AI Driven GrowthImproving EBITDA Through Efficiencies and Untapped Opportunities
Efficiency gains protect margin. Opportunity gains change the growth story.
Strategy, build, adoption, and measurement in one operating loop.
Prepared by CS Ventures from public research on AI value creation in private equity.
PE Conviction Sits at 84% While Enterprise-Scale Deployment Sits at 7%, so the Binding Constraint Is Operating Model, Not Belief
Conviction
High Conviction, Low Execution Clarity
PE has conviction, not execution clarity: 84% of funds expect AI to transform portfolio value.
Only 7% reach enterprise-scale deployment; 43% stay experimental, limited, or immaterial.
The Stall
Why Execution Stalls
Talent and skills gaps hold back 35% of funds; data readiness holds back 33% more.
Time-to-value concerns account for a further 29% -- an operating-model problem, not a belief problem.
The Close
What Closes the Gap
Technical fluency ships the build. Operator fluency connects it to EBITDA.
Adoption discipline changes how work is actually performed -- the piece most engagements skip.
The Ask
30–60 Days
Select one portfolio company for a 30–60-day AI-to-EBITDA diagnostic.
CS Ventures provides the expertise and data-backed strategy to turn that gap into real returns for your portfolio and your bottom line.
Source: EY, "How AI is sustainably transforming value creation in private equity," 2026 [S001]; FTI Consulting, "2026 Private Equity AI Radar" (200 PE decision makers, December 2025) [S002].
2
The Opportunity
AI Value Splits Into Two Engines: Efficiency Gains That Protect Margin and Opportunity Gains That Change the Growth Story
Efficiency gains
Perform existing work faster, cheaper, or more accurately.
Customer support and sales workflows automated end to end.
Finance workflows -- AP/AR and close -- automated end to end.
Procurement and vendor management costs reduced.
Low-value contractor spend reduced.
Process automation cuts cycle time and error rate across core operations.
Create capabilities the portfolio company could not previously support economically.
Continuous risk and customer surveillance
Predictive retention and expansion
Enterprise-grade account intelligence at SMB economics
Faster product development
New AI-enabled products or revenue models
Impact: revenue growth, pricing power, retention, and a stronger exit narrative.
Source: AI-to-EBITDA framework, two engines of AI value [S006]
3
The Opportunity
Replit Reports 2.9x Code Output per Engineer After Wiring Agents Into Company Systems, With Review Latency Flat
Replit self-reported, internal deployment across its own engineering organization
2.9x
code output per engineer
consistent cohort, company-reported
30%
human PR review time saved
review latency held flat, company-reported
60%
faster resolution of the hardest support tickets
company-reported
1
seven-figure SaaS product eliminated
after an internal build outperformed it, company-reported
The gain is not the chatbot, it is the wiring: agents connected to company systems, trusted data, permissions, playbooks, verification loops, and human escalation. That operating pattern, not the raw multiple, is what a portfolio company can replicate.
Source: Replit, "The Self-Driving Company," July 16, 2026; company-reported figures, not third-party audited [S004]
4
The Opportunity
Opportunity Gains Turn Periodic, Reactive Portfolio Review Into Continuous Surveillance at SMB Headcount
Before · manual portfolio review
Periodic, reactive
Risk reviewed on a quarterly or ad hoc cadence
Signals surface only after results already moved
Enterprise-grade analytics require a large dedicated team
Churn, pricing, and contract risk mined by hand, if at all
→
After · opportunity engine applied
Continuous, proactive
Portfolio risk surveilled on a continuous basis
Signals flagged ahead of P&L impact
Enterprise-grade coverage at SMB headcount and economics
Calls, tickets, and contracts screened automatically for risk signal
Measurement categories a diagnostic would baseline and an engagement would track -- not observed results
Median Revenue Multiples Rise From 14x to 31x Across McKinsey's AI-Maturity Levels, an Association the Sample Cannot Prove Causal
Median revenue multiple by AI maturity level (McKinsey defines four), x revenue
Level 1 · Opportunistic adoption
not reported¹
Level 2 · Operating enhancement
14x
Level 3 · Product transformation
20x
Level 4 · Business building
31x
The four levels
1 · Opportunistic adoption: individuals use AI tools; useful but hard to measure.
2 · Operating enhancement: AI is embedded in core workflows; productivity improves.
3 · Product transformation: AI changes what the customer buys.
4 · Business building: AI creates new revenue streams and businesses.
Read as association, not proof of cause: higher AI-maturity levels travel with higher median multiples as companies progress from operating enhancement to product transformation to business building. The sample design cannot establish that AI drove the increase.
Source: McKinsey, "Beyond productivity: How AI creates value in private equity" -- 471 PE-backed companies, $1M–$250M revenue, 30 countries; association across maturity levels, not a causal estimate [S003]. McKinsey's model has exactly four levels. ¹No median multiple reported for Level 1; Level 2 shows ~20% higher revenue per employee than Level 1. Level 3's multiple is 43% above Level 2's; Level 4's revenue per employee is 52% above Level 3's.
