This category uses AI to gather, summarize, and analyze financial and market information for research and advisory work. It helps analysts assess opportunities and prepare client-facing insights more efficiently.
Use cases
22
Examples
22
Industries
9
Timeline
11 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
By case publish month · completed months only
15 cases documented across 37 months (Jul 23 – Jul 26), peaking at 4 in May 2026.
AI Use Cases Hub
1 earlier case before Jul 23 not shown
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
3Innovativeness3/5Differentiated3/5 - Differentiated. More advanced than a simple copilot because it combines Gemini Enterprise with Vertex AI custom agents, multimodal/multilingual synthesis, and governance controls for investment workflows; similar to recent Vertex AI investment-research cases rather than a breakthrough architecture.
Premji Invest (India) needed to rapidly assemble, summarize, and cross-reference large amounts of market, legal, and compliance information across an investment life cycle while meeting governance and regulatory requirements.It built an AI intelligence layer using Gemini Enterprise and Vertex AI to synthesize multimodal and multilingual market intelligence into structured, actionable research, with custom agents and orchestration for regulatory update tracking, legal research, term sheet generation, and NDA review.BigQuery and Cloud Storage provide the data foundation, while Cloud Identity supports governance.The platform generates initial insights and structured extractions so investment teams can validate and grade decisions.
3Innovativeness3/5Differentiated3/5 - Differentiated. A solid but not highly novel enterprise Copilot Studio deployment: the main value is centralized insight access and document synthesis at scale, similar to other recent AI search/assistant cases rather than a breakthrough architecture.
With nearly 25 brands under the ELC umbrella, the company generates a high volume of insights about product development and marketing. To remain competitive, it needed a solution to help find insights faster and inform consumer-centric decisions.ELC worked with Microsoft to build an AI agent, ConsumerIQ, with Copilot Studio. ConsumerIQ is underpinned by Azure OpenAI Service to analyze documents, identify trends, and provide strategic recommendations.By centralizing information and applying a sophisticated AI tool, ELC has reduced the time required to gather data from weeks to minutes. This allows product developers and marketers to get to market faster and respond quickly to trend shifts.
3Innovativeness3/5Differentiated3/5 - Differentiated. A practical but not frontier-gen AI use case: runtime full-text prompting plus multilingual article summaries on Amazon Bedrock, with some workflow integration, but it remains a fairly standard RAG-like summarization pattern relative to recent AWS enterprise cases.
EBSCO Information Services uses Amazon Nova Lite to optimize the research experience and inference costs.EBSCO built an AI Insights feature that generates on-demand insights into top concepts in academic articles, with multilingual support and runtime full-text prompting.
2.5Innovativeness2.5/5Differentiated2.5/5 - Differentiated. This is a practical Copilot adoption story with supporting Azure AI Foundry/Azure OpenAI references, but the article mainly shows faster access to insights and video review rather than an unusual architecture or breakthrough workflow.
The National Football League (NFL) uses Microsoft solutions to help clubs optimize post-game data analysis, practice time, and offseason player evaluations.Club personnel use Microsoft 365 Copilot to access critical data instantly, improve prospect evaluations, and support draft preparation.At the NFL Combine, clubs use Microsoft Copilot to access real-time insights, compare top prospects instantly, and make faster decisions for football operations.
4Innovativeness4/5Advanced4/5 - Advanced. Advanced, bankwide adoption of generative AI agents integrated with proprietary investment frameworks and a multi-layered AI productivity strategy.
Berenberg, Europe's oldest private bank, partnered with Google Cloud to automate and enhance banking workflows, equity research, and investment analysis.The bank developed BegoChat, a custom AI assistant built on Vertex AI to aggregate and analyze financial data with proprietary investment frameworks.They adopted Gemini Enterprise for role-specific AI agents and NotebookLM for everyday AI productivity tools across the bank.The AI implementation resulted in 85-90% faster content generation for market briefs, improved research consumption, and higher-quality investment decisions.Berenberg follows a pyramid strategy balancing proprietary contextual AI with standard tools, retaining humans in the decision loop for empathy and judgment.
3.4Innovativeness3.4/5Differentiated3.4/5 - Differentiated. Compared with recent agentic finance cases, this is a differentiated data-and-workflow integration but not a frontier architecture: it combines Copilot Studio, MCP connectors, and governed financial data to embed AI into day-to-day workflows.
LSEG and Microsoft are enabling financial firms to use LSEG-licensed trusted financial data inside Microsoft 365 Copilot across apps such as Teams and Excel, as well as within customers' own channels and applications.The solution also uses MCP connectors to help users build AI agents that can access governed data and embedded context for specific financial workflows.The collaboration combines LSEG Workspace, Microsoft applications, and Copilot so professionals can analyze data faster and work with fewer manual steps.
