Part 3 — The OMG Protocol structure
3.1 The three pillars
The OMG Protocol organizes authority engineering into three pillars: Optimize, Manifest, Generative. The order is not arbitrary. It corresponds to the three minimum gates a brand passes through to be cited in an AI-generated answer.
| Pillar | Gate | Failure mode |
|---|---|---|
| Optimize | Can AI systems engage with the brand at all? | The brand cannot be crawled, parsed, or structurally understood by automated systems |
| Manifest | Does the engagement produce an accurate representation? | AI systems form an inaccurate or shallow understanding of what the brand is |
| Generative | Is the represented brand trusted enough to be cited? | The brand is well-represented but not surfaced because external validation is absent |
Failure at any pillar is sufficient to prevent citation. A brand may be technically pristine (Optimize complete) and content-rich (Manifest complete) yet receive no AI citations because external authority signals are absent (Generative deficient). Conversely, a brand may have abundant external authority (Generative strong) but receive degraded citations because AI systems cannot parse its content (Manifest weak). The three pillars are independently necessary.
The pillars also map to distinct organizational ownership and distinct rhythms of work:
| Pillar | Owning team archetype | Rhythm of work |
|---|---|---|
| Optimize | Web engineering, technical SEO | Sprint-shaped — work decomposes into discrete fixes that ship and verify quickly |
| Manifest | Content, editorial, subject-matter experts | Cycle-shaped — work decomposes into editorial commitments that span content production and ingestion windows |
| Generative | Communications, public relations, partnerships, knowledge graph operations | Campaign-shaped — work spans relationships, third-party publication, and authority compounding |
A single team attempting to address all three pillars simultaneously typically fails at all three. Optimize requires engineering velocity. Manifest requires editorial discipline and domain expertise. Generative requires relationships and time. Conflating them in operational planning is the most common reason AVO programs stall — the team scopes work without distinguishing the rhythms, and the slowest rhythm becomes the bottleneck for all of them.
For the practitioner scoping engagement structure, the three-pillar mapping informs which roles within the brand’s organization (or which external partners) are involved in which work. A brand without engineering velocity will struggle on Optimize. A brand without editorial capacity will struggle on Manifest. A brand without communications relationships will struggle on Generative. The practitioner’s role in scoping includes identifying these capacity questions before the work begins, not discovering them mid-engagement.
3.2 Why three pillars and not two or four
The three-pillar structure is not arbitrary count. It maps to the three minimum gates a brand passes through to be cited in an AI answer. A discipline with fewer pillars conflates gates that should be measured separately. A discipline with more pillars fragments interventions across team boundaries that do not exist in real organizations.
Two-pillar alternatives typically merge Manifest and Generative into a single “content authority” pillar. This conflation is an error because it obscures whether a brand’s invisibility is a content problem (the brand has not produced sufficient depth or quality of content) or a recognition problem (the brand has produced excellent content but no third party has validated it). These are different problems with different solutions, and conflating them causes practitioners to apply content-pillar interventions when recognition-pillar interventions are needed, or vice versa.
Four-pillar alternatives typically split one of the existing pillars in two. For example, separating “structured data” from “technical health” into distinct pillars. The split is unproductive because the actions within Optimize all share the same operational rhythm (sprint-shaped, engineering-owned) and the same measurement scaffolding. Splitting them into separate pillars produces administrative overhead without diagnostic benefit.
The three-pillar count is the minimum that preserves the diagnostic distinction between consumption (can the AI use the site at all), representation (does the AI form an accurate picture), and trust (does the AI rely on the picture). These are the three distinct failure modes that produce non-citation, and they are the three distinct interventions an AVO program must be able to direct independently.
3.3 The pillar–team–horizon mapping
Beyond the abstract pillar definitions, the practical implementation of each pillar requires distinct organizational capacity. This mapping is the most common point of friction in early-stage engagements, and the practitioner should make it explicit during engagement scoping.
Optimize
Owning team archetype: Web engineering, technical SEO, devops.
What this team must be capable of: shipping technical changes to the brand’s web properties, managing infrastructure (CDN, server configuration, performance), implementing structured data (Schema.org markup), maintaining canonical URL structure, configuring crawler permissions (robots.txt, robots meta tags).
