AI has moved past the pilot stage in investment management. In KPMG’s Q4 2025 AI Pulse Survey, 68% of asset management and private equity leaders said they are piloting AI agents, and about 24% already deploy them in production. According to Deloitte Switzerland, 67% of investment managers now plan to increase AI investment after they saw strong early value.
AI adds measurable value in six workflow areas: research, portfolio construction, trading, risk and compliance, operations, and client reporting. Custom investment software development often turns those gains from pilots into production systems. The market reflects the shift: AI in asset management will likely grow from $3.8 billion in 2025 to $21.7 billion by 2035 at a 27.7% CAGR. This guide walks through each workflow area with real firm examples. It then explains how to decide what to build and what to buy.
What Is Custom Investment Software Development, and Why Does AI Change the Equation?
Custom investment software development means a firm designs and builds its own trading, portfolio, risk, and reporting systems. It can also mean the firm builds the integrations and AI layers that connect commercial platforms. Either way, the firm avoids running its whole operation on off-the-shelf tools.
AI changes the math because generic platforms struggle to embed a firm’s own research process, factor framework, or agentic workflows. Firms now tend to custom-build three layers:
- The alpha layer, which reflects how the firm finds and acts on ideas
- The data pipelines that feed models with clean, governed inputs
- The AI integration points, such as retrieval, orchestration, and review screens
They build all three on top of purchased market data, OMS, and accounting cores. FactSet’s build-versus-buy framework supports this combined approach. It advises firms to buy the generic infrastructure and build the proprietary edge.
Data quality decides whether any of this works. Deloitte advises firms to build governed data infrastructure alongside AI rather than after it, because models cannot run well on broken data.
The budget and ROI signals look strong. KPMG found that leaders plan an average of $101M in AI investment over 12 months, and 78% expect AI to stay a top priority even in a recession. In the same survey, 40% expect measurable ROI within 12 months and 53% expect it within 12 to 24 months.
Where Does AI Add the Most Value in Investment Research Workflows?
Research shows AI’s clearest and most defensible gains. The work involves many documents and repeats often. Human analysts already review the output and can catch errors.
AI summarizes earnings calls, filings, broker notes, and news into structured outputs. It scales sentiment analysis across thousands of documents. It also pulls data from charts and tables that keyword search misses, and it surfaces signals that could change an investment thesis. Analysts still decide what matters and what to act on.
Several firms show how this works in practice:
- AllianceBernstein deployed an NLP platform to analyze healthcare and policy documents. Its regulatory impact research cycles reportedly fell from weeks to hours.
- Minotaur Capital runs its “Taurient” platform over about 35,000 articles each week. According to SmartDev, the fund reported a 23.5% YTD return vs. 17.4% for the MSCI ACWI and a 60% cut in research cycle time. No one has independently verified how much of that return the AI system drove.
- BlackRock’s Asimov agent combines internal, regulatory, and market information and sends alerts to portfolio managers, who make the final decisions.
- AlphaSense Smart Summaries turn earnings calls into positives, negatives, outlook, and Q&A. AlphaSense links each point back to its source, so an analyst can check it in one click.
Firms that build research platforms in-house typically pair retrieval-augmented generation (RAG) over their own research archives with their own factor framework. No off-the-shelf vendor can copy that combination exactly.
How Can AI Improve Portfolio Construction and Rebalancing?
In portfolio construction, AI works as a decision-support layer. Portfolio managers keep control of mandate limits and final allocation choices.
AI compares current holdings to target allocations and flags drift. It also supports optimization inputs, budget tracking error, and drafts order ticket fields. According to LeewayHertz, a human approves each ticket before the order moves forward. For robo and passive strategies, AI can also automate tax-loss harvesting and dividend reinvestment.
Here is how large firms apply it:
- Man Group (AHL) uses neural networks and reinforcement learning to adjust strategies across asset classes within mandate constraints. CNBC reporting, cited by SmartDev, described competitive returns at lower relative volatility.
- BlackRock Aladdin simulates portfolio stress scenarios and processes structured and unstructured data in real time to support portfolio managers.
- Betterment runs AI-driven rebalancing and tax-loss harvesting and manages over $45 billion in assets.
- A global manager with $13.5T+ AUM deployed a daily-use risk tool that gives real-time exposure insights. The tool supports investment judgment and does not replace it.
Optimization inputs, drift thresholds, and scenario definitions all encode a firm’s investment philosophy. Firms usually custom-build these components on top of a purchased risk engine or OMS, not inside one.
What Role Does AI Play in Trading and Execution Oversight?
Trading is where the human-in-the-loop requirement matters most. AI drafts and supports, and people confirm anything that moves money.
AI enriches trade blotters and checks restricted lists and liquidity before an order goes out. It also supports routing choices, documents best execution, and flags unusual order-flow or pricing patterns. Algorithmic execution tools can improve execution quality. However, SmartDev notes that autonomous trading needs circuit breakers and position limits.
Speed raises the stakes. Algorithmic systems can execute thousands of trades in milliseconds, so firms need firm guardrails before they put AI anywhere near order flow.
BlackRock offers a useful model. Its Aladdin Copilot runs compliance-limit and available-cash checks in parallel for multi-step workflows such as trade placement.
Pre-trade check logic and execution-cost models differ widely from firm to firm. The OMS executes the trade, but the oversight layer that approves and audits it usually lives in custom code.
How Does AI Strengthen Risk, Compliance, and Surveillance?
In risk and compliance, AI’s job is to triage. It separates the small share of items that need human judgment from the large volume that does not.
