RQ-107 deep research 生出力 (openai / 2026-06-18・$3.44 / 13 min)。突合・採択は Synthesis 参照。

Executive Summary

• Common Decision-Making Roles: Across many frameworks, a core set of roles consistently appears in strategic decision-making. These include a Proposer/Driver who researches and pushes a decision, one or more Contributors/Reviewers who provide expert input (or vet proposals), a Decider/Approver with final authority, and an Implementer who executes the decision. Many models also explicitly include roles for those to be Informed of the outcome (www.centercode.com) (www.centercode.com). Separating the “Driver” from the final “Decider” is a common theme to avoid bottlenecks and bias (www.centercode.com). Unique roles like a dedicated veto authority (e.g. a compliance veto) appear in only a few high-stakes frameworks (www.centercode.com), whereas the core roles above recur in 3+ frameworks and are considered robust essentials.

• Scaling Roles by Org Size & Industry: In startups (<50), one person often wears multiple hats – the founder might propose, decide, and implement – with minimal formal process. As organizations grow to mid-size (50–500), roles become more specialized (e.g. distinct product owner, architect, compliance officer) and some segregation of duties is introduced (e.g. an engineer’s proposal reviewed by an architect, approved by a manager). Large enterprises (500+) and multinationals formalize roles heavily: e.g. separate committees for proposals and approvals, and strict four-eyes principle on critical decisions (proposer ≠ approver) especially under regulations (SOX, J-SOX, ISO 27001) (fitsmallbusiness.com). Regulated industries like finance or healthcare require hard separation of certain duties regardless of company size (for instance, no single actor can initiate and approve a high-risk decision) – small firms in these sectors use compensating controls or external reviewers to achieve compliance (wheat-biz.com). In contrast, less-regulated sectors (SaaS, small consultancies) have more latitude to collapse roles safely, although best practices still suggest independent peer review where feasible.

• AI Agents in Decision Roles: Emerging practice treats AI participants as “virtual team members” fulfilling proposer or reviewer roles, while keeping humans as final arbiters. Multi-agent AI systems (e.g. OpenAI’s Swarm framework or Anthropic’s Constitutional AI approach) assign different LLM agents distinct roles – e.g. one agent generates a proposal and another critiques it – to mimic the checks and balances of human roles. This can prevent a single AI from effectively “rubber-stamping” its own ideas. Key governance controls include preventing any one AI agent from both proposing and approving a decision without oversight, and maintaining an audit trail of AI contributions (wheat-biz.com). For instance, Anthropic’s model uses a separate AI as a constitutional critic to review outputs against set principles, and some coding agent systems require a human to review an AI-written code patch before merge. Organizations are beginning to formally log which portions of a decision were AI-generated or AI-reviewed, ensuring transparency and allowing after-the-fact audits of AI vs human inputs.

• Role Compression for a Solo Human + AI Team: In a scenario with one human plus AI assistants, many traditional roles can be collapsed onto the human/AI combo, but certain critical separations should be preserved or compensated. The human can act as proposer, decider, and implementer, with AI agents handling various review steps – this is viable for speed, but it concentrates power. To maintain integrity, some roles require compensating controls when one person holds them: e.g. if the human both proposes and approves a decision, an AI “socratic reviewer” or an external consultant’s sign-off can add a semblance of independent check. Roles involving compliance or audit should not be solely automated or collapsed – e.g. a security-risk check for an architectural decision might require at least consultation with an external expert if no second human is on the team. A decision matrix (provided below) outlines which roles a solo operator + AI can safely merge (with or without controls) and which must eventually be split out as the company grows or faces audit requirements. Generally, strategic approval and compliance oversight are the last roles to fully automate or collapse – even a solo founder should seek a second pair of eyes (another human or a well-designed AI critique) on high-impact, irreversible decisions.

• Recommendations for the ADR Pipeline: For our internal ADR pipeline, we should introduce or strengthen a few key roles. A dedicated compliance checker role (ensuring decisions align with legal, security, and policy constraints) is a must-have – currently it’s ad-hoc, so formalizing it (even if filled by an AI agent) will reduce risk. Likewise, implementing a “four-eyes” principle for final approval is a should-have: since we have only one human decision-maker, this could mean requiring a second review (by a teammate or an AI in a devil’s-advocate role) before the CEO’s final acceptance. A “recorder/librarian” role to maintain and update the decision log is recommended (should-have) so that decisions remain current and accessible – this could be partially automated. Some existing roles can be consolidated: for example, the multiple AI review gates can be grouped under a general “peer review” function to simplify the process. Overall, the priority is to add independent review and compliance steps (even if via AI or external input) to counterbalance the solo decision-maker, while combining redundant review roles to streamline the pipeline. These adjustments will align our practice with industry baselines and ensure the decision process remains robust and audit-ready as we scale.

Role Decomposition in Decision Frameworks (Q1)

In mature organizations, decision-making is often guided by formal frameworks that define who does what at each stage of a strategic decision’s lifecycle. Despite differences in terminology, these frameworks converge on a few core roles. When roles are unclear, decision processes suffer: “Who decides in the end? Who delivers input? Who implements, and who must be informed? If these questions stay open, meetings circle in confusion, tasks fizzle out, and later no one can explain why something was decided.” (dectrack.com) Clear role definitions help avoid such chaos.

Common frameworks like RACI, DACI, and RAPID explicitly name these roles. Others, such as Amazon’s Type-1 vs Type-2 decision model, Holacracy, Sociocracy 3.0, the Spotify model, and corporate governance standards, implicitly rely on similar functions even if under different names. The table below synthesizes a cross-framework view of decision-making roles. It lists each role’s responsibilities, typical deliverables, separation requirements, and how the role hands off to the next step. Roles found in at least 3 frameworks are marked as core (widely adopted), whereas those unique to one framework are noted as specific.

Cross-Framework Decision Role Table

Notes: Roles in bold are widely recognized across multiple frameworks. For example, the Proposer/Driver appears as the “Responsible” in RACI (person doing the work) and “Driver” in DACI (www.centercode.com), and the concept of someone recommending a decision is explicit in RAPID’s Recommend role (www.centercode.com). Likewise, a singular Approver/Decider is universal – whether called “Accountable” in RACI or “Approver” in DACI or simply the boss in a startup. (www.centercode.com) (www.centercode.com) The Contributor/Reviewer role (seeking input from experts) is also common to RACI (Consulted), DACI (Contributor), RAPID (Input) (www.centercode.com), and is inherent in consensus-based models (everyone provides input).

Some roles are framework-specific. For instance, RAPID’s “Agree” veto role (often legal/compliance) is not present in RACI or DACI (www.centercode.com). It reflects that in some contexts, certain stakeholders have blocking power. Holacracy and Sociocracy emphasize a Facilitator role in group decisions (to run the process) and use a consent-based Decider (no single boss, but group consent) – this facilitator is unique to those self-management frameworks. Traditional corporate governance adds roles like Board vs. Management (e.g. Board approves strategy, Management executes), which don’t appear in team-level frameworks like DACI but are crucial in an OECD governance context.

Separation of duties is noted above for roles that should be distinct for checks and balances. Many frameworks stress that the person driving a decision should not be the only one to approve it (www.centercode.com). DACI, for example, gained speed by explicitly splitting the “Driver” and “Approver” roles – if one person fills both, it “turns DACI into a slow single-approver bottleneck” (www.centercode.com). Similarly, RAPID distinguishes those who Recommend from the one who Decides, and even from those who must Agree (veto) (www.centercode.com). RACI by itself can fail for decisions because it lacks a clear decider role (www.centercode.com) (www.centercode.com), highlighting that having a defined Approver is critical.

