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      "action_titles": 78,
      "mece_structure": 72,
      "closing_strength": 64,
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      "clarity_of_thesis": 80,
      "production_quality": 76,
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    "totalScore": 74,
    "coveragePct": 100,
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      "scqa_arc": "The flow has a usable Situation-Complication-Answer spine from R&D value creation on slide 4 to operating-model rewiring on slide 9 and pillars on slide 11, but the core question is not made explicit and the middle reads partly as an analytical tour.",
      "action_titles": "Most core slides use insight-bearing headlines, e.g., slide 9: \"Operating model needs rewiring to fully unlock AI benefits at company level,\" but slides 2-3 are labels and many later titles keep topic-prefix scaffolding.",
      "mece_structure": "The deck covers a mostly complete transformation system across process, agents, data, IP, talent, and roadmap on slides 11-22, though AI agents, AI layer, data architecture, and IP operating model partially overlap.",
      "closing_strength": "Slides 21-22 provide a roadmap and attention areas, but the deck then moves to contacts and a BCG end slide without a sharp recommendation, decision ask, or concrete next-step owner.",
      "evidence_quality": "Several claims are supported by quantified impact and examples on slides 3, 8, 11, and 12, but broader assertions on IP, talent, and operating-model change rely more on consultant logic than visible proof.",
      "clarity_of_thesis": "The thesis is identifiable by slides 3-5 as AI can materially improve R&D outcomes, but it is distributed across the executive summary, value-creation framing, and first use-case slide rather than stated as one crisp answer.",
      "production_quality": "The deck shows strong consulting production discipline and high action-title density across slides 4-22, though dense pages, repeated title prefixes, and uncertain footnote discipline keep it short of top-tier polish.",
      "visual_storytelling": "The visual tools generally fit the message, with from-to comparisons on slides 4 and 13, a radar chart on slide 5, case tables on slide 8, architecture diagrams on slides 17-18, and roadmap logic on slide 21."
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    "suggestions": [
      "Cut or merge slide 2 (Introduction) into slide 3 so the deck opens with the quantified thesis within the first two pages",
      "Insert a 'so-what' synthesis slide between slide 8 and slide 9 that explicitly bridges case-study evidence to the operating-model argument",
      "Replace or augment slide 22's six-imperative list with a single prioritized call-to-action: the one move leadership should make in the next 90 days",
      "Foreshadow the 5-pillar framework on slide 3 with a one-line preview so readers anticipate the structure before slide 11",
      "Rename slide 23 from 'Key contacts for R&D AI transformation' to an action-bearing close like 'Start the conversation with your regional R&D AI lead'"
    ],
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    "openingScore": 78,
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      "Quantified executive summary on slide 3 leads with the answer (10-20% time-to-market, 2x productivity)",
      "Action titles are mostly declarative insights, not topic labels (e.g., slides 4, 9, 11, 12)"
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      "The jump from case studies (slides 7–8) into operating-model framing (slide 9) lacks an explicit synthesis slide tying evidence to the 5-pillar framework on slide 11",
      "The close (slides 21–22) is a roadmap + checklist but lacks a single memorable 'one thing to do now' recommendation"
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    "closingCritique": "Slide 21 lays out a phased roadmap and slide 22 enumerates six attention areas, providing a credible call to action, but the close is more checklist than memorable. There is no single punchy 'what to do Monday morning' slide before the team-bio and brand sign-off.",
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    "titleQualityScore": 82,
    "titleQualityCritique": "Most action titles are declarative and insight-bearing — e.g., slide 9 'Operating model needs rewiring to fully unlock AI benefits' and slide 12 'Boost is driven by quicker iteration loops'. A few remain topic labels (slide 2 'Introduction', slide 23 'Key contacts')."
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      "frameworks": [],
      "arcBeats": [],
      "loops": [],
      "locked": true
    }
  ],
  "arcBeats": [
    {
      "from": 1,
      "to": 6,
      "label": "Situation & Context",
      "description": "Intro, R&D as backbone, AI capability framing (p1-6)"
    },
    {
      "from": 9,
      "to": 10,
      "label": "Problem & Complication",
      "description": "Operating model needs rewiring; benefits trapped at individual level"
    },
    {
      "from": 11,
      "to": 20,
      "label": "Solution & Approach",
      "description": "5 pillars deep dive: process, agents, data, IP, talent"
    },
    {
      "from": 7,
      "to": 8,
      "label": "Evidence & Proof",
      "description": "Cross-industry case studies with specific KPI gains"
    },
    {
      "from": 21,
      "to": 23,
      "label": "Impact & Next Steps",
      "description": "Phased roadmap, key attention areas, BCG contacts"
    }
  ],
  "loops": [
    {
      "from": 4,
      "to": 6,
      "label": "Golden Circle",
      "description": "Frame WHY R&D matters, HOW AI reshapes it, WHAT agents do"
    },
    {
      "from": 7,
      "to": 8,
      "label": "Pattern Hunter",
      "description": "Aggregate cross-industry evidence to prove AI in R&D works"
    },
    {
      "from": 9,
      "to": 10,
      "label": "Ripple Effect",
      "description": "Show value scaling from individual to team to company"
    },
    {
      "from": 11,
      "to": 20,
      "label": "Mece Breakdown",
      "description": "Decompose AI integration into 5 distinct, exhaustive pillars"
    },
    {
      "from": 21,
      "to": 22,
      "label": "Maturity Curve",
      "description": "Show phased path to AI maturity and the attention areas en route"
    }
  ]
}