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Last reviewed: September 2, 2026
2025 was the year AI in finance stopped being a pilot project. JPMorgan Chase put its LLM Suite in front of 200,000 employees and ran 450 proof-of-concept projects; Cash App launched Moneybot to consumers; 46% of accountants now use AI daily; and agentic systems began executing multi-step workflows instead of answering prompts. The result is a widening gap: some institutions run AI as infrastructure, others are still running experiments, and the distance between them may take years to close.
Key takeaways:
In January 2025, every financial institution had an "AI strategy." By December, strategy and execution had split into two very different groups. This is the story of that year, and what it means for everyone still experimenting.
The clearest signal that AI had moved from pilot to production came from the largest U.S. bank.
JPMorgan Chase deployed its LLM Suite to 200,000 employees and ran over 450 proof-of-concept projects through 2025.
What LLM Suite does:
Why it matters:
When the world's largest bank gives 200,000 employees access to AI tools, it's not an experiment. It's a statement: AI is infrastructure now.
The 450 proof-of-concept projects signal something equally important—JPMorgan isn't just deploying what works today. They're systematically discovering what will work tomorrow.
Square's Cash App introduced Moneybot, described as an "advanced financial chatbot."
What Moneybot does:
The shift:
Previous financial chatbots waited for questions. Moneybot actively monitors, identifies opportunities, and takes action within user-defined parameters.
This is agentic AI in consumer finance—not just answering questions, but making things happen.
The Intuit QuickBooks 2025 Accountant Technology Survey revealed that AI adoption among accountants outpaces nearly every other profession:
| Metric | Accountants | Small Businesses |
|---|---|---|
| Daily AI usage | 46% | 28% |
| AI boosts productivity | 81% | — |
| AI reduces mental load | 86% | — |
| Use AI for advisory | 93% | — |
What changed:
AI moved from "optional enhancement" to "competitive requirement." Firms not using AI daily are falling behind on speed, accuracy, and capacity.
2024 was Generative AI experimentation. 2025 was the Year of the Agent.
Traditional AI (including chatbots):
Agentic AI:
As Mitch Rutledge, CEO of Vertice AI, put it:
"Agents, agents and more agents—all the leading fintechs are embracing agentic AI functions to empower consumers and staff members."
Autonomous member service:
Self-driving back office:
Proactive financial guidance:
The institutions that deployed successfully weren't necessarily the most aggressive. They were the ones with proper controls:
"Many think they're ready for AI, but they're not. Guardrails, monitoring, oversight—most credit unions haven't even started." — Bill Illis, MDT
What successful deployment required:
The gap between AI leaders and laggards widened significantly in 2025:
AI Leaders:
AI Laggards:
The gap has a number attached. MIT's NANDA initiative published The GenAI Divide: State of AI in Business 2025 in August 2025, built on 150 executive interviews, a 350-employee survey, and 300 public deployments: 95% of enterprise generative-AI pilots delivered no measurable P&L impact. The failures were not model failures. Generic tools did not learn the organization's workflows, integration was flawed, and more than half of AI budgets went to sales and marketing while the largest returns sat in back-office automation. The institutions that bought from specialized vendors succeeded about 67% of the time; internal builds succeeded a third as often. Every bank and credit union still in pilot mode should read that as a description of its own project list.
Why the gap is hard to close:
AI systems improve with use. The institutions that deployed in 2025 now have:
Those still piloting have none of this. Every month they delay, the gap widens.
CCG Catalyst reported that 93% of bank digital transformation initiatives fell short of expectations.
But here's the nuance: AI initiatives were not the primary failure mode.
What failed:
What succeeded:
The lesson: AI works when deployed with focus. It fails when treated as a magic solution for systemic problems.
The most powerful AI models in the world still fail without context.
Example:
Generic AI: "Transaction of $14,457. Category: Miscellaneous."
Context-aware AI: "Conference expenses at Jack Henry Connect, September 13-15. Categories: Travel ($4,200), Lodging ($3,800), Meals ($2,100), Booth ($4,357). Total deductible: $14,457."
Same data. Radically different value.
Institutions with strong governance deployed faster, not slower.
Why:
Clear boundaries mean less fear. When everyone knows what AI can and can't do:
The successful AI deployments of 2025 were often modest:
Institutions that tried to "transform everything with AI" mostly ended up with PowerPoints.
The most effective implementations kept humans in the loop:
Pure automation without oversight created risks. Pure manual processes missed the efficiency gains. The sweet spot was hybrid.
If you deployed in 2025:
If you're still piloting:
Update, September 2026. The production phase arrived on schedule. In May 2026, CCG Catalyst reported that Anthropic had released ten purpose-built financial-services agents covering general-ledger reconciliation, month-end close, financial-statement audits, and KYC file assembly, with first deployments at BMO and Amalgamated Bank through FIS, and that M&A activity around AI agents had grown roughly tenfold in 2025 to nearly 100 deals. Pre-built, bank-specific agents with named institutions attached is what "AI as infrastructure" looks like eight months after this article was written; the pilot-or-production question is now settled for the largest players, and the community institutions that depend on three core providers for 70% of the market are waiting on those vendors to ship.
What to expect:
What to look for:
2025 established that AI is infrastructure, not innovation.
The conversation in 2026 won't be "should we use AI?" It will be "how do we use AI better than our competitors?"
This shift has implications:
Here's what nobody wants to say publicly:
Many institutions that claim to be "AI-first" are actually "AI-adjacent." They have AI features in vendor products. They've run demos. They've attended conferences.
But they haven't deployed AI in a way that changes their operations.
The institutions that actually deployed—that accumulated real data, learned real lessons, and proved real ROI—have a head start that will be very hard to overcome.
The pilot phase is over. The deployment phase has begun.
Those still experimenting need to decide: join the leaders, or accept the growing gap.
I'm Anna Khalzova, Co-founder of Jupid. We built Jupid to be AI-native from day one—not AI-assisted, not AI-enhanced, but AI at the core. Because we believe that's what small businesses deserve, and that's where finance is headed. If you're interested in what AI-native accounting looks like, let's connect.
CBO & Co-Founder
Business leader with 18 years embedding fintech into U.S. banks, leading 200+ integrations across products and partnerships. Deep expertise in digital banking and fintech partnerships, building lasting relationships with financial institutions across the US.
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