For Private Equity
Comparable AI progress across every company you own.
Portfolio-wide AI claims are difficult to compare because every company measures differently. FuseIQ applies one operating standard company by company, so maturity, adoption, execution, and value roll up in the same terms.
Every company reports AI differently.
Operating partners are asked to assess AI risk and opportunity across companies with different systems, different maturity, and different definitions of progress. Without a common standard, portfolio reporting collapses into anecdote.
- 01
Diligence claims outpace operating reality
AI is increasingly part of the equity story at entry. Confirming what is actually in production, owned, and governed requires a consistent way to look. - 02
Company-by-company reinvention is expensive
Each management team building its own AI operating model repeats the same discovery, tooling, and governance work at full cost. - 03
Adoption is the constraint, not tooling
Portfolio companies buy comparable tools and get incomparable results, because workforce adoption and workflow ownership differ far more than technology does. - 04
Exit narratives need evidence
A buyer discounts unverifiable AI claims. Value measured with a method the company's own finance team agreed to survives scrutiny.
One operating model, applied per company.
FuseIQ is deployed inside each company and operated with its management team through the FuseIQ Program. The sponsor sees the same structure everywhere without dictating each company's use cases.
- Common maturity baseline
- Every company assessed on the same dimensions, so a portfolio comparison means something.
- Local priorities, shared method
- Use cases stay specific to each business; scoring, ownership, and stage definitions are shared.
- Governance consistency
- Approval, human checkpoints, and change history are recorded the same way across the portfolio.
- One value method
- Results verified with each company's finance function before they roll up to the fund view.
Where FuseIQ is applied.
Diligence
AI risk and opportunity read
First 100 days
Baseline and portfolio
Hold
Adoption and production
Exit
Verified value evidence
- Diligence support
- An assessment of what an acquisition target actually runs, owns, and governs today, and what it would take to make AI a real value lever.
- Post-close activation
- Maturity baseline, ranked use-case portfolio, and named owners inside the first operating cycle rather than at the first annual review.
- Hold-period execution
- Forward-deployed delivery of the highest-value workflows, with adoption and governance recorded as they go live.
- Exit preparation
- A defensible record of what was built, who uses it, how it is controlled, and what it produced.
Fund-level standard, company-level accountability.
PraxisIQ contracts at the fund or company level depending on how the sponsor operates. In both cases, management teams own their use cases — the standard does not remove company accountability, it makes it visible.
- 01
Start with one or two companies
Prove the operating standard where the workflow economics are clearest before rolling it across the portfolio. - 02
Reuse what transfers
Assessment method, scoring, governance patterns, and enablement tracks transfer between companies; use cases do not have to. - 03
Report on one cadence
Company operating reviews and fund-level reporting read from the same record rather than from separate decks.
FuseIQ
Start with the companies where AI is already funded.
A portfolio briefing covering the operating standard, what a first company deployment looks like, and how value is verified before it reaches a fund report.