Beyond acronyms, other frameworks map to these roles in concept:

• Amazon’s Type-1 vs Type-2 model: Jeff Bezos classifies decisions into irreversible “Type 1” (high stakes) and reversible “Type 2” (low stakes). In Type-1 decisions, Amazon implicitly uses more formality – often involving multiple senior approvers or at least broad consultation – whereas Type-2 decisions are delegated to a single Responsible decider (like the two-pizza team lead) for speed. Bezos advises always identifying which type you’re facing, as reversible decisions can be made by individuals or small groups quickly, while one-way decisions merit more caution and oversight (nobl.io). Thus, an Amazon “Type-2” decision might collapse Proposer, Reviewer, and Approver into one empowered role (with the team lead acting fast), whereas “Type-1” might require the proposer to write a 6-page narrative, gather input from all stakeholders in a meeting, and get approval from the senior leadership group (multiple approvers with the final say often by an executive committee). The roles are not formally named in Bezos’s model, but the practice enforces that big decisions get a separate driver (document author) and approver (the exec or committee), plus consulted parties – aligning with core roles above.

• Holacracy (and Sociocracy 3.0): These self-organizing models replace a singular approver with a group consent process. In Holacracy’s Integrative Decision-Making, any circle member can be a Proposer (raising a tension and proposal), and then everyone else acts as Reviewer by raising clarifying questions and objections (reinventingorganizationswiki.com). A Facilitator role (and a Secretary) ensure the process is followed but do not decide content (reinventingorganizationswiki.com). The proposal is adopted only if no one has a remaining objection – meaning the “Approver” role is essentially distributed (everyone must consent, or at least not object) (reinventingorganizationswiki.com). Operational decisions in Holacracy default to role authority – i.e. whoever holds the role can decide autocratically in their domain, following an “advice process” (seek input from others before acting) (reinventingorganizationswiki.com). Sociocracy 3.0 similarly uses consent: the team as a whole is the Decider via no-objection, and specific roles like Facilitator and Secretary mirror Holacracy. These frameworks don’t use RACI-style acronyms, but they still have someone propose, others give input, and a decision reached (with the facilitator ensuring fairness). The absence of a single Approver is a differentiator – instead the group fulfills the Approver role by collective consent. (Notably, even in these models, someone – often the Secretary – records the decision, and others are informed via governance records.)

• Spotify Model: The Spotify engineering culture popularized autonomous squads as the primary decision units. Each squad (team) has a Product Owner (acting as something like a Driver/Decider for product decisions) and is accountable “end-to-end” for their feature (www.scribd.com). This means many decisions are made within the squad without needing higher approval – effectively the squad’s Product Owner or Tech Lead is the Approver, the team members consult each other as Contributors, and they implement themselves. For cross-squad issues or architecture, Spotify relied on community roles: Chapters (disciplinary guilds) and Guilds (interest groups) allow sharing information, and sometimes a System Architect or an Architecture guild would provide input (Contributor role) on broader decisions. But there isn’t a formal centralized architecture board in the classic Spotify model – the bias is toward decentralized Type-2 style decisions at the squad level, with informal consultation across the organization. In practice, as Spotify grew, some coordination roles emerged (e.g. “Alliance lead” or Tribe leadership acting as Approvers for multi-squad decisions), but autonomy remained high. So the core roles exist but are distributed: each squad decides within guardrails, and alignment is achieved by making information accessible (everyone informed via internal blogs, etc.) rather than top-down approval on every detail.

• J-SOX and Internal Control (4-eye principle): Corporate internal control frameworks (COSO, Sarbanes–Oxley, and Japan’s J-SOX) emphasize certain roles to prevent errors and fraud. A common pattern is the 4-step transaction cycle which can be applied to decisions: (1) one person initiates/proposes, (2) a different person approves, (3) another executes/records the outcome, and (4) an independent party monitors or audits it. For example, in accounting: one employee requests a payment, a manager approves it, another staff processes the payment and records it, and later an auditor checks the records. The same principle in strategic decisions would mean the Proposer and Approver should be different people, and perhaps an auditor later reviews major decisions. J-SOX explicitly requires management to design controls so that no one individual can manipulate financial outcomes (fitsmallbusiness.com). In practice, for big decisions this could mean requiring board approval (so management proposes, board approves), or requiring dual signatures. These frameworks don’t give catchy acronyms for roles, but they reinforce the need for segregation of duties: ”no person should have control over an entire process from start to finish” (fitsmallbusiness.com). Thus the Proposer vs Approver vs Implementer vs Auditor are clearly separated roles in well-controlled organizations (especially in finance or public companies). If roles must be combined (e.g. small company), then compensating controls like extra monitoring are expected (wheat-biz.com).

• OECD Corporate Governance Principles: At the highest level, roles are delineated between Shareholders, the Board of Directors, and Executive Management. Strategic decisions in large companies often follow this flow: Management (CEO and team) acts as Proposer, formulating major plans or investments; the Board (or a Board committee) serves as Approver for decisions of significant impact (e.g. strategy, mergers) – they have authority and also a duty to challenge management’s proposals; once approved, Management then implements. The Board may also assign a Monitor role to subcommittees – for instance, an Audit Committee (a subset of board members) oversees compliance and audits decisions and processes, and a Risk Committee might vet proposals for risk before they reach the full Board. The OECD principles stress that the Board should be independent and not involved in day-to-day execution (www.oecd.org) – essentially formalizing separation between the Approver (Board) and Implementer (Management). They also emphasize transparency to shareholders (the Informed public in this case) – e.g. material decisions must be disclosed. This is a more institutional level view of “roles,” but it mirrors the same lifecycle: a proposal (by management) → review (by committees) → approval (by Board) → implementation (by management) → evaluation (by auditors or the board’s oversight).

Across these frameworks, we see the robust roles (Propose, Review, Decide, Implement, Record, Inform) appear again and again, albeit sometimes combined or redistributed. Roles unique to one framework (like RAPID’s “Agree” veto or Holacracy’s facilitator) address specific needs – e.g. veto power for regulatory compliance, or structured meetings for egalitarian groups – but the fundamental activities remain comparable.

In summary, a large organization’s decision role taxonomy typically ensures: someone initiates and researches, multiple parties provide input, a clear authority decides, different people carry out the decision, and checks/balances are in place via documentation and oversight. Smaller or newer frameworks achieve the decision via group mechanisms (consent or advice process) instead of hierarchy, but they still identify who is proposing and how a decision is finalized. The next sections explore how these roles scale with organization size, how they might be allocated differently by industry, how AI can play some of these roles, and finally how to compress them back down when you only have one human and some AI helpers.

Role Variation by Organization Size and Industry (Q2)

The composition and separation of decision-making roles change significantly as organizations grow and as compliance demands vary by industry. Key differences include the number of distinct roles, the specialization of roles, and the degree of separation (checks and balances) required. Below, we examine how a startup might allocate decision roles versus a large enterprise, and how a SaaS company’s approach might differ from a bank’s.

