The Future of M&A Advisory

A Market That Rewards the Prepared

The global M&A market is back, but not for everyone. Deal values rose 36% between 2024 and 2025, driven largely by a surge in megadeals exceeding $5 billion, even as overall transaction volumes barely moved (PwC, 2026). The recovery is, in a word, K-shaped: concentrated at the top, with capital and competitive advantage accruing to the best-capitalised and most research-driven participants, while mid-market and smaller transactions remain subdued.

For M&A advisors, this bifurcation is not merely a market observation, it is a strategic inflection point. The question is no longer whether to invest in research and analytical capabilities, but how fast and how deeply to do so. The traditional playbook, built on relationships, qualitative judgment, and spreadsheet analysis, is being outpaced by a new model: one where systematic research, AI-enabled analytics, and deep sector expertise drive origination, underwriting, and value creation across the deal lifecycle.

This is the future of M&A advisory. And for firms willing to build it deliberately, the competitive upside is significant.

The Shifting Foundations of Advisory

The M&A advisory market is itself expanding, projected to grow from approximately $28.6 billion in 2025 to $34.8 billion by 2033 (Cognitive Market Research, 2025). Advisory revenues in North America and Asia-Pacific are expected to compound at roughly 2.3-3.0% annually, reflecting both rising transaction complexity and growing client demand for specialised counsel.

Yet growth alone does not guarantee relevance. Deloitte observes that the traditional approach of relying solely on spreadsheets is rapidly becoming a relic of the past, replaced by advanced, integrated, and dynamic analytical tools that combine big-picture insight with granular precision. The volume and velocity of data have also exploded, roughly 90% of the world's available data has been generated in just the past few years, forcing deal teams to process unprecedented volumes of information under compressed timelines (Doggett & King, 2023).

The implication is clear: competitive advantage is shifting toward advisors who can systematically harness data, analytics, and sector research to construct investment theses, map markets, and quantify risk-reward with genuine rigour.

The Cost of Thin Research

Weak research has a price - and it shows up most visibly in failed or underperforming transactions. Nearly one in three deals fail partly due to prolonged execution time, driven by the difficulty of manually reviewing hundreds of contracts and datasets under pressure (Doggett & King, 2023).

Average time-to-close has increased by more than 30% over the past decade, as stakeholders demand deeper, multi-dimensional diligence that extends well beyond traditional financial metrics.

Digital Journal (2025) underscores a persistent structural vulnerability: research that is shallow, episodic, or narrowly financial routinely leaves buyers exposed to hidden operational, regulatory, or market risks, risks that surface only after closing, when the cost of correction is at its highest. In this environment, the quality of pre-deal research is not a procedural matter. It is a value-at-risk question.

AI and Data: The New Deal Toolkit

Across the M&A lifecycle, AI and advanced analytics are transitioning from experimental enhancements to essential infrastructure. iDeals' 2026 survey of over 100 professionals found that approximately two-thirds of M&A practitioners already use AI or automation tools, primarily for document review, deal sourcing, and financial modelling, with speed and efficiency ranked as the top perceived benefits.

Deloitte similarly argues that AI-powered analytics are enabling smarter, faster decisions across the deal lifecycle relative to spreadsheet-based approaches. However, both sources are unambiguous on one point: AI is not a substitute for human judgment. In the iDeals research, improved accuracy did not register among the top five reported benefits of AI tools, underscoring that human oversight, interpretive judgment, and domain expertise remain indispensable in high-stakes dealmaking (iDeals, 2026). AI is only as good as the data it is trained on; poorly governed systems can misinterpret unusual contractual terms or miss emerging trends (Doggett & King, 2023).

The AI Investment Supercycle

AI is also reshaping the macroeconomic context for M&A itself. PwC (2026) estimates that between $5 and $8 trillion may be required over the next five years to fund AI technologies and enabling infrastructure, data centres, semiconductors, networks, and energy capacity. While this capex supercycle may temporarily divert capital from M&A, it is simultaneously setting up an innovation supercycle expected to reignite dealmaking as AI begins to transform productivity and cost structures across industries.

Bain & Company (2026) notes that 2025 saw the second-highest global deal value on record, as companies used M&A to reinvent themselves in response to technology disruption, post-globalisation realignment, and shifting profit pools. Roughly one-third of the 100 largest corporate deals in 2025 explicitly cited AI in their strategic rationale (PwC, 2026), a signal that AI-capability acquisition is no longer a niche rationale. It is a mainstream deal driver.

