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In the next 2 years the proportion of PE firms using AI to enable their operations is set to rise to 66%
CURRENT USERS
NON USERS
33%
37%
30%
FUTURE USERS
The proportion of funds relying entirely on external partners – vs. 40% leveraging a hybrid approach and 18% developing entirely AI in-house. 15% of funds find understanding costs and building a business case a challenge to further invest in AI.
14%
Only 4% of PE executives (nearly 1 out of 20) believe their firms are highly effective in employing data analytics, with 47% highlighting a disconnection between insights from data and their firm’s strategic objectives, and 19% citing data quality and maintenance as a key challenge to adopt AI.
Asset managers can reduce their cross-portfolio reporting costs by up to 75% by leveraging AI technology. By training AI to analyze documents in various formats and compile the information into a consistent format, they can significantly decrease both time and errors.
Automated target screening tools allow web interfaces to quickly develop customized search criteria. Analyst teams can identify thousands of targets, shortlisting using weighted criteria and Natural Language Processing (NLP), to reduce time spent from traditional methods by 50 to 60 percent. (Accenture – M&A Analytics)
Efficiency Needs
Reporting Issues
Underutilized Data
Tech Strategy Gaps
Risk of Disruption
Turning these challenges into strengths can be accomplished with incremental investment and quickly become a self-funding program
New AI-enabled vs. AI-driven funds pose a competitive threat.
Funds often use tools ad-hoc with a comprehensive technology, data, and AI strategy.
Portfolio companies often lack proper reporting tools and clear KPIs/KPEs, affecting decision-making.
With stagnant or shrinking fund sizes, firms must do more with less.
PE firms gain visibility into investment performance
Reduces effort, time, and costs at each investment
Justifies direct access to investment financial and operational data
Private equity firms need a strategy to unlock their data's value. This can take 6 to 18 months, so using select Commercial Off-The-Shelf (COTS) tools in parallel helps familiarize professionals with new workflows and create data use cases.
Unlocking portfolio data enhances investment value through comparative analysis and data science models. Advanced funds will use bespoke AI tools for AI-assisted investing like deal-scoring.
Building data consolidation, reporting at the fund level, and providing it to portfolio companies offers several benefits:
Technology, Data, and AI Strategy
Tech strategy realization can be self-funding through cost avoidance and establishing a Data-as-a-Service (DaaS) entity.
Firms are not fully leveraging the vast data available to them.
(e.g., raw material pricing, credit card data, Experian, demographics)
External Data Source
PortCo1 Systems
PortCo1 Reporting & Analytics
PortCo2 Systems
PortCo2 Reporting & Analytics
PortCo3 Systems
PortCo3 Reporting & Analytics
1.
Products
LPs Portal
VC Assistant/
Process Mining
AI/ML Factory
Data Lakehouse
Internal Apps/ Interfaces
7.
6.
5.
4.
3.
2.
PE VALUE CHAIN – MACRO USE CASES
INCREASE FUND EFFICIENCY WITH AI-ASSISTED INVESTING
INCREASE PORTFOLIO VALUE WITH DATA CAPABILIITES
Deal Sourcing and Screening
Advanced
Mature
Pipeline Mgmt.
Financial Consolidation
Deal Scoring
Operational Reporting
Investment Memo Creation
Target Sourcing
ML/AI COE at Portfolio Companies
Buyer Sourcing
Automated Due Diligence
Sell Side Data Cube
Digital LBO
Talk with your Docs
Foundation
Foundation
Fund of the Future: Key Roadmap Steps
West Monroe Value Proposition
Experience
Tailored Tech Strategies
Speed to Value
Unique Partnership Model
Data Science Expertise
20+ years serving hundreds of private equity clients
Custom strategies for PE funds of all sizes
Rapid value realization through modern data platforms
Innovative partnerships within the PE tech ecosystem
Deploying data science factories to create significant investment value
Keith Campbell
Brad Haller
Generative AI is revolutionizing private equity by streamlining due diligence, portfolio management, and deal sourcing. A focus on RAG architecture and data lakehouses helps overcome challenges, while a hybrid approach ensures data privacy.
Ready to unlock AI's potential?
