The Alpha Imperative: Why AI Is No Longer Optional for Asset Managers
As the industry matures, the operating environment for asset managers is getting increasingly difficult for Alpha generation and for profitable growth.
- Nov 20, 2025,
- Updated Nov 24, 2025 11:03 AM IST
Co-authored by:
Sathyanarayanan Palaniappan - Practice Head, Global India Banking & Financial Services Consulting Practice, Cognizant
Amitava Mukhopadhyay - Data, Analytics & AI Leader, Cognizant
In the last 5 years we have seen a phenomenal increase in assets under management of mutual funds, growing at a CAGR of 24%, driven by the accelerating financialization of household savings. As we compare the performance of large cap funds for last 5 years, only 26% of large cap funds achieved a positive information ratio, underscoring the challenge of consistently outperforming the index and generating a positive Alpha (Source: AMFI). We can attribute this to democratization of information and maturity of the market, and it becomes increasingly difficult for identification of opportunities, leveraging structured data analysis.
As the industry matures, the operating environment for asset managers is getting increasingly difficult for Alpha generation and for profitable growth. In the developed markets like United States — the proportion of inflows into actively managed funds has steadily declined in favor of passive strategies. We have consistently witnessed a consolidation of the market players.
With wider adoption of machine learning and artificial intelligence, investment managers must relook at the operating environment, build infrastructure and capabilities to stay ahead of competition. We have highlighted two critical, interconnected trends shaping the future of active asset management: the quest for the persistent alpha; and the operational shift towards democratized, data-driven research.
Organizations must adopt a "platform" mindset, transitioning from siloed, quant-centric research to a collaborative ‘alpha factory’ model where portfolio managers and analysts can rapidly test hypotheses. The goal is to augment human intuition with scalable data processing and quantitative rigor. To accomplish this vision, firms need to build critical capabilities, to stay ahead of competition. In our view, there are five key foundational enablers where firms need to build capabilities on:
Foundational data infrastructure: As the volume and veracity of data explodes in the organization, building the right data foundation is a non-negotiable bedrock. Building the right data foundation involves covering unstructured data pipelines along with structured data, including lakehouse architecture, enabling natural language processing and feature engineering options. As the industry matures, what would continue to differentiate amongst the mutual fund industry players is a personalized experience for advisors/mutual fund distributors and clients. And to drive personalization, in addition to strong data foundation and analytical layer, firms should focus on process automation, with machine-first and human-next layer. Mutual funds firms need to create an environment of seamless information flowing from front to back.
Low Code/No Code model builder: Firms need to build capabilities to create new investment models, leveraging structured and unstructured data, simulation, and back testing of the models. With low-code, no-code model building platforms, firms can reduce the time to market new models and enable portfolio managers and analysts to harness technology without needing to code. This is where the magic of democratization happens. The UI/UX must be powerful yet intuitive for financial experts who are not programmers. Key capabilities would be - Visual Workflow Designer, Pre-built Blocks, and Formula Builder.
Back testing and simulation engine: This is the rigorous "proving ground" that separates a good idea from a robust strategy for execution. It must be scientifically sound, computationally efficient and aligned with real-world execution constraints. Some of the key capabilities that firms need to invest in include Point-in-Time Data, Cost Modelling, Factor Exposure / Rebalancing logic and Performance Analytics.
Data governance & operational capabilities: Robust data governance will enable the enterprise to derive value from data investments. We suggest a data product approach with data ownership from the business and operations team, which would enable the enterprise to drive higher ROI. And a well-defined Machine Learning Operations (MLOps) environment. This ensures the platform is secure, scalable, and compliant. Key capabilities would be - Version Control, Access Management,
Model Deployment, model monitoring, and predictive and corrective actions for model drift, supported by a robust governance framework.
“New ways of working”: As artificial intelligence and digital agents become mainstream, mutual fund houses must invest in enabling the workforce for “new ways of working”. A structured organization change management exercise is essential for adoption of newer technologies, embracing the change through structured organizational transformation and workforce enablement.
For asset managers, developing this capability is not merely a tech upgrade—it’s a strategic imperative. A well-implemented no-code platform that harnesses unstructured data doesn’t just streamline workflows; it industrializes pursuit of alpha generation. By transforming investment decisions from isolated, artisanal efforts into a scalable, repeatable “alpha factory,” firms gain a decisive edge—enabling faster insights, broader coverage, and more consistent performance than competition!