Bottleneck signals: a brand with no in-house engineering capacity (or whose engineering capacity is fully occupied with other priorities) will be Optimize-bottlenecked regardless of AS finding. The practitioner should identify this bottleneck during scoping. Solutions include external engineering retainers, engagement-scoped engineering hires, or scope reduction to engagement-feasible Optimize work.
Manifest
Owning team archetype: Content, editorial, subject-matter experts.
What this team must be capable of: producing original long-form content with editorial standards, citing sources accurately, maintaining publication and update cadences, organizing content into pillar-and-cluster structures, refreshing existing content as facts change.
Bottleneck signals: a brand producing thin or duplicative content, or relying entirely on syndicated or aggregated content, will be Manifest-bottlenecked regardless of AS finding. The practitioner should identify this bottleneck during scoping. Solutions include hiring editorial staff, engaging external content production with editorial oversight, or commissioning subject-matter experts for specific content programs.
Generative
Owning team archetype: Communications, public relations, partnerships, knowledge graph operations.
What this team must be capable of: building and maintaining media relationships, commissioning original research, executing Wikipedia and Wikidata work with policy compliance, earning citations from authoritative publications, managing the brand’s structured-knowledge presence across multiple platforms.
Bottleneck signals: a brand with no communications function, no media relationships, and no original research output will be Generative-bottlenecked regardless of AS finding. The practitioner should identify this bottleneck during scoping. This is the most expensive bottleneck to remedy and the most often misjudged at engagement start. A brand stakeholder who states “we’ll handle the communications work in-house” without a dedicated communications function will produce minimal Generative-pillar progress.
The work of engagement scoping includes mapping the brand’s actual organizational capacity to these three teams’ archetypes and identifying which gaps need to be addressed before AVO loop work can produce results. A brand may need to hire, contract, or scope-reduce before the loop can run effectively.
3.4 The six measurement vectors
Within the three pillars, OMG specifies six vectors. Each vector groups datapoints that share a measurement type and an intervention type. Vectors are the level at which strategic prioritization happens — the question what should we work on next is answered at the vector level, before drilling down to specific datapoints.
| Pillar | Vector | What it covers |
|---|---|---|
| Optimize | V1.1 Signal Architecture | Structured data, schema markup, semantic HTML, metadata quality |
| Optimize | V1.2 Technical Health | Crawler accessibility, performance, security, canonical consistency, multilingual setup |
| Manifest | V2.1 Semantic Density | Topical relevance, content depth, attribution, entity recognition, claim density, originality, structural quality |
| Manifest | V2.2 Structural Legibility | Hierarchy, formatting, accessibility, identity clarity, update signals, chunk extractability |
| Generative | V3.1 Knowledge Validation | Citation strength, AI citation presence, Wikidata presence, knowledge graph depth |
| Generative | V3.2 Trust Alignment | Domain authority, trust signals, transparency indicators, external validation, content freshness |
The within-pillar splits are diagnostic distinctions:
| Split | What it isolates |
|---|---|
| V1.1 / V1.2 within Optimize | Signals (machine-readable structure) versus infrastructure (the site’s ability to be crawled and rendered). Both fail modes look like “AI cannot use this site,” but they require different fixes — V1.1 is content engineering, V1.2 is operational engineering. |
| V2.1 / V2.2 within Manifest | Substance (content depth, attribution, originality) versus legibility (structural clarity, chunkability, formatting). Content can be deep but unparseable, or legible but shallow. |
| V3.1 / V3.2 within Generative | Entity recognition (the brand exists in structured-knowledge systems) versus source trust (the brand is treated as authoritative by AI systems). A brand can be recognized but not trusted, or trusted but not recognized as the right kind of entity. |
For the practitioner, vector-level analysis is the primary level for action selection. A brand with low Optimize-pillar score and the deficit concentrated in V1.1 needs structured-data work; a brand with the same low Optimize pillar but the deficit concentrated in V1.2 needs infrastructure work. The two needs are addressed by different actions and different teams.
