documentation/research/ai-adoption/strategy-blueprint.md

Eldorado AI adoption — strategy implementation blueprint

Status: target operating model for review; not an approved rollout or an inventory of deployed controls. This handoff is documentation design only. It does not authorize installing agents, collecting employee telemetry, changing permissions or enabling autonomous delivery.

Implementation contract

Create eight substantive pages in the independent Strategy navigation, in this order: index, target-operating-model, skills-governance, skills-quality, skills-adoption, measurement, skills-maintenance, roadmap. The structured companion is strategy-model.json; its KPI definitions and identifiers are authoritative for the measurement prototype. Preserve Organization Structure, Collaboration and SDLC and their exact department/team names. Cross-link rather than duplicate reporting charts. The existing organizational documentation is an operating model, not certification of staffing.

Use the existing Archify JSON renderer and shared icon-only modal control. Diagram logical ID: ai-adoption-target; editable source: diagrams/ai-adoption-target.architecture.json. Do not expose downloadable JSON. Keep source mapping and open-decision material outside docs/static/search. Reader-facing prose below can be adapted into MDX without bibliography, evidence codes, recruitment commentary or repeated caveats. Use a visible Target operating model introduction and prospective verbs where status matters. Never present synthetic calculations as Eldorado results.

1. Overview — index

Govern skills first, expand authority only with evidence

Eldorado's target is to make AI-assisted work dependable across the organization: useful guidance, clear human ownership, controlled execution and measurable outcomes. The first investment is a governed skills system, not an autonomous replacement for the organization. A skill packages reusable working knowledge, prerequisites and verification steps. It helps an agent perform a task; it does not grant permission or guarantee that the work is correct.

The target combines one logical SDLC coordinating agent with developer choice on local laptops. Copilot, Claude Code and Cline remain client options within a qualified support matrix. They receive the same reviewed portable skill content through client-specific adapters. Repository, CI and release services remain the enforcement points, independently of what any agent says or which local tool produced a change.

The strategy describes the intended operating model and the evidence required to introduce it. It does not establish that these controls are installed, that every client is supported, or that AI has improved Eldorado's performance. The current reference is the documented six-department organization. Rollout begins by validating actual clients, responsibilities, data boundaries and available evidence.

Success means accepted outcomes with maintained quality, controlled risk and understood total cost. Faster activity without reliable outcomes is not success. More skill invocations, generated lines or tool subscriptions are not productivity targets. People remain accountable for product, customer, financial and personnel decisions.

2. Target operating model — target-operating-model

One coordination function, distributed contributions

Data & AI owns the coordinating function, with the existing AI Architect function connecting it to Technology leadership. The logical coordinator maintains approved work records, dependencies, evidence requests, bounded retries and escalation. It can summarize, propose work and route decisions. It cannot declare its own work approved, change protected checks, authorize production release or replace the responsible human.

One logical coordinator does not mean one fragile process. A durable task ledger, explicit state transitions, leases and idempotency keys allow recovery or multiple replicas without duplicate delivery. Inference outages stop new agent decisions, not the organization's ability to take over. People can resume work through the normal deterministic workflow. Evidence and authorization services fail closed for privileged actions; missing evidence never becomes permission.

The delivery path is: an approved problem and acceptance criteria; an authorized task; local or isolated worker proposals; independently reviewed repository changes; exact-revision CI evidence; a human release decision; execution by the release service; then operational feedback. Product Management accepts the outcome hypothesis and business result. Product Design reviews experience and accessibility. Product Engineering owns the code and maintainability. Quality Engineering assesses quality independently. Architecture & Security assesses risk. Platform & IT operates the delivery boundary. No arrow from a laptop or coordinator goes directly to production authority.

Trust and authority boundaries

People authenticate to the work system; the coordinator receives only the project context it needs. Untrusted issue text, repository content, web pages and tool output are task data, never an authorization source. Local clients operate under device controls and project-scoped access. Hard containment requires OS/container isolation, filesystem and network restrictions and separate short-lived identities; a skill folder, prompt or working directory is not a sandbox.

The registry distributes reviewed immutable content and adapters, not ambient credentials. Executable scripts, plugins, MCP servers and dependencies are separately reviewed and pinned. Model-provider routing is allowlisted for the data classification, with approved retention and training-use terms before sensitive data crosses that boundary. Fallback to another provider must not silently change the data policy.

