AI hits asset-management inflection
AI is moving from pilots to core investment workflows—firms embedding it across investment processes and risk management now look poised to outpace rivals, according to Markets Media reported. That shift is pushing hiring toward engineers who can deliver production-grade ML pipelines, not just prototypes—so quantitative shops are favoring end-to-end MLOps skills over isolated model-building.
Ben Lucas of Amundi Technology made the comment on a panel at the Investment Association’s EnTech Global conference on 12 March 2026, saying recent months showed more material advancement translating to top-line and margin impact than the prior 18 months. marketsmedia.com A ThoughtLab survey of 500 senior executives reported by Grant Thornton found 73% of asset-management leaders say AI is critical to their organisation’s future. grantthornton.com Mercer’s manager survey found 91% of asset managers already using or planning to use AI in investment strategy or asset-class research (54% current, 37% planned). mercer.com LinkedIn analytics summarised by PeopleInAI showed MLOps job listings ballooned (LinkedIn’s Emerging Jobs flagged ~9.8× growth over five years) and recruiters reported compensation for ML/MLOps roles rising roughly 20% year‑over‑year. peopleinai.com Hiring guides for enterprise teams now list CI/CD, Docker/Kubernetes orchestration, model monitoring, and reproducible experiment tracking as mandatory MLOps skills for production deployment. expertshub.ai Concrete end‑to‑end project templates recommended by practitioners include building an experiment-tracking + model-registry pipeline with MLflow, containerised inference on Kubernetes, and a feature store for point‑in‑time correctness; step‑by‑step tutorials for this stack are available (MLflow docs and a Kubernetes+MLflow tutorial). mlflow.org Open-source learning paths like DataTalks.Club’s MLOps Zoomcamp provide notebooks and CI examples for MLflow-based pipelines used in production teams. github.com Amundi and Schroders executives publicly described technology moves toward client-facing automation and recorded-advice scaling at the same EnTech panel, signalling incumbent asset managers are piloting production rollouts. marketsmedia.com Citi’s October 2025 industry paper maps the shift from efficiency use cases to AI contributing to alpha generation and automated decision workflows across investment research and execution. citigroup.com Surveys flag technical barriers that slow that production pivot: Mercer names data quality and integration as the top obstacles cited by managers, and Grant Thornton notes many firms “struggle to translate ambition into successful AI transformation.” mercer.com