3.5 The thirty actions catalog overview
The OMG Protocol includes a catalog of thirty canonical actions — the operational work practitioners perform within the three pillars. The full action catalog is the subject of Document 2: The OMG Action Playbook. This Foundations document presents the catalog at the level required to understand the protocol’s structure.
The pillar distribution is asymmetric:
| Pillar | Action count | Examples |
|---|---|---|
| Optimize | 7 actions | Competitive analysis, analytics infrastructure, technical foundation, structured data foundation |
| Manifest | 10 actions | Question-based opportunity mapping, answer-first architecture, FAQ hubs, evidence-based content, content refresh |
| Generative | 13 actions | Entity verification, advanced topic clustering, long-form content, media outreach, original research, academic citations, Wikipedia/Wikidata, partnerships |
The asymmetry reflects operational reality. Optimize work is concentrated in a smaller number of high-leverage actions because the pillar’s scope (technical readiness) is narrower. Manifest and Generative work is distributed across more actions because each addresses a narrower aspect of a broader remit (content quality and external authority, respectively).
A complete OMG practice does not require all thirty actions in every engagement. A typical engagement covers a subset selected based on what AS measurement reveals: brand maturity, current pillar/vector imbalance, and operational priority. Action selection is part of the practitioner’s craft, directed by AS findings rather than by intuition.
The thirty actions are catalogued with action IDs (O-1 through O-7, M-1 through M-10, G-1 through G-13). The IDs are stable across the methodology and the bible — when the team refers to “G-11” in conversation, they refer to a specific named action with a defined chapter in the Action Playbook.
3.6 Action sequencing logic
The thirty actions are not selected at random or by intuition. They have prerequisite structure that the practitioner must respect.
Hard prerequisites are conditions that must be true before an action can be attempted at all. Attempting an action without its hard prerequisites does not produce reduced effect; it produces failure or actively negative outcome. Examples:
- G-11 (Wikipedia and Wikidata Optimization) cannot be attempted without G-1 (Entity Verification) substantially complete and at least 3-5 substantive third-party citations available. Attempting G-11 without these prerequisites produces rejected articles, deleted Wikidata entries, and persistent damage with the volunteer editor communities that govern these platforms.
- G-3 (Comprehensive Long-Form Content) cannot be attempted without G-2 (Advanced Topic Clustering) at least partially complete. Without topic clustering, long-form content is produced without the structural authority architecture that makes it citable.
- M-2 (Answer-First Content Architecture) cannot be attempted without M-1 (Question-Based Opportunity Mapping). Without M-1, the answer-first work is directed at questions the brand’s audience does not actually ask.
Soft prerequisites are conditions that improve an action’s success rate but are not strictly required. Examples:
- G-4 (High-Authority Media Outreach) is more successful when G-3 (Comprehensive Long-Form Content) is complete because journalists more readily cite brands with substantial reference content than brands without.
- G-9 (Academic & Niche Citations) benefits from G-3 and G-8 (Original Research) being complete because academic citation is more accessible to brands with publishable research output.
Stage assessment is the practitioner’s judgment of what stage of brand maturity an action belongs to. AVO recognizes implicit stages of maturity that govern which actions are appropriate. These stages are not time-bounded; they are state-bounded. A brand transitions from one stage to the next when AS measurement indicates the prior stage’s work is sufficiently complete.
The stages, named for what the brand is engineered for at each:
| Stage | What is being built | Pillar emphasis | What AS looks like |
|---|---|---|---|
| Foundations | Machine-readability and basic citability | Optimize-heavy with early Manifest | AS rising from floor through low-Developing range |
| Depth | Content authority and structural legibility | Manifest-heavy with continued Optimize | AS in mid-to-high Developing range |
| Authority | External validation and knowledge-graph presence | Generative-heavy with continued Manifest | AS approaching Strong band |
| Sustained authority | Compounding and maintenance | Balanced across all three with maintenance discipline | AS in Strong-to-Elite range |
Action selection respects stage. Foundations-stage brands should not attempt G-pillar actions; the prerequisites are not in place. Authority-stage brands should not abandon Optimize and Manifest work; the foundations require maintenance.