Central controls verify protected-branch review, actual artifact identity, exact commit and policy revision, required checks and a scoped human approval. Candidate changes cannot edit their own authoritative gate or reuse approval for a different artifact. Deployment credentials belong to the release service, not the local agent. Approval expires or is invalidated by changed content. Emergency stop revokes mutation credentials and stops scheduling independently of the model, preserves evidence and enables human takeover.

Department contributions

Department / existing team	Contribution and retained human decision	First bounded workflow and evidence
Product / Product Management	Own pilot priorities, acceptance criteria and outcome hypotheses; decide product value.	Draft acceptance criteria from sanitized discovery notes; independent completeness review and accepted-task success.
Product / Product Design	Own design quality, accessibility and research handling.	Review a proposed flow against an accessibility checklist; verify findings against the interface.
Technology / Product Engineering	Own skill authoring, code review and maintainability; remain accountable for generated code.	Test scaffolding and change summaries in a disposable branch; independent tests and escaped-quality follow-up.
Technology / Platform & IT	Own supported client inventory, distribution, identity, isolation, CI/release integration, transport and emergency revocation.	Qualify a pinned bundle and exercise rollback; inventory and exact-revision gate evidence.
Technology / Architecture & Security	Own risk classification, data boundaries, security review and bounded exceptions.	Review injected-input and unauthorized-tool cases; independent control evidence and policy-effect counts.
Technology / Quality Engineering	Own regression/adversarial suites, rubric calibration and acceptance evidence.	Evaluate candidate versus approved skills on repeated representative tasks; retain all failures.
Technology / Data & AI	Own catalog coordination, coordinator evaluation and measurement design, through existing AI enablement responsibilities.	Curate a small skill set and analyze a prospective pilot; no new AI department.
Operations / Operations Squad	Own service acceptance, disputes, fraud, refunds and customer communications.	Draft an answer from sanitized resolved cases; Operational Quality workstream checks correctness before any send. No automated dispute/refund decision.
Marketing / Marketing Squad	Own brand, factual claims, content rights, publication and spend approvals.	Draft a campaign brief against approved brand material; human claim/rights review before publication.
Finance & Accounting / Finance & Accounting Squad	Own allocation of total costs, financial evidence and segregation of approvals.	Explain a sanitized reconciliation exception; no posting, payout or payment authority.
HR & Administration / People & Administration Squad	Own coaching, acceptable use, feedback privacy and people decisions; partner with Platform & IT on access lifecycle.	Draft onboarding guidance from approved policy; human accuracy review. No hiring or performance decision automation.

Product keeps two teams; Technology keeps five; the other four departments keep one squad each. Coordination, skill ownership, approval and cover are assignments inside these teams, not new standing departments, roles or headcount. Department leaders retain accountability and designate real approvers before a pilot starts.

3. Skills governance — skills-governance

A contract that travels, controls that stay authoritative

Maintain one catalog entry per stable skill ID, with immutable revisions and a concise portable SKILL.md body. Required content includes when to use and when not to use the skill, prerequisites, bounded steps, expected artifacts, verification, failure handling and escalation. The companion model defines the complete metadata contract: owner and independent reviewer, risk tier, supported clients and versions, dependencies, digest, provenance and license, evaluations, approval, review date, deprecation and rollback.

The release manifest pins the content, dependencies and adapter revisions by full immutable commit or digest. Names use an eldorado- prefix to reduce collisions. Friendly versions aid communication but never replace the digest. No arbitrary upstream automatic update is admitted into the approved bundle. Changes to scripts, resources or dependencies count as skill changes, not harmless documentation edits.

Adapters place content in the qualified client's discovery paths and implement only verified client-specific behavior. Qualification records the host, exact build, OS, model configuration, discovery, explicit invocation, collisions, disabled skills, permission behavior, context compaction and reload behavior. A common file format is not common precedence or a portable hooks API. Claude and Cline skill-name precedence differs from rule/memory behavior; Copilot support varies by surface. Test the actual build, not the brand name.

