PI · PERFORMANCE IMPROVEMENT
Performance improvement for teams where the operating system is the constraint
Stalled growth is usually not one broken function. It is the interaction between sales process, delivery capacity, technical debt, finance cadence, and leadership focus. We rebuild the system around measurable outcomes.
BEST FIT
Who this service is for, and when to use it.
The mandate follows the constraint, not the menu. This service line solves a specific operating problem; the trigger below tells you when it is the right opening move.
- AUDIENCE
- Founder-CEOs, PE operating partners, CFOs, COOs, CROs, and CTOs
- TRIGGER
- Use this when growth stalls, margins compress, delivery velocity drops, forecast accuracy degrades, or the leadership team keeps treating symptoms.
- SERVICE CODE
- PI
ENGAGEMENT TIMELINE
Performance Improvement primarily lives in implementation.
Each service line lives inside the four-phase operating journey. This phase is where this engagement spends most of its operating cadence.
PHASE 03
Implementation
Days 22–90
Performance improvement is the implementation engine — pipeline, win rate, delivery margin, and operating cadence all reset against baseline.
- Commercial bottleneck diagnostic with quantified improvement levers
- Forecast accuracy installed; win-rate trend reversed
- Delivery utilization and partner economics tightened
OPERATOR RESULTS
Performance work starts where the operating system breaks
A stalled company rarely has one broken metric. The same system usually explains win rate, forecast accuracy, delivery drag, and margin compression. We fix the operating architecture before prescribing another hire.
ENGAGEMENT OUTCOMES
What the work produces.
Outcomes are what the engagement leaves behind for the executive team to operate with. They are not intermediate deliverables; they are operating moves.
- OUTCOME 01
- 90-day performance baseline
- OUTCOME 02
- Revenue and delivery operating cadence
- OUTCOME 03
- Margin and velocity improvement roadmap
A stalled company rarely has one broken metric. The same system usually explains win rate, forecast accuracy, delivery drag, and margin compression. We fix the operating architecture before prescribing another hire.
RELATED INTELLIGENCE
Field notes that support performance improvement.
Read insights
BRIEF · PI
Your AI Center of Excellence Is a Filing Cabinet, Not an Org Chart
Most AI Centers of Excellence are an org chart with no paperwork behind it. Here are the four documents that decide whether your models survive M&A diligence.
BRIEF · PI
Stop Selling Prompts. Sell the Workflow They Break Inside Of.
A prompt pack is a one-time invoice that decays. Here is how a tech-services firm turns prompt engineering into a recurring, governed, defensible service line.
BRIEF · PI
The Margin That Wasn't There: Auditing AI Vendor Dependency Before You Sign
A SaaS target's 82% gross margin can hide a single-vendor API bill that quietly halves it. How to diligence AI dependency, model drift, and COGS before LOI.
BRIEF · PI
The MLOps Audit: How to Price an AI Target Before the Models Quietly Rot
AI targets don't fail in the codebase—they fail in the retraining pipeline. A buyer's field guide to auditing MLOps maturity, model drift, and registry gaps.
BRIEF · PI
How to Diligence a GenAI Acquisition: Reading the CIM Against the Inference Bill
A PE diligence playbook for tech M&A: separate a real GenAI moat from a $25/month API wrapper, audit the IP chain, and price inference cost before you sign.
BRIEF · PI
The Brittle System Problem: When a Dashboard Tweak Takes Down Billing
A two-line change to a reporting page shouldn't crash your payment gateway. When it can, buyers cut the price. Here's how brittleness becomes a 22% discount.
DECISION GUIDES
When this service is the right move.
- AI Agent vs. Workflow Automation: Decision Guide A decision guide for choosing an AI agent, internal copilot, or workflow automation for a business process.
- AI Audit vs. AI Implementation Sprint: Decision Guide A decision guide for choosing an AI audit, AI transformation blueprint, or implementation sprint based on readiness, workflow clarity, and risk.
- AI Consultant vs. Automation Agency: Decision Guide A decision guide for choosing an AI consultant, automation agency, or implementation partner when a growing business needs practical AI workflow improvement.
- AI Governance Sprint vs. Employee Training: Decision Guide A decision guide for choosing AI governance, employee training, or both when a small or medium business wants safer AI adoption.
- AI Knowledge System vs. Chatbot: Decision Guide A decision guide for choosing an internal AI knowledge system, support copilot, or customer-facing chatbot.
- Fractional AI Partner vs. Full-Time AI Hire: Decision Guide A decision guide for choosing fractional AI transformation leadership, a full-time AI hire, or vendor-led ownership.
- Interim CTO vs. Technical Advisor: Technology Leadership Decision Guide A decision guide for choosing interim CTO, technical advisor, or embedded technical operator support when technology execution, architecture, or engineering leadership is under pressure.
- Office of the CFO vs. Fractional CFO: Finance Leadership Decision Guide A decision guide for choosing fractional CFO, Office of the CFO, or interim finance operator support when technology companies need trusted numbers and board-ready finance infrastructure.
- Turnaround Advisor vs. Management Consultant: Board Decision Guide A decision guide for choosing turnaround advisor, management consultant, or interim operator support when a technology company needs analysis, authority, or stabilization.
OPERATOR RESOURCES
Checklists and scorecards for this service line.
- 14-Day Turnaround Diagnostic A board-ready diagnostic sequence for technology companies facing missed numbers, runway pressure, stalled initiatives, or integration failure.
- AI Acceptable-Use Policy Template A starter policy for employee AI use covering approved tools, restricted data, human review, customer-facing output, and escalation.
- AI ROI Spreadsheet A worksheet for translating AI use cases into time savings, quality improvement, revenue response, cost avoidance, and payback assumptions.
- AI Vendor Selection Checklist A practical checklist for comparing AI tools, automation vendors, and implementation partners before a growing business signs.
- Integration Risk Checklist A pre-close and Day 1 checklist for technology acquisitions where customer retention, staff retention, data migration, and synergy capture depend on execution quality.
- Technical Debt EBITDA Worksheet A finance-and-engineering worksheet for translating release drag, rework, incidents, and platform fragility into EBITDA and valuation exposure.
COMMON QUESTIONS
Operator-grade answers.
The questions that come up before the first call. Relevant outcomes are listed on the results page.
-
What is the first 30 days of performance improvement?
We baseline the metrics, find the constraints, and separate symptoms from root causes. That usually means revenue architecture, delivery bottlenecks, technical debt, finance cadence, and leadership decision rights.
-
How do you measure impact?
Win rate, forecast accuracy, CAC payback, NRR, gross margin, utilization, delivery velocity, working capital, and EBITDA expansion. The exact scorecard depends on the operating constraint.
RELATED SERVICES