In an era of AI-driven analysis and automated deal flow, the most unpredictable variable in any transaction remains people.
Founders misrepresent track records, senior hires carry undisclosed history, and partners bring hidden conflicts that only surface after commitments are made. The human element has always shaped deal outcomes, but for decades it sat outside the structured diligence process, assessed through interviews, references, and instinct rather than systematic analysis.
That is changing. Technology is now making it possible to systematically capture, structure, and analyse human capital data at scale, transforming what was once informal assessment into defensible, auditable process. The question is no longer whether this intelligence is available, but whether your firm can access it, interpret it, and defend the decisions you make with it.
Human Capital Moves to the Centre of Diligence
Recent research from Deloitte and KPMG confirms a shift that many practitioners already recognise: in return-constrained markets, traditional financial engineering delivers diminishing returns, and the real difference increasingly comes from leadership capability, cultural alignment, and the ability to execute.
KPMG's 2024 report on the human side of due diligence identifies three critical phases that determine deal success: change-in-control triggers and key person risk, talent market positioning, and cultural integration readiness. Deloitte's work on human levers in private equity goes further, positioning CEO assessments and talent diagnostics as core drivers of value creation rather than post-close afterthoughts.
Recent research confirms a shift that many practitioners already recognise. Mercer's 2025 research with the Transaction Advisors Institute found that while nearly all acquirers say they consider human capital pre-deal, only 41% actually apply it when targeting and formulating transactions. Mercer's broader findings are stark, with 60% of deals failing due to unaddressed people-related issues. In 2026 the message is simple: Human Capital is the deal.
This applies across every sector that manages human capital risk. Law firms assessing lateral partner hires face the same dynamic, accountancy practices onboarding high-net-worth clients carry similar exposure, and executive search firms presenting shortlists to boards bear reputational risk if a candidate's history surfaces later. PE firms increasingly recognise that founder risk matters as much as the balance sheet. In each case, the human element is no longer peripheral to the decision—it is the decision.
The Codification of Human Risk
What has changed is the ability to systematically gather and structure human capital intelligence at speed and scale. Open-source data, corporate filings, litigation records, adverse media, regulatory actions, and digital footprints can now be aggregated, cross-referenced, and surfaced in minutes rather than weeks through what is increasingly known as Open Source Intelligence (OSINT).
This creates a new category of diligence output: structured, auditable intelligence on individuals that goes far beyond what manual searches could achieve. Where compliance teams once relied on fragmented searches across Companies House, LinkedIn, and Google, they can now access consolidated reports covering directorships, UBO relationships, PEP and sanctions exposure, media presence, and corporate associations in a single view. These new processes are repeatable, sourced and critically highlights pertinent data.
OSINT exists because the scale of what can be known about an individual has increased dramatically, but the real question is whether due diligence processes have kept pace with this flood of available data.
The Verification Problem
The EU AI Act, which entered into force in August 2024, introduces transparency and due diligence obligations for AI systems used in high-risk contexts through a phased rollout from 2025 to 2027. Prohibited practices took effect in February 2025, with high-risk system requirements becoming applicable from August 2026 and full implementation by August 2027.
Separately, the C2PA (Coalition for Content Provenance and Authenticity) is developing open technical standards for certifying the source and history of digital content. Backed by Adobe, Microsoft, Intel, and others, the C2PA specification is on track for ISO adoption and is increasingly referenced in regulatory guidance on digital transparency. Content Credentials, as the standard is known, function as a provenance layer for digital objects, providing verifiable signals about origin and modification that can help establish whether content has been altered.
The emergence of OSINT creates a difficult backdrop for compliance and risk teams, but the need to use available data is growing rather than diminishing. The authenticity of data used in decision-making is becoming a governance issue in its own right, and firms relying on unverified sources face both regulatory and reputational exposure.
Where Automation Meets Judgment
The promise of AI-enabled diligence is compelling: platforms can now process volumes of structured and unstructured data that no human team could review manually, with pattern recognition, entity resolution, and risk classification accelerating screening and surfacing issues that might otherwise be missed.
But the limits are equally clear. MIT's NANDA initiative reports that 95% of generative AI pilots in enterprise settings fail to deliver measurable returns. The risk is not that automation produces bad data, but that structured outputs are treated as conclusions rather than inputs to human judgment.
The most defensible approach treats automated intelligence as the starting point for analysis, not a substitute for it. Findings should be traceable, sources should be linked, and outputs should inform decisions rather than make them.
How Firms Are Using People Intelligence
YOONO sits at this intersection between automated data gathering and human decision-making. The platform delivers automated public-data intelligence reportsthat consolidate public data on individuals, covering corporate affiliations, litigation, adverse media, regulatory exposure, and more, with reports that are structured, source-linked, and audit-ready.
What distinguishes effective people intelligence from fragmented search results is the ability to see data as a complete picture rather than isolated findings. YOONO addresses this by using high-integrity data sources and building relationships between data points in a graph database, connecting corporate records, media references, regulatory filings, and digital footprints to understand how information relates and where inconsistencies or patterns emerge. This relational approach helps establish the authenticity and context of data, surfacing not just what is known about an individual but how different sources corroborate or contradict each other.
Critically, YOONO does not automate the decision. It enables the compliance, risk, or deal team to make better-informed judgments with defensible process evidence. Whether the context is client onboarding, partner vetting, founder screening, or pre-hire checks, the output is intelligence, not a verdict.
OSINT will be on every Scorecard
Human capital risk has always been present in deal flow, hiring, and client relationships, but what has changed is the expectation that firms will see it clearly, early, and at scale. OSINT-derived people intelligence is no longer a supplementary check—it is becoming a standard component of due diligence scorecards across regulated industries, from PE deal memos to law firm lateral hire assessments to AML client onboarding files.
The firms gaining ground are those treating people intelligence as a structured, repeatable process with clear documentation and audit trails. Those that treat automated outputs as a shortcut will find themselves exposed when decisions are challenged. Those that use structured intelligence to inform, rather than replace, human judgment will be better positioned to demonstrate thoroughness when it matters most.