Measure AI before you scale it.
This dashboard separates AI cost, evidence-supported value and forecast assumptions so you can make accountable investment decisions.
Value vs investment
Baseline, observed and forecast views shown separately from unsupported time-saved claims.
Where to act
Ranked next actions by value, evidence and risk.
Operating signals
Spend, business results and adoption — the inputs that should move before investment moves.
Spend vs budget
Month-to-date actual and projected month-end.
Measured result index
Baseline 100 → observed → forecast across measured use cases.
Decision queue
Highest-priority use cases based on the latest entered data.
Know what AI costs before you scale it.
Enter the monthly budget, current spend and as-of date. ITONKA projects month-end spend, flags budget risk and shows which tools are driving the cost base.
Budget forecast
Current period spend projected to month-end from entered actuals.
Spend trend & projection
Prior period actuals plus current month-to-date spend and projected month-end.
Program ceiling
Budget usage versus projected month-end spend.
Tool budget view
Compare each approved tool's current monthly run rate with its assigned budget.
AI use cases
Enter the baseline, observed result, dates, value assumptions and evidence. The platform recalculates the forecast automatically.
See what changed — and where.
Drill into business area or AI tool. Results are normalized to each use case's baseline so unlike metrics can be viewed together without pretending they are the same unit.
Portfolio drill-down
Filters update the result, adoption and value views below.
Business result trend
Baseline = 100. Higher index means improvement regardless of whether the underlying metric should rise or fall.
Adoption trajectory
Current adoption compared with the entered forecast adoption target.
Value by business area
Forecast annual net value from monetized use cases.
Decision mix
Current recommendation count after evidence, value, cost and risk are applied.
Change the date. See the prediction.
Forecasts extrapolate the measured operational trend using an explicit continuation assumption and optional adoption change. Nothing is hidden.
Scenario controls
These assumptions apply to every forecast card below.
Use-case predictions
Every important number should have a source.
Trace the baseline, system result, business value and total AI cost behind each recommendation, then move into the Proof Standard when the decision requires a higher level of assurance.
Use the level of proof the decision actually warrants.
ITONKA can operate without blinded review. Start with business evidence, add structured human review when it helps, and invoke patent-pending Bioficial Intelligence™ calibration only when higher assurance is justified.
Portfolio proof register
See the current proof level, the level ITONKA recommends, and exactly what would increase assurance. Blinded calibration is never required just to use the platform.
Is AI—and this approach—the right fit for the work?
Score each use case before more money is committed. The assessment weighs task frequency, repeatability, data structure, volume, readiness, sensitivity, error cost and required professional judgment.
What increases fit
AI is more likely to create reliable value when work is frequent, repeatable, measurable and supported by accessible data.
What constrains fit
High data sensitivity, high cost of error and heavy reliance on professional judgment require stronger controls—or a non-AI solution.
Use-case fit register
The recommended approach is explainable and editable; it is not an automated vendor endorsement.
Turn an AI idea into an accountable decision.
Move through Baseline, Prioritize, Pilot, Measure, Decide and Review with named owners, target dates, gate requirements and an explicit next action. A pilot advances because its evidence gate is complete—not because AI activity increased.
Pilot portfolio
See the current gate, what is missing, who owns the next action and when management must review the result.
Record the decision—not just the dashboard result.
Preserve what management decided, the economics and proof level at that moment, who approved it, what happens next and when the decision must be reviewed.
Decision register
Scale, adjust, hold, measure or stop—with the economics, proof, owner, next action and review obligation visible beside the decision.
Control AI risk without losing the business case.
Track approved tools, high-risk use cases and the minimum policies required before broader deployment.
Policy controls
Demo controls that can later map to formal policy documents and approval workflows.
Risk & approval register
Unapproved tools and high-risk use cases remain visible even when they are not being scaled.
AI tools
Track approved and unapproved AI tools, monthly run rate, utilization, budget and risk.
Tool inventory
Edit cost, utilization, assigned budget, approval and risk.
Demo workspace
This demo saves changes in your browser so you can test the platform without creating an account.
Workspace details
Customize the client and review period.
Create a workspace
Choose an industry model, then open its standard baseline, a researched company model, or a blank workspace.