Lifecycle and decision rights

A proposal names the problem, owner, data scope and testable outcome. Experimental revisions run only in approved disposable environments. Promotion requires independent review, static and executable dependency checks, per-client behavioral evaluations and a bounded canary. Approval records bind the exact artifact and permitted uses. Deprecated revisions carry a migration path and deadline; retired revisions are unavailable for new work. Any active revision can be quarantined immediately.

The author never supplies the only approval. Data & AI maintains catalog state; the owning team is accountable for content; Quality Engineering approves evaluation evidence; Architecture & Security approves higher-risk boundaries; Platform & IT verifies distribution and revocation. A business owner accepts workflow outcomes. These functions can be covered by existing staff, but incompatible author/approver duties must remain separate for the same release.

An exception contains its ID, skill/client revision, scope, business reason, risk owner, compensating checks, approver, start and expiry, affected users/projects and rollback route. It does not disable protected release, payment or identity controls. Expired or unverifiable exceptions deny privileged actions. Repeated exceptions trigger a supported-design change rather than permanent informal bypass.

Distribution, stale clients and rollback

Stage a verified bundle atomically, check its digest, load it through the qualified adapter and read back its effective revision. Test already-open sessions; changing files is not proof of reloading instructions. Record immutable last-known-good content and adapter dependencies. Canary upgrades precede wider distribution.

A cached verified bundle may support explicitly permitted low-risk work if its model and tool dependencies are available. It does not make a cloud-backed client work offline. Stale or unsupported sessions cannot obtain centrally mediated privileged access. Reconnection revalidates support, revocation and policy before credentials are issued. Central artifact review protects delivery even when a laptop declaration is absent or false; a client report cannot prove which private prompt was actually followed.

Rollback restores the approved manifest and dependencies, invalidates affected sessions, revokes unsafe revisions, runs a smoke test and verifies effective state. It cannot undo data disclosure or commands already executed. Those require incident response.

4. Skills quality — skills-quality

Quality has four separate questions: was the right skill selected, was the procedure executed safely, did the artifact pass independent checks, and did the workflow produce useful outcomes? A successful invocation answers none of the remaining questions by itself.

The proposed qualification contract uses three tiers. R1 is read-only or drafting with nonsensitive approved material; R2 creates code or material with customer impact but no privileged external effect; R3 touches privileged, financial, sensitive-personal or production decisions. R3 remains human-authorized and out of the initial autonomous scope.

Every tier requires all mandatory deterministic safety, schema, authorization and provenance checks to pass, with no observed critical unauthorized action. An observed critical effect blocks promotion. The model gives proposed pilot evaluation floors and success thresholds; these are design defaults for owner ratification, not externally validated guarantees or approved service levels. If sample coverage, acceptance criteria or responsible reviewers are missing, qualification remains incomplete. No aggregate score can trade off a failed mandatory safety check.

Use sanitized, predeclared task sets containing positive triggers, near misses, negative cases and the normal workload mix. Compare no-skill, current-approved and candidate paths with the same starting artifacts; repeat stochastic trials and retain failed and abandoned attempts. Independently inspect output state. Deterministic tests grade checkable constraints; calibrated humans grade usefulness, maintainability and experience. A model judge is supplementary, versioned and checked against human disagreements, never the sole safety or promotion authority.

Include adversarial repository text, malicious referenced code, wrong permissions, client toggles, name shadowing, unsupported versions, hook timeout/crash/malformed response, stale sessions, offline recovery and rollback. Run central checks against forged/missing client declarations. Hook presence is not fail-closed enforcement: failure semantics must be tested on each client, and central authorization remains independent.

Report denominators, task mix, trial counts, per-client results and uncertainty. A passing benchmark is evidence within a tested envelope, not proof that all future tasks are safe. Material model, client, adapter or dependency changes trigger requalification.

5. Adoption — skills-adoption

Start with a small set of useful workflows chosen by the existing teams. Product Management and the departmental owner identify the outcome; Data & AI helps turn it into a skill; Quality Engineering establishes acceptance; Platform & IT qualifies the client path; Architecture & Security clears the data boundary. People & Administration Squad coordinates learning and accessible feedback.

Onboarding should teach people to select the right skill, inspect the actual artifact, recognize untrusted instructions, use the exception and incident routes and recover without AI. A practice task ends with verified work and an explanation of limits, not just a logged invocation. Demonstrate the same portable skill on the supported local clients without promising identical agent behavior.

