“We engage Compello Partners for diligence and post-close integrations. They understand the PE playbook and focus early on the critical areas that impact investment decisions.”
Partner, Mid-Market Private Equity Firm ($1B AUM)
Private EquityYour portfolio companies are already using AI, mostly without a policy, an owner or a plan. Our AIM-PE framework establishes governance, proves value with working pilots, then scales AI across the portfolio, with every result tracked to EBITDA.
Our AI consulting services help private equity firms safely and profitably scale AI across their portfolio. Our methodology finds where AI is already in use, establishes guardrails around it, and builds use cases that move EBITDA. As a result, it turns AI from a board-level conversation into realized shareholder value.
Shadow AI. Ungoverned tools are already handling company data.
Data leakage. Public tools can keep, or train on, what people type in.
No access controls. AI lacks the role-based permissions an ERP has.
IP and compliance exposure. Customer contracts, privacy and regulatory duties get breached.
Unreviewed outputs. Inaccurate AI answers drive decisions with no human check.
Stalled value. Pilots never reach production, or EBITDA.
OPEX reduction from workflow automation
annual savings in high-volume back-office work
ROI on production AI workflows
cycle-time compression, concept to value
AI Maturity for Private Equity. Five stages, sequenced by the roadmap. Start where the company is today and move one stage at a time.
AI readiness due diligence and an AI maturity assessment: risk, maturity and opportunity. We find where AI is already in use and with what data, score the company’s AI maturity, and size the use cases worth building.
An AI maturity score and a target level
AI use and data-risk findings
A prioritized list of the use cases that pay first
The policy, security and data foundation AI needs before it touches company data: a tailored policy, training and guardrails, set once and adopted company-wide.
AI usage policy, roles and approval path
Data classification and approved tools
Workshops and training by role, from the board to the front line
The highest-impact use cases are tested with working pilots and a measured return, so only ideas that have proved their value are funded to scale.
Working pilots on the prioritized use cases
ROI measured against the business case
A clear go or no-go for each pilot
Proven use cases are built into everyday workflows: a foundational release first, then scaled, on a data foundation designed to carry them.
Foundational release into live workflows
An AI-ready data foundation and integrations
Monitoring, controls and ongoing oversight
Cost reduction, growth and efficiency tracked to EBITDA, so the sponsor sees what AI is worth in the numbers that matter at exit.
Value tracked to EBITDA, not activity
A portfolio scorecard, reported quarterly
A roadmap to the next maturity level
The AI maturity model is the backbone of AIM-PE. It scores each company, shows where it stands against a target, and turns the gaps into a roadmap that’s governed before it’s scaled.
The roadmap closes the gap between today and the target, one stage at a time.
An AI maturity score for the company today, and a target level.
Where the company stands, and which use cases pay first.
A sequenced, costed path to the target level, governed before it’s scaled and tracked to EBITDA.
Left to each company, AI risk appetite varies and fixes come after incidents. We help the sponsor set one AI standard for the whole portfolio, help every company adopt it, and roll the results up into one view.
Talk to a PartnerYour portfolio AI view
Platform company
Add-on 1
Add-on 2
Compello Partners
Sets one AI standard and helps every company adopt it
Platform company
Add-on
Add-on
Each company adopts, trains and attests
Six ways private equity AI consulting work begins. Each maps to a stage of AIM-PE, so whatever starts the engagement, the next step is already sequenced.
“Is the target’s AI an asset or a liability?”
Before you sign, we assess the target’s AI use, data readiness and AI security as part of IT diligence, so AI risk and upside are priced into the deal.
AI data, security and governance review
AI readiness of systems and data
Early qualifiers and disqualifiers for the deal team
“How mature is our AI use, and where should it go next?”
A short, senior-led assessment that scores the company’s AI maturity, finds the AI tools people already use and the data going into them, then turns it into two lists: risk to fix and value to build.
AI maturity score
Unsafe usage to remediate
High-return use cases to build
“What do we tell the board and our LPs about AI?”
One AI policy, approval path and control set the whole portfolio can follow, built to the sponsor’s risk appetite and tightened by sector where it needs to be.
Usage policy and hard nos
Roles, AI committee and approval tiers
Sector guidance: healthcare, financial services, industrials
“Do our people know what’s safe to put into AI?”
Training tailored to every level, from a briefing for executives, the board and the deal team to short front-line sessions on the daily do’s and don’ts.
Executive, board and deal-team briefing
Workshops for functional leaders
Technical deep dives for IT, product and security
“Why do our AI pilots never reach production?”
We take the highest-value use cases from pilot to production with a measured return, the data foundation underneath them, and guardrails built in from day one.