6
The Opportunity
Talent, Data Readiness, and Time-to-Value Keep AI Stuck in Pilot Mode, Which Is an Operating-Model Problem Rather Than an Awareness Problem
Share of PE decision makers citing each barrier to scaling AI across the portfolio.
Top barriers to scaling AI
Why it matters
35%
Talent & skills shortage -- not enough people who can build and run AI systems.
33%
Data readiness -- systems and records not clean or accessible enough to use.
29%
Time-to-value -- deployment speed too slow to show results before priorities shift.
28%
Legacy integration -- technical debt slows connections into existing systems.
25%
Change management -- organizational resistance to adopting new workflows.
Not a tooling gap -- a people-and-process gap.
Requires operating support, workflow expertise, and governance together.
PE does not have an AI-awareness problem, it has an operating-model problem -- conviction is already high; what is missing is the talent, data, and delivery speed to execute at scale.
Source: FTI Consulting, "2026 Private Equity AI Radar" (survey of 200 PE decision makers, December 2025) [S002].
7
Why Execution Stalls
The Market Splits Strategy, Build, and Adoption Across Providers, so the Engagement Fails Wherever the Handoffs Sit
Capability
Strategy-Led Advisor
Build-Led Technology Partner
Integrated Builder-Operator
Business and EBITDA framing
Technical build fluency
Workflow and operating-model redesign
Frontline adoption
KPI baselining and measurement
Governance and scale
Coverage:gappartialfull
The engagement fails if strategy, build, adoption, and measurement sit with disconnected owners -- only the integrated model carries full coverage on all six capabilities. CS Ventures is built for that column: AI strategy that ships inside your existing systems, for tangible operating benefits.
Source: illustrative capability positioning, not independently researched provider assessment [S006].
8
Why Execution Stalls
One Governed Loop -- Prioritize, Baseline, Connect, Build, Adopt, Measure -- Converts AI Strategy Into Measured EBITDA
Prioritize
Select workflows on value, feasibility, and verifiability.
Baseline
Establish current cost, cycle time, quality, revenue, and risk.
Connect
Governed access to systems, knowledge, and trusted data.
Build
Encode the workflow, decision rules, tools, and human escalation.
Adopt
Embed in the tools and routines employees already use.
Measure & scale
Compare with the baseline; expand only where the economics hold.
A 30-60-Day Diagnostic Ships Eleven Concrete Deliverables and Ends in a Fundable 90-Day Roadmap
What the portfolio company receives from one 30–60-day engagement -- ending in a fundable 90-day roadmap
Value map
Efficiency and opportunity value map
Prioritized use-case portfolio
Baselines
Current-state KPI baselines
Workflow and adoption assessment
Readiness
Data and integration readiness assessment
Economics
Business case and return model
Build-versus-buy recommendations
Governance
Governance and risk requirements
Build plan
Two quick-win build specifications
Two strategic redesign opportunities
90-day implementation roadmap
The output is fundable, not conceptual -- a business case, a governance sign-off, and two quick-win specifications ready to build in the next 90 days.
Source: CS Ventures diagnostic scope, 30–60 days [S006]
10
The Execution Model
Engagement Runs Diagnose, Then Build and Adopt, Then Scale, With Each Phase Gated on the Prior Phase's Measured Economics
Phase 1 · Diagnose
30–60 days
Phase 2 · Build & Adopt
Following the diagnostic
Phase 3 · Scale
Gated on proven economics
What happens
Identify and prioritize the highest-value efficiency and opportunity gains
Establish baselines and implementation requirements
Ship two quick wins and one or two strategic workflow redesigns
Embed them in real operating routines
Train users and measure results
Expand across functions, portfolio companies, or the fund
Standardize governance, KPIs, shared components, and board reporting
Pursue AI-enabled products and business building where appropriate
Gate to next phase
Baselines and a business case earn the build.
Measured savings and adoption earn the scale-up.
Expansion continues only where the unit economics hold.
No phase is assumed -- each one earns the next on its own measured economics, from a 30–60-day diagnostic to fund-wide scale.
Source: CS Ventures engagement model [S006]
11
The Execution Model
Turn AI Conviction Into a Measured EBITDA Lever by Selecting One Portfolio Company for a 30-60-Day Diagnostic
1
PE conviction is high, but enterprise-scale execution remains rare.
Most portfolio companies still run AI as scattered pilots rather than as a governed operating discipline connected to EBITDA.
2
The opportunity spans margin protection and the growth and exit story.
Efficiency gains protect margin today; opportunity gains build the capabilities that change how the growth story -- and the exit story -- get told.
3
Capturing it takes one operating loop, not disconnected motions.