Co-authored by:
Sathyanarayanan Palaniappan - Practice Head, Global India Banking & Financial Services Consulting Practice, Cognizant
Amitava Mukhopadhyay - Data, Analytics & AI Leader, Cognizant
In the last 5 years we have seen a phenomenal increase in assets under management of mutual funds, growing at a CAGR of 24%, driven by the accelerating financialization of household savings. As we compare the performance of large cap funds for last 5 years, only 26% of large cap funds achieved a positive information ratio, underscoring the challenge of consistently outperforming the index and generating a positive Alpha (Source: AMFI). We can attribute this to democratization of information and maturity of the market, and it becomes increasingly difficult for identification of opportunities, leveraging structured data analysis.
As the industry matures, the operating environment for asset managers is getting increasingly difficult for Alpha generation and for profitable growth. In the developed markets like United States — the proportion of inflows into actively managed funds has steadily declined in favor of passive strategies. We have consistently witnessed a consolidation of the market players.
With wider adoption of machine learning and artificial intelligence, investment managers must relook at the operating environment, build infrastructure and capabilities to stay ahead of competition. We have highlighted two critical, interconnected trends shaping the future of active asset management: the quest for the persistent alpha; and the operational shift towards democratized, data-driven research.
Organizations must adopt a "platform" mindset, transitioning from siloed, quant-centric research to a collaborative ‘alpha factory’ model where portfolio managers and analysts can rapidly test hypotheses. The goal is to augment human intuition with scalable data processing and quantitative rigor. To accomplish this vision, firms need to build critical capabilities, to stay ahead of competition. In our view, there are five key foundational enablers where firms need to build capabilities on:
Foundational data infrastructure: As the volume and veracity of data explodes in the organization, building the right data foundation is a non-negotiable bedrock. Building the right data foundation involves covering unstructured data pipelines along with structured data, including lakehouse architecture, enabling natural language processing and feature engineering options. As the industry matures, what would continue to differentiate amongst the mutual fund industry players is a personalized experience for advisors/mutual fund distributors and clients. And to drive personalization, in addition to strong data foundation and analytical layer, firms should focus on process automation, with machine-first and human-next layer. Mutual funds firms need to create an environment of seamless information flowing from front to back.
Low Code/No Code model builder: Firms need to build capabilities to create new investment models, leveraging structured and unstructured data, simulation, and back testing of the models. With low-code, no-code model building platforms, firms can reduce the time to market new models and enable portfolio managers and analysts to harness technology without needing to code. This is where the magic of democratization happens. The UI/UX must be powerful yet intuitive for financial experts who are not programmers. Key capabilities would be - Visual Workflow Designer, Pre-built Blocks, and Formula Builder.
Back testing and simulation engine: This is the rigorous "proving ground" that separates a good idea from a robust strategy for execution. It must be scientifically sound, computationally efficient and aligned with real-world execution constraints. Some of the key capabilities that firms need to invest in include Point-in-Time Data, Cost Modelling, Factor Exposure / Rebalancing logic and Performance Analytics.
Data governance & operational capabilities: Robust data governance will enable the enterprise to derive value from data investments. We suggest a data product approach with data ownership from the business and operations team, which would enable the enterprise to drive higher ROI. And a well-defined Machine Learning Operations (MLOps) environment. This ensures the platform is secure, scalable, and compliant. Key capabilities would be - Version Control, Access Management,
Model Deployment, model monitoring, and predictive and corrective actions for model drift, supported by a robust governance framework.
“New ways of working”: As artificial intelligence and digital agents become mainstream, mutual fund houses must invest in enabling the workforce for “new ways of working”. A structured organization change management exercise is essential for adoption of newer technologies, embracing the change through structured organizational transformation and workforce enablement.
For asset managers, developing this capability is not merely a tech upgrade—it’s a strategic imperative. A well-implemented no-code platform that harnesses unstructured data doesn’t just streamline workflows; it industrializes pursuit of alpha generation. By transforming investment decisions from isolated, artisanal efforts into a scalable, repeatable “alpha factory,” firms gain a decisive edge—enabling faster insights, broader coverage, and more consistent performance than competition!