In collaboration with Asia Tech Lens
What Does AI Really Do
To Cashflows?
Field notes from an AI practitioner on turning AI ambition into measurable EBITDA and growth for investors.
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The Challenge
AI is now priced in.
The returns aren't.
In most boardrooms, AI is now hygiene. The absence of a credible AI trajectory increasingly attracts a discount. Yet 80–95% of enterprise AI projects fail to reach production or deliver measurable business impact. (MIT, 2025)
Most deal models assume a singular 200–300 basis point margin expansion — without accounting for the execution reality that sits between the AI narrative and the P&L.
"A hundred pilots, no scale. AI becomes tourism: demos, proofs of concept, innovation days, and dashboards that never move the P&L."
In a 5–7 year hold, AI will either compress cash flows faster than expected — or create value that never made it into the model. The gap between those outcomes is not who "uses AI" and who does not, but how.
What's Inside
A disciplined lens for pricing AI
across the deal lifecycle
The report introduces two complementary frameworks that make AI pricing explicit and disciplined — turning a narrative variable into a financial one.
Framework
AI Transformation Yield
Y = (AI Opportunity − AI Disruption) × Absorption Capacity
Opportunity
Credible upside from cost reduction, revenue growth, pricing power, data monetisation
Disruption
Revenue erosion from AI-native entrants, substitution, or regulatory constraints
Absorption Capacity
Share of net opportunity realistically capturable within hold period (0.1 to 0.6)
Market & Model
How does AI change demand, pricing power, and the competitive set?
Infrastructure & Information
Does the company have the technical and data spine to execute AI at pace?
Capabilities & Culture
Does the organisation have the people and operating model to turn AI from PowerPoint to P&L?
Edge & Economics
What does AI do to structural advantage and unit economics?
Key Findings
Three patterns that determine yield
The Pricing Gap
AI is treated as a narrative label — not a disciplined financial variable. What looks like execution failure often starts as mispricing.
Absorption Capacity
Strong assets capture 40–60% of AI upside within hold. Weak infrastructure and culture? 10–20%. Same sector, very different yield.
Exit Premium
Assets that evidence AI Transformation Yield will trade first — and at better multiples. Those that merely mention AI will not.
Evidence in the Wild
The gap is not technological.
It's organizational.
The report documents both positive and negative yield in detail — from DHL's operational transformation to Volkswagen's Cariad disaster.
80–95%
of enterprise AI projects fail to deliver measurable business impact (MIT, 2025)
30%
reduction in logistics costs at DHL through AI-driven route optimisation and warehouse automation
$300M
per year in inventory savings at one large consumer company through AI-driven demand forecasting
$7.5B
in losses at Volkswagen's Cariad — a case study in negative yield from weak infrastructure and culture
Inside the Report
Apply it to your next deal
- The AI Transformation Yield formula — a single metric for pricing AI value-at-stake across any deal
- The MICE framework — four lenses PE investors and operating partners already understand
- Deal lifecycle application — how to use both frameworks across sourcing, due diligence, value creation, and exit
- Positive and negative yield case studies — DHL, CPG supply chains, Volkswagen's Cariad, US health insurers
- Monday Morning Questions — ready-to-use prompts for your next IC meeting or board session
"Resilience is the new multiple. In a world where exits remain constrained relative to AUM, the assets that trade first — and at better multiples — will be those that can evidence AI Transformation Yield, not those that merely mention AI."— RebootUp × Asia Tech Lens
Ready to price AI properly?
Download the free report and apply the AI Transformation Yield framework to your next deal.
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