5 biggest AI + BI adoption challenges and how leaders are solving them

Beata Socha

September 17, 2025

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5 challenges of AI + BI adoption in financial services

Without consistent answers, clear definitions, or governance, AI can create confusion instead of clarity. According to the 2025 Global Survey on AI-powered analytics, financial services leaders point to five major challenges in AI + BI adoption:

• 80% cite compliance and regulation as the top challenge

• 51%report difficulty integrating AI + BI tools with existing systems and siloed data

• 49%struggle with inconsistent answers from AI due to a lack of a universal semantic layer

• 40%say they lack internal AI + BI Centers of Excellence to guide implementation

• 37% name the cost of scaling as a primary blocker

If left unresolved, these challenges risk adding complexity instead of value.
The good news: the solutions aren't quick fixes-they're chances to modernize analytics for the long term.

And for most financial services leaders, modernization begins with governance.

How AI supports FSI data governance

Data is only as good as its source. Without a strong data foundation, AI + BI risks producing inconsistent, untrusted answers. As financial institutions process growing volumes of information, governance becomes even more critical.

Nearly half of FSI organizations report that their AI tools produce incorrect or hallucinated responses-often stemming from fragmented, siloed data. When data is fragmented across applications and platforms, metrics become jumbled, and insights get lost. The result is a domino effect: reporting becomes confusing, decisions slow down, and in the rush for "quick fixes," governance practices are often disregarded.

Modern AI + BI platforms solve this problem with a universal semantic layer.By eliminating inconsistent data and creating a single source of truth, they ensure that everyone-from analysts to advisors-is working with the same definitions, metrics, and business logic.

The outcome: connected data, consistent KPIs, and unified reporting across the enterprise-without sacrificing governance.

How FSI leaders are adopting AI-powered analytics

Despite these challenges, leading financial services institutions are fully operationalizing AI + BI across departments. Two examples stand out:

Case Study: Fannie Mae

Fannie Mae is one of the largest mortgage financing institutions in the United States, backing over 1.5 million home loans. To guide high-stakes trading decisions, its Treasury and Risk teams run daily forecasting models.

The challenge: Insights were buried in spreadsheets, macros, and disconnected reports-slowing decision-making.

The solution: Fannie Mae modernized its reporting stack with a universal semantic layer, REST APIs, and role-based access controls. Centralized data and real-time metrics were exposed directly to trading applications.

The outcome:duplication and manual collation were eliminated, creating a governed, real-time foundation for self-service analytics and faster, more confident decisions.

- Sheel Ratan, Software Engineering Manager, Fannie Mae

Case Study: goeasy

goeasy, a Canadian non-prime lender serving over a million customers, faced a different challenge: data inconsistency.

The challenge: Teams were working with overlapping KPIs, duplicate dashboards, and disconnected tools.

The solution: goeasy built a BI Center of Excellence using Strategy's tools, unifying semantic logic across the business. Metrics were certified, standardized, and dashboards tailored by data maturity.

The outcome:

• 93% of reporting is governed by the BI Center of Excellence
• 2,200+ active users with consistent KPIs and metrics
• 150K+ views on a single governed intraday dashboard

By using AI + BI to enforce governance, goeasy delivered faster insights and more trustworthy decision-making.

- Jide Adeoye, Director of Business Intelligence, goeasy

What makes a successful AI + BI adoption

Both FSI leaders tackled different problems, but their strategies followed a common path:

• Investing in governance from the start
• Prioritizing standardization and trust over speed alone
• Supporting both technical teams and frontline users
• Developing systems that scale without sacrificing security

As AI-powered analytics become a strategic differentiator in financial services, the real challenge isn't access-it's strategic alignment.

According to the survey,

• Establish a universal semantic layer as a strong foundation

• Define clear, consistent KPIs

• Train users and build internal Centers of Excellence

• Balance innovation with governance and compliance

The goal isn't speed-it's getting analytics right.

One thing is for certain: AI-powered analytics will transform how decisions are made-but only if the data behind those decisions is consistent, governed, and trusted.