Beyond spreadsheets and legal reviews: AI is redefining investments
Written by Viktor Ekberg and Frida Holzhausen
Investment and advisory rely on data, relationships, and deep expertise. Success means managing complexity quickly and precisely - whether it’s spotting high-growth targets or fine-tuning asset allocation. However, despite advances in technology and analytics, many of the fundamental tasks in investment and advisory, such as due diligence, financial modeling, deal sourcing, and compliance, remain highly manual, labor-intensive, and prone to inefficiencies. Traditionally, these tasks have been handled by junior analysts and associates, but agentic AI can now act as digital colleagues, managing many of these tasks with speed, consistency, and precision.
Investment firms face more pressure than ever. Geopolitical uncertainties, new regulatory frameworks, and market volatility are creating an environment where decision-making must be faster, more data-driven, and highly precise. Longer deal cycles and increasing complexity of financial transactions make it difficult for firms to maintain their competitive edge. This is where AI can make a difference, changing how investment firms operate. It enables firms to analyze data faster, spot opportunities sooner, and reduce risks with precision.
AI does more than automate tasks; it sharpens strategies, cuts costs, boosts returns, and closes deals faster.
“We’re seeing a shift where AI doesn’t just support investment processes - it actively participates. It allows teams to spend less time on manual analysis and more time on the strategic decisions that actually drive value.”
-Viktor Ekberg, Partner
The M&A process: Why it takes longer today and what’s driving the change
M&A have always been complex - demanding evaluation, financial structuring, and negotiation. Today, economic, regulatory, and market shifts make them even more unpredictable. Investment firms, private equity players, and corporate acquirers are facing challenges across every stage of the deal lifecycle - from sourcing and due diligence to valuation, financing, and PMI. These obstacles are causing extended timelines, higher transaction costs, and greater execution risks. A 2023 McKinsey report shows AI cuts due diligence time in half and boosts valuation accuracy by 30%. Tasks that junior analysts would spend weeks on, like reviewing financials and contracts, can now be done by AI agents in hours or even minutes, freeing up deal teams to focus on strategy and negotiations.
How M&A timelines have evolved
Key drivers behind longer M&A timelines
Increased risk aversion: In economic uncertainty, investors are more cautious about financial stability, demanding deeper due diligence on past earnings, debt exposure, and operational risks. As a result, dealmakers are doing more extensive audits, stress testing business models, and engaging in prolonged negotiations.
Regulatory hurdles and compliance complexity: Countries have tightened compliance requirements, antitrust regulations, and foreign investment controls. Cross-border transactions face greater scrutiny, leading to longer regulatory approval processes, additional legal reviews, and increased reporting obligations.
Market volatility and economic pressures: Macroeconomic instability, including inflationary pressures, supply chain disruptions, and geopolitical conflicts, is making valuation modeling more challenging. Fluctuating earnings forecasts and changing interest rate environments are forcing dealmakers to revisit financial assumptions multiple times, delaying transactions.
Higher cost of capital and financing constraints: Rising interest rates have made debt-financed acquisitions significantly more expensive. Leveraged buyouts and structured financing deals now require more complex capital structuring, leading to lengthier negotiations between buyers, lenders, and institutional investors.
Shift in investment priorities: Investors are moving away from a pure growth-centric approach and prioritizing profitability, sustainable cash flows, and resilience against economic downturns. This shift requires more sophisticated financial modeling, deeper scenario analysis, and greater emphasis on operational synergies, all of which add time to the M&A process.
How AI is changing M&A
AI can be used to overcome M&A roadblocks. AI agents enables faster, smarter, and more efficient deal execution by acting as a digital deal team member, handling many of the tasks typically assigned to junior employees.
Agentic AI for due diligence and risk assessment: Just as a junior associate would go through financials, contracts, and market reports, an AI agent can do the same and flag potential risks, cutting manual review time and improving accuracy.
Predictive financial modeling: AI agents can assess vast amounts of financial data to create more accurate predictive models for valuation, future earnings projections, and market trends.
Deal sourcing and target identification: AI Agents speeds up deal sourcing by quickly identifying promising targets based on financial health and industry trends - just as a junior associate would, but at a scale and speed no human can match.
Regulatory and compliance automation: An agent can function as a digital compliance officer, ensuring firms stay ahead of evolving regulations while automating documentation.
Post-merger integration optimization: AI can facilitate post-merger transitions by analyzing workforce structures, culture and operational synergies, allowing firms to integrate assets and teams more efficiently.
Automating tedious tasks and improving financial models isn’t optional. Investment firms that find ways to use AI in their deal-making processes will shorten deal cycles, create greater value, reduce execution risks, and improve overall investment performance.