Working pilots with measured ROI
Production rollout across workflows
Monitoring, MLOps and guardrails
“Is our Microsoft 365 ready for Copilot?”
A Microsoft partner staffed by alumni of Microsoft’s Azure and Modern Workplace units. We get permissions, data and policy right before Copilot switches on.
Copilot readiness: permissions, data and policy
Rollout with single sign-on and role-based access
Power Platform automation
“We engage Compello Partners for diligence and post-close integrations. They understand the PE playbook and focus early on the critical areas that impact investment decisions.”
Partner, Mid-Market Private Equity Firm ($1B AUM)
Private Equity“Compello isn’t a typical consulting group. They take the ‘Partner’ title seriously… they position us for our best future.”
CFO, $250M PE-Backed Financial Services Firm
Financial Services“Since partnering with Compello, I don’t hear about IT problems anymore. Their approach has streamlined our operations, saving us time and money.”
CFO, Healthcare Provider
Healthcare
For a PE sponsor’s manufacturing portfolio, we formalized a portfolio-wide AI policy, trained the first business, and inventoried and prioritized use cases.
Read the case study →
Multimodal AI prescription intake for a $400M pharmacy cut about 10 minutes per faxed script to seconds.
Read the case study →
A $200M HVAC and plumbing platform built from fragmented local brands, with inconsistent technician training. A continuously learning AI platform unified training and operations across the brands.
Read the case study →Something we haven’t covered? A partner can answer it on a short call.
AIM-PE (AI Maturity for Private Equity) is Compello’s framework for taking a portfolio company from AI risk to measurable EBITDA. It runs in five stages: Assess, Readiness and Governance, Pilot and Prove Value, Scale and Embed, and EBITDA Value Creation.
An AI maturity model is the backbone. It scores the company, benchmarks it, and sets a sequenced roadmap to the target level.
AI due diligence, or an AI readiness review, answers two questions about a target: where is AI already creating risk, and where could it create value? We look at how employees use AI tools and with what data, the AI policy and governance in place, AI in the product and vendor stack, and whether the data can support the use cases in the investment thesis.
It runs as one of the lenses in our IT due diligence, so there is no separate workstream. After close, the same findings become the starting point for the AI maturity assessment.
Three things. First, a clear view of where AI is already in use and how mature the company is. Second, one governance baseline (policy, data rules, controls and training) set at the sponsor’s risk appetite. Third, a prioritized list of use cases tied to EBITDA, with a roadmap from pilot to production.
Most AI strategies stall because they skip the first two, or never get past the third.
Shadow AI is employees using AI tools the company hasn’t approved, often with company or customer data. It creates data-leakage, IP and compliance exposure, and it can surface as a finding in a buyer’s diligence.
Banning it tends to push it underground. Giving people a governed, better option is what removes it, which is why governance and adoption are one motion in AIM-PE, not two.
No. You start where the company is, and only move to the next stage when the last one has delivered.
Many engagements start with an AI maturity assessment; others start at governance or with a single pilot.
Both. We set the strategy and governance, then deliver the pilots and production builds with senior AI engineers, with every build measured against its financial impact.
Results include a custom AI intake engine that lifted a law firm’s client conversion from 50% to 82%, and secured production AI agents for a financial advisory firm after an 18-month stall.
A briefing for executives, the board and the deal team on the policy, the hard nos and how AI affects strategy and investment decisions. Inside each company, training runs by role: use-case workshops for functional leaders, technical deep dives for product, IT and security, and short front-line sessions on the daily do’s and don’ts. Every participant signs the policy attestation.
Rule of thumb: if you wouldn’t email it outside the company, don’t put it into AI.
They run on one governance model: the same owners, committee, controls and scorecard. They meet where AI writes code and where AI tools carry their own vulnerabilities, so our security practice has a voice in every AI decision.
Both. Governance is defined once at the fund level, each portfolio company adopts it with our support, and the results roll up into one portfolio view for the sponsor.
The same baseline doubles as AI due diligence on new targets, so questions about AI become early qualifiers or disqualifiers for the deal team.
Everything your private equity firm needs to create value with IT, from diligence to exit.
IT due diligence, carve-outs and TSAs, merger and add-on integration, and the PMO to run it.
Fractional and interim CIO, CTO, CISO and VP Ops.
Managed cybersecurity (MDR, SOC and vCISO), pen testing and compliance readiness.
Lean, supply chain and sourcing work that lifts EBITDA.
Bring Shadow AI under control with portfolio-wide policy, then build the AI use cases that pay off.
Help desk, NOC and Microsoft 365 on one portfolio standard.
NetSuite implementation, managed support and health checks.
Start with an AI maturity assessment: a senior-led look at how mature the company’s AI use is, what data it touches, and which use cases will pay back first.