The Action Playbook (Document 2) makes the prerequisite structure and stage assessment explicit per action. Foundations introduces the structure so the practitioner reading the Playbook understands what the prerequisites mean.
3.7 The Acceleration Layer
Some organizations choose to accelerate AVO outcomes through paid amplification — paid social, paid search, paid distribution of authority-building content. This work is not part of OMG methodology because OMG measures and improves earned authority and earned visibility. Paid amplification is a different discipline operating on a different theory of change.
AVO recognizes the Acceleration Layer as an optional service category outside the thirty OMG actions. For the practitioner, the distinction matters for two reasons:
- Measurement integrity. AS and VS measure earned authority and earned visibility. Paid amplification produces visibility that is not earned and should not be conflated with VS measurement. A brand whose VS lifts because of paid distribution is not the same as a brand whose VS lifts because of authority compounding.
- Strategic clarity. Paid amplification is appropriate when the brand needs visibility now while authority builds. It is not a substitute for authority work. A brand that runs paid amplification continuously without authority work produces visibility that disappears the moment paid spend stops. AVO produces visibility that compounds and persists.
The practitioner’s framing of the Acceleration Layer to brand stakeholders is straightforward: paid amplification is sometimes the right tool for the brand’s commercial needs, but it is a separate discipline with separate measurement. AVO can recommend when acceleration is appropriate (typically during the gap between OMG action and visibility manifestation), but the work itself is outside AVO scope.
Bagian 3 — Struktur OMG Protocol
3.1 Tiga pillars
OMG Protocol mengorganisasikan authority engineering ke dalam tiga pillars: Optimize, Manifest, Generative. Urutan ini bukan tanpa alasan. Urutan ini mencerminkan tiga gerbang minimum yang harus dilalui sebuah brand agar dapat dikutip dalam jawaban yang dihasilkan oleh AI.
| Pillar | Gerbang | Mode kegagalan |
|---|---|---|
| Optimize | Apakah sistem AI dapat berinteraksi dengan brand sama sekali? | Brand tidak dapat di-crawl, di-parse, atau dipahami secara struktural oleh sistem otomatis |
| Manifest | Apakah engagement menghasilkan representasi yang akurat? | Sistem AI membentuk pemahaman yang tidak akurat atau dangkal tentang brand |
| Generative | Apakah brand yang telah direpresentasikan cukup dipercaya untuk dikutip? | Brand telah direpresentasikan dengan baik tetapi tidak dimunculkan karena validasi eksternal tidak ada |
Kegagalan pada salah satu pillar sudah cukup untuk mencegah kutipan. Sebuah brand mungkin secara teknis sempurna (Optimize selesai) dan kaya konten (Manifest selesai), namun tetap tidak mendapatkan kutipan dari AI karena sinyal otoritas eksternal tidak ada (Generative defisien). Sebaliknya, sebuah brand mungkin memiliki otoritas eksternal yang melimpah (Generative kuat) tetapi mendapatkan kutipan yang merosot karena sistem AI tidak dapat mem-parse kontennya (Manifest lemah). Ketiga pillars bersifat independen dan masing-masing diperlukan.
Ketiga pillars juga memetakan kepada kepemilikan organisasi yang berbeda dan ritme kerja yang berbeda:
| Pillar | Arketipe tim pemilik | Ritme kerja |
|---|---|---|
| Optimize | Web engineering, technical SEO | Berbentuk sprint — pekerjaan terpecah menjadi perbaikan diskret yang dapat dirilis dan diverifikasi dengan cepat |
| Manifest | Konten, editorial, pakar bidang | Berbentuk siklus — pekerjaan terpecah menjadi komitmen editorial yang mencakup jendela produksi konten dan ingestion |
| Generative | Komunikasi, hubungan masyarakat, kemitraan, operasi knowledge graph | Berbentuk kampanye — pekerjaan mencakup hubungan, publikasi pihak ketiga, dan compounding otoritas |
Satu tim yang mencoba menangani ketiga pillars secara bersamaan biasanya gagal di ketiganya. Optimize membutuhkan kecepatan engineering. Manifest membutuhkan disiplin editorial dan keahlian domain. Generative membutuhkan hubungan dan waktu. Mencampuradukkan ketiganya dalam perencanaan operasional adalah alasan paling umum program AVO terhenti — tim menetapkan ruang lingkup pekerjaan tanpa membedakan ritme, dan ritme yang paling lambat menjadi bottleneck bagi semuanya.
