Enterprise AI
Morgan Stanley AI Assistant Reaches 98 Percent Advisor Team Adoption
Financial services firm Morgan Stanley deployed a proprietary internal chatbot that draws exclusively on company documents to support advisors, resulting in near universal team usage and expanded query capacity.
The AI @ Morgan Stanley Assistant is Morgan Stanley's internal generative AI chatbot that enables financial advisors to retrieve and synthesize information from the firm's proprietary document corpus.
Executive Summary
Morgan Stanley operates in the financial services sector and has deployed the AI @ Morgan Stanley Assistant as its company-owned chatbot. The system operates exclusively on internal documents to deliver trusted responses for advisor queries.
The primary quantified business outcome is active usage by over 98 percent of advisor teams. This adoption level reflects successful production deployment within daily advisory workflows.
The rollout forms part of a wider pattern among large enterprises that favor proprietary internal copilots over public AI portals. Comparable systems appear at McKinsey, Moderna, Walmart, AstraZeneca, IKEA, Siemens, Goldman Sachs and BNY.
Background and Context
Fortune 500 organizations across consulting, pharmaceuticals, retail, finance and industrial sectors are replacing public AI sandboxes with internal systems trained on proprietary data. This change supports production-grade, trusted deployment while maintaining control over sensitive information.
McKinsey introduced Lilli firmwide in July 2023. The platform aggregates more than 40 internal knowledge sources and has recorded 72 percent of the workforce active on the system along with more than 500,000 monthly prompts.
Moderna launched mChat in early 2023 as a purpose-built internal instance of ChatGPT on OpenAI APIs. Employee adoption exceeded 80 percent across the company.
Walmart created Element as a cloud-agnostic machine learning platform to scale AI application development. AstraZeneca operates the iLab facility in Gothenburg where AI and automation handle end-to-end compound synthesis and drug discovery.
IKEA Billie, introduced by Ingka Group in 2021, manages customer inquiries and resolved approximately 47 percent of interactions during its initial period. Siemens Industrial Copilot assists with industrial tasks including code generation and fault diagnosis through natural language.
Goldman Sachs deployed its GS AI Assistant firmwide for document summarization, content drafting and data analysis. BNY Eliza now supports over 125 live use cases with 20,000 employees building agents.
Deployment of the AI @ Morgan Stanley Assistant
Morgan Stanley activated the AI @ Morgan Stanley Assistant as an internal chatbot available to financial advisors. The tool answers queries drawn directly from the firm's document collection without external model exposure.
Advisor teams integrated the chatbot into routine information retrieval processes. The resulting usage covers more than 98 percent of teams, establishing the system as a standard resource within the organization.
The deployment aligns with similar moves at peer institutions that seek data sovereignty and customization unavailable through public vendor interfaces.
Technical Specifics and Capabilities
The AI @ Morgan Stanley Assistant processes natural language questions against an internal corpus of 100,000 documents. Responses draw exclusively from company-owned content to maintain accuracy and confidentiality.
Prior capabilities permitted answers to roughly 7,000 questions. The current implementation extends coverage to any query within the full document set.
BNY Eliza demonstrates parallel technical scaling by supporting 125 operational use cases while enabling 20,000 employees to construct additional agents on the same platform.
| Company | Tool Name | Key Adoption Metric | Sector |
|---|---|---|---|
| Morgan Stanley | AI @ Morgan Stanley Assistant | over 98% of advisor teams | Financial Services |
| McKinsey | Lilli | 72% workforce active, 500,000 monthly prompts | Consulting |
| Moderna | mChat | more than 80% employee adoption | Pharmaceuticals |
| BNY | Eliza | 20,000 employees building agents, 125 use cases | Banking |
| IKEA | Billie | resolved approximately 47% of interactions | Retail |
Market and Stakeholder Implications
Chief information officers and chief technology officers in financial services observe that internal AI systems deliver productivity gains while preserving regulatory compliance. Data remains within firm boundaries throughout processing.
Stakeholders gain access to synthesized knowledge that matches each client's distinct requirements. The approach augments advisor expertise without introducing external data risks.
Peer executives evaluating similar initiatives can reference the Morgan Stanley adoption figure and the documented expansion of query capacity as measurable benchmarks for internal AI performance.
- High adoption rates signal internal trust in proprietary systems over public alternatives.
- Query capacity increased from 7,000 to coverage of any question within 100,000 documents.
- Banking platforms such as BNY Eliza now run 125 concurrent use cases.
- Consulting tools such as McKinsey Lilli deliver up to 30 percent time savings in knowledge tasks.
- Retail and pharmaceutical examples confirm cross-sector applicability of internal copilots.
Expert Reactions
Firmwide AI leadership at Morgan Stanley has described the technology as equalizing access to organizational expertise.
This technology makes you as smart as the smartest person in the organization. Each client is different, and AI helps us cater to each client’s unique needs.Jeff McMillan, Head of Firmwide AI at Morgan Stanley
Product and architecture strategy leaders have quantified the increase in document coverage achieved by the internal system.
We went from being able to answer 7,000 questions to a place where we can now effectively answer any question from a corpus of 100,000 documents.David Wu, Head of Firmwide AI Product & Architecture Strategy at Morgan Stanley
Moderna CEO Stéphane Bancel has expressed a parallel view on redesigning business processes around AI across legal, research, manufacturing and commercial functions.
What's Next
Financial services firms continue to expand internal AI capabilities by adding data sources and refining agent-building features observed at BNY.
Executives at comparable organizations may adopt the documented metrics from Morgan Stanley and McKinsey as reference points when planning their own proprietary deployments.
The pattern of high adoption and expanded query capacity is expected to influence investment decisions across the sector as more companies replicate the internal model.
Ongoing monitoring of live use cases at platforms such as Eliza will provide further data on scalability of employee-driven agent development.
Frequently asked
What adoption rate has Morgan Stanley achieved with its internal AI assistant?
Over 98 percent of advisor teams actively use the AI @ Morgan Stanley Assistant.
How many documents can the Morgan Stanley AI system now address?
The system covers any question from a corpus of 100,000 documents, an increase from the prior limit of 7,000 questions.
Which other large firms have reported internal AI adoption statistics?
McKinsey reports 72 percent workforce activity on Lilli with over 500,000 monthly prompts, while BNY reports 20,000 employees building agents on Eliza.