3Innovativeness3/5Differentiated3/5 - Differentiated. Automates real estate valuation/investment workflows by aggregating internal/external data into an AIDA dashboard with continuous updates, interactive Power BI visualization, and predictive models for deal origination and market trend identification.
Colliers Italy, a subsidiary of the global real estate advisory firm, has embarked on automating and optimizing its property valuation and investment analysis processes. Using a combination of Microsoft technologies, including Power BI and Microsoft Copilot, Colliers Italy's Data Analytics & Capital Markets team in Milan aggregates and analyzes real estate and investment data from various internal and external sources. By implementing automated solutions for continuous data updating, and integrating open-source databases into their internal AIDA dashboard, Colliers enables data-driven decision making for staff and stakeholders. The company is focused on creating interactive dashboards for visualization, predictive models for investment evaluation, and automated reporting workflows. AI technologies and Microsoft Copilot assist in identifying market trends and streamlining deal origination. These efforts have led to more immediate decision-making and improved operational efficiency, contributing to Colliers's aim of accelerating transactions, boosting transparency, and supporting climate neutrality in their Italian operations.
4Innovativeness4/5Advanced4/5 - Advanced. The case indicates an enterprise AI assistant ('UBS Red') for real-time customer support across five divisions, combining Azure OpenAI with AI Search for compliant retrieval, suggesting more complex deployment and operational integration than standard single-feature chat.
UBS partnered with Microsoft Azure to transform operations by implementing AI-powered tools across five divisions, introducing 'UBS Red,' an AI-based assistant for real-time support with customers. Using Azure OpenAI Service and AI Search capabilities, UBS enhanced content accessibility, improved compliance adherence, and optimized operations. The transformation highlights significant advancements in integrating AI into financial services for better customer responsiveness and efficiency.
3.8Innovativeness3.8/5Advanced3.8/5 - Advanced. This is a differentiated production migration combining managed RAG, semantic chunking, isolated knowledge bases, guardrails, and a zero-downtime cloud consolidation. It is more advanced than routine chatbot deployments, but still falls short of the multi-agent or frontier architectures seen in top-end cases.
Octus (formerly Reorg) migrated its flagship CreditAI conversational chatbot to AWS using Amazon Bedrock, Amazon OpenSearch Service, Amazon Textract, Amazon S3, AWS Fargate, Amazon ECS, Amazon MSK, and Amazon Bedrock Guardrails. The application supports natural-language queries over proprietary credit intelligence and streamlines research workflows for investment professionals.
3Innovativeness3/5Differentiated3/5 - Differentiated. Multiple public-sector tools were deployed (e.g., GOV.UK Chat using RAG, moderation and sensitivity review workflows), showing applied AI integration but without evidence of uncommon architecture or agentic orchestration beyond standard LLM/RAG and ML use.
The UK Government departments and public sector bodies have implemented Microsoft AI technologies including AI agents, Microsoft 365 Copilot, and Azure AI Foundry. Their implementation spans across key departments such as Cabinet Office, NHS, Department for Education, Home Office, and others.The AI-driven implementations include AI-assisted tools for document summarization, natural language processing, chatbot services (like GOV.UK Chat), automated review moderation, commercial procurement recommendation systems, AI-driven digital sensitivity review, and user research tools.This wide-ranging adoption has significantly enhanced productivity and service delivery in the UK public sector, with results including improved efficiency and accuracy, reduced workloads, faster document and review processing, and strengthened transparency and compliance with ethical AI deployment safeguards.
How many investment research use cases are documented?
The AI Use Case Hub documents 22 real investment research deployments across 9 industries, with 22 detailed company examples you can browse.
Which industries adopt investment research the most?
Investment research is most common in Finance (55%), Real Estate (9%) and Professional Services (9%).
Which countries lead in investment research?
United States leads documented investment research deployments, followed by United Kingdom and Germany.
What technologies are used for investment research?
Teams most often build investment research with Azure OpenAI, Vertex AI and Azure AI.
What AI capabilities power investment research?
Across the documented deployments, the most common capability patterns are Agent (59%), RAG (36%) and Copilot (23%).
What results do companies report from investment research?
Across the 22 deployments reporting outcomes, companies most often cite speed & agility (82%), new product / capability (73%) and better decisions & insight (50%). Where impact is quantified, the strongest evidence is in time & speed: a median +87.5% across 3 reported metrics.