Bagi praktisi yang menetapkan struktur engagement, pemetaan tiga pillar ini menginformasikan peran mana di dalam organisasi brand (atau mitra eksternal mana) yang terlibat dalam pekerjaan mana. Brand tanpa kecepatan engineering akan kesulitan di Optimize. Brand tanpa kapasitas editorial akan kesulitan di Manifest. Brand tanpa hubungan komunikasi akan kesulitan di Generative. Peran praktisi dalam scoping mencakup identifikasi pertanyaan kapasitas ini sebelum pekerjaan dimulai, bukan menemukannya di tengah engagement.
3.2 Mengapa tiga pillars dan bukan dua atau empat
Struktur tiga pillars bukan hitungan yang sembarangan. Struktur ini memetakan tiga gerbang minimum yang harus dilalui sebuah brand untuk dikutip dalam jawaban AI. Disiplin dengan lebih sedikit pillars mencampuradukkan gerbang-gerbang yang seharusnya diukur secara terpisah. Disiplin dengan lebih banyak pillars memecah intervensi ke lintas batas tim yang tidak ada dalam organisasi nyata.
Alternatif dua pillars biasanya menggabungkan Manifest dan Generative menjadi satu pillar “content authority”. Penggabungan ini adalah kesalahan karena ia mengaburkan apakah ketidakterlihatan sebuah brand merupakan masalah konten (brand belum menghasilkan kedalaman atau kualitas konten yang cukup) ataukah masalah pengakuan (brand telah menghasilkan konten yang sangat baik tetapi tidak ada pihak ketiga yang memvalidasinya). Keduanya adalah masalah yang berbeda dengan solusi yang berbeda, dan mencampuradukkannya menyebabkan praktisi menerapkan intervensi content-pillar ketika intervensi recognition-pillar yang dibutuhkan, atau sebaliknya.
Alternatif empat pillars biasanya memecah salah satu pillars yang ada menjadi dua. Misalnya, memisahkan “structured data” dari “technical health” menjadi pillars yang berbeda. Pemisahan tersebut tidak produktif karena aksi-aksi dalam Optimize semuanya berbagi ritme operasional yang sama (berbentuk sprint, dimiliki oleh engineering) dan scaffolding pengukuran yang sama. Memisahkannya menjadi pillars terpisah menghasilkan overhead administratif tanpa manfaat diagnostik.
Jumlah tiga pillars adalah jumlah minimum yang mempertahankan distinsi diagnostik antara consumption (apakah AI dapat menggunakan situs sama sekali), representation (apakah AI membentuk gambaran yang akurat), dan trust (apakah AI mengandalkan gambaran tersebut). Inilah tiga mode kegagalan berbeda yang menghasilkan non-kutipan, dan inilah tiga intervensi berbeda yang harus dapat diarahkan secara independen oleh program AVO.
3.3 Pemetaan pillar–tim–horizon
Di luar definisi pillar yang abstrak, implementasi praktis setiap pillar membutuhkan kapasitas organisasi yang berbeda. Pemetaan ini adalah titik gesekan paling umum dalam engagement tahap awal, dan praktisi harus membuatnya eksplisit selama scoping engagement.
Optimize
Arketipe tim pemilik: Web engineering, technical SEO, devops.
Kemampuan yang harus dimiliki tim ini: merilis perubahan teknis pada properti web brand, mengelola infrastruktur (CDN, konfigurasi server, performa), mengimplementasikan structured data (markup Schema.org), mempertahankan struktur URL kanonik, mengonfigurasi izin crawler (robots.txt, robots meta tags).