Research-Led Origination

Deal origination is perhaps the clearest arena where research-led models are displacing informal, reactive sourcing. Magistral Consulting (2024) reports that over 60% of major investment banks and advisory firms already use AI- and machine-learning-based tools to enhance deal sourcing and valuation, and that firms using predictive analytics in origination close roughly 15% more deals than those relying on traditional approaches, as algorithms surface promising targets based on historical transaction patterns and real-time market conditions.

Firmex (2023) offers an important counterpoint: effective deal sourcing still blends structured, data-driven research with relationship-based outreach and long-cycle nurturing. Their advisor survey found that it can take an average of 3.5 years from first contact to deal closure, a figure that underscores the value of early, research-informed mapping of potential sellers, well before any formal process begins. Research and relationships are not in competition. In a well-run origination function, they reinforce each other.

Where the Opportunities Are

Sector and regional specificity matter enormously in origination:

Technology M&A reached approximately $1.2 trillion in 2023, representing over 30% of global deal value (Magistral Consulting, 2024).
Healthcare deal origination approached $800 billion in the same period, with software and biotech as particularly active sub-sectors.
In India, M&A deal value rose from roughly $25.24 billion in July 2023 to $36.14 billion by July 2024, led by manufacturing, healthcare, technology, AI, fintech, and cloud computing (MergerDomo, 2025).
Cross-border M&A involving emerging markets rose approximately 15%, reaching $650 billion in 2023 (Magistral Consulting, 2024).

For advisors operating in a K-shaped market, where roughly 600 transactions above $1 billion drove the lion's share of 2025's 36% value increase, proprietary, thesis-driven origination is increasingly the decisive differentiator (PwC, 2026). Clients seeking advisors with deep industry specialisation and geographic insight have also driven the rise of sector-specific boutique firms alongside global banks (Cognitive Market Research, 2025).

Due Diligence as a Research Lab

Financial due diligence remains the backbone of M&A, but its scope is expanding dramatically. KPMG (2025) observes that as transaction complexity increases, particularly in private equity, comprehensive, timely, and data-driven diligence is essential to support sound decisions. Data analytics can reduce review time by up to 90% in some diligence tasks (Doggett & King, 2023), freeing professionals to focus on higher-value activities: synthesising financial, operational, customer, and contractual data into forward-looking insights about resilience and value creation.

This repositions due diligence from a compliance exercise to a research function, one that is expected to generate strategic intelligence, not merely verify representations.

AI in Document-Heavy Reviews

iDeals (2026) reports that many dealmakers now use AI to review and summarise large document sets in virtual data rooms, including contracts and regulatory filings, significantly reducing the burden of exhaustive manual review. AI can also identify, anomalies across contracts, extract key terms such as pricing and timelines, and enable coverage across thousands of documents that traditional sampling approaches cannot match (Doggett & King, 2023).

That said, both sources caution that AI tools remain vulnerable to data-quality issues, misinterpretation of unusual clauses, and privacy risks where sensitive client information is handled without adequate safeguards. Many firms are consequently adopting "trust but verify" frameworks, curated datasets, controlled prompts, and expert review, rather than allowing AI outputs to stand unvalidated.

When AI Is the Target

When AI capabilities are central to deal rationale, the diligence agenda shifts further. Katten (2026) notes that buyers are increasingly pursuing targets with advanced AI capabilities, valuable datasets, or AI-enabling infrastructure, and treating laggard businesses with greater caution. In such cases, due diligence must probe:

How AI systems were developed and trained
The quality and provenance of training data
Intellectual-property ownership and licensing
Regulatory and data-protection compliance

PwC (2026) adds that AI readiness is now a key driver of valuation, with leading private equity firms reporting that investment committees dedicate substantial time to evaluating whether portfolio companies can harness AI for productivity gains, or risk disruption if they cannot. Future-focused research on AI strategy, competitive threats, and regulatory trajectories is accordingly beginning to sit alongside traditional financial models in investment committee materials.

Valuing AI-driven businesses also introduces structural uncertainty. Katten (2026) observes that buyers and sellers frequently hold divergent views on what AI businesses are worth, leading to more frequent use of earn-outs, equity rollovers, and escrow mechanisms to bridge valuation gaps and link consideration to performance outcomes, deal structures that themselves demand well-researched scenario analysis on technological obsolescence and regulatory trajectory.

