The top challenges in AI analytics and how leaders are overcoming them

Beata Socha

September 26, 2025

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AI is fundamentally changing the way businesses collect, connect, and analyze data.

Its applications offer clear benefits-from conversational analytics that deliver precise insights to self-service tools that enable faster reporting. No wonder organizations plan to triple workforce accessto AI-powered business intelligence by 2026.

However, adoption issues remain. Our 2025 Global Surveyof 200+ professionals across 38 countries aimed to report on enterprise adoption of AI-powered analytics, and the findings highlight what's coming next.

Common barriers to scaling AI-powered analytics in the enterprise

According to the survey, the top roadblocks to adoption are:

• Inaccurate or inconsistent answers (43.4%)

• High costs with unclear ROI (41.7%)

• Integration challenges with existing systems (41.5%)

• Compliance and regulatory risks (52%)

• Lack of in-house AI expertise (31.2%)

Source: The State of AI+BI Analytics Global 2025 Report

Each of these issues can slow progress, but none is insurmountable.

One thing is clear: the success of AI hinges on consistency, trust, and governance.

Why consistency matters in AI-powered analytics

According to the report, 43.4% of respondents cite inaccurate or inconsistent answers as a main blocker for AI adoption. These so-called "hallucinations" undermine trust and force teams to spend extra time on manual quality assurance.

However, the issue doesn't always lie with the AI itself. While some AI models run into technical difficulties, most AI output is driven by the data it consumes. If data inputs are inconsistent, AI outputs will reflect it-every time.

The solution? A Universal Semantic Layer can connect, clean, and prepare data for analytics. It ensures all metrics, relationships, and hierarchies remain consistent-delivering datasets that are AI-ready.

Bridging the trust gap in AI-powered analytics and BI

Trust is the foundation of data analytics, driving everything from KPIs in reports to executive-level decisions. But if trust is low, teams fragment, and executives hesitate to act on insights.

Similarly, without confidence in the accuracy of AI-powered insights, even the most advanced tools and teams are underutilized.

When asked about the reliability of their AI+BI, enterprises remained cautiously optimistic: while 65.9% felt at least confident in their AI-powered analytics, but only 8.1% reported being extremely confident.

Why? As Brett Sheppard, the author of the report, puts it:

Why AI compliance and modern data governance are critical in 2026

Perhaps the most striking finding is that compliance has now overtaken cost as the top barrier to enterprise adoption.

52% of respondents cite concerns about bias in AI models, data privacy, and regulatory obligations. This reflects the broader global shift toward tightening AI oversight, from the EU AI Act to the NIST AI Risk Management Framework.

Source: The State of AI+BI Analytics Global 2025 Report

Strong data governance and clear accountability are becoming non-negotiable.

Organizations that embed compliance into their analytics strategies-rather than treat it as an afterthought-will be best positioned to scale responsibly.

How data leaders are overcoming AI and analytics obstacles

Here are four ways leaders are tackling data challenges to maximize AI adoption across teams and locations:

1. Improving governance and data quality
   By prioritizing investments in semantic layers and governance frameworks, companies are addressing both compliance and accuracy challenges. The report backs this up: nearly 40% of organizations highlight governance as a top investment area over the next three years.
   

2. Setting SMART goals for ROI
   Leaders are linking AI analytics projects to measurable business outcomes such as cost savings, productivity gains, and customer satisfaction. This helps justify budgets and avoid pilot fatigue.
   

3. Expanding training and literacy
   Data and AI literacy initiatives are spreading across enterprises, ensuring employees not only have access to AI tools but also know how to use them effectively.
   

4. Adopting a multi-cloud, multi-vendor approach
   To avoid vendor lock-in and improve flexibility, many organizations are balancing multiple cloud providers and BI platforms. This approach reduces risk and helps keep costs predictable.

What's next for AI + BI in your industry?

The roadblocks to AI-powered analytics are real, but they're the growing pains of a technology moving from novelty to necessity. For leaders, the takeaway is clear: success lies in pairing technology with governance, training, and measurable business outcomes.

AI analytics isn't stalling-it's maturing. The organizations that address trust, compliance, and ROI head-on will unlock the full potential of intelligence everywhere, turning today's challenges into tomorrow's competitive advantage.