REDPIXEL - stock.adobe.com
How to get reliable BI insights from AI-augmented analytics
Before adopting AI-powered BI applications, organizations need stronger business context, trusted workflows and users prepared to assess AI outputs.
"Bolting AI onto legacy BI doesn't create intelligence, it just scales confusion faster."
That observation comes from Mike Trkay, CIO and chief customer officer at FICO, an analytics software company. As organizations rush to adopt AI-powered BI tools, many discover that sophisticated analytics capabilities can't compensate for inconsistent data, unclear metrics and weak governance.
Industry analysts and practitioners say organizations hoping to capitalize on AI-enabled BI need more than new software features. Many enterprises are applying generative AI (GenAI) tools and AI models to analytics environments designed for historical reporting on fragmented data rather than AI-driven decision-making. That gap is forcing data leaders to treat AI-augmented BI as an architecture, governance and operating model challenge, not just a software rollout.
Trkay said this mismatch is rooted in how many older analytics environments were designed.
"Legacy platforms were traditionally built for rear-view reporting on static, often siloed data, not the dynamic demands of generative AI," he said. "If your underlying data isn't semantically consistent, clean and securely ring-fenced, feeding it to a large language model is a recipe for disaster. You are essentially just accelerating the generation of inaccurate insights and potentially exposing high-value data."
Those limitations are already showing up in BI deployments. Data quality and accessibility issues often limit the effectiveness of AI capabilities embedded in BI tools, said Chris Hutchins, founder and CEO of Hutchins Data Strategy Consulting. Organizations are also finding that employees frequently lack the data, process improvement and technical skills needed to use AI-augmented analytics effectively.
Building the AI-ready analytics foundation
For data leaders, the required work starts below the BI interface. Building data environments that produce consistent, explainable AI outputs takes effort, said Boris Evelson, vice president and principal analyst at Forrester Research.
"We've been talking about data democratization for ages, and the vendors are putting out there that, now with [these AI features], we can democratize data, and any person will be able to ask any data question. But you don't just buy it and get all that out of the box. It requires a few specific investments, and you have to build a lot of stuff to make that happen," Evelson said.
Evelson said organizations need trusted data foundations, clear metric definitions, semantic consistency and modern platforms that can support AI-augmented analytics at scale. Those capabilities also must be tied to business workflows rather than treated as standalone BI features.
Along with shared definitions for key business metrics, organizations need clear data ownership and documentation, said Fern Halper, vice president and senior research director for advanced analytics at TDWI, which provides education and research for data and analytics professionals. She's also the author of the book Data Makes the World Go 'Round: The Data, Tech, and Trust Behind AI Success.
A semantic layer is especially important in conversational BI applications because it connects business language to enterprise data, Halper said. Without it, similar questions can produce different answers depending on the wording.
"These systems need context," Halper said. Otherwise, "they're just going to use whatever definition they want, and you won't get consistent output."
Employees also need enough data literacy to challenge AI-generated results.
"Democratizing analytics and [deploying] conversational interfaces don't eliminate the need for critical thinking," Halper said. "Users still need to understand how to interpret results and identify errors and evaluate the evidence."
Those requirements do not diminish the value of BI. Rather, they underscore why BI remains a critical part of enterprise decision-making, even as AI changes how workers interact with analytics tools.
"There's always going to be a place for BI, because there are certain types of data that organizations need to have standardized and reported on," Halper said.
BI tools were once used primarily by BI professionals and other analytics specialists who had the technical acumen and skills to parse data and understand definitions, context and quality.
Over time, BI tools became easier to use, enabling more business users to create dashboards, reports and data visualizations. But many still relied on predefined queries or BI and analytics teams for specialized deep dives into the data.
GenAI and LLMs are changing self-service analytics, Halper said. They power conversational interfaces, assisted analysis, narrative explanations, anomaly detection and root cause analysis that workers in all functional areas -- not just those in the data science department -- can use.
A phased roadmap for deployment
Once those foundations are in place, organizations can start to expand AI-powered analytics more broadly across the business.
Sripathi Jagannathan, head of data engineering at global digital technology and IT services company UST, recommends a phased approach that balances innovation with governance and oversight. He said organizations should start with technology deployments to improve analyst productivity, then introduce guardrails such as semantic models and certified datasets, before expanding AI-enabled analytics to more business users.
To prepare for conversational BI and GenAI-assisted analysis, visualization and reporting, Jagannathan said organizations must rethink workflows, metrics and decision-making processes.
- Workflows. Organizations need to move beyond static reporting and design workflows that let users explore data through interactive, conversational exchanges. Those workflows should support follow-up questions, maintain context across queries and be limited to trusted datasets and semantic models. GenAI provides less value when applied only to static reports, Jagannathan said, adding that it becomes more powerful when enabling "exploration, explanation and guided analysis."
- Metrics. Organizations also need to move away from ad hoc metric creation and establish a governed metrics layer. Metrics should be clearly defined, standardized, reusable and certified for business use. That governance helps reduce misinterpretation when conversational tools dynamically generate queries, Jagannathan said.
- Decision processes. Organizations should "adopt a human-in-the-loop model where AI augments analysis but does not own decisions," Jagannathan said. That requires validation, transparency into how results were generated, confidence levels and traceability so employees can evaluate AI-generated insights before acting on them. The goal, he said, is to preserve "human oversight for judgment and accountability."
Organizations that treat the use of AI in BI applications as merely a technology purchase risk replicating the same problems that plagued their existing analytics environments. Those that approach it as a broader data management and operating model initiative will be better positioned to turn AI-augmented analytics into reliable business insight.
Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.