“The traditional M&A process is not following today’s fast-paced environment. Firms can manage complexity with far greater precision using agentic AI - reviewing documents and providing decision support while maintaining quality”
- Frida Holzhausen, Management consultant
Automating repetitive tasks: From investment memorandums to due diligence and legal documentation
Investment professionals, especially junior analysts and associates, spend countless hours drafting memos, reviewing contracts, and assembling reports - tasks that, while critical, take time away from high-value work. An AI agent can now work as a junior digital colleague, handling these labor-intensive tasks at scale, ensuring consistency, accuracy, and efficiency. This allows deal teams to focus on negotiations, relationship management, and decision making rather than paperwork.
How AI automates document preparation
AI tech, particularly NLP and ML, are changing how investment firms handle documentation:
Extracting key insights: Agents can scan and summarizes financial reports, competitor analysis, and regulatory filings, highlighting critical insights for decision-making.
Automated report generation: Agents can generate standardized IMs, due diligence reports, and financial summaries in minutes, based on all historically produced assets, ensuring consistency and accuracy.
Legal review: Agents can analyze contracts, identify potential risks, and suggest edits, reducing manual workload while improving compliance and mitigating legal exposure. According to Deloitte, AI tools have cut legal review times by up to 70%.
Case studies on AI in private equity
Private equity firms operate in a high-stakes environment where speed, accuracy, and compliance are critical to success. However, manual processes, ranging from IM creation to legal reviews and due diligence, can slow down deal flow and introduce inefficiencies. AI-driven automation is reshaping these processes, allowing firms to move faster while improving accuracy and risk management.
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A leading European private equity firm implemented an AI agent to generate documents and speed up investment memo creation. This agent:
Extracted key insights: Pulled financial data, market analysis, and past transaction details automatically.
Standardized IM structure: Ensured consistency by automatically generating reports with key deal metrics, risk assessments, and strategic rationales.
Reduced preparation time: Cut IM drafting time from 20 hours to just 2 hours per deal.
Impact: By automating IM creation, the firm’s deal team freed up time which allowed them to analyze three times more opportunities per quarter, significantly enhancing its ability to close high-value deals.
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Legal contract review is one of the most time-consuming aspects of private equity transactions. A global private equity firm deployed an AI agent to perform contract analysis:
Scanned legal documents at scale: Reviewed thousands of contracts, identifying inconsistencies, non-standard clauses, and potential compliance risks.
Automated risk flagging: Highlighted regulatory concerns and flagged potential deal-breakers for legal teams.
Accelerated negotiations: Suggested redlines and revisions, reducing the time spent on manual contract review by 70%.
Impact: The firm reduced transaction closure times by 30%, allowing them to act faster in competitive M&A deals.
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Conducting thorough due diligence is crucial in private equity, but it often requires analyzing vast amounts of data across financials, legal documents, and operational performance. A due diligence AI agent can:
Automate data collection: Extract insights from financial reports, ESG disclosures, and operational performance data.
Identify red flags: Use machine learning to detect anomalies in financial statements and assess potential risks.
Enhance decision-making: Provide predictive risk analysis, helping firms proactively address potential deal risks.
Impact: A mid-sized PE firm cut due diligence timelines by 50%, accelerating decision-making and reducing risk exposure.
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Beyond deal origination, AI also plays a role in portfolio management by continuously analyzing company performance and market conditions. AI agents can:
Monitor portfolio companies in real time: Track financial performance, market trends, and operational KPIs.
Predict potential issues: Use predictive analytics to flag financial distress or operational inefficiencies before they escalate.
Optimize exit strategies: Provide data-driven recommendations on the best timing and structure for portfolio company exits.
Impact: A private equity firm leveraging AI-powered monitoring reduced portfolio risk and improved investment returns by dynamically adjusting strategies based on real-time data.
Case studies on AI in corporate finance advisory
Corporate finance advisory firms play a key role in M&A transactions, capital raising, and financial restructuring. However, these are often time-intensive, requiring exhaustive document review and risk assessment under time pressure. AI is significantly changing these processes, allowing advisors to execute deals faster, enhance accuracy, and strengthen client outcomes.
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A boutique M&A advisory firm implemented AI agent to perform contract reviews to improve its due diligence process. This AI agent:
Scanned thousands of legal documents: Analyzed contracts, shareholder agreements, and regulatory filings for inconsistencies, risks, and compliance issues.
Automated redlining and risk flagging: Identified non-standard clauses, potential deal-breakers, and suggested revisions, reducing the manual workload for legal teams.
Cut legal review time by 70%: Enabled faster and more thorough due diligence while improving accuracy.
Impact: By automating legal and compliance checks, the firm reduced transaction closure times by 30%, giving them a competitive edge in fast-moving M&A auctions and negotiations.
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AI-powered financial modeling enhance corporate finance advisory by automating data analysis and valuation processes. Firms using AI for financial assessments benefit from:
Automated data extraction: AI pulls key financial insights from balance sheets, income statements, and historical transactions.
