Investment firms entering 2026 face new opportunities and higher expectations: what Deloitte describes as the transition “from endless pilots to real business value.” After years of testing promising applications of AI in financial services, firms now need production‑scale infrastructure.
Moreover, that infrastructure must be capable of handling exponential compute demands, security models that defend against machine‑learning‑driven threats, and governance frameworks that satisfy both regulators and boards.
Here, we’ll explore how firms can break free of the pilot stage to realize real gains from AI in financial services.
Implementing AI in Financial Services for Long-Term Value
Global AI spending is projected to top $2 trillion in 2026, continuing the steep year‑over‑year growth in AI investment seen across industries. Yet a sobering MIT study shows that 95% of enterprise GenAI pilot projects failed to deliver measurable ROI. Meanwhile, Forrester’s 2026 predictions note that “one-quarter of CIOs will be asked to bail out business-led AI failures” in their organizations in 2026.
If AI is to succeed in long-term operational roles, firms must identify and take on paths to long-term business value. But the pressure is particularly acute for hedge funds, private equity, and asset managers. McKinsey research shows that fintechs account for nearly 70% of AI initiatives tracked, despite making up just 40% of the dataset, while “many banks remain stuck in pilot mode.”
Now, “AI is acting “as an equalizer that allows agile players to challenge incumbents in revenue-rich areas.” Established firms stand to lose the most to agile competitors that can quickly pilot, test, and operationalize effective AI in financial services.
“Cloud-First” Has Limitations for AI in Financial Services
The cloud‑first strategies that defined the 2010s are not sufficient on their own to support production‑grade AI in financial services. Deloitte predicts that inference (running AI models in production) will account for roughly two-thirds of all AI computing power in 2026, requiring new data centers worth nearly half a trillion dollars and AI chips worth over $200 billion.
This represents a staggering escalation in edge computing capacity. Bank of America Research notes that Meta, Oracle, and others issued $75 billion in bonds and loans in September and October 2025 to fund AI data center buildouts, Yahoo! Finance reports.
Investment firms face a particular challenge. Portfolio analysis, risk modeling, and trading algorithms demand both computational intensity and data sovereignty. Public cloud economics break down when models must process sensitive client portfolios, comply with cross-border data restrictions, and deliver millisecond latency for real-time decision-making.
The solution is a hybrid architecture: secure private cloud environments for regulated workloads, combined with public cloud burst capacity for model training and batch processing. In another article, Forrester predicts that private AI factories will reach 20% adoption, with on-premises servers capturing 50% of inference workloads—driven precisely by the sovereignty, cost control, and latency requirements that define financial services.
For firms without hyperscaler budgets, managed infrastructure partners that can deliver AI-optimized compute, secure virtual private cloud environments, and compliant data handling become strategic differentiators. The key question is no longer whether to invest in AI infrastructure, but whether to build it in-house or partner to gain speed to value.
Governance is Essential as AI in Financial Services Evolves
Production AI demands production governance. Gartner predicts that 90% of finance functions will deploy at least one AI-enabled technology solution by 2026 to “fully harness AI in finance.” This rapid acceleration means that governance frameworks designed for traditional software cannot keep pace with AI’s probabilistic outputs, multi-step reasoning, and autonomous decision-making.
McKinsey’s analysis reveals why governance matters: “Despite their scale and data advantages, incumbent banks trail fintechs in deploying AI with measurable business impact” because they “face greater regulatory complexity, fragmented technology stacks, and organizational inertia.” In practice, this means that governance paralysis often costs more than disciplined, proactive governance.
Investment firms must establish clear policies for model approval, ongoing monitoring, and decision auditability. This includes determining which AI outputs require human validation, establishing accuracy thresholds for automated recommendations, and creating audit trails that regulators can verify. Domain-specific AI models tailored to existing investment workflows, such as portfolio rebalancing logic, compliance screening, and trade execution parameters, deliver better outcomes than generic large language models. They embed industry knowledge and regulatory constraints directly into the architecture.
The World Economic Forum frames the challenge clearly: “The real ROI emerges when technology investments are matched by human elements, including skills, trust, and time to adapt.” Governance is the foundation for sustainable AI value.
FINRA’s New Supervisory Framework
For the first time, FINRA’s 2026 Regulatory Oversight Report includes a dedicated GenAI section, establishing supervisory expectations for investment firms. The guidance is explicit. FINRA expects firms to:
- Implement formal review and approval processes
- Establish AI governance frameworks with clear policies and procedures
- Conduct ongoing monitoring of prompts and outputs
- Track agent actions and decisions
- Store prompt output logs for accountability.