Use short worked examples, peer pairing, office hours and a clear catalog search path. Gather reasons for non-use and rejected suggestions without penalizing them. Preserve a manual path. Training feedback improves the skill only through review, not automatic self-modification of the approved catalog. Departmental workflow outcomes matter more than uniform uptake.

No developer rankings, individual productivity scores, minimum invocation quotas or inference of effort from token counts are part of this strategy. Report cohort-level evidence with privacy protection and balanced quality, flow, risk and cost. A participant may reasonably stop using a skill that creates more work or uncertainty.

6. Measurement — measurement

Decide what counts before counting

Use the machine-readable KPI catalog for exact definitions. Every report states its eligible population, observation window, numerator and denominator or distribution sample count, source coverage, exclusions and calculation version. Zero denominators return “not available.” Missing telemetry is unknown, not inactivity, compliance or success. Examples are explicitly synthetic and illustrative.

Begin with task acceptance, repository, CI, deployment and incident systems because they can independently establish outcomes. Add catalog/version inventory and task-level declarations. Client telemetry is supplementary and version-dependent; enabling rich tool details may collect sensitive commands and identifiers. Do not enable it merely to count skills. A normalized adapter may report approved skill identifiers without forwarding raw arguments.

Validated success uses all eligible initiated tasks whose predeclared observation window has matured. Failed and abandoned tasks remain nonsuccess. Unresolved or unobserved outcomes remain in the denominator, with known-outcome coverage shown alongside the conservative confirmed-success share. They are not relabeled as observed failure. A task cancelled before start is excluded; cancellation after start remains visible. Count a task once across retries and multiple skills. For timeliness, also show unfinished work and age; accepted-only lead-time distributions have survivorship limitations.

Track all five service-level DORA measures: commit-to-production lead time, deployment frequency, change fail rate, failed deployment recovery time and deployment rework rate. Do not substitute PR-open-to-merge time or all-incident MTTR. Pair them with matured escaped-quality rates, policy effects, total cost per accepted outcome, support coverage, adoption, human experience and catalog maintenance. Keep customer/business outcomes linked to their departmental hypotheses; do not infer conversion, trust or revenue improvement from delivery telemetry.

Baseline and interpretation

Before a pilot, freeze eligibility and acceptance rules and verify event joins. Collect prospective no-skill/current-practice evidence over a complete comparable workflow cycle and until the preregistered precision/sample requirement is met. Do not select a convenient end date after seeing results. Retain task class, service, skill, client/model configuration and concurrent changes. A matched or randomized comparison may be used when practical and ethical; label observational trends as descriptive and report confounding, selection and uncertainty.

Elapsed time and human effort are different quantities. Parallel agents may reduce waiting while increasing review or compute. Include failed attempts and human review/rework in cost, do not multiply a multi-skill task's value across every invocation, and do not convert survey perceptions into proven time savings. Small or incomplete populations yield evidence limits, not an ROI percentage.

Event and privacy contract

Use the event envelope and per-event required fields in the model. Source producer plus event ID deduplicates transport retries. Conflicting payloads under the same key are quarantined. Attempts have distinct IDs under one task. Domain IDs deduplicate deployments, defects and approvals even when multiple feeds describe them. Late arrivals produce versioned aggregate corrections; never silently overwrite a published result. Preserve occurrence and receipt times, reject impossible orderings and flag clock skew. Missing terminal events remain unresolved until the work owner reconciles them.

Many-to-many task/attempt/skill/client associations preserve exact immutable versions. Unknown version stays null with a reason; never assign the latest revision retroactively. Client declarations, server facts and self-reports remain separately labeled. A skill-loaded event is evidence of loading only. The minimal prototype can operate without actor identity except where a lawful, necessary adoption denominator requires pseudonymous cohort membership.

The privacy starting proposal is 30 days for raw structured analytics events and 12 months for aggregated trends, subject to the responsible owners' review. Keep the approved catalog/approval record for the supported revision's lifetime and its required audit period, which must be decided separately. Incident evidence uses a restricted, explicitly authorized hold and deletion schedule, not an indefinite analytics exception. Suppress reports below five people and prohibit drill-down or overlapping cohort slices that reconstruct identities. Five is a proposed minimum, not a guarantee against re-identification. No raw prompts, code, screens, customer cases or secrets are collected by default. Publish the purpose, lawful collection basis, access/deletion route and retention before collecting. Consent is not presumed to be an appropriate employment-law basis.

