IBM x Oracle research validates the urgent need for Agentic AI in Fund Administration and Asset Management
By Team DwellFi
The financial services industry stands at an inflection point. According to IBM's groundbreaking 2025 report "Agentic AI's Strategic Ascent," 78% of C-suite executives now recognize that achieving maximum benefit from AI requires not just new technology, but an entirely new operating model. For private markets, fund administrators, and asset managers drowning in manual processes and compliance complexity, this isn't just a trend—it's a survival imperative.
The data is unequivocal: organizations that excel in implementing agentic AI operating models are 32 times more likely to achieve top-tier business performance than those stuck in traditional automation approaches. But what does this mean for the private markets ecosystem, and why are platforms like DwellFi emerging as critical infrastructure for this transformation?
The Transformation Imperative: From Automation to Orchestration
IBM's research, based on surveys of 800 C-suite executives across 20 countries and 19 industries, reveals a stark divide in how organizations approach AI. While 78% of AI investment has focused on improving existing processes—extracting data from PDFs faster, automating simple workflows—the real winners are those creating net-new capabilities that were previously impossible.
This is the difference between automation and orchestration. Traditional automation handles deterministic, rule-based tasks. Agentic AI, by contrast, deploys autonomous agents capable of reasoning, multi-step planning, and making decisions with minimal human oversight. In private markets, this means moving beyond "faster data entry" to fundamentally reimagining how fund operations work.
The Numbers Don't Lie: Projected Impact by 2027
IBM's research projects dramatic improvements across financial services operations by 2027:
These aren't marginal gains—they represent fundamental operational transformation:
- 71% of customer support inquiries handled touchlessly from order to cash
- 43% increase in real-time spend visibility
- 36% improvement in procurement compliance ratings
- 29% improvement in days sales outstanding (DSO)
- 24% improvement in predictive financial modeling accuracy
For fund administrators managing hundreds of investor relationships, capital calls, and NAV calculations, these improvements translate directly to competitive advantage. The question isn't whether to adopt agentic AI—it's whether you can afford not to.
The Adoption Timeline: AI Transformation Isn't a Forecast, It's a Deadline
The pace of change is accelerating faster than most executives realize. IBM's data shows:
- Today: 24% of executives report AI agents taking independent action in their organizations
- By 2027: 67% expect AI agents to operate autonomously—a 2.8x increase in just two years
- By 2030: Fully autonomous robotic systems with embodied AI will be operational realities across industries
Even in highly regulated areas like compliance and risk management, transformation-driven organizations are moving aggressively. Executives expect agentic AI to automate an average of 29% of risk and compliance operations by 2027. For private markets firms navigating complex regulatory frameworks across multiple jurisdictions, this represents a paradigm shift from reactive compliance to proactive, AI-enabled governance.
The Three Critical Drivers: Your 32x Performance Multiplier
IBM's analysis identified three specific capabilities that separate transformation leaders from laggards. Organizations that excel in all three areas achieve exponential performance gains—not incremental improvements.
1. Integrating Cybersecurity into AI Initiatives
When AI systems make decisions independently, the security stakes change entirely. A compromised agentic system becomes a weapon capable of making countless harmful decisions before humans detect the breach. For fund administrators handling sensitive investor data and financial transactions, robust cybersecurity isn't optional—it's the foundation that determines whether autonomous AI becomes a competitive advantage or an existential threat.
2. Embedding Ethics Analysis in AI Deployments
Autonomous systems operating at machine speed and scale need ethical guardrails to prevent decisions that conflict with human values. Without proper frameworks, AI agents can inadvertently perpetuate biases, violate privacy, or make choices that seem algorithmically logical but fundamentally wrong. Ethics analysis creates the framework for fairness, accountability, and transparency—building the public trust these systems need to succeed.
3. Implementing Workflow-Specific Small Language Models
Generic AI struggles with industry jargon and unique process complexities. Workflow-specific small language models are specialized translators that understand the terminology, procedures, and context defining how work actually gets done in private markets. They excel at breaking down complex workflows—like multi-jurisdiction compliance monitoring or fund-specific reconciliation—into logical steps, bridging the gap between AI capability and business reality.
Why Private Markets Need This Now: The DwellFi Case Study
The private markets industry faces unique challenges that make agentic AI not just beneficial, but essential:
- Complexity at Scale: Fund administrators manage unstructured data across hundreds of documents—capital call notices, investor statements, subscription agreements, LPAs—each with fund-specific language and formats that change constantly.
- Regulatory Pressure: Compliance requirements span multiple jurisdictions with evolving rules. Manual monitoring is no longer sustainable.
- Talent Scarcity: The industry faces a critical shortage of experienced professionals. 56% of the workforce will require reskilling due to AI-driven automation by 2027, according to IBM's research.
- Margin Compression: Traditional fund administration operates on thin margins. The ability to scale without proportional headcount increases is existential.