Role Scaling with Organization Size

• Startup / Small Team (< 50 people): Roles are fluid and often collapsed onto a few individuals. It’s typical to see one person (e.g. the founder or team lead) acting as Proposer, Decider, and even Implementer for strategic choices. Formal review may be minimal – a startup might make decisions in an impromptu meeting or even via a Slack chat. Consultation tends to be ad-hoc: the decision-maker informally asks a few team members or mentors for input (covering the Contributor role in a lightweight way) and then decides. Documentation might be light as well (perhaps a short ADR or none at all if pivoting fast). This compression is efficient but has governance risks: there’s essentially no built-in oversight on the decision-maker, and mistakes or biases can go unchecked. In unregulated domains, this is acceptable if managed by trust and quick iterations; however, even small startups try to introduce a semblance of checks for critical decisions – e.g. getting co-founder agreement (if there are co-founders, that’s a natural “two-eye” principle) or having an advisor review an important strategy. In regulated settings, a startup is often forced to assign certain roles despite headcount: for example, a fintech startup must designate someone as a compliance officer separate from operations, even if that’s an external consultant on retainer. In summary, small teams collapse roles out of necessity, but they should compensate with extra transparency and periodic external review to avoid blind spots (wheat-biz.com). For instance, one tactic is to have monthly check-ins with an advisor or board member who effectively acts as an independent reviewer of key decisions.

• Mid-size Company (≈50–500): With growth, the organization adds specialized roles and formal processes. By 50+ employees, you often see roles like a Product Manager, Tech Lead/Architect, Engineering Manager, QA Lead, etc., which naturally distribute decision responsibilities. A new product feature decision, for example, might involve: a PM as Proposer (driving the vision), an Architect and Ops Lead as Reviewers (checking technical feasibility, costs), a Director or VP as Approver, and then engineering teams as Implementers. At this stage, companies often adopt frameworks like RACI or DACI for clarity on big projects – not for every small decision, but for strategic ones or cross-functional ones. Role count increases: instead of one person doing everything, you have committees or boards (e.g. an Architecture Review Board might appear around the 100–200 employee mark in tech companies, introducing multiple reviewers and a formal approval for architectural decisions). Delegation also increases: the CEO/founder will delegate many decisions to VPs or managers (creating a multi-tier Decider role: minor decisions made by team leads, major ones still by execs). Audit and compliance roles start taking shape: if preparing for public financial audits or certifications, a company will establish an internal control function, e.g. a Finance Manager ensures no one person can both request and approve large expenditures. In terms of decision lifecycle, mid-size firms try to enforce the “proposer ≠ approver” rule wherever practical, at least for high-impact decisions – for example, a salesperson cannot unilaterally approve their own big discount to a client; it needs finance or management sign-off (a separation of sales vs finance roles mandated by policy). Industry practices also kick in: a mid-size software/SaaS company might not be legally forced to separate duties, but to obtain something like an ISO 27001 certification or pass security-conscious clients’ vendor checks, they will institute role separation for changes (e.g. code changes require peer review, operations changes require manager approval). Meanwhile, a mid-size bank or healthcare org will by law need clear segregation (e.g. any decision affecting customer funds or patient safety must go through compliance and risk officers). Thus, in mid-size companies role specialization brings better oversight, but it can also introduce some bureaucracy. A common challenge here is to avoid over-engineering: if a company adds too many reviewers or unclear accountability, decisions slow down. Successful mid-caps strike a balance by delegating Type-2 (reversible) decisions downward (e.g. to teams or middle managers) for agility, and reserving Type-1 (big, irreversible) decisions for a higher-level process with more roles involved (cross-functional review, senior approval) ([nobl.io](https://nobl.io/changemaker/how-jeff-bezos-frames-decisions-for-speed-and-inventiveness/#::text=How%20Jeff%20Bezos%20Frames%20Decisions,caution%20for%20the%20situation%20at)).

• Large Enterprise (500–5000): Large organizations formalize roles into well-defined departments and often multiple layers of approval. There is usually a clear hierarchy of decision committees: e.g. a product initiative might need team approval, then departmental approval, then perhaps executive committee or even board approval if it’s strategic enough. At this scale, segregation of duties is a fundamental principle – not only for compliance, but also operationally to manage complexity. Every key decision likely has a RA(D)CI chart or equivalent: for instance, a large tech enterprise might have a Design Review Board (group of architects as Reviewers) who must sign off before a new system architecture decision (approver could be the Chief Architect or CTO). Similarly, in manufacturing or aerospace, a formal Change Control Board evaluates engineering changes (with roles like Change Proposer (engineer), independent testers or quality engineers as Reviewers, a board chair or chief engineer as Approver). Large enterprises in regulated industries (finance, pharma, aviation, etc.) have additional required roles: Compliance Officers, Risk Managers, Safety Officers who approve or veto decisions in their domain. For example, a bank’s new product launch decision must go through a Risk Committee and Compliance Committee – they serve as specialized Approvers focusing on their risk/compliance mandates, separate from the business owner who proposed the product. Audit requirements (e.g. SOX 404 for public companies) are strict: companies must document that for any process impacting financial reporting, no single person can create and approve a decision without a second check (fitsmallbusiness.com). This is where practices like the “four-eye principle” (two individuals sign off) or even “six-eye” in critical cases become standard. As a result, role count is high – you might have 10+ people meaningfully involved in a major decision (multiple consulted experts, multiple approvers in sequence or consensus). Large enterprises also institute oversight roles on an ongoing basis: Internal Audit departments periodically review decisions and processes to ensure roles were properly separated and no policy was bypassed. The trade-off is slower decision speed, which many large companies attempt to counteract by empowering smaller teams for minor decisions (to avoid everything bogging down in bureaucracy). Many adopt a hybrid model: e.g. “Guardrails” are set by risk/compliance roles (so any decision within guardrails can be made quickly by product teams, but anything outside triggers an escalated approval). In summary, large enterprises present the most specialized and segregated role decomposition, often mirroring the frameworks to the letter (you will see RACI charts in project plans, DACI tables in decision memos, etc.), and they embed roles like compliance and auditor formally to meet legal mandates.

• Multinational / Global Corporation (5000+): At this scale, all the large-enterprise characteristics apply, plus the complexity of geographical and legal divisions. Roles may exist in each region – for instance, a multinational might have country-level compliance officers (who act as Contributors/vetoers for local regulatory decisions) feeding into a global Chief Compliance Officer (ultimate Approver for compliance matters). Decision processes often have to account for both local decision-making and global oversight. A strategic technical decision in a multinational might start in a business unit (BU) with a BU architect proposing, local tech leaders reviewing, then go to a Global Architecture Board for cross-BU impact review, and finally need sign-off from the global CTO. There’s also an element of duplicated roles: e.g. each division might have a mini decision board, with a corporate-level one ensuring consistency. In industries like manufacturing with global operations, you might find matrix structures: a plant manager can make many operational decisions (role of Approver at plant-level) but corporate engineering must approve any change that deviates from global standards. Additionally, public multinationals adhere to OECD governance best practices – meaning boards with independent directors, separation of CEO and Chairman roles, etc., which is macro-level role segregation to prevent concentration of power. For instance, many multinationals ensure that the person proposing a major acquisition (often the CEO) cannot unilaterally approve it – it requires Board approval and sometimes shareholder approval by law. Also, stakeholder communication roles become formal – e.g. an Investor Relations officer (Communicator role) informs shareholders of decisions, and a Corporate Secretary (Recorder role) ensures all board decisions are documented and filed legally. In summary, a multinational operates with layers of role decompositions: vertical layers (local, regional, global decision roles) and horizontal specialization (each aspect of a decision – technical, financial, risk – has a separate review and sign-off). This ensures resiliency and compliance, but demands strong coordination.

