Data and Analytics Trends for 2026

Another year has flown by at breakneck speed. In the data and analytics space, the past year has seen an AI gold rush where BI and analytics solutions raced to offer AI-powered insights to best meet end users’ rising expectations. But despite AI being named the most important trend of the next five years in an insightsoftware survey, it isn’t an exact science as organizations contend with AI projects that fail to launch and the risk of AI hallucinations.

What will the rest of 2026 have in store? Now’s the time to plan for the most pressing data and analytics trends for 2026.

Balancing Enterprise AI Solutions With Trusted BI

This year, we predict organizations will continue to race to keep up with the AI revolution, but not by leaning on AI alone. Although it’s already everywhere, enterprises still struggle to launch AI projects and provide value with it. According to MIT research, 95% of organizations see zero return on their AI investment. And Gartner predicts that over 40% of agentic AI projects will be scrapped by 2027. Why is this and how can you keep up as technology continues to evolve throughout the year?

One option is to turn to trusted, traditional BI tools, which provide helpful dashboards for valuable insights into business operations. With BI, users can craft reports that present pertinent information to leadership and stakeholders in a familiar way.  However, that might not be enough to meet user needs.

“Customers want products that actually make them smarter, not just prettier dashboards,” said Matt Belkin, President and General Manager, Data + Analytics at insightsoftware.

While AI projects can face roadblocks, AI tools can help fill BI gaps, allowing users to move beyond static dashboards with greater interactivity. While AI doesn’t replace BI, it enables users to gain dynamic and conversational insights. AI-powered analytics built into your application can take into account:

• Your data model and relationships between entities
• Common industry-specific terminology that’s important
• Historical patterns that indicate what’s normal versus noteworthy
• The questions your users ask most often (and the follow-ups they’ll need)

With the right balance of AI and BI, the analytics you provide in your application can help meet user demand with trusted BI insights and advanced AI that can help them answer critical questions quickly and accurately.

On-Premises AI for Highly Regulated Industries

Enterprise AI deployment requirements are increasingly driven by regulatory compliance needs that traditional BI tools struggle to address effectively. While most AI analytics vendors expect cloud or SaaS delivery models, regulated markets present different challenges. And with the EU’s Artificial Intelligence Act coming into effect in August 2026, keeping your analytics and AI tools compliant will be a pressing trend.

Healthcare, finance, and manufacturing organizations especially face strict data residency and compliance requirements like HIPAA and European data protection regulations. These requirements create a push for on-premises solutions.

However, many AI analytics solutions on the market today offer their technologies only if you’re on their cloud platform. If your users, like those in highly regulated industries, need flexibility or on-premises analytics, they lose out. To avoid vendor lock-in, look for a solution that allows you to meet users wherever they are on their cloud journeys. This empowers users with:

• Complete data control: Organizations maintain full ownership and control of their data throughout the AI analytics process.
• Compliance framework alignment: On-premises deployments can be configured to meet specific regulatory requirements without compromise.
• Reduced vendor dependency: Companies avoid the ongoing risks associated with cloud provider policy changes or service interruptions.

By providing cloud-agnostic insights, you can deliver mature AI analytics capabilities for any user, even those who work under strict compliance requirements.

Contending With AI Hallucinations

In the past few years, we’ve seen AI spread everywhere, leading to both innovations and challenges across all industries. One major challenge, and an analytics trend we see for 2026, is reconciling AI hallucinations.

AI is designed to provide answers. It can comb through overwhelming amounts of data in the blink of an eye and respond to questions users have. However, the information they get back could be incorrect.

This phenomenon is an AI hallucination. It happens when an AI-generated response sounds confident and correct but contains false or misleading information. This can quickly erode trust, especially when organizations depend on accurate data for regulatory reporting, presenting to leadership, and keeping business running smoothly.

“When AI gives you different answers each time or can’t explain where it got its information from, that’s unacceptable in production environments,” Belkin explained to Intelligent CIO. “Leaders can’t defend decisions they can’t trust or verify. Today, data teams spend more time cleaning and managing permissions than delivering AI applications that drive business value.”

Core Causes of AI Hallucinations

• No live data connection forces models to guess. AI creates believable responses from training patterns because it can’t access your real-time enterprise systems to verify facts.
• Business logic gaps produce wrong calculations. Models generate answers that ignore your company’s specific rules, formulas, and compliance requirements embedded in production systems.
• Governance blocks create blind spots. Security teams correctly restrict database access, leaving models unable to cross-check responses against actual source data.
• Enterprise complexity overwhelms pattern matching. Hundreds of related tables with custom joins and dependencies require deep context that models lack without proper semantic mapping.

Your users need AI that connects to real data, not invented answers. When looking for analytics and connectivity solutions, search for one that connects directly to user data. The right technology will show a trackable data trail so that your users can easily trace back the data to its source and be able to confirm that it’s correct.

Smart Ways To Build In AI Insights

After years of rapid innovation, 2026 is shaping up to be a year where developers balance the benefits and pitfalls of the AI revolution. AI-powered insights can provide a powerful edge against your users’ competition, but it’s critical to be careful that you’re not forcing them into cloud platforms that can pose a compliance risk or providing analytics that risk answers that are more machine-generated hallucinations than fact.

There’s no doubt that this year will be a balancing act for developers, but staying on track doesn’t have to be intimidating. With Logi Symphony from insightsoftware, developers and product teams can embed powerful business intelligence and AI analytics into your SaaS applications. Logi Symphony goes beyond static BI dashboards to provide AI-driven insights without vendor lock-in, allowing end users to make decisive and informed choices.

And to avoid AI hallucinations, Simba Intelligence’s answers are tied to live data and show the trail so that your users can easily track, verify, and stand by results. While traditional BI tools surface dashboards and data integration tools move data, Simba Intelligence connects AI directly to governed enterprise data without copying or losing control.

Ready to learn more? Check out our white paper on how to move beyond dashboards in the AI-first era.

The post Data and Analytics Trends for 2026 appeared first on insightsoftware.