Applied AI

Applied AI and workflow automation for asset managers.

Deploy governed AI across asset-management workflows with model selection, approved data, integrations, evaluations, human review, and production controls.

Assess an AI workflow See what is included

AI becomes valuable when it has context, controls, and a path to action.

Asset managers do not need another abstract AI strategy presentation. They need a disciplined way to deploy useful models inside distribution, product, marketing, reporting, diligence, and investment-operations workflows.

That means connecting AI to approved product content, investment commentary, RFP and DDQ libraries, CRM and advisor intelligence, reporting data, business rules, and review steps. A useful implementation knows which sources and entitlements apply, what output it should produce, when a human must review it, and how the result reaches the next person or system.

AUMOps helps asset managers prioritize bounded use cases, prepare the data and integrations, build controlled AI-assisted workflows, evaluate their behavior, and measure whether they reduce cycle time, rework, or missed follow-up. The objective is not AI adoption for its own sake. It is a more capable operating process with clear accountability.

Investment operations and reporting workflows

Workflow solutions for asset managers.

A useful workflow solution connects a trigger to a controlled business output. Use deterministic automation for known rules and calculations, then apply AI where retrieval, drafting, or explanation adds value. The designs below are illustrative; acceptance measures are criteria to establish, not achieved client results.

Month-end factsheet release

Trigger and inputs: The reporting period closes and expected source files arrive. Approved product/share-class records, performance, benchmark data, commentary, disclosures, and template.
Processing and AI boundary: Match identities, validate dates/calculations, render proofs, and route required exceptions. Calculations and publication gates use deterministic rules; AI may assist a cited commentary draft only when permitted.
Exceptions and human review: Product operations resolves data breaks; authorized reviewers approve content and the final version. Missing required inputs block publication.
Output and owner: One approved PDF/web release with source manifest and approval evidence. Publication owner, with product and data owners assigned to exceptions.
Acceptance and measurement: Outputs reconcile to approved inputs; destination release IDs agree; track source-to-approval time and post-proof corrections.

Fund factsheet production and automation

Investment operations: administrator-to-AUM reconciliation

Trigger and inputs: A new administrator file or correction arrives for the selected close. Beginning/ending assets, subscriptions, redemptions, market movement, product crosswalks, and agreed timing definitions.
Processing and AI boundary: Normalize and reconcile control totals by product and period. AI can help summarize an explained break; it does not invent a balancing adjustment or replace reconciliation rules.
Exceptions and human review: Fund/data operations investigates missing records, classification differences, and late deliveries. The metric owner approves adjustments or restatements.
Output and owner: A versioned AUM/flow view and break queue with source evidence, status, and owners. Metric owner supported by fund and data operations.
Acceptance and measurement: Approved views reconcile under agreed rules; track unresolved break count/age and manual adjustments.

Worked AUM and flow reconciliation example

RFP and DDQ preparation

Trigger and inputs: An approved diligence request enters the work queue. Question set, current approved answer library, product documents, source dates, user permissions, and deadline.
Processing and AI boundary: Retrieve evidence and prepare cited drafts. Flag unsupported answers, source conflicts, and expired content rather than guessing.
Exceptions and human review: Subject-matter owners review their answers; the authorized diligence owner approves the response before external delivery.
Output and owner: A reviewed response package with citations, version history, unresolved items, and approval record. Diligence owner with assigned product, operations, and review contributors.
Acceptance and measurement: Every material response has current support or an explicit escalation; track draft-to-approval time and reviewer rework.

RFP and DDQ automation workflow

Where the work breaks down

The gap is not access to a model. It is operationalizing it safely.

Experiments never reach the workflow

Teams test general-purpose tools, but the output remains disconnected from approved data, systems of record, and the actions employees need to take.

Approved knowledge is fragmented

Prospectuses, factsheets, commentary, RFP answers, DDQs, procedures, and research live across files and platforms without a permission-aware retrieval layer.

Risk and ownership are unclear

No one has defined permitted data, review requirements, exception handling, retention, or responsibility for an AI-assisted decision.

Value is not measured

The firm cannot tell whether an AI initiative reduced cycle time, improved quality, increased capacity, or simply created another tool to manage.

Practical starting points

Begin with a defined deliverable and a decision about what follows.

AI workflow opportunity assessment

Deliverable: A prioritized use-case map with the target action, source systems, controls, accountable owner, value hypothesis, and implementation recommendation for each candidate workflow.
Inputs: Current workflow notes, recurring work queues, approved information sources, relevant policies, system access constraints, and interviews with the people who perform and supervise the work.
Success measure: The firm selects one bounded use case with an owner, baseline, acceptance criteria, and a documented reason to proceed or stop.
Next step: Prepare the data, evaluation set, controls, model and deployment comparison, and technical design for a focused prototype.

Approved-knowledge assistant pilot

Deliverable: A permission-aware assistant grounded in a defined body of approved product, process, research, or diligence content, with citations, review controls, and an evaluation set.
Inputs: Approved source documents, user roles, representative questions, expected answer patterns, candidate provider or cloud constraints, disclosure requirements, and security and retention requirements.
Success measure: Authorized users can retrieve supported answers against firm-defined accuracy, citation, access, and review criteria.
Next step: Connect the assistant to a distribution, product, reporting, or diligence workflow and measure adoption and exceptions.