The table below summarizes how role counts and distribution tend to scale by org size, with notes on where roles can be combined or must be separated:

Differences by Industry

Industry norms and regulations heavily influence how roles are allocated and which must be separate. A few examples:

• Software / SaaS (unregulated tech): These companies, especially if privately held, have a lot of flexibility. A small SaaS might have engineers making architectural decisions with almost no oversight (perhaps just informally telling the CTO). Speed is often prioritized over formal process. As the SaaS firm grows, customer expectations (like reliability, security) push it towards more formal roles – e.g. introducing an SRE team that must review major infrastructure decisions (splitting the implementer vs reviewer roles for ops), or a Change Advisory Board for production deployments in an ITIL sense. There is no external law forcing internal decision separation, but adopting some separation is wise to avoid costly mistakes. SaaS enterprises may adopt internal policies like “all code must be reviewed by someone else” (peer code review as a role expectation) or “architectural ADRs require at least one senior engineer’s sign-off besides the author.” In the absence of regulation, role combination is a business risk decision: many tech companies err on the side of lightweight process with trust in individuals (DevOps culture: “You build it, you run it” means the dev is Proposer, Decider, Implementer for infrastructure, with only a blameless post-mortem as a retrospective audit). However, when a SaaS is aiming for high assurance (say selling to enterprise clients), it might voluntarily comply with standards that emulate regulated industries (SOC2, ISO27001) which mandate certain separations – e.g. developers should not have unchecked production access, changes should undergo approval, etc. In summary, SaaS industry tends to allow a collapse of roles for agility, up until the point trust or safety becomes a concern, then it imports more formal roles.

• Systems Integration / IT Services (consulting): In a consulting or project-based SI firm, decisions often revolve around project governance and client requirements. Industry frameworks like PMI or PRINCE2 explicitly define roles such as Project Sponsor (often client or exec, Approver), Project Manager (Driver/Proposer of plans), Technical Lead/Team (Implementers), and Steering Committee (Reviewers/approvers for major project decisions). Even a small SI outfit might mimic these to appear professional to clients – e.g. having an internal “quality reviewer” for any proposal before it goes to a client. As SI firms grow, they introduce independent review roles to manage risk: for instance, a bid proposal might require review by a separate “Risk Management” person to ensure the approach is sound. The “four eyes principle” is often instituted on deliverables: one consultant drafts (proposes) a solution design, another consultant must peer review it before it’s sent as final. Delegation in consulting is tricky because decisions often need client approval (the client is an external Approver for key decisions). So an SI must manage internal decision roles (to produce a recommendation) and then external sign-off (the client playing Approver). This means roles like Client Engagement Manager (who communicates and negotiates decisions with the client) are key – essentially a communicator and facilitator role not seen in product companies. Industry standards (ISO9001 for quality, or CMMI in software dev) encourage well-defined processes with stage reviews – an SI aiming for these will formalize roles such as independent verifier or quality assurance lead separate from the project team to audit decisions. So in SI, by practice, even small orgs might separate roles to ensure quality control (because a failed decision has immediate client impact). By 50+ people, an SI likely has a formal QA department or at least a senior “red team” that reviews proposals/architectures – a cultural norm. There’s typically no law forcing this, but competitive pressure does, since mistakes can ruin client trust.

• Finance (banking, insurance, fintech): Here, regulation strongly dictates roles and their separation. For any process that touches money, there will be a strict maker-checker requirement. A bank, for example, will not allow a trader (Proposer of a transaction) to also be the one who authorizes it in the system – a separate operations or risk officer must approve. In strategic decisions, banks have risk committees, compliance committees that must review major initiatives (so the Contributor/Reviewer role is filled by risk managers, independent of the business line that proposed it). Often, compliance or risk can veto at any stage – e.g. compliance might say “this new product idea is not allowable under regulations” stopping a proposal before it gets to the CEO. Audit and regulatory bodies also expect evidence of role separation: e.g. under SOX/J-SOX, any significant change in financial reporting processes must show that the person who designed the change isn’t the only one who tested and approved it. Even in a fintech startup, if it’s dealing with payments or investments, regulators might require that a Compliance Officer role exist and is not the CEO (to avoid conflicts of interest) – practically, the CEO might still make all calls, but formally they’ll bring in an outside expert to sign off compliance-critical decisions. Delegation in finance is very cautious – junior staff have limited decision powers, and clear escalation paths are defined (like a credit approval: under a threshold, a branch manager can approve, over it, need credit committee). For instance, a loan decision process might break down roles: a front officer proposes the loan, a credit analyst (separate) reviews and recommends, and a credit committee approves, then an internal audit might review that the procedure was followed later. In summary, finance industry demands multi-person involvement by design – one person show is virtually illegal when client assets or public trust is at stake.

• Manufacturing & Engineering: These industries often follow engineering best practices and safety regulations that enforce role separation. For example, in construction or aerospace engineering, it’s common that a design must be checked and signed off by an independent engineer (e.g. one engineer calculates a load, a second engineer verifies those calculations). Similarly, changes on an assembly line might require a process engineer to propose, a quality engineer to review, and a plant manager to approve. Many manufacturing companies use a Stage-Gate project model, where at each gate different roles must sign off (R&D, manufacturing, quality, finance all approve their aspect). ISO 9001 (Quality Management) doesn’t explicitly dictate roles, but it requires documented procedures for design control, including review, verification, and validation steps by people other than the designer. So you inherently get roles: Designer (proposer), Reviewer (peer or expert who verifies design output), Approver (usually a project manager or engineering manager who authorizes moving forward), Tester (validates in practice), etc. Safety-critical fields (like automotive or medical device manufacturing) have regulatory requirements that, for example, the person who verifies a product requirement is not the same person who implemented it, to ensure independent testing. In smaller manufacturing outfits, an engineer might wear multiple hats but they will still attempt to do peer reviews (e.g. have another engineer or an external consultant review critical calculations, because liability is a concern). Manufacturing companies also separate decision roles to manage cost and efficiency: production may propose a process change to speed up output, but finance must approve the budget (so here propose vs approve roles are in different departments with potentially conflicting priorities – a built-in check). Delegation in manufacturing is often structured by a chain of command: team leads make many on-floor decisions, but anything affecting safety, quality, or cost beyond a threshold gets escalated to management or a review board. Interestingly, manufacturing has long used the concept of “stop-the-line authority” – if a worker sees a defect, they can stop production (equivalent to giving even a junior person a certain decision power). That’s an example of how roles can be empowering in one dimension (anyone can act as a quality checker to halt, fulfilling a bit of a Reviewer/Auditor role in real-time). But to restart or change a process, usually a manager’s approval is needed.

• Healthcare (hospitals, pharma): Decisions here often involve life-or-death stakes, so formal checks are routine. A doctor may propose a treatment (Proposer) but if it’s experimental, an ethics board or a second doctor must concur (Reviewer/approver). In pharmacies or labs, there’s the rule of independent verification (one person prepares, another checks). Pharma companies have rigorous clinical trial protocols where changes must go through review boards. Essentially, the pattern is that any single decision that could harm a patient must be double-checked by someone else. Even administrative decisions in hospitals often require committee approval (e.g. adopting a new policy involves medical board sign-off). This industry is heavily regulated like finance, so small clinics to big hospitals all need clear delineation (the concept of “peer review” is deeply ingrained – e.g. one surgeon reviews another’s surgical plan for complex surgeries).

