While banking executives agree that AI can significantly benefit their business, a new Personetics report says most have a long way to travel before reaping the benefits. Personetics 2026 Global Banker Survey: From Aspiration to Execution, which surveyed 902 senior executives across North America, APAC and EMEA, is available here.
Every one of the 902 executives said it is at least somewhat important to link digital engagement to business outcomes. Half deem it “extremely important.”
Personetics said the gap between digital engagement and measurable business results is the industry’s biggest untapped growth lever. While institutions on average convert 53% of their digital customer engagement into measurable business outcomes, only 14% do it at least 75% of the time.
Institutions struggle mightily to deliver contextualized offers when it most matters to customers. Only 48% of customer bases are “accurately served with offers that meet predicted needs and are timely and relevant, based on transaction data.”
When those offers come, they are often way too slow. Only 9% deliver real-time offers.
- Why are those offers so slow?
- Data silos between business lines (56%);
- Inability to build a unified, real-time customer profile (55%);
- Regulatory and compliance constraints (44%);
- Batch-processing infrastructure (43%);
- Inadequate martech (43%); and
- Lack of AI/ML capability (11%).
Too many institutions rely on their calendar instead of their customers’; only 42% of consumer outreach is prompted by what’s happening in their clients’ lives, with much of the rest coming from pre-planned campaigns. Most ideas (93%) take between one and six months to launch.
“A bank can aspire to contextual engagement, but if every new insight or offer takes three months to ship, the institution can never keep pace with customers’ actual financial moments,” the Personetics report states. “This finding quantifies the operational drag that makes contextualized engagement aspirational rather than achievable for most.”
The biggest obstacles to launching new personalized content quickly:
- Data availability and integration challenges (71%);
- Internal approval and compliance review processes (69%);
- Heavy IT development requirements (49%); and
- Dependence on vendor roadmaps and release cycles (41%).
Roughly four out of five executives say Gen AI is either a significant (53%) or transformational (26%) opportunity. None said it was small or non-existent.
The use of Gen AI will also grow as more executives trust it. The greatest barriers to scaling Gen AI from experiment to full deployment are:
- Ensuring the accuracy, reliability, and compliance of Gen AI outputs (31%);
- Integration into existing technology infrastructure (19%);
- Regulatory uncertainty (17%);
- Data quality and availability (14%)
- Internal AI/ML expertise (7%);
- Use-case prioritization (7%); and
- Cultural resistance (5%).