Advanced valuation models: Machine learning algorithms analyze comparable transactions, industry benchmarks, and macroeconomic indicators to generate precise valuations. AI-driven financial modeling improves valuation accuracy by 30% compared to traditional models (World Economic Forum AI Finance Report, 2023).
Scenario-based forecasting: AI simulates different economic conditions, stress-testing financial projections and deal viability.
Impact: AI-driven financial modeling reduces valuation errors, accelerates deal assessments, and improves confidence in investment decisions.
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AI is transforming risk management by continuously analyzing financial, legal, and regulatory risks across transactions. AI-driven risk assessment tools:
Identify red flags: Detect financial anomalies, compliance gaps, and potential litigation risks in due diligence data.
Monitor regulatory changes: Use NLP to track updates in financial regulations, ensuring deals align with evolving compliance requirements.
Improve credit and counterparty risk analysis: AI assesses borrower creditworthiness and potential risks in acquisition financing.
Impact: AI-enhanced risk analysis allows firms to proactively mitigate risks, reducing deal failures and ensuring regulatory compliance.
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AI is also streamlining corporate finance negotiations by analyzing past deals and market trends to optimize deal structures. AI agents can:
Analyze historical deal terms: Identify common negotiation patterns and best practices in similar transactions.
Predict counterparty behavior: Use data-driven insights to anticipate negotiation tactics and prepare optimized counteroffers.
Automate term sheet drafting: AI generates initial deal terms based on industry benchmarks and legal precedents.
Impact: By leveraging AI-driven insights, advisors can negotiate more effectively, increasing the likelihood of successful deal closures.
The competitive advantage of AI in corporate finance advisory
AI can accelerate due diligence, enhance financial analysis, and improve risk management. By reducing manual inefficiencies and enhancing data-driven decision-making, firms can execute deals faster and with greater accuracy. As AI tech advances, firms will gain an advantage in deal origination, structuring, and execution.
AI in wealth management: Enhancing personalization and efficiency
Wealth management firms are under pressure to deliver personalized financial strategies while ensuring regulatory compliance and effective risk management. Clients expect investment solutions that align with their financial goals, risk tolerance, and market conditions. At the same time, firms must navigate complex regulatory frameworks and shifting geopolitical and economic landscapes.
AI lets firms personalize financial plans, monitor risks in real time, and automate investments. By using AI, wealth managers can enhance client outcomes, improve operational efficiency, and create a competitive edge:
AI-driven portfolio management: AI dynamically adjusts asset allocation and investment strategies based on real-time market trends, client goals, and individual risk tolerance. These systems continuously optimize portfolios for maximum efficiency.
Predictive analytics for market trends: AI analyzes vast amounts of global financial data, identifying market trends and potential risks. This allows wealth managers to proactively adjust client portfolios, mitigating downside risk before market fluctuations occur.
Automated tax optimization: AI-driven tax strategies help minimize liabilities by identifying investment opportunities, including real-time tax-loss harvesting and capital gains deferral strategies. This ensures clients retain more of their wealth over time.
Case studies on AI in wealth management
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A leading private wealth management firm integrated AI-powered asset allocation tools to enhance portfolio performance. The AI:
Analyzed five years of client investment behaviors and market trends to improve diversification and risk balancing.
Automated tax optimization strategies, reducing clients’ annual tax liabilities by an average of 3%.
Provided dynamic rebalancing recommendations, ensuring portfolios remained aligned with changing market conditions.
Impact: The AI-powered portfolio optimization resulted in improved long-term investment performance, stronger returns, and reduced tax burdens, leading to increased client satisfaction and loyalty.
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A multi-family office implemented an AI-driven risk management platform to enhance portfolio oversight and downside protection. The AI:
Continuously analyzed global market conditions, identifying potential threats to client portfolios.
Conducted stress testing across multiple economic scenarios to ensure proactive risk mitigation.
Generated real-time risk alerts and recommended hedging strategies tailored to each client’s risk profile.
Impact: The AI-driven risk monitoring reduced overall portfolio volatility by 12%, lowering downside risk for clients, which increased trust, retention rates, and confidence in the firm’s ability to navigate uncertain markets.
The competitive advantage of AI in wealth management
By deploying AI agents into wealth management operations, firms can offer clients highly personalized, data-driven financial strategies while improving efficiency and compliance. The agents can help improve portfolio management, risk mitigation, and tax-optimized investing, all of which contribute to better client outcomes.
How to get started with AI in investment and advisory
Ready to get started with AI? Follow these steps to ensure a smooth and effective implementation process.
AI is a competitive edge in investment and advisory
The investment and advisory industries are undergoing a major transformation, driven by economic shifts, regulatory pressures, and the growing need for efficiency. Firms aiming to outperform competitors must speed up deal-making and maximize returns.
AI agents helps companies cut costs, spot trends earlier, and make better risk decisions - ensuring stability and competitive strength over time.