To illustrate how org size and industry intersect, consider a small fintech startup vs a similarly sized small SaaS startup: the fintech is likely legally required to have a compliance officer and maybe an external auditor – so even with 10 people, it cannot have the founder alone approve everything related to finance or security; some decisions will need sign-off by that compliance role (even if it’s just the founder wearing a different hat formally, or a part-time contractor). The SaaS startup with 10 people and no such external requirement could in theory have the CTO make all architecture decisions alone with no formal review. Now in practice, good practice for the SaaS would be to mimic some of the controls – e.g. have code reviewed by at least one other developer – but it’s not enforced by law.

By mid-size, both the fintech and SaaS will have separated many roles, but the fintech will be ahead in formalization (because it had to earlier). The SaaS might start separating roles mainly to reduce bottlenecks (e.g. too many decisions waiting on the CEO is bad for speed), and to prepare for scale. By large size, both will look quite similar in having multifaceted decision processes, though the fintech (or any finance co) will have more compliance review layers by default.

Audit-driven separations (like those mandated by SOX 404, J-SOX, or ISO standards) usually crystallize specific points where roles cannot be combined. Examples include:

• In financial processes: the person who creates a transaction (like raising a purchase order or initiating a wire transfer) must be different from the person who approves or executes it – this is not just best practice, but a compliance requirement (fitsmallbusiness.com). For a decision pipeline, that translates to: the person who proposes a major expenditure or deal cannot be the sole approver. We see companies enforce this via approval workflows (e.g. any expense > $5k auto-routes to someone’s manager for approval). A solo operator workaround is documenting approval by an external party – for example, a startup CEO could have their board chair or an investor sign off an unusually large expense, to simulate that control.

• In software change management (especially under ISO27001 or banking IT guidelines): the developer who writes code should not be the one deploying it to production without an independent code review and test (to prevent malicious or error changes). This means the Implementer role must be overseen by a separate Reviewer/Approver for production pushes. Small teams can do peer review; if one-developer, they might use automated tests or code scanning as a compensating control, but strict environments might still require a second human (some companies have even hired external code reviewers or used managed services to satisfy this when internal headcount was low).

• Compliance approvals: in sectors like pharma, any decision affecting regulatory compliance (e.g. a change in manufacturing process) requires sign-off by a qualified regulatory specialist. This is non-negotiable; if you don’t have that in-house, you must hire a consultant to perform that role. It’s an example where a solo operator simply “cannot” collapse that role – they legally must involve an independent qualified person.

• Governance decisions: public companies by law require certain decisions to be made by the Board, not an individual executive. If you’re a sole founder of a startup, you can decide everything yourself; but once public, you as CEO cannot single-handedly decide to, say, acquire another company beyond a certain size or pay yourself more – the Board (Approver) must approve those, to protect shareholders. This is where roles like Board Chair, independent directors, etc., become part of the decision-making chain.

Where can a solo operator collapse roles safely? Generally, in small-scale and low-stakes situations. For internal architectural decisions (like our ADRs) in a non-regulated software startup, a single person can indeed perform multiple roles (propose, review mentally, decide) without legal issues – the risk is mainly quality and bias, not compliance. They should avoid collapsing roles when it leads to decisions that one is not an expert in or has a conflict of interest in. For example, a solo founder might handle technical decisions solo (if they’re the technical expert) but if a decision has legal implications, they shouldn’t “collapse” the legal review role – better to run it by a lawyer agent or actual lawyer. So even solo, one should identify “which hat am I wearing” and sometimes briefly “take off one hat and put on another” to challenge oneself, or bring in an external perspective occasionally.

Where can roles NOT be collapsed? If there’s a legal or safety requirement for independent oversight, you must staff that. For instance, you cannot be your own external auditor for financial statements – you must engage a certified auditor. If you’re writing software for medical devices, you cannot be the sole tester to validate it meets regulatory standards – you need an independent test and validation by someone else per FDA rules. Where human life, investor protection, or customer trust under regulation is on the line, roles like compliance reviewer, auditor, or independent approver must be filled by someone besides the primary actor.

To summarize, as organizations grow and as industry stakes rise, the role count increases and overlap decreases. Startups collapse roles to move fast but assume more risk; large enterprises segregate roles to manage risk but move slower. Regulated industries enforce segregation early, whereas unregulated ones have more freedom to decide when and how to introduce it. High audit-exposure scenarios (public company, regulated product) essentially dictate a minimum role decomposition no matter the size – even a small public company must have an Audit Committee chair (independent) reviewing big decisions, for example. The next section will explore how, in modern contexts, AI agents can step in to occupy some of these roles, and how that intersects with the need for role separation.

AI Agents as Formal Decision Roles (Q3)

As organizations incorporate AI into their workflows, a natural question arises: can AI systems take on some of the decision-making roles defined above (proposer, reviewer, etc.)? The current consensus is that AI can assist and even formally occupy certain roles (like analysis or review), but final accountability should reside with humans. Multi-agent AI frameworks explicitly experiment with assigning roles to different AI agents to mirror a human decision team. Below, we examine patterns and considerations for embedding AI as decision participants:

• AI as Proposers (Idea Generation): AI systems (like GPT-4 or Claude) are increasingly used to draft proposals, strategies, or design documents. For example, an AI can consolidate technical research and produce a recommended architecture decision. In our context, an AI could generate an initial ADR draft (acting as a “Proposer” role assistant). This can greatly speed up the insight-gathering and option-formulation stage. Organizations like Amazon have reportedly used internal generative tools to draft portions of their six-pagers or to outline decision options. The pattern here is AI as a research and drafting agent. Importantly, a human typically sponsors or prompts this AI proposer and then becomes the human ‘approver’ of the draft before it circulates. So the AI is not autonomously deciding to propose X; it’s working under a human’s guidance to produce a proposal artifact.

• AI as Reviewers (Critics/Validators): This is one of the most prevalent and effective uses. AI can be employed to review human decisions or even other AI’s outputs. For example, Anthropic’s Constitutional AI approach uses an AI model to critique another model’s answers against a set of principles, effectively performing a reviewer role to ensure the output is safe and high-quality. In coding, GitHub’s Copilot Labs and research prototypes have an AI review code for bugs or style issues – an AI code reviewer. In strategic decisions, one might use an AI to do a “socratic critique” of a proposal (we do this in our pipeline: an AI agent is tasked with critiquing the ADR draft, finding gaps or risks). Another agent might do a consistency check with prior decisions. The key pattern is AI providing an independent perspective or check on a decision, much like a peer reviewer or analyst would. An observed best practice is to ensure this AI reviewer is sufficiently separated from the AI proposer to avoid simply reinforcing the same errors. This can be done by using different models (or at least different prompts) for the two – e.g. one GPT-4 instance generates the plan, a second GPT-4 instance with a “critical evaluator” prompt evaluates it. Some frameworks use different temperatures or even different LLM providers to get variation in viewpoint. The idea is akin to a human having a colleague double-check their work – here your AI-written proposal is double-checked by another AI (and then by a human). Self-review by the same AI model with the same prompt is considered an anti-pattern, because it tends to be blind to its own biases and often will just reinforce the initial output. For instance, an AI agent that plans a task might “evaluate” its own plan but since it doesn’t truly have new information, it may rubber-stamp it. Patterns like debate or adversarial agents have been researched: OpenAI’s debate framework had two AIs argue opposite sides of a question in front of a human judge, and Redwood Research explored one AI generating scenarios and another trying to find flaws. These approaches intentionally create multiple agents with conflicting objectives to stress-test a decision. The multi-agent toolkits (e.g. OpenAI’s Swarm or Microsoft’s AutoGen) allow users to script such roles: one agent could be the “Planner”, another the “Executer”, another the “Devil’s Advocate” that only seeks holes in the plan. By structuring AI interactions this way, we avoid letting a single AI system go unchallenged. This is crucial when using AI in serious decisions: you always want either another AI or a human to independently verify the output.

