Investment management firms want more from AI. Is your firm ready to move from pilots to measurable benefits?
Despite widespread adoption of AI in the industry, recent advances in the technology require firms to go beyond incremental planning toward a fundamentally different model for interacting with AI.
Key takeaways
- Investment management firms have broadly adopted AI use cases into their daily operations, but technological evolution requires firms to engineer AI as an operating infrastructure rather than a collection of productivity tools.
- Moving AI prototypes into production requires foundations to be ready for scale, including rewiring the workflows, establishing a compatible architecture, and strong, trustworthy AI governance.
- Deloitte supports investment management firms at every stage of their AI journey, with deep investment management industry expertise, proprietary AI implementation experience, and a global network of technology alliances.
Deloitte’s 2026 The State of AI in the Enterprise report shows that by 2025, most firms had at least 40% of their AI projects in production. Investment management firms have embedded AI solutions throughout investment lifecycles and across their operating capabilities, creating meaningful value at every layer.
As outlined in Deloitte's AI’s impact on investment management report, AI tools can provide better investment insights, increase efficiency, and help manage risk. Examples include:
- Generating alpha and research synthesis. Managers can use AI to ingest, synthesize, and summarize thousands of documents, including earnings transcripts, regulatory filings, equity research, and macroeconomic commentary. This synthesis compresses hours of analyst review into minutes and surfaces non-obvious signals.
- Portfolio intelligence and risk surveillance. AI systems can continuously monitor portfolio allocations against target risk parameters and automatically rebalance in response to market shifts across thousands of securities.
- Compliance. AI enables firms to interpret complex regulatory requirements, flag documentation gaps, and automate regulatory submission preparation across multiple jurisdictions and regulatory regimes.
- Client reporting and relationship management. Generative AI (GenAI) is compressing bespoke client reporting pack production from hours to minutes and enabling advisors to respond faster with more compelling messages.
- Operations. Reconciliation, cash management, trade exception handling, and counterparty data management are the highest-volume, lowest-ambiguity workflows in investment operations. Early agentic deployments are demonstrating previously unattainable straight-through processing rates.
Through Deloitte’s engagement with investment management firms to build AI use cases, we have seen measurable cost savings of 20–60%, depending on the level of automation, process maturity, AI fluency, and function of each case. These savings accumulate on top of the qualitative benefits of knowledge compounding and talent elevation.
AI's rapidly improving accessibility and scalability have opened a new frontier. The product development lifecycle is shortening, the interaction between humans and AI is becoming more democratic, and the penetration of automation is deeper across business processes. As a result, the expectation of ROI from any AI investment has been higher than ever.
A tool that summarizes research in isolation will evolve into a redesigned portfolio-construction workflow. AI systems can continuously monitor portfolio allocations against target risk parameters and automatically rebalance in response to market shifts—an operational cadence that human teams cannot maintain manually across thousands of securities. A research summary saves minutes, and a workflow reshapes competitive positioning.
The challenge of achieving that expectation and fully utilizing AI for measurable benefits is not technical, but structural.
How to invest in AI differently
Rather than diffusing budgets across isolated pilots, firms must concentrate investment in high-value, enterprise-wide AI workflows in four key ways.
Build data foundations as a parallel strategic investment
Data adds context to the AI pipe: the data platform and technology architecture need to be future-proof, but past-compatible. Data treatment is a prerequisite, but it should be treated as a continuous, governed process alongside AI development. The supplier-driven IBOR and operational data system need to be consolidated into an organization-level knowledge layer with a common, business-understandable language and model that AI can consume.
AI can also help with the classification of unstructured data, the building of ontologies and semantic models, and the generation of metadata and lineage for text, PDFs, and spreadsheets.
Concentrate investment in a small number of end-to-end priorities
Engineer a small number of priority workflows end-to-end. Rather than distributing budgets across dozens of disconnected pilots, identify three to five workflows where outcomes are measurable, the competitive stakes are highest, and AI can redesign the process.
For example, alternative data extraction into signals, automated compliance surveillance triage, or AI-assisted client reporting packs with controlled data sources.
Embed AI into decision flows, not alongside them
The most consequential shift is integration: allowing research signals to flow into portfolio/risk decisions with defined human escalation. Controls become continuous, and operations move toward straight-through processing with exception-based staffing.
Because fiduciary and regulatory expectations are non-negotiable, these decision workflows require stronger governance than most other industries.
Govern AI with the same rigour applied to investment risk
AI use cases pass through a value-and-risk approval framework that finance and compliance must recognize as credible. The operating model balances democratized access, enabling analysts and advisors to use AI tools directly, with enterprise-grade controls that prevent diffuse adoption from becoming a governance liability.
How can Deloitte help?
Deloitte can support your efforts to scale AI pilots into AI-enabled workflows. Our Investment Management AI practice offers end-to-end advisory, implementation, and managed services across five domains:
- AI Strategy and Business Process Transformation. Define your AI vision, prioritize your highest-value workflows, and build the business case for board and executive leadership.
- Data and Infrastructure Readiness. Assess and remediate your data foundations, design your AI-ready data architecture, and establish governance frameworks that meet regulatory requirements.
- AI Operating Model Design. Stand up your Centre of Excellence, design agentic workflow architectures, and define the human-in-the-loop governance model appropriate for your firm.
- AI Development and Deployment. Build and deploy AI solutions across front, middle, and back office—from AI-augmented research to autonomous compliance monitoring to GenAI-powered client communications.
- AI Risk, Governance, and Regulatory Compliance. Navigate an increasingly complex AI regulatory environment with confidence, embedding responsible AI practices from design through deployment.
We can support investment management firms at every stage of their AI journey, with our deep investment management industry expertise, proprietary AI implementation experience, and a global network of technology alliances.