Sinyal bottleneck: brand tanpa kapasitas engineering internal (atau yang kapasitas engineeringnya sepenuhnya tersita oleh prioritas lain) akan mengalami bottleneck di Optimize terlepas dari temuan AS. Praktisi harus mengidentifikasi bottleneck ini selama scoping. Solusinya mencakup retainer engineering eksternal, perekrutan engineering khusus untuk engagement, atau pengurangan lingkup menjadi pekerjaan Optimize yang layak dilakukan dalam engagement.
Manifest
Arketipe tim pemilik: Konten, editorial, pakar bidang.
Kemampuan yang harus dimiliki tim ini: menghasilkan konten panjang orisinal dengan standar editorial, mengutip sumber secara akurat, mempertahankan kadence publikasi dan pembaruan, mengorganisasikan konten ke dalam struktur pillar-and-cluster, me-refresh konten yang sudah ada seiring perubahan fakta.
Sinyal bottleneck: brand yang menghasilkan konten tipis atau duplikatif, atau yang sepenuhnya mengandalkan konten sindikasi atau agregasi, akan mengalami bottleneck di Manifest terlepas dari temuan AS. Praktisi harus mengidentifikasi bottleneck ini selama scoping. Solusinya mencakup perekrutan staf editorial, menjalin kemitraan produksi konten eksternal dengan pengawasan editorial, atau menugaskan pakar bidang untuk program konten tertentu.
Generative
Arketipe tim pemilik: Komunikasi, hubungan masyarakat, kemitraan, operasi knowledge graph.
Kemampuan yang harus dimiliki tim ini: membangun dan mempertahankan hubungan media, menugaskan riset orisinal, mengerjakan Wikipedia dan Wikidata dengan kepatuhan kebijakan, mendapatkan kutipan dari publikasi otoritatif, mengelola kehadiran pengetahuan terstruktur brand di berbagai platform.
Sinyal bottleneck: brand tanpa fungsi komunikasi, tanpa hubungan media, dan tanpa output riset orisinal akan mengalami bottleneck di Generative terlepas dari temuan AS. Praktisi harus mengidentifikasi bottleneck ini selama scoping. Ini adalah bottleneck yang paling mahal untuk diperbaiki dan paling sering dinilai keliru di awal engagement. Pemangku kepentingan brand yang menyatakan “kami akan menangani pekerjaan komunikasi secara internal” tanpa fungsi komunikasi khusus akan menghasilkan kemajuan Generative-pillar yang minimal.
Pekerjaan scoping engagement mencakup pemetaan kapasitas organisasi brand yang sebenarnya ke arketipe ketiga tim ini dan mengidentifikasi kesenjangan mana yang perlu diatasi sebelum pekerjaan AVO loop dapat menghasilkan hasil. Brand mungkin perlu merekrut, berkontrak, atau mengurangi lingkup sebelum loop dapat berjalan efektif.
3.4 Enam vectors pengukuran
Dalam ketiga pillars, OMG menetapkan enam vectors. Setiap vector mengelompokkan datapoints yang berbagi tipe pengukuran dan tipe intervensi. Vectors adalah level di mana prioritisasi strategis berlangsung — pertanyaan apa yang harus kita kerjakan selanjutnya dijawab pada level vector, sebelum mendalami datapoints spesifik.
