Liquidity and cash
13-week forecasting, working capital, treasury, scenario design, lender communication, and cash decision cadence.
Diagnostic method
Start with the variance. End with the cause.
Karl’s finance work begins by establishing a credible baseline: revenue quality, gross margin, cash conversion, working-capital behavior, operating leverage, covenant capacity, and the reliability of the underlying data.
Benchmark the economics. Isolate the anomaly. Trace it through the operating model, incentives, systems, and decisions behind the numbers.
That sequence avoids two common failures: treating every adverse variance as a finance problem, and treating every operational explanation as sufficient without reconciling it to the ledger and cash.
Core finance architecture
FP&A and economics
Driver-based forecasting, margin analysis, capacity economics, job cost and WIP, plan-versus-actual learning, and decision support.
Capital and stakeholders
Capital planning, lender and investor materials, sponsor accountability, transaction readiness, and evidence-based performance narratives.
Finance-function buildout
Close, controls, reporting, systems, roles, policy, data ownership, management cadence, and a practical path from fragile processes to durable capability.
Technology finance
Build-vs-buy analysis, technology ROI, total cost of ownership, security and customization tradeoffs, cost-center design, and internal recharge.
Commercial judgment
Contract economics, risk allocation, negotiation support, issue framing, and coordination with specialist legal, tax, accounting, and transaction advisors.
What good looks like
Visibility that changes behavior.
A finance system succeeds when operators can see the consequences of decisions early enough to act. That requires common definitions, authoritative sources, clear ownership, bounded exceptions, and reporting designed around decisions rather than departmental output.
The same standard applies to AI in finance. A tool does not repair an unclear chart of accounts, stale master data, missing process ownership, or inconsistent policy. Instrument the process first; automate only what can explain itself.