This is precisely why DwellFi's agentic AI platform represents a strategic inflection point for the industry. Unlike horizontal AI tools that require extensive customization, DwellFi delivers vertical AI purpose-built for fund administration and private markets—embodying all three of IBM's critical drivers.
How DwellFi Delivers on the Three Critical Drivers
1. Enterprise-Grade Security Architecture
DwellFi's platform integrates cybersecurity at the foundation, with SOC 2 Type 2 compliance, enterprise-grade encryption, and the ability to deploy in clients' own cloud environments. This addresses the autonomous agent security challenge head-on, ensuring that AI-driven decisions operate within robust security frameworks.
2. Transparent, Auditable AI Governance
Every DwellFi agent operates with built-in transparency and auditability. The platform doesn't just automate—it creates audit trails showing exactly how decisions were made, addressing the 45% of executives who cite lack of visibility into agent decision-making as a significant barrier to implementation.
3. Fund Administration-Specific Intelligence
DwellFi's specialized agents are trained specifically on fund administration workflows. They understand the difference between "Net Capital Contribution" in one LPA versus another, can reconcile entities across thousands of unstructured records, and adapt when formats change mid-year.
This isn't generic AI applied to finance—it's workflow-specific intelligence designed for the messy, nuanced reality of private markets operations.
The Human + AI Operating Model in Practice
IBM's research emphasizes a critical point: 79% of leaders believe they need to protect and value human critical thinking as algorithms become commoditized. The goal isn't to replace teams—it's to stop them from wasting their talent on repetitive work.
The Human + AI operating model looks like a highly automated factory: not empty of people, but full of skilled operators who manage systems, handle exceptions, and focus on high-value work like cultivating investor relationships and unearthing critical portfolio insights.
DwellFi enables this model by:
- Automating the tedious: AI handles data extraction, reconciliation, and routine compliance checks
- Augmenting the strategic: Humans focus on judgment, relationship management, and complex decision-making
- Enabling scalability: Operations scale without proportional headcount increases
Fund administrators using DwellFi report up to 90% reduction in manual processing time—freeing teams to focus on strategic advisory services that command premium fees.
The Competitive Separator: Operating Model Redesign
IBM's research identifies operating model redesign as the ultimate competitive separator. Organizations achieving genuine transformation aren't just implementing AI tools—they're fundamentally redesigning how work gets done.
76% of transformation leaders indicate that focusing on complex, high-leverage problems yields competitive advantage. This insight cuts to the heart of strategic thinking: using agentic AI to solve previously unsolvable problems rather than simply solving solvable problems faster.
For private markets, this means:
- Real-time compliance monitoring across multiple jurisdictions instead of quarterly reviews
- Predictive cash flow modeling that updates continuously instead of monthly forecasts
- Automated investor communications that personalize at scale instead of generic updates
- Dynamic risk assessment that identifies issues before they materialize instead of reactive fire-fighting
DwellFi's Agentic OS approach enables this transformation by providing the foundational layer where specialized agents collaborate, learn from your data, and continuously improve—creating a system where people and AI think together, faster and more accurately than either could alone.
The Path Forward: Action Steps for Fund Administrators
Based on IBM's research and DwellFi's implementation experience, here's how to begin your agentic AI transformation:
1. Assess Your Current State
Are you optimizing existing processes or creating net-new capabilities? Only 42% of process-oriented organizations have developed new KPIs to monitor AI impact—a critical gap.
2. Identify High-Value Use Cases
Start with areas where autonomous intelligence delivers immediate competitive advantages:
- Investor inquiry handling and document retrieval
- Capital call processing and reconciliation
- Compliance monitoring and regulatory change management
- NAV calculation validation and exception handling
3. Demand the Three Critical Drivers
Evaluate potential AI partners on cybersecurity integration, ethics frameworks, and workflow-specific intelligence. Generic AI won't cut it in private markets.
4. Redesign Around Outcomes, Not Tasks
Don't just automate existing workflows—reimagine what becomes possible when AI handles complexity at scale. What strategic services could you offer if your team wasn't buried in manual operations?
5. Invest in Your People
47% of organizations cite inadequate employee skills as a barrier. The most successful firms pair AI rollouts with intentional change management, reskilling programs, and clear career pathways in the AI-enabled future.
IBM's research makes one thing abundantly clear: AI transformation isn't a forecast, it's a deadline. By 2027, autonomous decision-making will be the norm, not the exception. Organizations that wait will find themselves competing against rivals operating at fundamentally different efficiency and capability levels.
For private markets and fund administration, the choice is stark: embrace the Human + AI operating model now, or risk irrelevance as transformation-driven competitors capture market share with superior service delivery, lower costs, and capabilities you can't match.
The leaders of tomorrow won't be those with the most AI "features." They'll be the organizations that designed their systems so people and AI can think together—faster, more accurately, and at scale.
DwellFi represents that future, available today. Purpose-built for private markets, grounded in the three critical drivers IBM identified, and proven to deliver the operational transformation the industry desperately needs.
The question isn't whether agentic AI will transform fund administration. The question is whether you'll lead that transformation—or be left behind by it.