• AI in Compliance-Check Roles: AI can rapidly ingest and apply rules and policies, making them suited to a compliance assistant role. For example, given a set of corporate policies or regulatory guidelines, an AI reviewer could scan a proposed decision or design and flag any potential non-compliance (“This design doesn’t encrypt data at rest, which violates Policy X”). In fact, one of our AI agents in the pipeline is doing a policy alignment check – that’s an AI acting as a compliance checker. Companies are starting to formalize this by training or prompting models with their policy documents. For instance, an AI might be given the GDPR text and asked to review a product decision for GDPR compliance issues. While AI is not legally accountable, it can dramatically reduce the human workload by catching obvious issues. The pattern is to treat the AI’s output as a recommendation or alert: e.g. if the AI compliance agent says “risk of data privacy issue”, a human compliance officer or decision-maker takes that under advisement and investigates further. AI can also maintain an updated checklist of compliance requirements and tick them off for each proposal (something humans might forget). What controls are needed? One, as mentioned, is not relying on AI alone – a human should verify the AI’s compliance assessment. And two, ensuring the AI is kept updated with current laws (models can get outdated knowledge) and doesn’t hallucinate rules that don’t exist.

• AI as Decision-Maker? – Current governance literature and ethical guidelines strongly recommend that final decisions, especially those with significant consequences, remain with humans. This principle of human-in-the-loop or meaningful human control appears in many AI policies (e.g. the EU’s draft AI Act will require human oversight for high-risk AI systems). So while an AI can propose “Option A” and even say “Option A scores highest on all criteria,” the formal Approver role is reserved for a human. This is both for accountability reasons and practical reasons – an AI has no skin in the game or legal responsibility or intuitive judgment for novel trade-offs. There are some experimental exceptions: for low-stakes automated decisions (like A/B testing minor UI changes), companies might let algorithms decide (e.g. a multi-armed bandit algorithm effectively “decides” which variant to show more users). But in strategic or architectural decisions, it’s rare to give an AI the authority to sign off. One could imagine an AI in a collective decision scenario – e.g. if an AI sits among voting members in a committee as a tie-breaker or advisor – but again, a human governing body typically frames that AI’s role as advisory. For instance, there was an experimental venture capital firm that gave an AI a vote on investment decisions; however, legally the human partners still carried the liability and could override it.

• AI as Implementer/Executor: Separate from deciding, AI can also directly carry out decisions now (for example, code-writing agents that implement a decided design). Tools like Cognition’s Devin project aim to have an AI “software engineer” that can plan, write, test, and ship code autonomously (brixo.com) (aws.amazon.com). In such cases, the AI is fulfilling the Implementer role. This is already happening in narrow scopes (like automated deployments or script generation). The catch is ensuring the AI implementer follows the intent of the human decision and doesn’t introduce errors – thus often an AI implementer’s work is reviewed by a human or another AI. For example, if an AI writes code (implementer), a human or a separate AI might do the code review (reviewer) before it’s merged.

• Controls to Prevent AI Self-Review and Autonomy Loops: One major risk when using multiple AI agents is inadvertently removing human oversight entirely. If one agent proposes and another agent approves and we consider it “done,” we might have allowed a fully AI-driven decision without realizing. To prevent this, organizations set boundaries:

  • Always designate certain roles as human-only, often the final decision-maker and the final sign-off on implementation. In our pipeline, for instance, the Accept/Reject gate is explicitly human-only – no AI agent is allowed to auto-approve an ADR. This ensures an AI doesn’t effectively approve its own proposal.
  • Use logging and approvals for AI actions: For example, if an AI agent is tasked with merging code, the system might still require a human to click “confirm” on the agent’s suggestion. This practice of a human gatekeeper ensures nothing slips by unchecked.
  • Maintain a clear record of AI contributions. It’s recommended to annotate decision documents or logs with which parts were AI-generated or AI-checked. This transparency helps later audit trails – if a bad decision is made, one can trace whether an AI’s analysis was flawed or human judgment failed. (Some organizations even have policies that “AI-generated content must be labeled as such” internally, so that decision-makers reading a draft know if it came from a human analyst or an AI).
  • Diverse agent opinions: When feasible, use more than one AI to review a critical item, similar to getting second opinions from multiple experts. If two independent AI agents (perhaps from different vendors or using different methods) both flag the same concern in a proposal, you have higher confidence the concern is real. Conversely, if only one AI in the chain was used, a systematic error or bias in it could go unnoticed. This is analogous to not depending on a single human’s opinion in big decisions.
  • Human audit and periodic review of AI decisions: For example, a compliance team might periodically review a sample of AI-generated recommendations to ensure the AI is performing correctly and not exhibiting drift or bias. This is a way to not “set and forget” AI roles.

• Audit Trail for AI vs Human contributions: As AI agents become part of the workflow, companies are establishing audit practices. One convention is to treat AI outputs similar to how we treat junior staff outputs that require sign-off. For instance, if an AI writes an ADR draft, the record might include an annotation or metadata that “Drafted by AI agent X, based on prompt by Y, reviewed and edited by human Z.” If an AI makes an analysis and a human decision-maker overrides it, documenting the rationale (“AI recommended Option A with score 8, but human chose Option B due to factors AI didn’t consider…”) can be important for learning and accountability. In regulated industries, there is emerging guidance that use of AI in decision processes should be transparent and auditable – for example, the U.S. FDA has guidance on AI in medical decision support that says the reasoning must be explainable to a human and the clinician must ultimately be the one deciding. In finance, if an AI produces a trading signal, the trader is still accountable and must keep evidence of why they acted (possibly referencing the AI’s signal among other things). So practically, an audit trail might include the AI’s recommendation text or log files showing the multi-agent discussion, which are preserved for later inspection. Our pipeline, by design, keeps logs of each AI gate’s output for this reason – we can show which agent (and prompt) contributed what to the final ADR.

• Formalizing AI as “team members”: Some organizations give AI agents personas or even email accounts to integrate them into workflows (e.g. an AI might be CC’d on an email thread to automatically provide suggestions). There are reports of AI bots being added to Slack channels to watch discussions and interject information (not unlike a silent team member who speaks up when relevant). Governance-wise, these AI are tools, but treating them as roles can help clarify boundaries. For instance, if “AI Assistant” is listed as a participant in a meeting for note-taking, everyone knows that role is being fulfilled automatically and a human isn’t doing it. Similarly, one could imagine an architecture review meeting where an AI is officially assigned the role of “compliance advisor” – it listens and checks each idea against a checklist, then speaks up if needed. This formal embedding ensures people trust and understand the AI’s function (and also not confuse an AI’s voice with a human’s). IBM has done something similar with their Watson for regulatory compliance – essentially an AI that sits alongside human analysts.