Model and deployment strategy

Choose the model, hosting route, and controls together.

AUMOps does not force every workflow onto one provider. The right choice depends on the task, approved information, existing cloud environment, data controls, evaluation evidence, latency, cost, and the actions the workflow may take.

The model and the deployment route are separate decisions. A firm may use a provider directly, access a model through its preferred cloud, route different tasks to different approved models, or operate an open-weight model when the additional responsibility is justified.

OpenAI API

Direct access to OpenAI models and platform capabilities when the firm’s evaluation, retention, security, and integration requirements support that route.

Official provider information

Anthropic Claude

Direct Anthropic API access for workflows where Claude meets the firm’s quality, context, tool-use, safety, and commercial requirements.

Official provider information

Azure OpenAI

OpenAI models delivered through the firm’s Microsoft Azure environment when Azure identity, networking, geography, and governance are important.

Official provider information

Amazon Bedrock

A managed AWS control plane for accessing and evaluating supported model families within an AWS-centered architecture.

Official provider information

Gemini on Google Vertex AI

Google’s Gemini model family delivered through the Vertex AI enterprise platform for managed evaluation, deployment, monitoring, identity, and cloud controls.

Official provider information

Approved open-weight deployment

Private or managed inference for an approved open-weight model when control, customization, portability, or workload economics justify the operating burden.

Architecture determined by the approved use case

Selection and routing criteria

Workflow fit

Match the provider to the actual job: retrieval, extraction, drafting, reasoning, multimodal analysis, tool use, or controlled action.

Information controls

Evaluate data classification, retention, residency, encryption, identity, networking, logging, and contractual requirements.

Measured quality

Compare providers against representative firm examples, required citations, structured outputs, prohibited behavior, and reviewer decisions.

Operating economics

Measure latency, throughput, context usage, infrastructure, support, and cost per accepted workflow result—not token price alone.

Integration fit

Consider the firm’s cloud, CRM, repositories, data platforms, observability, procurement, and existing security controls.

Resilience and lifecycle

Define approved versions, change testing, fallback behavior, routing rules, deprecation handling, and ownership before production.

Provider names identify potential model and deployment options. They do not imply an AUMOps partnership, endorsement, certified implementation, or guaranteed availability. The asset manager approves the provider, commercial terms, data handling, security requirements, and permitted use.

What AUMOps can deliver

A focused implementation shaped around your operating model.

AI opportunity assessment

A prioritized map of distribution, product, reporting, diligence, marketing, and operations workflows where AI can produce a measurable benefit.

Product knowledge assistants

Permission-aware retrieval grounded in approved product documents, performance context, disclosures, commentary, research, and procedures.

Asset-management copilots

AI-assisted wholesaler preparation, RFP and DDQ drafting, commentary support, classification, research, and next-action workflows embedded in existing tools.

Agentic automations

Controlled multi-step processes that gather context, use tools, propose or take actions, and escalate exceptions.

System integrations

Connections to CRM, document stores, reporting systems, data providers, portals, and internal applications.

Model strategy, governance & measurement

Provider selection, routing, access controls, logging, review gates, evaluations, monitoring, and business-impact reporting.

Implementation approach

Improve the system without disrupting the business.

Choose the action

Define the decision, deliverable, or system update the workflow should improve and the metric that proves value.

Ground the model

Connect approved information, resolve permissions, define tools, and establish the source of truth for each task.

Control the workflow

Add structured outputs, evaluations, human review, failure handling, auditability, and limits on what the system may do.

Deploy and improve

Release to a focused user group, measure quality and adoption, review exceptions, and expand only when the evidence supports it.

Frequently asked questions

Applied AI: questions to resolve before an engagement.

What does it mean to make AI actionable?

It means moving beyond a standalone chat tool. The model receives approved business context, produces a defined output, and connects to a real next step such as updating a CRM record, preparing a review package, routing an exception, or creating an approved draft.

Do we need to replace our existing systems?

Usually not. The strongest early use cases add an intelligence layer around the CRM, document repositories, reporting tools, data platforms, and custom applications the firm already uses.

Can AI workflows include human review?

Yes. Review gates are often essential. A workflow can prepare, classify, compare, or recommend while an authorized employee approves the output before it is published, sent, or written back to a system of record.

Which AI model providers can AUMOps work with?

AUMOps is model- and cloud-neutral. Depending on the use case and the firm’s approved environment, an implementation may use the OpenAI API, Anthropic Claude, Azure OpenAI, Amazon Bedrock, Google Gemini directly or through Vertex AI, or an approved open-weight deployment. Selection is based on evaluation results, data controls, security, latency, cost, integration fit, and lifecycle requirements—not a default vendor preference.

How should an asset manager manage AI risk?

The implementation should define approved data, access permissions, model and vendor constraints, review requirements, logging, retention, evaluations, monitoring, and accountable owners. The asset manager determines its legal, compliance, privacy, and cybersecurity requirements.

Where should a firm start?

Start with a frequent, bounded workflow that has clear source material and a measurable cost today. Good candidates often involve research synthesis, content retrieval, drafting, classification, data review, meeting preparation, or exception triage.

Related work and guidance

Choose one asset-management workflow where AI can earn broader adoption.

Define the business action, approved knowledge, controls, evaluation criteria, and owner before selecting a model or building a broad platform.

Assess an AI workflow