AI-Augmented Diagnosis: Faster Thinking, Fewer Guess Cycles

{
  "id": "ams-008",
  "title": "AI-Augmented Diagnosis: Faster Thinking, Fewer Guess Cycles",
  "hook": "SAP incidents are rarely hard because they are complex. They are hard because diagnosis starts blind.",
  "idea": "AI in SAP AMS is not about auto-fixing. It’s about collapsing the time between symptom and a credible hypothesis — with evidence.",
  "sap_context": {
    "typical_pain": [
      "Same issue described differently by users",
      "Logs scattered across transactions and systems",
      "Senior experts pulled in just to ask the first right questions",
      "Long loops of ‘can you check one more thing’"
    ],
    "truth": "Most diagnosis follows patterns — humans just remember them poorly under pressure."
  },
  "diagnostic_model": {
    "inputs": [
      "Error messages and message classes",
      "Dump signatures (not full dumps)",
      "Object identifiers (BP, material, order, IDoc)",
      "Recent changes (transports, config, data loads)",
      "Timing correlations (jobs, interfaces, peaks)"
    ],
    "output": "Ranked hypotheses with confidence level and next checks."
  },
  "automation": {
    "copilot_moves": [
      "Normalize SAP errors into canonical symptom patterns",
      "Correlate current issue with historical incidents and fixes",
      "Suggest the top 3 most likely causes with evidence links",
      "Generate a focused check-list instead of a fishing expedition"
    ],
    "what_it_never_does": [
      "Decide final root cause",
      "Change production state",
      "Override functional ownership"
    ]
  },
  "operating_rules": [
    "No escalation without a hypothesis and supporting signals.",
    "If AI confidence is low, collect missing data — don’t guess.",
    "Hypotheses are refined or killed fast; no zombie theories."
  ],
  "why_this_reduces_cost": [
    "Senior experts spend time confirming, not discovering from scratch.",
    "Fewer handovers and dead-end checks.",
    "Shorter outages for business-critical flows."
  ],
  "anti_patterns_to_kill": [
    "Reading full dumps as a first step",
    "Random checks driven by habit",
    "Escalations with no analysis attached"
  ],
  "metrics_that_matter": [
    "Time from incident start to first viable hypothesis",
    "Number of diagnostic loops per incident",
    "Percent of escalations with evidence-backed hypotheses"
  ],
  "design_question": [
    "What does a good diagnosis look like — and how fast can we reach it?"
  ],
  "meta": {
    "schema": "dkharlanau.dataset.byte",
    "schema_version": "1.1",
    "dataset": "ams",
    "source_project": "cv-ai",
    "source_path": "ams/ams-008.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-008.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": "SAP incidents are rarely hard because they are complex. They are hard because diagnosis starts blind."
  }
}