FINRA highlights specific risks that should concern every investment management CIO. First, “AI agents acting autonomously without human validation and approval” create scenarios where portfolio recommendations, trade executions, or client communications occur without appropriate oversight. Second, “complicated, multi-step agent reasoning tasks can make outcomes difficult to trace or explain,” undermining the auditability that regulators demand.
The guidance also addresses data quality: “Bias may arise from limited, outdated, or skewed training data, potentially influencing GenAI outputs in ways that reflect historical data patterns rather than current conditions.” For AI in financial services, this is not theoretical. For example, a model trained on pre-pandemic market behavior may produce recommendations misaligned with current volatility patterns, leading to portfolio underperformance or compliance violations.
FINRA further requires firms to assess whether their cybersecurity programs appropriately contemplate “risks associated with the firm’s and its third-party vendors’ use of GenAI” and “how its technology tools, data provenance, and processes identify how threat actors use AI or GenAI against the firm or its customers.” This dual mandate—securing AI systems while defending against AI-powered attacks—represents a fundamentally new challenge.
Cybersecurity at Machine Speed
In cybersecurity, AI accelerates both attack sophistication and defensive requirements. IBM warns that “autonomous AI agents are reshaping enterprise risk, and legacy security models will crack under the pressure,” with identity becoming “a strategic security priority on par with networks and cloud.”
What’s more, AI agents capable of autonomous portfolio management or client communication create new insider threat surfaces that operate at machine speed, beyond traditional monitoring capabilities.
In response, 43% of technology decision-makers plan to increase IT security spending in excess of inflation in 2026, CIO Dive reports. Investment firms must deploy:
- Continuous discovery platforms that map all AI agents and their access privileges
- Runtime protection that detects anomalous behavior before damage occurs
- Behavioral analytics that establish baseline patterns for the agent
Traditional, reactive security designed for human-speed threats will leave them vulnerable to AI-powered attacks that exploit vulnerabilities faster than humans can respond.
The Budget Discipline Mandate
While 85% of companies expect IT spending increases in 2026, 36% already believe they are overspending on AI, according to CIO Dive. CIOs face unprecedented scrutiny as a result. In its 2026 predictions, Forrester predicts that roughly two‑thirds of CIOs will be required to justify technology budgets by linking spend directly to business value, including measurable results from AI initiatives.
Leaders must be able to tie their implementations of AI in financial services to specific business metrics, such as:
- portfolio returns improvement (measured in basis points)
- operational efficiency gains (cost per trade, time to settlement)
- risk reduction (compliance violations avoided, false positive rate in AML screening)
- client acquisition and retention
Vague promises of “enhanced decision-making” or “competitive positioning” are unlikely to secure funding.
The McKinsey findings provide a cautionary benchmark: fintechs excel in “agentic AI and revenue-driving use cases,” including “advanced predictive decision management, AI-driven financial analytics, and multi-asset trading platforms.” Investment firms that cannot demonstrate a similar revenue impact or cost reduction will find budgets redirected to firms that can.
The Agentic Future and Its Risks
‘Agentic AI’ refers to autonomous systems capable of perceiving, deciding, and acting independently. McKinsey describes them as having “the most transformative potential” in financial services. Yet FINRA’s warnings about autonomous agents operating “beyond the user’s actual or intended scope and authority” highlight the operational risk inherent in this transformation.
Investment firms must balance efficiency with control. Autonomous agents can accelerate portfolio rebalancing, compliance screening, and market analysis, but they require monitoring frameworks that track system access, establish human-in-the-loop oversight protocols where judgment matters, and create guardrails that constrain agent behavior within acceptable parameters.
IBM emphasizes the urgency, warning that firms that delay their agentic strategy will find themselves competing against organizations that have already operationalized autonomous intelligence.
Moving Forward at Machine Speed
When using AI in financial services in 2026, firms must make deliberate choices about infrastructure, governance, security, and talent. The firms that will thrive are those that move decisively beyond experimentation to production deployment—building hybrid infrastructure that balances sovereignty with scale, establishing governance that enables rather than blocks innovation, deploying security that operates at machine speed, and developing talent capable of working alongside autonomous intelligence.
As McKinsey concludes, “AI is more than just another automation wave. It acts as an equalizer that allows agile players to challenge incumbents in revenue‑rich areas.”
Your AI-Driven Future with Option One Technologies
Partnering with specialized providers that understand both next-generation technology and financial-grade compliance can help firms turn this roadmap into an executable plan. Contact one of our experts today to learn how Option One Technologies can help.