Strategy, build, adoption, governance, and measurement have to run together -- prioritize, baseline, connect, build, adopt, measure and scale.
Next step
1Select the portfolio company
2Identify an executive sponsor
3Schedule a data and workflow scoping session
Source: CS Ventures engagement model [S006]
12
The Execution Model
CS Ventures
Appendix
Inside an At-Scale AI Deployment
What one company-reported deployment shows about the operating pattern behind the gains, and how a portfolio company can test it.
#
Replit Reports 2.9x Output per Engineer and 5.8x Total Code Contributed While Incident and Review-Latency Trends Stayed Flat
Replit self-reported, internal deployment across its own engineering organization
2.9x
Code output per engineer
Consistent cohort, company-reported
5.8x
Total code contributed
Early January to late June, company-reported
30%
Human PR review time saved
Company-reported
60%
Faster resolution of the hardest support tickets
Company-reported
Flat
Review latency
Held steady as output rose, company-reported
Flat
Reversion and incident trends
No increase in defects, company-reported
Down
Mean time to mitigation
Declined, company-reported
Up
Project completion
Increased, company-reported
The output gain did not come with a quality trade-off: review latency, defect trends, and incident trends all held or improved as the same cohort shipped more.
Source: Replit, "The Self-Driving Company," July 16, 2026; company-reported figures, not third-party audited [S004]
14
Appendix
The Gain Came From a Governed Agent Operating System With Human Escalation, Not From a Standalone AI Tool
Reference architecture the loop on top, the components it is built from, and the governance it rests on
01
Operating loop
one cycle, repeated
Employee sets the outcome→
Manager agent decomposes the work→
Specialist agents act across systems→
Outputs are evaluated→
Exceptions escalate to people→
Results improve the next cycle
02
Components
what the loop is built from
Manager agent capable of spawning multiple agentsVerifiable agent loopsConnections to operating systems and knowledge basesRemote and isolated execution infrastructureFunction-specific skills and playbooksTrusted data and semantic definitionsHuman escalation for judgment and accountability
03
Governance controls
the rail generalized on the core loop
Access policiesToken proxiesAudit loggingZero-trust networkApproval thresholdsSecurity and cost controls
Source: Replit, "The Self-Driving Company," July 16, 2026; company-reported [S004]
15
Appendix
Adoption Spread Because Agents Entered an Existing Slack Workflow and Used Trusted Company Data
01
Meet people where they work
A Slack interface made the agent system visible and accessible to employees, not walled off in a separate tool.
02
Ask, then act
Employees could ask questions and then have agents take the follow-up actions directly, in the same thread.
03
Wire in company systems
Connections to internal systems made every output company-specific rather than generic.
04
Trust the data first
A semantic layer identified which data and relationships were trustworthy before agents acted on them.
05
Let teams build their own skills
Teams contributed their own skills and integrations, extending the system into their own workflows.
06
Keep a human in the loop
Human escalation preserved judgment and accountability wherever an agent reached a limit.
07
Let wins recruit the next team
Visible wins in one group pulled additional teams into the system on their own.
Source: Replit, "The Self-Driving Company," July 16, 2026; company-reported [S004]
16
Appendix
The Pattern Transfers Wherever Code Is Replaced by a Verifiable Business Work Product, Though the Magnitude Does Not
Same operating pattern, different function a governed agent producing a verifiable work product, checked, then escalated on exception
Function
Example work product
Verification method
Primary economic KPI
Finance
Reconciliation, collections prioritization, close package
The transferable lesson is the operating pattern, not the magnitude of the gain: a non-code function should expect a governed loop to work, not a 2.9x multiple.
Source: pattern translation from Replit, "The Self-Driving Company," July 16, 2026 [S004]; function mapping per AI-to-EBITDA framework [S006]
17
Appendix
A Portfolio Company Can Test the Model in 90 Days on One High-Volume, Verifiable Workflow
Days 0–15
Select and Baseline
Choose a high-volume workflow with a measurable output
Baseline cost, time, quality, revenue, and risk
Identify systems, data, owners, and approval requirements
Days 16–45
Connect and Pilot
Connect the minimum required systems and knowledge
Encode the workflow, its standard operating procedure, quality tests, and escalation rules
Launch with a controlled user group
Days 46–75
Measure and Harden
Measure economics, quality, adoption, and exception rates
Add permissions, logging, monitoring, and cost controls
Improve the workflow using observed failures
Days 76–90
Scale Decision
Compare results with the baseline
Decide whether to scale, redesign, or stop
Document reusable components and rollout requirements
Decision metrics: hours eliminated or redeployed, cycle-time reduction, error-rate reduction, revenue captured or retained, risk detected earlier, adoption and override rates, and AI and infrastructure cost per completed work item. Prompt volume and tool-login counts are not value metrics.
Source: CS Ventures 90-day portfolio-company pilot design [S006]