Observed flow, not idealized process
Real steps, handoffs, waits, roles, tools, and exceptions are captured.
KAVOSH AI OPPORTUNITY LAB / 15
Test a real process with AI
Before a large investment, we test feasibility with a small and measurable experiment.
Evidence before buildBounded pilot scopeDecision before scale
NOT A DEMO SHOWROOM
Review the process
AI creates value when it meets real decisions, documents, waits, handoffs, and exceptions. We capture that operating flow first, then decide where AI, deterministic automation, or a considered hybrid is the credible answer.
Our first question is not “which model?” It is “which change must become measurable?”
OBSERVE
Available data, not assumed data
Representative samples, quality, access rights, and preparation cost are examined.
Measurable outcome, not broad promise
The baseline, acceptance measure, and post-pilot decision are framed early.
Users and owners, not technology alone
The outcome owner and the people expected to adopt the solution shape the test.
AI OPPORTUNITY SCANNER
Find the bottleneck
Choose an operating field to see common signals, credible opportunity candidates, and the first evidence needed for a useful test.
This scanner is neither a project estimate nor a definitive diagnosis. It turns a broad concern into sharper questions for a discovery conversation.
OPERATIONS SIGNALS
When queues, answers, and coordination do not scale.
Look for points where experts spend time finding answers, triaging requests, or repeatedly passing a case between people.
Request volume scales directly with headcountAnswers depend on scattered knowledgeLate or incorrect routing keeps recurring
CANDIDATEKnowledge-grounded service assistant
First test: a bounded set of real questions and expert reference answers.
CANDIDATEIntelligent request triage and routing
First test: historical requests, correct destination, and handling time.
PROOF METRICResponse time, routing accuracy, verified quality
A metric needs both a baseline and an accountable owner before pilot.
KNOWLEDGE SIGNALS
When documents abound, but trusted answers do not.
Opportunity appears where finding, comparing, or extracting information is slow and the ability to trace an answer back to evidence matters.
Search spans many PDFs and filesForms and letters require manual entryConflicting versions obscure the source
CANDIDATEStructured extraction from documents
First test: a representative pack of standard and exceptional formats.
CANDIDATECited question answering over trusted sources
First test: real questions with verifiable reference answers.
PROOF METRICField accuracy, source coverage, review time
An answer without provenance or a review path is not deployment-ready.
DECISION SIGNALS
When the data exists, but action arrives too late.
Analytics opportunities begin with a specific management question—not more charts or an isolated forecast without a decision owner.
Reports arrive after the eventMetric definitions differ across teamsDecisions still depend on one person's intuition
CANDIDATEEarly warning for a defined event
First test: historical data, decision point, and cost of error.
CANDIDATEForecasting with scenarios and explanation
First test: comparison with a simple baseline and current decision method.
PROOF METRICWarning lead time, decision quality, action rate
A better model matters only if it enables a better decision.
WORKFLOW SIGNALS
When people become the manual bridge between tools.
Not every repetitive task needs AI. Integration and explicit rules can be faster, safer, and more dependable than an intelligent model.
Information is copied across systemsStatus lives in fragmented messages and filesPredictable approvals repeat every cycle
CANDIDATERule-based automation with exception handling
First test: workflow map, rules, and examples of exceptions.
CANDIDATEAn agent for steps requiring interpretation
First test: approved tools, authority boundary, and human checkpoints.
PROOF METRICCycle time, error rate, human intervention
The aim is not to remove people; it is to remove low-value repetition.
THE SIX-STAGE LAB JOURNEY
The test route
Each output should reduce a specific uncertainty. When evidence is weak, stopping or redesigning is a valid result—not a failed project.
Observe the Process
Real work, roles, tools, handoffs, and friction.
Map Opportunities
Candidates, value, dependencies, and test priority.
Assess Feasibility
Data, quality, security, integration, and limits.
Build a Prototype
Test the highest-risk value or technical assumption.
Run a Controlled Pilot
Real users, a clear boundary, and acceptance criteria.
Develop the Solution
Durable architecture, deployment, monitoring, improvement.
PILOT EVIDENCE GATE
Pilot checklist
Select the criteria for which you already have early evidence. This exposes conversation gaps; it does not replace a professional assessment.
Select the criteria that already have early evidence.0 / 4
DECISION-GRADE OUTPUTS
What you receive
The exact package follows the engagement stage, but the intent stays constant: clarify what is worth building, how it should be tested, and what move comes next.
Explore AI & Data CapabilitiesPrioritized Opportunity Map
Candidates, value, readiness, risk, and dependencies.
Challenge and Success Frame
Boundary, baseline, owner, and decision question.
Feasibility Evidence Report
Findings across data, technology, security, and operations.
Testable Prototype
A bounded slice focused on the riskiest assumption.
Pilot Design and Report
Acceptance criteria, results, limits, and learning.
Delivery and Deployment Roadmap
Architecture, phases, monitoring, and ownership.
BRING ONE REAL PROCESS. LEAVE WITH A CREDIBLE NEXT MOVE.
Suggest a real process
You do not need to name a model or platform. The current flow, its main friction, and the change you want to see are enough for the first conversation.