| Pillar | Vector | Yang dicakup |
|---|---|---|
| Optimize | V1.1 Signal Architecture | Structured data, markup schema, semantic HTML, kualitas metadata |
| Optimize | V1.2 Technical Health | Aksesibilitas crawler, performa, keamanan, konsistensi kanonik, pengaturan multibahasa |
| Manifest | V2.1 Semantic Density | Relevansi topikal, kedalaman konten, atribusi, pengenalan entitas, kepadatan klaim, orisinalitas, kualitas struktural |
| Manifest | V2.2 Structural Legibility | Hierarki, pemformatan, aksesibilitas, kejelasan identitas, sinyal pembaruan, extractability chunk |
| Generative | V3.1 Knowledge Validation | Kekuatan kutipan, kehadiran kutipan AI, kehadiran Wikidata, kedalaman knowledge graph |
| Generative | V3.2 Trust Alignment | Domain authority, sinyal kepercayaan, indikator transparansi, validasi eksternal, kesegaran konten |
Pemisahan dalam setiap pillar adalah distinsi diagnostik:
| Pemisahan | Yang diisolasi |
|---|---|
| V1.1 / V1.2 dalam Optimize | Sinyal (struktur yang dapat dibaca mesin) VS infrastruktur (kemampuan situs untuk di-crawl dan di-render). Kedua mode kegagalan tampak seperti “AI tidak dapat menggunakan situs ini,” tetapi membutuhkan perbaikan yang berbeda — V1.1 adalah content engineering, V1.2 adalah operational engineering. |
| V2.1 / V2.2 dalam Manifest | Substansi (kedalaman konten, atribusi, orisinalitas) VS keterbacaan (kejelasan struktural, chunkability, pemformatan). Konten bisa mendalam tetapi tidak dapat di-parse, atau terbaca tetapi dangkal. |
| V3.1 / V3.2 dalam Generative | Pengenalan entitas (brand ada dalam sistem pengetahuan terstruktur) VS kepercayaan sumber (brand diperlakukan sebagai otoritatif oleh sistem AI). Brand bisa dikenali tetapi tidak dipercaya, atau dipercaya tetapi tidak dikenali sebagai jenis entitas yang tepat. |
Bagi praktisi, analisis tingkat vector adalah level utama untuk pemilihan aksi. Brand dengan skor Optimize-pillar rendah dan defisit yang terkonsentrasi di V1.1 membutuhkan pekerjaan structured-data; brand dengan pillar Optimize yang sama rendahnya tetapi defisit yang terkonsentrasi di V1.2 membutuhkan pekerjaan infrastruktur. Kedua kebutuhan tersebut ditangani oleh aksi dan tim yang berbeda.
3.5 Ikhtisar katalog tiga puluh aksi
OMG Protocol mencakup katalog tiga puluh aksi kanonik — pekerjaan operasional yang dilakukan praktisi dalam tiga pillars. Katalog aksi lengkap adalah subyek Dokumen 2: OMG Action Playbook. Dokumen Foundations ini menyajikan katalog pada level yang diperlukan untuk memahami struktur protokol.
Distribusi pillar bersifat asimetris:
| Pillar | Jumlah aksi | Contoh |
|---|---|---|
| Optimize | 7 aksi | Analisis kompetitif, infrastruktur analytics, pondasi teknis, pondasi structured data |
| Manifest | 10 aksi | Pemetaan peluang berbasis pertanyaan, answer-first architecture, FAQ hub, konten berbasis bukti, content refresh |
| Generative | 13 aksi | Verifikasi entitas, topic clustering lanjutan, konten panjang, media outreach, riset orisinal, kutipan akademis, Wikipedia/Wikidata, kemitraan |
Asimetri ini mencerminkan realitas operasional. Pekerjaan Optimize terkonsentrasi dalam jumlah aksi yang lebih kecil dengan leverage tinggi karena ruang lingkup pillar (kesiapan teknis) lebih sempit. Pekerjaan Manifest dan Generative didistribusikan ke lebih banyak aksi karena masing-masing menangani aspek yang lebih sempit dari cakupan yang lebih luas (kualitas konten dan otoritas eksternal, masing-masing).
Praktik OMG yang lengkap tidak mengharuskan semua tiga puluh aksi dalam setiap engagement. Engagement tipikal mencakup subset yang dipilih berdasarkan apa yang diungkapkan oleh pengukuran AS: kematangan brand, ketidakseimbangan pillar/vector saat ini, dan prioritas operasional. Pemilihan aksi adalah bagian dari keahlian praktisi, diarahkan oleh temuan AS bukan oleh intuisi.
Tiga puluh aksi dikatalogkan dengan ID aksi (O-1 hingga O-7, M-1 hingga M-10, G-1 hingga G-13). ID-ID tersebut stabil di seluruh metodologi dan bible — ketika tim menyebut “G-11” dalam percakapan, mereka merujuk pada aksi bernama spesifik dengan bab yang ditetapkan dalam Action Playbook.
3.6 Logika pengurutan aksi
Tiga puluh aksi tidak dipilih secara ac