The Broader Research Agenda: ESG and Geopolitics

Research-led advisory must also account for ESG and geopolitical dynamics that increasingly shape deal selection and execution. Magistral Consulting (2024) projects that ESG-oriented M&A could account for around 25% of total M&A activity, as companies align acquisitions with sustainability goals and conduct more detailed ESG diligence on targets.

Geopolitical considerations are equally pressing. The US accounted for approximately 60% of global deal value in 2025, while CEOs in the Middle East and India reported particularly strong acquisition intent (PwC, 2026). Cross-border theses require robust geostrategy research, covering trade policy, regulatory regimes, and local capital markets, to underpin valuation assumptions with credibility. For advisors building practices across dynamic markets, geostrategy is no longer a supplementary consideration. It is a core diligence input.

Governance is often an overlooked dimension. Katten (2026) reports that parties are now incorporating AI clauses into non-disclosure agreements to restrict AI tool usage during transactions, particularly where sensitive data or AI-heavy businesses are involved. Doggett and King (2023) warn that indiscriminate use of public AI tools can expose confidential client data, a risk that demands explicit policies on model training, data retention, and acceptable use. Research-led practice is, in this sense, as much about designing safe, compliant workflows as it is about analytical sophistication.

What Clients Will Expect

As boards and investment committees grow more familiar with AI and data-driven approaches, their expectations of advisors will rise correspondingly. Digital Journal (2025) notes that stakeholders now expect evaluations that integrate historical performance with forward-looking scenario analysis, using technology to surface hidden risks. PwC (2026) finds that 41% of CEOs plan a major acquisition within three years, and 92% of investors believe companies should increase capital allocation to technological transformation — signals that the deals ahead will be judged, in no small part, on their AI and technology dimensions.

Practically, clients will expect advisors to:

Evidence how data and research shaped opportunity origination
Demonstrate AI-enabled, analytics-driven diligence processes
Articulate clear AI and technology narratives within investment theses
Incorporate ESG, regulatory, and geopolitical insights into recommendations

Advisors who cannot show this research lineage may find it increasingly difficult to justify fees or to defend recommendations when deals underperform (Cognitive Market Research, 2025).

Building a Research-Led Practice: A Phased Approach

For firms looking to evolve, the consensus across sources is a phased, focused approach rather than wholesale transformation:

Start focused. Prioritise a few high-impact processes for AI and analytics, contract review, specific diligence modules, or sector mapping, leveraging third-party tools before building proprietary systems (Doggett & King, 2023).

Embed, do not bolt on. Integrated, dynamic analytical tools work best when embedded into end-to-end workflows rather than added as isolated experiments (Deloitte, n.d.).

Build origination discipline first. Sharpen client-strategy articulation, improve CRM hygiene, and systematically track sector and ownership data before layering in predictive analytics or deal-sourcing platforms (Firmex, 2023).

Layer in intelligence progressively. Once foundational data and processes are in place, machine-learning models for identifying likely acquirers, sellers, or consolidation plays can materially lift close rates (Magistral Consulting, 2024). The future diligence team will include not only financial analysts and lawyers, but also data scientists and technologists, making interdisciplinary research capabilities central to execution (Doggett & King, 2023).

Research as the Organising Principle

The most successful M&A advisors of the next decade will not be those with the deepest networks alone, they will be those who build repeatable insight systems. As Cognitive Market Research (2025) notes, clients are increasingly differentiating advisors based on industry expertise, geographic coverage, and depth of advisory services, particularly in the mid-market, where choice is abundant and differentiation is hard-won.

In a world where AI is accelerating both opportunity and disruption, and where capital is plentiful but structurally uneven, research-led dealmaking is becoming the core of that differentiation. Advisors who invest in robust data foundations, advanced analytics, disciplined governance, and deep sector research will be better positioned to originate proprietary ideas, underwrite complex risks, and design structures that create durable value (Bain & Company, 2026). Research, in other words, is no longer supporting material in M&A advisory. It is the organising principle.

At Yajur Knowledge Solutions, we believe that the quality of knowledge behind a deal is as consequential as the deal itself. As we build our capabilities in domain-specific research and AI-enabled knowledge workflows, we remain committed to helping advisory professionals, particularly those operating in dynamic markets like India and across Asia-Pacific, develop the research foundations that underpin better decisions, stronger mandates, and more durable outcomes.