As AI adoption becomes increasingly embedded across buy-side organizations, the conversation is shifting from experimentation to execution. Most firms today are no longer asking if AI should be part of the operating model, but rather where it can deliver the most value. And while GenAI pilots have proliferated across research, reporting, and operations, outcomes remain inconsistent and, in many cases, disconnected from core business priorities.
What separates successful AI programs from stalled initiatives is not model sophistication, but disciplined use-case selection. Without a clear evaluation lens, firms risk deploying AI in areas that generate activity without impact, creating complexity rather than efficiency. As the industry moves toward agentic AI, the ability to prioritize the right use cases will determine whether AI becomes a strategic advantage or an operational distraction.
The Complexity Behind Use Case Selection
Buy-side workflows differ fundamentally from those in many other industries. They are rarely linear, rarely isolated, and rarely driven by a single system or data source. Instead, they tend to be:
– Multi-step and cross-functional, spanning investment teams, operations, finance, risk, and investor relations
– Highly dependent on unstructured and semi-structured data, such as notices, legal documents, borrower financials, and emails
– Governed by strict fiduciary, regulatory, and audit requirements, where explainability and traceability are mandatory
– Extremely sensitive to accuracy and timing, as small errors can cascade into valuation issues, cash breaks, or compliance failures
Because of this complexity, not every AI use case, no matter how compelling it appears, is appropriate for the first wave of agentic AI adoption. Some processes are best served by assistive GenAI. Others demand the orchestration, autonomy, and governance that only agentic AI can deliver. The challenge lies in distinguishing between the two.
Understanding Where Agentic AI Fits
Agentic AI is most effective when applied to workflows that require continuous coordination rather than deep discretionary judgment.
In our recently published whitepaper, we described this through the lens of workflow complexity and reasoning complexity:
- Workflow complexity reflects how many systems, handoffs, and dependencies a process involves
- Reasoning complexity reflects how much interpretation, contextual judgment, and decision logic is required
Workflows with high workflow complexity but moderate reasoning complexity are ideal candidates for agentic AI. These processes are operationally heavy, repetitive, and time-consuming, yet governed by structured business rules and policies.
Critically, the objective is not to automate judgment, but to automate orchestration: the sequencing, coordination, validation, and exception handling that currently consumes disproportionate operational effort.
A Practical Framework for Selecting Agentic AI Use Cases
Buy-side firms should evaluate candidate use cases across five core dimensions:
1. ROI and Operational Impact
The starting point is tangible value. Strong agentic AI candidates deliver measurable improvements in:
– Cycle time reduction
– Accuracy and standardization
– Capacity creation through exception-based processing
Takeaway: If a use case cannot demonstrate clear operational leverage, it should not progress beyond experimentation.
2. Data Readiness
Agentic systems depend on trusted inputs. Use cases should be evaluated for:
– Availability of validated, consistent data
– Presence of golden records and defined ownership
– Ability to trace outputs back to source inputs
Takeaway: Poor data quality does not simply reduce model performance, it undermines governance and trust.
3. Integration Feasibility
Agentic AI creates value through orchestration. Firms must assess:
– API availability across core systems
– Ability to invoke downstream tools securely
– Effort required to integrate without destabilizing existing workflows
Takeaway: Use cases with excessive integration friction often fail to scale.
4. Governance and Risk Sensitivity
Not all workflows carry the same risk profile. Selection must account for:
– Audit and regulatory expectations
– Need for explainability and traceability
– Alignment with the firm’s risk appetite
Takeaway: Early agentic AI deployments should favor workflows where governance requirements are well understood and controllable.
5. Human Oversight Model
Finally, firms must define how humans remain in control. Strong candidate use cases support:
– Clear review and escalation points
– Confidence thresholds for autonomy
– Monitoring dashboards that expose decisions, not just outcomes
Takeaway: Agentic AI succeeds when humans supervise exceptions, not when they are removed entirely.
Why Getting This Right Early Matters
Agentic AI is not a plug-and-play technology. Poor early choices create lasting consequences:
– Misaligned use cases erode confidence
– Governance gaps increase risk exposure
– Integration-heavy pilots stall momentum
Conversely, disciplined use-case selection enables firms to:
– Demonstrate early ROI
– Build trust with risk and compliance teams
– Establish repeatable deployment patterns
– Scale horizontally across adjacent workflows
In effect, use case selection determines whether agentic AI becomes a strategic capability or a stalled experiment.
Conclusion
As buy-side firms transition from GenAI experimentation to agentic AI execution, the key differentiator will not be ambition, but discipline. Selecting the right agentic AI use cases requires a clear understanding of workflow complexity, governance requirements, data readiness, and operational impact.
Firms that succeed will not deploy autonomy everywhere. They will deploy it where it matters most, in workflows that demand coordination, accuracy, and scale. Agentic AI rewards precision in both technology and strategy. For buy-side leaders, choosing the right starting point is the most important step of all.
To explore the transition from GenAI to agentic AI in greater depth, including frameworks for readiness assessment, governance design, and phased adoption, download our whitepaper:
From GenAI to Agentic AI in Alternatives and Private Markets
At Indus Valley Partners, we help buy-side firms move from experimentation to execution by identifying the right agentic AI use cases, establishing governance foundations, and operationalizing autonomous workflows with confidence.
Connect with us to explore how agentic AI can deliver measurable impact across your operating model.
