11 December 2025
In an era of persistent margin compression and rising technology costs, many active asset managers find themselves caught between shrinking fee pools and stubbornly high costs to generate alpha. In Reducing the Cost of Alpha: A CIO’s Framework for Human + AI Integration, Michael Schopf, CFA, outlines a practical blueprint for asset management leaders - particularly Chief Investment Officers - to rethink the investment process and build a scalable, cost‑efficient engine for sustainable alpha generation.
Active management faces structural headwinds: passive strategies have siphoned assets and fee revenue, while producing alpha remains resource‑intensive due to large research teams, complex data demands and legacy systems. Traditional cost‑cutting rarely keeps pace, and years of technology investments have often failed to reduce costs due to entrenched infrastructure and resistance to change.
According to Schopf, the solution is not just new tools, but a reimagined investment process: a Human + AI “alpha factory” in which artificial intelligence amplifies human expertise and low‑value work is automated. The goal is to focus highly compensated investment professionals on high‑value judgment, while machines handle laborious data processing and screening.
A four‑stage Human + AI investment funnel
The core of the framework is a structured funnel that efficiently narrows investment universes, preserves human oversight and embeds AI where it can add measurable value:
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Pre‑screening: start with basic liquidity and size filters to reduce the universe to a manageable group of stocks.
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AI‑driven idea generation: AI models adapt dynamically to market conditions, surfacing investment ideas that traditional screens might miss.
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Deep analysis: generative AI conducts rich document and data analysis across filings, sentiment, competitive landscape and fundamentals, providing human analysts with distilled insights.
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Portfolio construction: the portfolio manager integrates human judgment with AI‑assisted risk and optimization tools to build a refined, risk‑aware portfolio.
This approach compresses investment cycle times and enhances process discipline while preserving human accountability where it matters most.
The new architecture: four portfolio pillars
Schopf also proposes a clear portfolio architecture that balances human and machine roles:
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AI‑driven top ideas: large allocations stemming from high‑conviction positions surfaced by AI and validated by human insight.
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Human expertise sleeve: specialist positions where deep domain knowledge and human judgment add unique value.
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Core stability: index‑anchored positions that ensure liquidity and limit tracking error.
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AI‑driven risk: diversifying exposures selected by AI to enhance risk‑adjusted returns.
This structure makes explicit how human judgment and AI contributions are layered and harmonized in portfolio design.
Shifting the Cost Equation
A key insight is that alpha generation needn’t imply rising costs. By outsourcing routine work to licensed external models - i.e., focusing intellectual property on tailored prompts and workflows rather than proprietary model development - firms can shift from heavy capital expenditures (capex) to more flexible operating costs (opex). Continuous model evaluation and integration become part of a dynamic, cost‑efficient investment engine.
For investment professionals in Italy and beyond, this framework offers valuable insights into how the investment profession is evolving in response to both technological disruption and market pressures. Rather than replacing human decision-making, artificial intelligence should be viewed as a tool to amplify professional judgment (fully in line with the CFA Charter’s emphasis on ethics, rigor and fiduciary responsibility).
The real competitive advantage no longer lies in building proprietary models, but in designing smart, integrated workflows where AI enhances, rather than dictates, the investment process.
In an environment of fee compression and rising regulatory complexity, cost efficiency becomes a central element of the value proposition for active managers. Firms that can embed AI into their processes without inflating costs or compromising investment quality are better positioned to compete with passive strategies.
Importantly, success in this transformation depends as much on cultural adaptation as it does on technological investment: investment teams must evolve their working methods, valuation frameworks and communication models to fully leverage the potential of Human + AI collaboration.
For investment professionals committed to excellence, this transition represents both a challenge and a strategic opportunity to redefine the role of active management in a fast-changing world.