• Emerging multi-agent systems: Projects such as OpenAI’s Swarm (an experimental framework) demonstrate orchestration of multiple agents with specific sub-tasks – e.g. one agent might fetch data, another makes a plan, another verifies the plan . AutoGPT and similar “autonomous AI” experiments initially had a single agent iterating on tasks (and often falling into loops due to lack of external critique), which highlighted why role separation is needed even among AIs. Newer approaches employ something like a “manager” agent that can spawn and coordinate “worker” agents (each with a defined role). For example, a “Manager AI” might read an ADR prompt and decide to create one agent to research databases, another agent to analyze cost, and then ask them to report back – after which the manager agent compiles a recommendation. This structure mimics a team with a team lead and two specialists. The benefit is each sub-agent focuses on a narrow role (less likely to go off track) and the manager can cross-verify their outputs (or even have them check each other). Such designs inherently reduce the risk of one agent’s bias dominating, and they provide a more traceable process (each agent’s output can be logged).

• Anti-patterns: Aside from the self-review issue mentioned (an AI marking its own homework), another anti-pattern is AI role confusion – not being clear about which decisions an AI is allowed to make vs where human intervention is needed. If this is ill-defined, teams might either over-rely on AI (letting it implicitly decide something critical) or ignore it completely. We should avoid scenarios like an AI generating a decision and the human approver just clicking “yes” without understanding (that’s basically giving the AI de facto final say). To prevent that, the human in the loop should be required to engage critically – perhaps by having to write a brief justification whenever they accept an AI-generated recommendation, to show they’ve thought it through. Another anti-pattern is using one AI agent with a monolithic prompt to do everything (e.g. “You are a proposal generator and also verify your proposal and finalize it”). This tends to skip the needed tension between roles that produces quality. It’s better to split that into two steps or two agents.

In terms of audit trail conventions: one approach is tagging content or commits with an author (human or AI). For example, Microsoft’s Azure OpenAI service has logging for all prompts and responses when used in workflows – these logs can serve as evidence of what the AI contributed. Internally, having a version history in documents (with changes by the AI vs by humans marked) can be useful. Some companies consider maintaining a registry of AI models and their intended use – so if an AI was used to make a decision, you can later check “was that an approved model version and what data was it trained on?” for accountability. These are evolving practices.

Overall, AI agents can play valuable roles in the decision lifecycle – primarily as Proposal Assistants, Reviewers (critics), and compliance or consistency checkers. They excel at sifting through information and providing unbiased (if well-tuned) analyses. However, organizations treat AIs as augmented collaborators, not autonomous decision-makers: final Accept/Reject remains with a human by policy in almost all frameworks. AI roles need guardrails to ensure they don’t become a single point of failure or a source of unchecked error. By structuring multiple AIs to check each other (or having humans check them), and by logging all AI contributions, one can integrate AI into decision-making while maintaining accountability and traceability.

Compression Strategy for One Human + AI Assistants (Q4)

Given the role taxonomy from Q1, we now consider how those roles can be compressed or consolidated when an organization is extremely small – in our case, effectively 1 human + a pool of AI agents. This is a unique scenario: many roles that are separate in a large org will inevitably be held by the same person, with AI possibly faking a “second set of eyes”. We need to identify which roles can be safely combined under one person (especially with AI support), which require compensating controls if combined, and which roles truly cannot be collapsed even in a one-person scenario (meaning you’d need to enlist outside help or accept some risk).

Below is a decision matrix that outlines role combinations vs conditions. It’s keyed by two dimensions: the organization’s stage (truly solo, growing, etc.) and audit exposure (i.e. external compliance pressure high or low). First, some general guidance:

• Must-Separate Roles (even for n=1): If an external rule or extreme risk consideration requires it, you cannot fully collapse these roles. In practice this means the solo human needs to bring in an external party or delay certain actions until they can be properly reviewed. For example, final financial approval might need a board or investor sign-off in some cases; security audits might need a certified external auditor. For our ADR context, there’s no law forcing a second approver on an architecture decision, but if we were pursuing something like SOC2 compliance, we might voluntarily require an independent review of changes.

• Collapsible with Compensating Controls: Many roles can be combined under one person if extra controls are in place to mitigate the risks. Compensating controls could be: thorough documentation, after-the-fact reviews, stronger automation checks, or periodic audits. For instance, one person can be both Proposer and Approver if we log the decision clearly and perhaps have an AI critique it – that way, there is a record that the decision was challenged, albeit non-humanly. Another example: if the same person implements and deploys a change, a compensating control is to run extensive automated tests or to do a post-deployment review to catch issues. The idea is to introduce something that either detects errors or deters misconduct to make up for the lack of a second human.

• Freely Collapsible Roles: Some role combinations don’t inherently pose a conflict of interest or risk and can be merged without special controls (apart from general good practice). For example, having the same person propose a solution and also document the ADR is generally fine – there’s no conflict, it might even be beneficial since they know the context best. In fact, in our pipeline currently the human acts as decision-maker and also often as the one who edits the final ADR text (with AI help) – that’s a reasonable combination.

Let’s present the matrix of roles vs. collapse safety. We’ll categorize roles into groups and note for each: if one person (with AI) does both, is it Safe, Safe with Controls, or Unsafe (Not advisable) especially under high audit conditions:

(In the above matrix, “high-audit or high-risk” implies scenarios where external auditors, regulators, or significant financial/safety impact is in play. Compensating controls mentioned include logs, AI reviews, external consultant reviews, automated testing, etc., all aimed to substitute for additional human roles.)

A key point from the matrix: some roles can merge without issue (communication, documentation), while others merge at the cost of oversight (proposal and approval). For a single human operation, it’s about mitigating those oversight gaps as creatively as possible.

In our specific pipeline (founder + AI), the human currently is Proposer, Approver, and often Implementer, while AI fills many Reviewer roles. This is workable but puts weight on the AI review quality. We identified the Final Accept (Approve) being solo as a risk – indeed, according to best practice, we are collapsing Proposer and Decider. To compensate, we run a parallel AI review and a Socratic critique to simulate two independent reviewers before that final decision. It’s not perfect “four eyes” but is better than one unchecked eye. As soon as our team grows by one (the junior engineer joining), a recommendation will be to involve that engineer in an actual second review (even if just to sanity-check or play devil’s advocate), because a human perspective adds qualitative insight AIs might miss (like gut feelings about an idea’s cultural fit, etc.).

Another role we noticed is missing explicitly: a Retroactive Evaluator (someone who looks back at past decisions to update or learn). Right now, nobody is officially doing that – the founder might informally, but it’s easy to neglect. This role can be collapsed with either the Decider or an AI agent. For example, the founder could set a quarterly task (in the CEO role) to review some past ADRs (acting as Evaluator). Or we could assign an AI to scan for outdated ADRs (though it’d need knowledge of what changed). If this role stays unfilled, we risk accumulating stale or misguiding decisions in our log. So even as a one-person setup, some roles we initially left vacant should be given to either the person or an agent.

