How marktgAI Evaluates and Communicates Evidence
A practical methodology for separating demonstrated capability, documented engagement outcomes, operating targets, and claims that require additional validation.
Evidence Before Claims
mAI is designed around measurable operating decisions. Public evidence should therefore state what was measured, the relevant time period and operating context, and the limits of what the evidence supports. We do not treat a demonstration as a client result, an engagement result as a universal benchmark, or a target as a guarantee.
Four Evidence Classes
1. Demonstration
Shows how an mAI workflow, decision pattern, or architecture can operate. A demonstration is capability evidence, not evidence of a client relationship or realized business outcome.
2. Applied Case Study
Describes an anonymized engagement and the documented outcomes associated with that operating context. Results remain engagement-specific and are not presented as guarantees.
3. Operating Target
Defines a measurable objective for an implementation. The current mAI 90-day framework targets approximately 15–20% productivity improvement and 10–25% precision improvement against an agreed baseline; actual outcomes vary.
4. Validated Claim
A statement supported by an appropriate source, documented measurement, or implementation evidence and reviewed for the context in which it will be communicated.
The mAI Measurement Contract
Before interpreting performance, the implementation should establish a measurement contract: the baseline, KPI definition, measurement window, data sources, material constraints, and approval requirements. The AI Marketing Brain can then surface hypotheses and recommendations while the AI Marketing OS connects Plan → Execute → Measure → Optimize.
- Baseline: define the starting condition and comparison period.
- KPI: define exactly what is being measured and how it is calculated.
- Window: state the time period over which change is observed.
- Context: record material changes in budget, channel mix, market conditions, offer, audience, or data quality.
- Confidence: distinguish observed association, experiment evidence, and causal conclusions.
- Governance: retain Human Command for material strategy, budget, audience, brand, regulated-claim, and compliance decisions.
How We Communicate Results
Public case studies may be anonymized to protect client confidentiality. Where a result is reported, the page should identify the engagement context and measurement period when available, and should not imply that the result is independently audited unless that is explicitly supported. Results should not be generalized into promises of future performance.
Compliance and regulated environments: mAI can be configured with governance, access, approval, and data-handling controls appropriate to an implementation. Compliance depends on the specific deployment, data flows, contracts, infrastructure, organizational controls, and applicable legal requirements; it is validated per implementation rather than assumed from the mAI Framework alone.
P²: Productivity + Precision
Productivity can include time-to-launch, operational hours saved, reporting latency, and automation coverage. Precision can include CTR, CVR, CPA/CPL, ROAS/ROMI, LTV, retention, pipeline quality, or another agreed business KPI. Targets only become meaningful when tied to a documented baseline and measurement design.
For implementation context, see the mAI Architecture and AI Marketing OS White Paper. For applied evidence, review the Case Studies; capability demonstrations and test deployments are separated in the mAI Hub.
Review the Evidence in Context
Explore applied case studies alongside the mAI technology and governance model to understand what was measured, how the system operates, and where Human Command remains essential.
Start a Strategic Conversation
Tell us what your organization is trying to improve. We’ll explore where the mAI Framework, Managed mAI or a Custom Enterprise mAI deployment could create measurable Productivity + Precision.
“Marketing’s future is a human-led AI Marketing OS guided by an AI Marketing Brain — amplifying expertise, automating the routine and improving precision while people remain accountable for strategy and brand integrity.”
