For the last two years, generative AI (GenAI) has dominated conversations across the buy-side, initially fueled by headline-level excitement and rapid experimentation. What began as curiosity quickly evolved into adoption. GenAI proved its ability to summarize documents, accelerate research cycles, and automate repetitive content-related tasks. The industry has now moved well beyond prototypes and pilots as AI is becoming embedded in everyday workflows.
But as firms gain practical experience, a key realization is emerging: higher productivity is not the finish line. The next competitive advantage will come from the ability to automate decision-centric processes, orchestrate multi-step workflows, and drive outcomes autonomously. This is the transition from GenAI to agentic AI, a fundamentally different capability that asset managers must prepare for now.
The First Wave: What GenAI Delivered
The first wave of AI adoption delivered measurable efficiency gains across the investment and operational ecosystem:
– Accelerated research and insights through automated summarization and synthesis
– Faster client communication cycles with AI-assisted drafting and content generation
– Improved access to unstructured data from financial documents, filings, and investor materials
– Operational relief for onboarding, reporting, and data extraction
For many funds and asset managers, these wins validated the value of AI and strengthened confidence in AI adoption. However, the outcomes remained primarily assistive. GenAI still relied heavily on human prompting, supervision, and manual handoffs between systems and teams. Its role was to support decisions, not to execute them.
What’s Missing: Where GenAI Falls Short
Despite its strength in intelligence and language, GenAI has limitations that prevent it from scaling into fully automated enterprise workflows. Specifically, GenAI:
– Can’t autonomously trigger or complete multi-step actions
– Lacks state awareness across systems and time
– Relies on manual oversight for accuracy and validation
– Can’t integrate and orchestrate end-to-end processes without external logic
– Struggles to manage conditional decision paths and exception handling
In high-stakes environments like asset management, where governance, traceability, and auditability are non-negotiable, these constraints become bottlenecks.
The Next Wave: Enter Agentic AI
Agentic AI extends GenAI beyond insight generation and into autonomous execution. Instead of responding to prompts, agentic systems can:
– Sense changes in data or workflow conditions
– Reason across structured and unstructured inputs
– Evaluate multiple possible actions and select the optimal one
– Orchestrate workflows across enterprise systems through tool-calling and APIs
– Escalate exceptions while maintaining a full audit trail
In other words, Agentic AI allows organizations to shift from task-level automation to workflow-level orchestration.
This evolution is not theoretical. Momentum for agentic AI adoption is accelerating. Firms now have:
● Stronger data foundations after years of digital transformation
● Increasing pressure to reduce manual dependency and operational costs
● Leadership awareness and strategic prioritization of AI investment
● More mature model governance frameworks and cloud-native architectures
The ecosystem is ready for the next step.
What Changes for the Buy-Side
As agentic AI becomes operational, buy-side leaders will need to rethink four factors.
Workflow Design
Processes previously built around human checkpoints can now be redesigned to focus on autonomous orchestration, exception-based management, and continuous monitoring.
Governance Models
Agentic AI requires stronger auditability, transparency, identity control, and human-in-the-loop intervention frameworks to maintain regulatory alignment.
Decision Making
Agents can propose or execute actions, shifting roles from execution to supervision and strategy.
Infrastructure and Integration
API-driven connectivity, data lineage, and observability become foundational rather than optional.
Conclusion
The industry has reached a pivotal moment: the era of experimentation is over and the era of intelligent execution has begun. GenAI delivered productivity, but agentic AI will reshape operating models, governance frameworks, and competitive differentiation across alternatives and private markets.
Now is the time for firms to assess readiness, identify high-impact processes, and establish the guardrails, data foundations, and integration capabilities required to operationalize autonomous workflows.
The move from GenAI to agentic AI is not incremental; it is transformative. Early adopters will gain structural advantages, and the cost of waiting will escalate quickly. Every delay widens the efficiency and capability gap between firms experimenting with AI and those actually executing it.
To dive deeper into the transition to agentic AI, including the frameworks, readiness models, and implementation roadmap for alternatives and private markets, download our latest whitepaper:
From GenAI to Agentic AI in Alternatives and Private Markets
At Indus Valley Partners, we help buy-side firms move confidently from GenAI experimentation to scalable agentic AI adoption. With deep expertise in data management, domain consulting, and AI-enabled operating models, we enable clients to identify high-impact use cases, establish governance foundations, and operationalize autonomous workflows that deliver measurable efficiency and control.
Connect with us to explore how agentic AI can transform your operation.