AI extracts mandate rules from governing documents and triages pre-trade and post-trade exceptions. It also ranks AML alerts, monitors employee communications, and detects factor exposure drift. Compliance staff still sign off on every escalation and filing.
Three examples show the pattern:
- Deloitte’s “Unify Compliance Workflow” reads investment contracts through OCR and fund documents, then uses AI to generate investment guidelines. According to Deloitte Switzerland, the system compares those guidelines with rules coded in the trading system and sends any gaps to experts for review.
- Goldman Sachs uses machine learning to monitor employee communications and flag possible conduct breaches. Trained analysts review every alert.
- BlackRock Aladdin Copilot uses a supervisor-based agentic architecture with LangGraph and GPT-4 function calling. According to the ZenML LLMOps Database, input guardrails screen for off-topic content, toxicity, and PII, while output guardrails check for hallucinations. The team also runs LLM-as-judge tests on every pull request.
Vendors cover standard rulesets well. Custom development encodes the firm’s own interpretation of each mandate, its triage logic, and the audit trail that regulators expect to see.
Where Can AI Streamline Operations, Settlement, and Reconciliation?
The middle and back office offers the strongest cost-savings case. High volume, standard processes, and costly errors combine to produce a clear, measurable return.
AI tracks T+1 affirmation status, predicts settlement fails, forecasts cash, and reconciles custodian positions. It also ranks aged breaks, flags stale prices, reviews NAV packages, and assembles audit evidence. Operations staff review each flagged item before they resolve it.
The numbers here look solid:
- A Citi report, cited by Deloitte Switzerland, found that early AI adopters saw about a 50% improvement in operational workflows, including trade matching, settlement, and document processing.
- One private markets division cut investment committee memo preparation from two weeks to two days.
- Deloitte’s own client work shows cost savings of 20 to 60%, depending on automation level and process maturity.
Keep in mind that firms with clean data infrastructure produced those gains. Firms with weak data foundations should not expect the same results.
Reconciliation logic almost always varies by firm, because custodian formats, exception rules, and cash-forecasting assumptions differ. Vendors provide the engine, and custom code encodes the firm’s own operating model.
How Does AI Reshape Client Reporting and Regulatory Filings?
AI drafts client-facing output fastest. This is also where human review must never be skipped.
AI drafts quarterly letters, factsheet tables, RFP responses, and portfolio commentary. It assembles Form N-PORT, 13F, and Schedule 13D data, checks advisor materials for unsupported claims, and supports Form ADV updates.
- UBS Red supports more than 30,000 employees with onboarding, document drafting, and internal queries. According to SmartDev, it handles over 1 million AI-assisted interactions each month.
- An AI-powered RFP generator matches client requests against asset and corporate data and drafts a response for the front office to review.
- One large asset manager launched GenAI-generated client communications in 2025 and reported gains in client engagement.
Regulated outputs need audit trails, version control, and a consistent firm voice. Custom development usually owns this layer.
How to Decide What to Build vs. Buy in Custom Investment Software Development
The right answer is neither “build everything” nor “buy everything.” Firms need to know which layer belongs in each category. The table below follows FactSet’s framework:
| Layer | Approach | Why |
| Market data, OMS, risk engines, accounting cores | Buy | These tools are mature, and building them pulls engineers away from higher-value work |
| Proprietary alpha layer | Build | It reflects the firm’s unique research process, factors, and signals |
| Workflow glue and data pipelines | Build | Vendor platforms assume a generic workflow, not yours |
| AI integration points | Build | Model orchestration, retrieval, agent workflows, and review screens depend on proprietary data |
Cost deserves a careful look. FactSet compares a $5 million custom build with $500,000 per year for basic services. According to Equity Data Science, former PwC Global CIO Mark Lutchen estimates that 70% of software costs occur after implementation. Over a 7 to 8 year lifecycle, this often favors buying generic platforms.
Firms should also weigh the risks. Base LLMs hallucinate 15 to 20% of the time without proper retrieval. Many claims that “AI delivered X% returns” lack independent checks. The EU AI Act, SEC guidance, and model-risk frameworks all still apply.
Firms that combine build and buy often bring in a specialized AI engineering partner for the custom layer. For example, Azumo delivers RAG implementations that cut LLM hallucination rates to under 5% in most enterprise cases, under SOC 2 compliance.
Conclusion
The same pattern runs through research, portfolio construction, trading, risk, operations, and reporting. AI retrieves evidence, ranks exceptions, and drafts output, and then a named reviewer confirms before anyone acts. The practical path is to buy the generic layer and custom-build the proprietary edge, the workflow glue, and the AI integration points.
The firms that invest in both clean data and solid AI integration have the best chance to capture the 20 to 60% cost gains Deloitte describes. A good first step is to map your own workflows against these six areas and pick the one where volume and error cost are highest. That workflow is usually the best place to start.
FAQs
Where does AI add the most value in investment management today?
Research and middle and back-office operations show the clearest gains. AllianceBernstein’s research cycles reportedly fell from weeks to hours, and Citi reports about a 50% improvement in trade matching, settlement, and document processing.
How much are asset managers spending on AI?
KPMG’s Q4 2025 Pulse Survey found that leaders plan an average of $101M in AI investment over the next 12 months.
Is custom investment software development worth it?
In most cases, the best approach combines both options. Firms buy generic infrastructure and custom-build the alpha engine, the workflow glue, and the AI integration points.
What are the biggest risks?
The main risks are hallucination, weak data foundations, unverified performance claims, and regulatory exposure. Every credible framework, including LeewayHertz’s 14-function map, requires human review before any action.
How long until ROI?
40% of leaders expect measurable ROI within 12 months, and 53% expect it within 12 to 24 months.
