Investment priorities 2026 whitepaper

Putting agentic AI to work

Business leaders are tracking dozens of AI developments, but agentic AI is the one that changes how work actually gets done.

These systems make decisions and act autonomously, moving beyond text processing into real action. That means last year's investment framework no longer fits. This whitepaper from Statista's Finance Industry Expert Raynor de Best identifies exactly where to focus in 2026 and why.

What's inside this whitepaper:

Identify which of the four agentic AI priorities applies to your organization right now
Make the case internally for where AI budgets should—and shouldn't—go in 2026
Benchmark your current AI posture against what leading organizations are already doing

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Putting agentic AI to work

The top AI investment priorities in 2026

Cybersecurity operations

Why do financial services have more at stake than any other industry?

Finance leaders already rank AI as their #1 budget priority in 2026. It’s because AI cuts both ways. Attackers are weaponizing it at the same speed it's being built into defenses.

Brand trust is the main reason consumers choose a bank. One AI failure can undo years of it.

Workflow automation tools

The move from experimentation to production is speeding up.

Around 75% of companies expect their IT spending and outsourcing budgets to increase over the next two years.

With agentic AI as the primary driver, the organizations moving fastest are choosing to buy rather than build. The reason is simple—companies need results today.

Customer experience

Your customers are already expecting AI to act on their behalf.

Customer expectations are shifting fast. Intelligent automation now tops CX investment priorities in financial services, because customers increasingly expect AI to handle decisions, not just answer questions.

Whether you do, your infrastructure needs to be ready for the transactions they'll generate.

Workforce & productivity

The biggest obstacle to your AI strategy isn't your technology.

Skills shortages have overtaken data quality as the main barrier to enterprise AI adoption. The technology is developing faster than most teams can realistically keep pace with.

Those who understand decades-old core banking systems are retiring—and the AI skills needed to replace or integrate with them are in short supply.