7. Maintenance — skills-maintenance

Every approved skill has an accountable owning team, independent reviewer, review due date, supported client matrix and rollback bundle. Review both usefulness and safety, not just metadata freshness. Proposed maximum review intervals are recorded by risk tier; incidents, dependency changes, model/client drift or repeated failures trigger earlier review.

Data & AI reviews catalog health with the owning teams. Quality Engineering samples accepted outputs and adds confirmed failures to regression sets without exposing sensitive content. Platform & IT watches unsupported, deprecated and revoked revisions and tests distribution recovery. Architecture & Security reviews emerging misuse and exception expiry. Remove redundant or unused entries deliberately, after examining whether poor discoverability or fit explains non-use.

A reported unsafe behavior opens an incident with artifact, policy and client versions. Architecture & Security coordinates containment; Platform & IT revokes authority, stops scheduling and quarantines affected revisions; the owning department handles customer or business consequences; Quality Engineering reproduces the failure; Data & AI coordinates catalog correction and trend analysis. Preserve minimal restricted evidence and involve People & Administration Squad for employee-data concerns and Finance & Accounting Squad for financial effects. Restart requires a reviewed fix, regression evidence, verified rollback/revocation and accountable human authorization. Closing the incident does not erase the historical failure from measurement.

8. Roadmap — roadmap

Phase	Entry evidence	Work	Exit evidence and hold condition
Foundation	Strategy scope accepted for planning; existing teams identified.	Inventory actual client hosts/builds, provider/data terms, repositories, authority, owners and event availability; select low-risk workflows and metric definitions.	Named accountable people and independent reviewers, bounded pilot charter, forbidden actions, data/retention decision and reproducible baseline plan. Hold if accountability or lawful collection is unresolved.
Governed skills pilot	Foundation evidence approved; no privileged autonomous effects.	Build a small registry, immutable contract, client adapters, evaluations, coaching and synthetic measurement verification.	Qualified client/skill tuples, mandatory safety checks, independent task acceptance, reconciled coverage and user feedback. Hold or narrow scope if quality or data coverage is inadequate.
Reliable distribution	Pilot quality and usefulness support continuing.	Canary updates, effective-version readback, version locking, exception expiry, stale/offline handling and rollback.	Tested revocation and recovery, no bypass of central privileged gates, owners can support the catalog. Hold if effective versions or controls cannot be verified.
Logical SDLC coordination	Distribution, authority and measurement evidence stable.	Observe and assemble evidence first, then propose and route work within scoped identities and budgets.	Exact-artifact approvals, idempotency, duplicate/outage recovery, separation of duties, audit and kill-switch drills pass; humans can take over. No authority expansion based on usage alone.
Departmental expansion	Each department supplies a workflow owner, outcome, data boundary and capacity.	Add Operations, Marketing, Finance & Accounting and HR & Administration workflows with domain-specific review; Product and Technology continue quality improvement.	Departmental accepted outcomes, guardrails, total cost and experience justify continuation; sensitive decisions stay human. Stop or roll back workflows without adequate evidence.

There are no invented launch dates, budgets, staffing commitments or guaranteed gains. Phase gates permit narrowing or stopping. Re-evaluate earlier phases when the underlying client, provider, data scope or authority changes.

Internal architecture brief for implementer

Use a small overview with six groups: (1) accountable people and approved work; (2) one logical coordinator and durable ledger; (3) governed registry plus qualified adapters; (4) local client contributors; (5) independent repository/CI/release boundary with separate human approval; (6) audit/measurement feedback. Put external model-provider access across a labeled data/egress boundary, with approved routing. Show registry-to-coordinator and registry-to-local-client distribution; coordinator-to-work proposals; clients-to-repository proposals; CI-to-human approval evidence; approval-to-release service; resulting evidence-to-audit and audit-to-people/coordinator feedback. Distinguish proposal/evidence arrows from authorization arrows. No coordinator-to-production or laptop-to-production edge. Diagram alt text must state target status, human accountability and lack of direct production authority. Keep full contract detail in prose, not tiny diagram boxes. Organization page links identify existing teams, not a new hierarchy.

