AMS Reputation Metrics: Measuring Trust, Not Ticket Volume

{
  "id": "ams-049",
  "title": "AMS Reputation Metrics: Measuring Trust, Not Ticket Volume",
  "hook": "Reputation in AMS is not what people say in meetings. It’s what they do before problems appear.",
  "idea": "Modern SAP AMS measures its reputation indirectly — through behavior changes around it. When AMS is trusted, chaos decreases upstream, not just tickets downstream.",
  "core_principle": "Trust is observable. If AMS advice is valued, the system will show it long before surveys do.",
  "reputation_dimensions": {
    "early_involvement": {
      "what_it_means": "Business and IT involve AMS before decisions and changes.",
      "signals": [
        "AMS invited before requirements are finalized",
        "AMS asked to review options, not just estimate effort",
        "AMS included in vendor discussions early"
      ]
    },
    "decision_influence": {
      "what_it_means": "AMS recommendations shape outcomes.",
      "signals": [
        "Chosen options align with AMS recommendations",
        "Explicit acceptance of AMS risk warnings",
        "Fewer overridden change gates"
      ]
    },
    "behavioral_trust": {
      "what_it_means": "People follow AMS processes even under pressure.",
      "signals": [
        "Reduced bypass of intake and approval gates",
        "Fewer side-channel requests",
        "Lower emergency-change abuse"
      ]
    },
    "predictive_confidence": {
      "what_it_means": "AMS forecasts are believed.",
      "signals": [
        "Stakeholders plan around AMS risk windows",
        "Change freezes respected during high-risk periods",
        "Fewer surprise escalations"
      ]
    },
    "strategic_pull": {
      "what_it_means": "AMS is seen as a long-term partner, not a cost center.",
      "signals": [
        "AMS asked to assess new capabilities and tools",
        "AMS input requested for roadmap and budgeting",
        "AMS involved in architecture and sourcing decisions"
      ]
    }
  },
  "reputation_metrics": {
    "leading_indicators": [
      {
        "metric": "Pre-Request Engagement Rate",
        "definition": "% of changes discussed with AMS before formal request",
        "interpretation": "High value = AMS seen as advisor"
      },
      {
        "metric": "Decision Adoption Rate",
        "definition": "% of decisions where AMS-recommended option is chosen",
        "interpretation": "High value = trust in judgment"
      },
      {
        "metric": "Gate Bypass Rate",
        "definition": "% of work attempting to bypass intake or approval gates",
        "interpretation": "Low value = respect for AMS governance"
      }
    ],
    "lagging_indicators": [
      {
        "metric": "Emergency Request Trend",
        "definition": "Change in volume of 'urgent' requests over time",
        "interpretation": "Downward trend = trust + predictability"
      },
      {
        "metric": "Repeat Advice Requests",
        "definition": "How often stakeholders return for guidance",
        "interpretation": "High value = AMS seen as safe reference point"
      },
      {
        "metric": "Post-Decision Regret Rate",
        "definition": "% of decisions reopened due to misunderstood risk",
        "interpretation": "Low value = credibility preserved"
      }
    ]
  },
  "reputation_failure_signs": [
    "AMS consulted only after decisions are already made",
    "Frequent ‘just do it’ pressure during risk windows",
    "Business bypasses AMS to talk directly to vendors",
    "Same warnings ignored repeatedly"
  ],
  "how_reputation_is_built": {
    "behaviors": [
      "Consistent framing of options and trade-offs",
      "Early, calm warning about risk",
      "Saying no with evidence, not emotion",
      "Admitting uncertainty explicitly"
    ],
    "systems": [
      "Decision briefs reused everywhere",
      "Metrics that expose consequences, not people",
      "Post-decision learning shared openly"
    ]
  },
  "automation": {
    "copilot_moves": [
      "Track when AMS is engaged relative to request timing.",
      "Measure recommendation adoption automatically.",
      "Detect governance bypass patterns.",
      "Correlate reputation signals with stability and cost trends."
    ],
    "outputs": [
      "AMS reputation dashboard",
      "Trust trend report (quarterly)",
      "Early warning signals for reputation erosion"
    ]
  },
  "why_this_matters": [
    "High reputation reduces noise before it hits AMS.",
    "Trust lowers coordination and escalation cost.",
    "AMS influence grows without formal authority."
  ],
  "anti_patterns_to_kill": [
    "Trying to be liked instead of being clear",
    "Overpromising to gain approval",
    "Using surveys instead of behavior signals",
    "Defensive explanations instead of evidence"
  ],
  "design_question": [
    "If AMS stopped working tomorrow, who would notice first — and why?"
  ],
  "meta": {
    "schema": "dkharlanau.dataset.byte",
    "schema_version": "1.1",
    "dataset": "ams",
    "source_project": "cv-ai",
    "source_path": "ams/ams-049.json",
    "generated_at_utc": "2026-02-03T14:33:32+00:00",
    "creator": {
      "name": "Dzmitryi Kharlanau",
      "role": "SAP Lead",
      "website": "https://dkharlanau.github.io",
      "linkedin": "https://www.linkedin.com/in/dkharlanau"
    },
    "attribution": {
      "attribution_required": true,
      "preferred_citation": "Dzmitryi Kharlanau (SAP Lead). Dataset bytes: https://dkharlanau.github.io"
    },
    "license": {
      "name": "",
      "spdx": "",
      "url": ""
    },
    "links": {
      "website": "https://dkharlanau.github.io",
      "linkedin": "https://www.linkedin.com/in/dkharlanau"
    },
    "contact": {
      "preferred": "linkedin",
      "linkedin": "https://www.linkedin.com/in/dkharlanau"
    },
    "canonical_url": "https://dkharlanau.github.io/datasets/ams/ams-049.json",
    "created_at_utc": "2026-02-03T14:33:32+00:00",
    "updated_at_utc": "2026-02-03T15:29:02+00:00",
    "provenance": {
      "source_type": "chat_export_extraction",
      "note": "Extracted and curated by Dzmitryi Kharlanau; enriched for attribution and crawler indexing."
    },
    "entity_type": "ams_byte",
    "entity_subtype": "",
    "summary": "Reputation in AMS is not what people say in meetings. It’s what they do before problems appear."
  }
}