Connect With Us Today
Improves decision-making efficiency, increasing deal closure rates.
BENEFITS
AI-driven evaluation and prioritization of investment opportunities.
DESCRIPTION
Investment Opportunity Management
Enhances productivity by reducing time spent on information retrieval.
BENEFITS
AI-powered search across all company documents and databases.
DESCRIPTION
Enterprise Search
Enhances productivity by reducing time spent on information retrieval.
BENEFITS
AI-powered search across all company documents and databases.
DESCRIPTION
Digital Underwriting
Strengthens investor trust and engagement through timely, tailored updates.
BENEFITS
AI-based management and personalization of communications with investors.
DESCRIPTION
Investor Relations
Ensures data privacy and compliance, fostering secure information exchange.
BENEFITS
Encrypted AI-enabled messaging platform for confidential communications.
DESCRIPTION
Secure Chat Capability
Saves time and enhances comprehension, aiding quicker decision-making.
BENEFITS
AI-driven summarization of lengthy documents and reports.
DESCRIPTION
Summarization of Documents
Improves efficiency and accuracy in retrieving necessary documents.
BENEFITS
AI-enhanced search functionality to find relevant documents rapidly.
DESCRIPTION
Document Search
Provides timely insights, facilitating agile business responses.
BENEFITS
Automated generation and updating of financial and operational reports using AI.
DESCRIPTION
Real-Time Reporting
Enhances strategic planning and value creation across the portfolio.
BENEFITS
AI analysis of data across portfolio companies to identify trends and opportunities.
DESCRIPTION
Cross-Portfolio Insights
Creates new revenue streams without additional operational costs.
BENEFITS
Leveraging portfolio company data for indirect revenue generation through AI insights.
DESCRIPTION
Indirect Data Monetization of Portco Data
Enhances objectivity and speed in deal assessments, improving success rates.
BENEFITS
AI-driven scoring system for evaluating potential deals.
DESCRIPTION
Deal Scoring
Supports strategic planning by predicting impacts of different decisions.
BENEFITS
AI-enabled simulation of various business scenarios and outcomes.
DESCRIPTION
What If/Scenario Analysis
Streamlines deployment and monitoring of AI models, driving operational efficiencies.
BENEFITS
Implementation of machine learning operations to optimize portfolio company performance.
DESCRIPTION
ML Ops for Portcos
Efficiency Needs
Reporting Issues
Underutilized Data
Tech Strategy Gaps
Risk of Disruption
Efficiency Needs
Efficiency Needs
Reporting Issues
Underutilized Data
Tech Strategy Gaps
Risk of Disruption
The PE firm can use its data to create revenue-generating products for the investment space, such as advanced deal screening tools, portfolio analytics, and automated reporting systems. These products enhance efficiency, decision-making, and position the firm as an innovation leader.
7.
LP reporting is streamlined with automated FAQs, chatbots, and regular reporting, providing timely updates and real-time insights, enhancing transparency and engagement.
6.
The PE firm’s data lakehouse integrates diverse data to improve investment activities and uncover early value. Currently, AI/ML efforts are siloed, but data sharing across the portfolio can create synergies, amplifying value creation.
5.
The AI/ML factory develops models from this data, deploying them to portfolio companies. This continuous feedback loop drives ongoing improvements and reinforces the value creation strategy.
4.
A process mining solution on top of the lakehouse analyzes these inputs, identifying inefficiencies and opportunities for value creation.
3.
The PE firm's data lakehouse serves as a central hub, where all portfolio companies’ systems feed into a unified data infrastructure.
2.
Each portfolio company has created a custom data lakehouse and AI/ML engine to support their unique goals, driving data-driven decisions and growth.
1.
Better Utilization of Third-Party Tools >
Better Utilization of Third-Party Tools >
Development of a Modern Data Platform >
Development of a Modern Data Platform >
Expanding the Modern Data Platform > to AI/ML Use Cases
Expanding the Modern Data Platform > to AI/ML Use Cases
Better Utilization of Third-Party Tools >
Development of a Modern Data Platform >
Expanding the Modern Data Platform > to AI/ML Use Cases
Deal Evaluation & Acquisition
Value Creation During Holding Period
Exit Preparation