Internal evidence mapping and open decisions

Source IDs below refer to research/ai-adoption/sources.json. They support principles and documented capabilities, not local deployment or approval. Organization mapping was read from repositories/documentation/docs/organization/{structure,collaboration,sdlc}.mdx at 14ba1aa1ef66c91525135f743d15c77e32db78d3.

Recommendation	Evidence	Design decision / limit
Portable contract plus exact-client adapters	1, 2, 4, 8, 9, 21, 26	Content format is portable; precedence and enforcement are not. Vendored immutable distribution is the proposed default; no Gitea installer compatibility established.
Independent gates, least privilege and human authority	3, 5, 6, 7, 15, 23	Central authorization and kill switch are target controls; local hooks are supplementary and may fail open.
One logical coordinator, resilient task ledger and bounded authority	Task direction; 11, 15, 25	Replica/idempotency/recovery topology is a design choice, not an observed installation or a vendor guarantee.
Exact existing-team ownership	Three organization pages; findings section 5	Assignments extend the documented model without asserting named staffing, capacity or approval.
Immutable provenance, review, dependency checks and rollback	1, 26, 15	Approval digest and distribution manifest are proposed operational contracts. Pinning alone does not make content safe.
Layered repeated evaluation and independent output checks	11, 15	Risk tiers, evaluation floors, review intervals and proposed percentages are local design defaults requiring ratification, not thresholds from the literature.
Balanced KPIs and all five delivery measures	12, 24	Service/team evidence; no individual ranking or AI causal claim from activity.
Comparative baseline, effort/elapsed distinction and no forecast ROI	16, 17, 18, 22	Studies vary by context; abstract-only evidence for 17/18 and known 2026 selection/timing limitations. Do not reproduce percentages as expected gains.
Version-aware optional client telemetry	19, 20, 23	Actual licensing, settings and host coverage unknown; normalize only minimally necessary events.
Lifecycle ownership, privacy and gates before expansion	13, 14, 25; 20	NIST voluntary framework is not certification; 14 inspected at landing-page level. Retention and cohort floor are proposals for review.

Open decisions (not blockers to writing target documentation; blockers to their corresponding rollout gates):

1. Platform & IT with Architecture & Security: actual supported hosts/builds/OSs, identity boundaries, authoritative CI/release controls, protected-policy administration and stale-bundle access rule.
2. Department leaders with Data & AI: named owner/reviewer/deputy coverage, pilot capacity and selected workflows; no new headcount is implied.
3. Architecture & Security with People & Administration Squad and the relevant data owner: provider processing terms, data classification, lawful telemetry basis, retention, incident holds and access/deletion handling. Obtain specialist legal advice where needed; no new permanent legal role is invented.
4. Quality Engineering with Data & AI and each workflow owner: ratify or replace the proposed risk-tier thresholds, task distributions, minimum samples, uncertainty criterion and quality follow-up windows before using them for promotion.
5. Finance & Accounting Squad: cost allocation, currency normalization, treatment of shared licenses, effort sampling and confidence required for investment decisions.
6. Product Management with departmental owners: business outcome hypotheses and whether a matched/randomized pilot is practical. No tool choice is coerced for an unreviewed experiment.
7. Platform & IT: private registry distribution method, digest verification, revocation semantics, coordinator recovery and feasible minimal adapters. Do not assume vendor-preview installation or Cline SDK telemetry is already available.

Executable handoff and verification

build_strategy_model.py writes the structured contract and illustrative values. sample_calculations.py is an executable reference/test plan using only labeled synthetic in-memory fixtures. The implementation child should port the same assertions into the documentation repository's established test tooling and add a CLI/fixture interface, rather than treating this reference as a production collector. Required cases are declared in the model. Do not ship research files or raw event fixtures into public static assets inadvertently.

Acceptance for this synthesis: both requested files parse/exist; eight page IDs and all eleven existing teams are exact; every KPI has the full calculation/evidence/privacy contract; source IDs resolve; illustrative arithmetic is executed; synthetic edge-case tests pass; documentation repository remains unchanged. Publication, browser review, diagrams and production measurement infrastructure belong to the downstream implementation task, not this synthesis.