Five Trends Shaping the Role of AI in Business Intelligence

Originally published February 2, 2024. Updated February 6, 2024

Artificial Intelligence (AI) is a leading topic in nearly every industry across the globe. From chatbots for customer support to Google Lens and the now-famous ChatGPT, individuals and businesses are exploring AI's powerful capabilities and many applications.   

AI has the unique ability to mimic human intelligence and automate tedious or complex tasks, both of which have the potential to revolutionize the way people work and interact with technology.  

Incorporating AI in business intelligence (BI), in particular, creates many new opportunities beyond simply automating data analysis and report creation. AI-powered analytics solutions can enable organizations to:  

  • Create Reports Using Natural Language Processing (NLP), reducing the need for complex queries and making data analysis more accessible to non-technical users.  
  • Find New Trends and Patterns using machine learning algorithms that can analyze vast amounts of data faster and more accurately than manual methods.  
  • Ensure Reliable Reporting by automating data cleansing tasks designed to identify and rectify errors or inconsistencies.   
  • Incorporate AI into Existing Tools and Technology so business users don't have to learn a brand new platform.  
  • Enable Timely, Informed Decisions through the faster and more accurate discovery of valuable data. 

The emergence of AI has paved the way for many exciting new ideas, use cases, and possibilities in the world of data analysis, but are organizations and end-users actually ready to integrate it into their workflows? And if so, how do they see themselves using AI in business intelligence? 

Dresner Advisory Service’s 2023 AI, Data Science, and Machine Learning Market Study looks to answer these questions and understand end-users' attitudes toward AI and similar technologies.  

This report is part of Dresner’s Wisdom of Crowds® series of research. It examines AI, data science (DS), and machine learning (ML) end-user deployment and trends, including analytical features and functions, generative AI, and AI/DS/ML use cases, among other topics.  

In this blog post, we’ll explore findings from Dresner’s study and discuss the top trends related to the role of AI in business intelligence.  


Trend #1: BI Success Correlates With AI Readiness  

This year, 79% of users surveyed by Dresner considered the topic of AI to be important or critically important. However, that doesn't mean they all see AI as central to their operations or consider themselves ready to adopt AI-enabled solutions or technologies.   

Dresner’s study found that organizations with a high level of BI success are more likely to consider AI a critical part of their business strategy than those only partially successful or even unsuccessful with their current business intelligence projects.  

According to the 2023 report, 31% of organizations that are completely successful with BI consider AI, data science, and machine learning to be critical, compared to only 17% of those that are somewhat successful with BI and 13% of those that are somewhat unsuccessful or unsuccessful.  


Trend #2: Midsize Organizations Are Focused on Customer-Centric Use Cases 

There are endless use cases for AI in business intelligence, from complex backend automation tasks to predictive insights for finance and other business areas. While priorities and interests vary from organization to organization based on industry, region, and more, Dresner’s study found some common interests among organizations of similar sizes  

In particular, midsize organizations of 101-1,000 employees reported higher-than-average interest in multiple customer-focused use cases and data points, including: 

  • Churn prevention 
  • Customer lifetime value 
  • Customer segmentation 
  • Next-best-action 
  • Risk management 

This trend highlights the diverse applications of AI-powered business intelligence, especially in its ability to generate predictive insights into things like customers’ buying habits and touchpoints that will affect their sentiment and overall brand experiences.  


Trend #3: Top Deployment Features Center Around Improving Data Quality  

Perhaps one of the most valuable capabilities that AI brings to business intelligence is its ability to rapidly cleanse and organize massive quantities of source data.  

Whether organizations need to identify and resolve errors, bring consistency to data from multiple sources, or filter through massive data libraries, AI can save them extensive amounts of time while eliminating the risk of human error.  

Dresner’s study found that the top AI/ DS / ML deployment features center on data management and data quality improvements. They include:  

  • Cleansing and enriching source data 
  • Detecting duplicates 
  • Set operations 
  • Complex filtering 


Trend #4: AI, DS, and ML Solutions Must Balance Agility and Usability 

In Dresner’s study, participants shared that access to advanced analytics, easy iteration, support and guidance, and fast cycle time are the most important usability features in AI, data science, and machine learning.  

This trend reinforces a longstanding trend in business intelligence. Along with capitalizing on innovative features and capabilities, organizations want to ensure their investments in AI and machine learning serve to increase data accessibility and empower end-users at every level of their organization. 

What’s more, these AI-powered solutions must be agile and easy to scale and customize as operational needs and strategic goals change. Finally, organizations want to implement AI in business intelligence alongside a partner that can offer expert support and guidance, especially as the AI landscape continues to evolve.  


Trend #5: Organizations Want to Scale AI On-Prem and in the Cloud  

Scalability is critical in business intelligence, and the introduction of AI has only further emphasized this point. Organizations looking to explore AI and machine learning technologies want to ensure their investments are scalable in multiple business environments.  

According to Dresner’s study, in-database and in-memory analytics are the most important scalability features for AI, data science, and machine learning, followed by multi-tenant cloud services, horizontal scaling, and hybrid / cloud bursting.  

This trend highlights the value of agility in the context of AI, as well as the need for business intelligence solutions that can quite literally meet organizations where they are — whether they work on-prem, in the cloud, or in a hybrid environment.  


Power the Future of Your BI and Analytics Practice With TARGIT 

While it’s been at the forefront of many conversations for a while, it’s clear that the specific capabilities and use cases for AI in business intelligence will continuously evolve as organizations explore various tools and features.  

But no matter the specifics, technologies like generative AI and predictive analytics will play a role in the future of business intelligence, especially as more organizations move their BI operations to the cloud and integrate multiple systems and data sources into their BI environments.  

At TARGIT, we’re now working with AI as an integrated part of our strategy and future roadmap, and as a way to enhance and accelerate our Friendly BI Platform, Industry Value Drivers, and Care for Customers approach 

You can hear more about our product strategy and get a detailed look at our 2023 product release in our latest on-demand release webinar, where we also discuss upcoming projects and focus areas like generative AI, NLP, and more.  


Source: 2023 Wisdom of Crowds AI, Data Science, and Machine Learning Market Study, Ó 2023 Dresner Advisory Services 

Get an In-Depth Look At TARGIT Decision Suite 2023

Originally published February 2, 2024. Updated February 6, 2024