The enterprise world has been buzzing about AI-infused data analytics for some time. Now that we’re several years into the hype cycle, and even a couple of years past the mainstream rollout of generative AI, it should be possible to look beyond the dazzle and see where and how companies are really deriving business intelligence value from these technologies.
It helps that adoption is soaring. Research that probes the ways that organizations are implementing AI-powered data analytics shows that there are specific areas where the impact is greatest. According to a recent Information Services Group (ISG) report, businesses today see the most value when AI makes it easier for them to find the right data insights and when it removes barriers to access for line-of-business users.
These benefits, in turn, unlock more timely and accurate data insights, improve operational efficiency, enable better data-based decision-making across the organization and raise standards for customer experience. Let’s take a closer look at each of these types of value and how exactly AI-powered analytics makes this all happen.
3 Benefits of AI-Infused Analytics
- More efficient business operations.
- Smarter decision-making across the organization.
- Improved customer experience.
More Efficient Business Operations
The ISG study found that, by 2026, more than two-thirds of workers will have immediate access to cross-functional analytics embedded in activities and processes. This helps make operational decision-making more efficient and effective by shortening the journey to relevant insights for all decision-makers, not just data science experts.
For example, when determining team work shifts for upcoming weeks, a manufacturing plant manager using AI analytics embedded in their factory management app might know that summer makes for higher demand in their industry. The manager can ask the AI engine to pull data from the company’s sales figures, compare that surge to previous years, and then to predict the factory output necessary to meet demand this summer. Based on the analytics, the manager knows how many people the factory will need.
Embedded analytics implementations highlight relevant insights exactly when and where they are needed. There’s no need to switch screens or manually migrate data sets across systems — the relevant information is there within your workflow. What’s more, AI analytics spots relationships between data sets, delivering a unified view of operations in visualizations that make it easy for users to come to data-supported conclusions that improve operational efficiency.
“You wouldn’t believe the difference between an accounting solution that has a great BI solution at the end of it and one that doesn't,” says Avi Perez, CTO of Pyramid Analytics. “One allows you to play with the numbers and, ‘Wow, I had no idea I was spending so much money on XYZ,’ and one doesn’t. This is probably the number one use case if I were to broadly describe the entire ecosystem.”
By giving everyone in the organization access to richer, more reliable insights, data teams gain the support they need for advanced initiatives, like cultivating a data-driven culture. Predictions are also more accurate, allowing operational decisions to be proactive instead of reactive. And by democratizing access to data, AI analytics frees up data and IT teams for strategic work that boosts operations.
Smarter Decision-Making Across the Organization
Natural language processing, a key component of AI analytics, underpins easier and more accurate search functionalities so every line-of-business user can access relevant insights more quickly. Intuitive interfaces allow them to interact with data directly, taking control of their data exploration journeys by literally having conversations with analytics engines.
“Gone are the days when business users had to rely on data teams to create new dashboards and reports. With AI in business analytics, you are now the architect of your data journey. You are free to engage with your data, ask questions, drill into new data points, and create custom visualizations – and this is just one part of the story,” says Vaishnavi Shah of ThoughtSpot. “Intuitive interfaces, paired with AI capabilities, empower even non-technical users to instantaneously spin up real-time visualizations and dashboards.”
In this way, data insights are spread across the organization, creating a data foundation for every decision in every department.
For example, when marketing teams have access to software product engagement data, they can run queries asking AI to reveal which app features are trending in terms of use frequency among loyal customers. Without having to request any extra cross-departmental coordination with the data science or product team, the marketing team can use these insights to create a new series of social media videos demonstrating the value of the relevant app features to prospective customers.
Dashboards can be customized for each team, opening up dynamic access to data visualizations and dashboards, ensuring that everyone is responding to the most up-to-date metrics.
Improved Customer Experience
Thanks to its ability to turn data into relevant, timely insights, AI data analytics also supports improved customer experience. Employees can receive updated alerts about changing customer expectations and pain points, better analysis of customer interactions with products and services, and more accurate predictions about preferences and trends.
This empowers teams to make proactive decisions about everything from product R&D to marketing campaigns. Messaging can be refined to be more engaging, and features can be developed to meet emerging customer needs, helping to ensure that customers feel understood and supported.
For example, when customer support ticket data is fed into an analytics solution, the AI can process unstructured comment text to identify common frustration over access to past invoices. The support team can then work with designers and developers to create an interface that makes it easier for customers to find their invoices, helping to improve the customer experience and to reduce the support team’s workload.
AI analysis can even detect issues in apps and other digital products so they can be fixed before customers become aware of them. By feeding user interaction signals into an AI analytics engine, teams can reveal friction faster and more accurately, highlighting user behavior and touchpoints that teams otherwise wouldn’t notice, enabling swifter and more impactful CI/CD cycles.
“The challenge for product teams is often the inability to accurately detect these user experience issues or understand which particular areas to prioritize. This is where AI models can help — finding patterns among user-perceived defects, e.g. a raised voice, repeating the same ask more emphatically,” observes Ash Sharma, an AI product lead at Amazon. “Here, AI-driven analytics help turn a problem of massive scale into something more wieldy to tackle.”
AI-Powered Data Analytics Can Deliver Excellence
Overall, improved operational efficiency, diffused data-driven decision-making, and enhanced CX are the use cases that drive the most value for organizations that have adopted AI data analytics.
However, every company is different, and the benefits you see from AI-infused analytics will vary depending on your vertical and specific business challenges. Today’s leaders need to examine their specific context to identify the use cases that will drive the most value for their functional domains.
