AI Is Rewriting Company Valuation. Is Your Commercial Due Diligence Ready?
Rethinking Company Evaluation as Intelligence Becomes Abundant
Multiple 2024–2025 labour market analyses (LinkedIn, Indeed Hiring Lab, and Goldman Sachs research) show a measurable slowdown in hiring for entry-level white-collar roles in Consulting, Finance, Legal services, Software development. But this may well be only the tip of the iceberg. As AI agents become capable of replacing high-skilled white-collar functions, intelligence itself becomes abundant. And when something becomes abundant, it loses economic pricing power.
In traditional economic cycles, productivity gains are broadly positive. Higher efficiency increases output, profits expand, and markets reward scalable models. But if AI systems displace large portions of knowledge workers — from software engineers to analysts, designers, legal professionals, and consultants — then a structural imbalance emerges. Companies may maintain or even expand margins due to automation, yet the broader economic base that fuels demand begins to weaken.
If humans are no longer the primary creators of high-value output, and if wage growth decouples from productivity growth, consumption power may stagnate even as corporate output increases. Machines generate “ghost GDP” — measurable productivity that does not recycle through household income or spending. This creates a paradox: companies appear operationally stronger, yet the long-term sustainability of demand becomes less certain.
Markets have already started reacting to this tension. Enterprise software and AI-exposed companies have experienced valuation volatility as investors reassess forward revenue assumptions. The market is beginning to price not just AI upside — but AI compression risk. If AI agents can replicate tasks previously monetised through subscription software or human services, then pricing power, retention, and growth durability must be reconsidered.
This shift is particularly destabilising for businesses whose value is primarily embedded in human expertise, professional judgment, relationship networks, or specialised operational know-how. Historically, such assets were scarce and defensible. Talent, experience, and industry knowledge formed durable moats. But when AI can replicate portions of cognitive labour — faster, cheaper, and at scale — those moats begin to erode.
In this context, traditional commercial due diligence frameworks may systematically overestimate value. Many CDD processes still assume:
Human intelligence is scarce and defensible
Expertise translates directly into pricing power
Customer relationships are durable
Operational know-how cannot be automated
These assumptions are no longer safe.
We are transitioning from people-centric organisations — where value is embedded in teams, networks, and institutional knowledge — toward AI-centric operations where intelligence is embedded in systems, models, and data layers. In such a world, valuation must shift from “who knows what” to “who can scale intelligence defensibly.”
This is not simply a technology upgrade cycle. It is a structural redefinition of competitive advantage.
And it demands a redefinition of commercial due diligence.
How AI Radically Changes Company Evaluation
Traditional CDD assumes human intelligence, relationships, and institutional knowledge as scarce and valuable assets. Many business models rely on salesperson relationships, customer networks, service delivery expertise, and specialized labor — all forms of human intelligence premium.
But as AI becomes capable of performing core tasks once thought uniquely human, this assumption is under pressure.
Moats based on human relationships weaken when AI can optimise pricing, routing, and decision-making without loyalty bias. Labor arbitrage shifts when agentic AI performs high-value tasks at marginal cost. Market valuations increasingly anchor on AI leverage potential — not just realised revenue performance.
The result is a fundamental question:
Is the company’s advantage based on human friction — or structural defensibility?
A services business that depends on specialised analysts may look attractive under traditional metrics. But if AI can replicate 60–70% of that cognitive workflow within three years, the revenue multiple deserves scrutiny.
A SaaS company may show strong recurring revenue today. But if its core feature set becomes commoditised by embedded AI models, retention assumptions need stress-testing.
A marketplace reliant on manual curation may see that advantage dissolve under algorithmic optimisation.
AI changes not just cost structure — it changes the durability of differentiation.
How CDD Must Change in the AI Era
To remain relevant and accurate, CDD needs to evolve in five key ways:
1. Reframe Competitive Advantage
Instead of assuming differentiation through human expertise or relationships, diligence must assess AI-enabled defensibility: Can the target leverage AI for sustainable advantage, or is its moat eroding due to automation and low friction?
2. AI Impact Sensitivity Testing
Scenario analysis should include AI displacement curves — mapping how quickly key functions can be automated and what that means for pricing, retention, and growth forecasts.
3. Validation That Goes Beyond Human Narratives
CDD must triangulate management narratives with AI-aware expert input — specifically experts who understand how AI integration alters core economics and competitive dynamics.
4. Market Demand Under AI Displacement
Assumptions about customer behavior and spending power need to consider a world where human labor plays a diminished role in economic circulation.
5. Technology Friction and True Value Creation
Analysts should distinguish between friction-based value (e.g., intermediary relationships that AI can replicate) and irreducible value (unique IP, regulatory licensing, real world network effects).
How Outman Can Support
At Outman, we recognise that the AI transition fundamentally alters how commercial realities should be assessed. We help investors adapt their CDD frameworks by integrating AI-aware analysis into every layer of diligence: market sizing, competitive benchmarking, channel validation, and customer behaviour. By blending rigorous modelling with direct, project-specific expert validation, we help investors move beyond outdated human-centric evaluations to evidence-based commercial insight fit for an AI-centric future.