A Perspective for the Asset Management Industry: Capturing ROI from GenAI Initiatives
There is a reason organizations have invested hundreds of billions of dollars into artificial intelligence. There is a reason AI has become a standing agenda item in boardrooms around the world. And there is a reason it sits at the center of geopolitical competition and economic policy.
The reason is simple. Leaders recognize that LLMs, and advancements in AI, represent far more than another technology innovation. They mark the next wave of the digital revolution. They represent a fundamental shift in how knowledge work is performed, how decisions are made, and where competitive advantage will be created.
While the technology is real, many organizations still struggle to demonstrate its value. Executives often find it difficult to explain to their boards what return those investments are generating. This is because organizations are still learning how to manage this rapidly evolving landscape.
Three Ways AI Value Goes Missing
The challenge can be summarized across three themes. The wrong initiatives were selected from the beginning, meaning value was never created. Employees are becoming more productive, but those gains never translate into financial performance because the organization fails to capture them. Finally, value is being generated but cannot be measured because no one established the metrics needed to demonstrate success. Understanding which of these problems exists is the first step toward solving it.
1. Creating Value: You Placed the Wrong Bets
GenAI initiatives must be aligned with the firm's overall business strategy. Technology alone does not create competitive advantage. The value comes from applying AI to the capabilities that matter most to the business and investing in the enterprise foundations that enable AI to scale.
Too many organizations focused on isolated productivity pilots instead of enterprise capabilities. AI was introduced within functional silos, while foundational investments in knowledge management, data quality, governance, and reusable platforms were deferred. At the same time, change management often centered on teaching employees how to use AI rather than helping leaders redesign how work should be performed. The result is dozens of disconnected pilots that never scale into competitive advantage.
What Asset Managers Should Do
Prioritize use cases that create a flywheel, where each successful implementation builds capabilities that accelerate future AI initiatives.
Invest in shared enterprise capabilities, such as model routing, reusable connectors, agent libraries, enterprise knowledge management, and application development platforms that enable AI to scale across the organization.
Shift from a bottom-up collection of AI ideas to a top-down AI portfolio aligned with the firm's strategic priorities, business objectives, and long-term competitive advantage.
2. Capturing Value: The Value Is Real, But No One Captured It
Sometimes the strategy is sound and the technology works. Employees genuinely become more productive. Yet nothing appears in the financial results because the value remains trapped at the individual level. As Robert Solow famously observed, "You can see the computer age everywhere but in the productivity statistics." Today, the same could be said about AI.
This is a failure of management systems. Individual productivity only becomes enterprise value when organizations deliberately harvest those gains. That means redesigning workloads, adjusting staffing models, redeploying capacity toward higher-value work, resetting service levels, and changing performance expectations.
Simply making AI available does not fundamentally change how work gets done. Most firms have completed deployment. Very few have completed operationalization. There is also a trust dimension. Organizations that clearly communicate how capacity will be reinvested into growth, client service, innovation, or employee development tend to achieve stronger adoption and better long-term outcomes.
What Asset Managers Should Do
Define your leadership philosophy for AI adoption, balancing accountability with incentives that encourage experimentation and sustained adoption.
Communicate transparently how AI will reshape roles, create opportunities, and deliver value to both the organization and employees.
Make managers, not end users, the primary drivers of change by holding them accountable for adoption, workflow redesign, and value realization.
3. Tracking Value: The Value Exists, But You Cannot See It
In many organizations, value is being created every day, but no one can prove it. No baseline was established before deployment. Success metrics were never defined. Benefits tracking was treated as an afterthought. Without disciplined measurement, every ROI conversation becomes subjective.
Organizations should think beyond traditional productivity metrics. AI creates value through faster decision-making, improved client experience, lower operational risk, increased scalability, stronger employee engagement, and greater organizational resilience. Not all of these benefits immediately appear on an income statement, but they should still be measured and communicated.
Leaders should also recognize the strategic cost of inaction. While difficult to quantify precisely, firms that fail to modernize their operating models risk falling behind competitors that deliver better client experiences, operate at lower cost, and innovate more rapidly. That opportunity cost should be part of every AI investment discussion.
What Asset Managers Should Do
Define success before deployment by establishing clear qualitative and quantitative outcomes.
Establish baseline performance metrics to objectively measure business impact over time.
Assign a business owner with clear accountability for delivering measurable value and business outcomes.
Build executive dashboards that connect adoption, operational, strategic, and financial metrics into a clear story for leadership and the board.
From Proving Value to Transforming the Enterprise
Fixing these three issues improves ROI, but it does not fully prepare firms for what comes next. The organizations that ultimately win will not simply deploy AI more effectively. They will redesign how the enterprise operates.
The goal is an operating model where AI becomes a structural component of how work gets done, allowing value to compound across functions rather than accumulating one pilot at a time. That transformation requires four commitments.
1. Reimagine What Your Processes Could Look Like
Today, GenAI is often embedded within deterministic workflows, acting as another step in an existing process. Over time, that model will reverse. Deterministic workflows will increasingly become components orchestrated by AI, with intelligent systems determining when structured processes should be invoked, integrating their outputs, and driving work toward business outcomes. Organizations that redesign around this new operating model will unlock far greater value than those that simply automate yesterday's processes.
Too many organizations approach AI by layering it onto existing processes. The greater opportunity is to redesign work from the ground up. Rather than asking how AI can make today's workflows more efficient, leaders should ask how those workflows would be designed if AI were available from the start.
2. Use AI to Break the Data Deadlock
Data is often the biggest hurdle to transformation. Organizations frequently postpone strategic initiatives because of concerns around data quality, accessibility, or governance. GenAI can help overcome these challenges by extracting, organizing, enriching, and validating data across structured and unstructured sources. More importantly, these initiatives create shared data and knowledge assets that become the foundation for future AI use cases, allowing each implementation to make the next one faster and more valuable.
3. Embrace Change Management as a Strategic Capability
GenAI is a general-purpose technology, much like the internet and smartphones. Its impact will extend far beyond productivity, fundamentally reshaping how organizations operate and compete. Realizing that potential requires more than deploying new tools. It requires leaders to create excitement, foster a culture of continuous learning, equip managers to lead through change, and make organizational adaptability a core capability.
4. Redesign the Operating Model
AI is changing more than how work gets done. It is changing how organizations should be structured. Many firms still operate through functional silos, with responsibilities divided across technology, operations, product, and the business. As intelligent systems become embedded across workflows, those boundaries become less effective. Leading organizations are shifting toward value-based or product-centric teams organized around outcomes rather than functions. The opportunity is not simply to automate work, but to rethink ownership, decision-making, and accountability.
The Opportunity Ahead
The pressure to demonstrate AI ROI is healthy. It is pushing organizations to move beyond pilots and start redesigning how work gets done. The firms that lead over the next decade will not be those with the most AI use cases. They will be the ones that redesign their operating models, rethink how work is organized, and build AI into the fabric of the enterprise. Every initiative should create business value while strengthening the data, knowledge, and capabilities that accelerate the next.
The opportunity ahead is far greater than proving ROI. It is about building an organization that can continuously learn, adapt, and create competitive advantage in the age of AI.
Reach out to BeaconAP to learn more.