Analytics Provider Gemineye Releases 2026 AI Sentiment Report

Examining the Correlation Between AI Usage and AI Sentiment in Community Financial Institutions

Sandwich, Mass (July 28th, 2026) – The financial services industry is at an unstable moment when the rise of organizational AI usage is colliding with the rise of personal AI skepticism. Imagine two opposite forces careening into each other in boardrooms and team meetings; one is the pressure to seize and leverage the operational benefits of AI, the other, a growing concern that AI isn’t going to produce any tangible outcomes.

Gartner’s VP Analyst Carlie Idoine spoke at the Gartner Data & Analytics Summit in June 2026. Her comments echoed what Gemineye is seeing among community financial institutions. “Organizations are moving rapidly toward an AI-first operating model, where AI is now a core consideration in every business decision, workflow and investment. Without a clear, enterprise-wide commitment, organizations will struggle to consistently realize its full potential across the business.” Yet, 70% of financial institutions are already using agentic AI in some capacity, reports MIT Technology Review Insights. And when it comes to credit unions in specific, 66% now plan to leverage AI for credit decisioning, reports Filene in their research paper The AI Adoption Journey: A Survey of Credit Union Leaders.

Indeed, the pressure for leadership to prove that credit unions and community banks can, in fact, benefit from AI is mounting. And yet, most teams aren’t in a position to take advantage of the true benefits of AI. How so? The majority of teams don’t have the right tools, governance, skills, and processes to really leverage AI.

The Results: Gemineye’s AI Sentiment Survey

Maggie Chopp is Director of Business Development at Gemineye and former credit union data analyst. She has seen the struggles community FIs face in developing and executing on an AI strategy.

“We see a lot of inertia issues when it comes to AI adoption. Credit Union leaders are stuck trying to select AI pilots that have sufficient upside while mitigating very real risks."

This dissonance is also reflected in a recent study we conducted among credit unions and community banks.

In May of 2026, Gemineye conducted primary source research on AI, with the goal of examining the correlation between AI usage and AI sentiment. 30 credit union and community bank employees from almost every business unit were surveyed.

Below is the respondent breakdown:

Select Titles: CEO, CFO, SVP Operations, VP of HR, Director of IT, Member Contact Center Manager, VP of Marketing, Director of Data Analytics, Data and IT Analysts

Asset Range: $250M to $8B

Date: May 2026

The first data point that Gemineye wanted to collect was the level of importance executive teams were placing on organizational usage of AI in the next year. On a scale of 0 (not important at all) to 10 (top priority), the average response was an 8, showing that organizational AI usage sooner rather than later is indeed a priority. Not a single response was lower than a 4.

Next, respondents were asked how many AI processes they had integrated at their financial institution, with the options being 0, 1-2, 3-5, 6-9, and 10+.

· 50% of respondents said they had 1 to 2 AI processes integrated

· 20% had 3 to 5

· 13% had zero

· 10% had 10 or more

· 7% had 6 to 9

This dataset illustrates the lag between the executive importance placed on AI processes and the operational capacity to integrate - and thus benefit - from them. While executive teams rank organizational AI usage an 8 out of 10 in importance, two-thirds of respondents have two or less AI processes integrated.

AI Concerns Dominate the Discussion

More interesting than the quantitative data from this survey was the qualitative data in the form of comments from recipients. The last question asked was open form: What do you think the biggest concern or opportunity is for AI?

A shocking 87% of respondents answered with a concern rather than an opportunity, illustrating the widespread fears that FI employees have regarding AI. Let’s explore their answers.

Concerns organically fell into two main buckets: accuracy and security.

“Too much reliance on AI without verification of the data or an understanding of the business.” – Data Analyst at a $2B Credit Union

“Degradation of work product by some - too reliant in AI vs critical thinking.” - President, $800M Credit Union

“Security, Privacy and Customers’ Information.” - Business Applications Manager, $1.6B Bank

However, several respondents were positive in their outlook on AI integrations, and sited efficiency as the biggest opportunity. Israel Spence, Business Intelligence Strategy Manager at $800M Service 1st CU, responded: “Automating repeatable, static structured tasks. Summarizing or condensing information for briefings/front pages. Natural language querying for business units.”

For deeply risk adverse teams, we recommend starting with internal (non-member facing), supportive (human decision makers), and observable (results are traceable and repeatable) models.

Lower Risk Higher Risk

Internal Facing Member Facing

Supports Human Decision Maker Makes Decisions for the CU

Observable Agents Black box solutions

Continuous Model Training One-Time deployed Models

Deployed in Private Virtual Environment Deployed in a co-owned or Saas model environment

Reproduceable Results Unverifiable Responses

Survey results from global insights leader Qualtrics validates Gemineye’s chart above. Their 2026 report on Consumer Experience Trends, which includes 20,000 responders across 14 countries, points to a degradation of consumer trust in AI customer service. Only 29% of consumers trust organizations to use AI responsibly, and misuse of personal data was their number one concern about AI. Internal facing AI usage with a dedicated human decision makers offers the best opportunity to experiment at this time.

Conclusion

The ability for financial institutions to balance risk with opportunity in their AI integration strategies will continue to be a key factor. Investing in AI tools that are natively designed with security and accuracy in mind, and recognizing those that are riskier will provide the strong foundation needed in this unpredictable time.

About Gemineye

Gemineye is a leader in FI analytics, data warehousing, and secure, contextual AI solutions. They deliver to community-based financial institutions to the same world-class technology that Fortune 50 companies use, lowering the barrier to accessibility and success. Their newest offering is the first-of-its kind AI platform which leverages a private deployment model rather than a shared infrastructure, which is more secure and more specific. Learn more about Gemineye at gemineye.com or emailing us at sales@gemineye.com.