Where should we use AI?
Rank recurring delivery workflows by economic exposure, feasibility, and quality risk.
AI workflow economics for professional services
Prove where AI improves delivery margin.
For consulting and IT-services firms with fixed-fee or managed-service work.
WorkIntel compares a real delivery workflow before and after AI—including labor, review, rework, software cost, and quality—so leaders know whether to scale, change, or stop it.
$12,500 fixed fee50% at kickoff · 50% at executive readout
Experiment WI-01Review
Managed service · Monthly reporting
Delivery economics
Contribution marginBaseline → Pilot
Quality gateHeldThreshold fixed before run
Decision recordITERATE
Margin signal is positive. Review effort must be reduced before scale.
Illustrative framework — not a customer result.
Did it actually work?
Compare margin, labor, review, rework, software cost, and quality on equivalent work.
What should we do next?
Issue a defensible SCALE, ITERATE, STOP, or INCONCLUSIVE operating decision.
No browser extension
No prompt or response capture
No employee scoring or leaderboards
No always-on monitoring
The software
One connected path from workflow to decision.
The product is an operating workspace for delivery leaders—not an AI adoption dashboard. It keeps the workflow design, controlled experiment, evidence, economics, and final decision connected.
Map
Document current and proposed delivery steps, owners, volume, and quality gates.
Experiment
Lock the comparison basis, baseline, quality threshold, and controls.
Prove
Attach aggregate financial, quality, and cost evidence with approvals.
Decide
Scale, iterate, stop, or remain inconclusive—and preserve the rationale.
Does WorkIntel use an AI API?
Yes—only when you ask.
The AI advisor turns the current experiment into an executive brief, identifies missing proof, and proposes next actions. Margin calculations, quality checks, and decision gates remain deterministic. Public pages never call a model, and organization names, evidence notes, prompts, AI responses, and client deliverables are excluded.
Walk through the demoThe gap
Usage is visible. Delivery economics are not.
Major AI platforms already report adoption and usage. That still leaves delivery leaders with the harder question: did AI improve the economics of a real workflow without weakening quality?
Who used which tool, and how often.
Useful for adoption. Insufficient for pricing, staffing, or delivery redesign.
Whether a workflow changed margin while quality held.
Evidence for a specific operating decision—not a proxy for business value.
Usage reporting is documented by OpenAI, Anthropic, Microsoft, and Google.
The method
One controlled experiment. One defensible decision.
We establish the comparison and quality guardrails before changing the workflow, then measure only what can be supported by evidence.
- 01
Map the economics
Rank up to five recurring workflows by financial opportunity, feasibility, repeatability, and quality risk.
- 02
Fix the baseline
Use at least three comparable historical work units or four weeks of representative delivery data.
- 03
Approve the controls
Set quality thresholds, human review, confidentiality rules, and a decision protocol before the experiment starts.
- 04
Run comparable work
Test one AI-assisted workflow across at least three comparable work units with structured evidence.
- 05
Make the decision
Compare margin and quality, then issue a SCALE, ITERATE, STOP, or INCONCLUSIVE decision.
Evidence rule
If the baseline has fewer than three comparable historical work units or less than four weeks of representative data, the result is labelled directional—never presented as ROI.
Fixed scope
The AI Margin Sprint
Designed to produce a decision in 30 days—not another transformation roadmap.
$12,500fixed fee
Included
- One service line
- Up to five workflow candidates
- One controlled AI-assisted experiment
- Up to 15 participating employees
- Executive readout and decision record
You leave with
- Prioritized workflow opportunity map
- Approved baseline and experiment design
- Evidence ledger with source and owner
- Labor, review, rework, quality, and margin comparison
- Redesigned workflow and operating playbook
- Pricing scenarios and executive proof pack
The output
A decision—not a vanity dashboard.
Every sprint closes with one of four explicit outcomes. A no-go can be valuable when it prevents waste, risk, or premature scaling.
Operationalize it
Quality holds, economics improve, and the workflow is ready for controlled expansion.
Fix the constraint
The signal is promising, but review effort, tooling, or process design still limits value.
Avoid the waste
The economics or quality do not justify further investment under the tested conditions.
Do not overclaim
The evidence is not strong enough to support a scale or stop decision.
Anonymous calculator
Estimate your margin exposure.
Compare one baseline work unit with an AI-assisted scenario. Use loaded costs—not billing rates.
Calculated locally in your browser
Example values are illustrative. No input is stored, transmitted, or converted into employee-level analytics.
Ideal customer
Built for firms where delivery economics matter.
The first cohort is deliberately narrow so the method can become repeatable before the software expands.
Consulting and IT-services firms
- 50–500 employees
- Meaningful fixed-fee or managed-service revenue
- Recurring, comparable delivery workflows
- An executive sponsor across delivery, finance, or operations
- Willingness to protect quality with human review
Situations we will not force
- Companies seeking employee surveillance or productivity rankings
- Experiments without a comparable baseline
- Projects that treat hours saved as cash automatically
- Legal or accounting deployments before required security controls exist
- Software procurement without a real workflow to test
Financial integrity
Hours saved are capacity—not money.
Capacity becomes realized value only when it lowers cost, increases throughput, avoids hiring, creates additional revenue, or produces another verifiable financial outcome. WorkIntel keeps those two claims separate.
Founding pilot cohort
Choose one workflow worth proving.
Tell us about your service line, delivery model, and the workflow you suspect AI could change. If the evidence cannot support a defensible experiment, we will say so before kickoff.