI technologies automate repetitive tasks used in data processing, cleansing, and preparation. By implementing generative AI, it helps to save time but also reduces the risk of human errors in handling data.

Are you starting to see the benefits and applications of using generative AI? Whether you work in data processing or content creation, generative AI can positively impact your business processes; but it’s also important to consider the precautions when using this type of technology.

Tips to Get the Most Out of Generative AI and Data Intelligence

AI can seem intimidating at first, but if you follow these tips, you’ll be a generative AI-pro in no time.

  1. Consider Security. Despite the benefits of using generative AI for security reasons, it’s important to be mindful of the information you’re sharing with AI-powered tools. The standards advise against sharing personal information or private company data to avoid risks of cyber-attacks and breaches.
  2. Develop Goals. What are you and your organization looking to achieve by utilizing generative AI for business processes? Your objectives might differ in their procedural requirements and could affect the types of information shared.
  3. Understand Your Organization’s Stance. There are some organizations that have generative AI built into the company culture and there are other companies that only use advanced technologies when prompted. Depending on where your organization stands, you might consider approaching the use of generative AI in short stints, starting with basic processes and working your way up to larger projects that could benefit from the efficiency boost.
  4. Don’t Solely Rely on AI. Just like all forms of technology, generative AI tools are missing one thing: human judgment. Remember to always review the final product – whether made through generative AI or other tools – before publishing, submitting, or sharing with stakeholders. Despite accuracy and validity being high among AI-powered tools, it’s important to review the work and consider any qualitative factors that are difficult for these data-focused tools to consider such as organizational culture and sentiment, prior experience and background knowledge, and stakeholder opinions – especially when it comes to recommendations.

Continue learning about AI solutions in our article Revolutionizing Text Data Analysis with AI Autocoding with NVivo, or explore our product line to see how our technology can enhance your business processes.

Three popular use cases for generative AI

Generative artificial intelligence (AI) is impacting every conceivable type of application, content, and industry, and according to a recent survey by Accenture, 72% of business executives believe that AI will be the most significant business advantage of the future. But how exactly is it influencing data?

As processes grow in complexity, we often find ourselves in the throes of new and emerging technology trends – one of those being generative AI – to help combat that complexity and increase efficiency. But many of us don’t realize the benefits generative AI can bring to our everyday tasks. For example, the data you’re sorting through? It can be analyzed quickly via AI. The sales you’re forecasting for the next five and 10 years? AI can easily forecast and predict the information for you.

Generative AI has countless use cases, and the truth is, you’re probably already using a form of AI and don’t even know it. Let’s take a look at three of the most popular applications of generative AI and data intelligence and how each impacts how we work with data:

  1. Data Analysis and Insights: Generative AI systems can analyze large volumes of data quickly and efficiently – providing valuable insights and finding patterns that might be challenging to identify. This enhances decision-making processes and allows businesses to make impactful data-driven decisions.
  2. Predictive Analytics: Generative AI algorithms can predict future trends and outcomes based on historical data. This is particularly useful in areas such as finance, healthcare, and marketing, where predicting future events can have a significant impact.
  3. Automation of Data Processing: Generative AI technologies automate repetitive tasks used in data processing, cleansing, and preparation. By implementing generative AI, it helps to save time but also reduces the risk of human errors in handling data.

Are you starting to see the benefits and applications of using generative AI? Whether you work in data processing or content creation, generative AI can positively impact your business processes; but it’s also important to consider the precautions when using this type of technology.

Tips to Get the Most Out of Generative AI and Data Intelligence

AI can seem intimidating at first, but if you follow these tips, you’ll be a generative AI-pro in no time.

  1. Consider Security. Despite the benefits of using generative AI for security reasons, it’s important to be mindful of the information you’re sharing with AI-powered tools. The standards advise against sharing personal information or private company data to avoid risks of cyber-attacks and breaches.
  2. Develop Goals. What are you and your organization looking to achieve by utilizing generative AI for business processes? Your objectives might differ in their procedural requirements and could affect the types of information shared.
  3. Understand Your Organization’s Stance. There are some organizations that have generative AI built into the company culture and there are other companies that only use advanced technologies when prompted. Depending on where your organization stands, you might consider approaching the use of generative AI in short stints, starting with basic processes and working your way up to larger projects that could benefit from the efficiency boost.
  4. Don’t Solely Rely on AI. Just like all forms of technology, generative AI tools are missing one thing: human judgment. Remember to always review the final product – whether made through generative AI or other tools – before publishing, submitting, or sharing with stakeholders. Despite accuracy and validity being high among AI-powered tools, it’s important to review the work and consider any qualitative factors that are difficult for these data-focused tools to consider such as organizational culture and sentiment, prior experience and background knowledge, and stakeholder opinions – especially when it comes to recommendations.

Continue learning about AI solutions in our article Revolutionizing Text Data Analysis with AI Autocoding with NVivo, or explore our product line to see how our technology can enhance your business processes.

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