Must-have vs optional separations for a solo+AI can also be considered in light of external exposure. If we were to pursue a security certification (audit exposure), one of the must-haves would be demonstrating change management with dual control. That could mean we’d need to show that for each ADR affecting security, we had an independent review (maybe our AI critique logs could serve as evidence in absence of a human reviewer). If insufficient, we might literally engage an external consultant on an hourly basis to do a quick review of decisions that have security implications – effectively “renting” that role when needed.

Conversely, in purely internal low-risk decisions (like choosing a library for a component), a single person can do everything (propose, decide, implement) and just note it down. That’s low impact if wrong, so we wouldn’t sweat formal separation there.

In sum, for n=1 + AI, no role is truly impossible to collapse (because it’s being done), but some collapses carry significant risk if not compensated. The above matrix highlights those and suggests controls. The overarching strategy: Add human or AI “checks” wherever two different humans would normally be required. And be aware of which combined roles leave you blind to certain problems – e.g. if you propose and decide entirely alone, you might be overconfident; inject a dose of outside perspective via an AI critic or later review.

Finally, we translate these insights into concrete recommendations for our pipeline: which roles to add or clarify, and how to consolidate smartly given our limited staffing.

Recommendations: Role Additions and Consolidations for Our ADR Pipeline

Considering the analysis, here’s a prioritized list of changes to our decision pipeline roles:

• Must-Have:

  1. Explicit Compliance/Risk Check Role – We should establish a formal “compliance reviewer” gate in the pipeline (if not already distinct). Currently, policy and risk checks happen in an ad-hoc or AI-driven way. We want a specific role (even if filled by AI for now) that checks each proposal against security, privacy, and regulatory constraints. This could be as simple as an AI agent that is given a checklist (GDPR, security policies, etc.) to run through, plus a human sanity check on anything flagged. By formalizing this role, we ensure we don’t skip it. This addresses risk #2 (unspoken roles like compliance). It’s must-have because missing a compliance step could lead to decisions that violate laws or our own standards.
  2. Independent “Second Eye” on Final Approval – While we can’t magically produce a second human approver, we should strengthen the simulation of the four-eye principle. One must-have solution is to require a second distinct AI review (or use the new junior engineer) right before final approval. For example, after our parallel AI reviews and critiques, but before the CEO hits “Accept”, have one more agent (maybe an ensemble or an agent that specifically tries to find any overlooked issue) give a final go/no-go recommendation. Alternatively, make it policy that the junior engineer (from 2026-10) must review the decision summary and concur before the CEO finalizes it. Even if the junior’s experience is less, the act of explanation and a fresh pair of eyes can catch something. This addition directly addresses risk #1 (solo final accept). It’s must-have for high-impact ADRs (perhaps those graded A or B in importance); for low-impact ones, it can be waived, but better to practice it always.
  3. Logging & Attributing AI Contributions – We should treat this as a role of “Recorder of AI assistance” or part of the recorder role. Ensure that the ADR document or an annex lists which parts of the process were done by AI vs human. This increases accountability. For instance: “Sections 2–4 of this ADR were drafted by AI reviewer agent, then edited by human.” This is must-have to maintain trust in the decisions as we rely heavily on AI. If later we audit why a decision was flawed, we can see if perhaps an AI’s input was taken too unquestioned. It formalizes the audit-trail convention we discussed.

• Should-Have:

  4. Role for Retrospective Updates (“Recaller/Curator”) – Assign an owner to periodically revisit and update past decisions. This could be an AI agent that every month scans for contradictions or obsolete references in the ADR registry, but a human needs to initiate it. Perhaps the junior engineer can take on the “ADR Librarian” role on a monthly basis (with an AI to assist). Mark this role in our RACI as responsible for versioning ADRs. It’s should-have because while not immediately critical, over time outdated or un-revisited decisions can mislead new team members or cause architectural debt. Having someone explicitly accountable to keep the decision knowledge base clean will pay off as we grow.
  5. Consolidate AI Review Gates – We have many AI gates (triage, critique, scoring, cross-validation, etc.). We should rationalize these into perhaps a single multi-dimensional review role or two roles: e.g. one “Technical Reviewer” agent that covers feasibility, cost, consistency checks, and one “Devil’s Advocate” agent that purposely looks for what could go wrong. If some gates are overlapping or not adding distinct value, they can be merged. This consolidation will simplify the pipeline and make it easier to explain roles. It’s should-have because it reduces complexity without losing coverage – for instance, maybe our “parallel review” and “cross-validation” and “consistency” gates overlap; we could merge them into one robust reviewer that checks consistency with past ADRs and cross-validates assumptions. This doesn’t remove oversight, it streamlines it.
  6. Elevate the Junior Engineer’s Role Gradually – Plan for the junior engineer to take on some subset of roles like Implementer on certain ADRs, and Reviewer on others, to start building a true multi-person dynamic. For example, when the junior is on board, in decisions where the CEO is Proposer, have the junior act as human Reviewer (reading the ADR and giving comments) before the CEO finalizes. And for some decisions the junior proposes (in time), the CEO can be the reviewer/approver. This formalizes a two-way mentorship and checks, and is a stepping stone to proper segregation. It’s should-have in the sense of growth: as soon as the junior is capable, we should practice this, because it establishes good habits and trust in roles.
  7. Policy for When to Seek External Review – Not exactly a “role to add” internally, but a guideline (which effectively creates an external role on demand): e.g. “If an ADR is about a significantly regulated area (e.g. handling of personal data, or a major financial model change) and we have no in-house expert, we will get an external consultant to review.” This would mean budgeting for a lawyer to glance at a data privacy affecting decision, etc. It’s a should-have because it’s scenario-dependent; many decisions won’t need it, but having the policy ensures we don’t forget to do it when it is needed.

• Nice-to-Have:

  8. Automated Decision Metrics and Postmortem Role – Introduce an AI or process that tracks metrics tied to an ADR’s outcome (e.g. performance improved, or user complaints reduced) and flags if a decision perhaps should be revisited. This AI could effectively fulfill a “monitor/evaluator” role by comparing expected benefits of a decision to actual results after some time. It could then suggest a retrospective meeting if things don’t match up. This is nice-to-have because it adds a layer of continuous improvement that many companies don’t formally do, but it would be beneficial for us in re-using and learning from decisions.
  9. Dedicated Facilitator Role for Decision Meetings – If and when our team grows a bit (maybe beyond 1 human), having someone facilitate the ADR review meeting (even if that meeting is just with AI participants) could ensure all perspectives (including AI outputs) are considered fairly. This is borrowing from Holacracy/Sociocracy. It could even be an AI assigned to moderate the others (“AI mediator” ensuring, say, that the cost-check agent and security-check agent both get equal attention in the discussion). It’s nice-to-have; currently the CEO basically does this mentally.
  10. Knowledge Disseminator/Trainer Role – In the future, as more staff join, someone should ensure that all team members are informed about decisions and understand them (maybe through brown-bags or an internal newsletter of ADR updates). Right now, with one person, this is moot, but as we get a second/third person, it’d be nice if one of us (or an AI summarizer) takes on the job of onboarding others to the ADR history. This ensures decisions are re-used effectively (people know them) and not just archived.

Priority Ranking for our current pipeline, summarizing must/should/nice:

• Must-Have: Formal Compliance Checker role; Strengthened independent review on final decisions (simulate or add second approver); Clear logging of AI vs human contributions.
• Should-Have: Add Retroactive ADR updater role; Simplify overlapping AI review roles; Utilize junior engineer as human reviewer/co-implementer asap; Policy triggers for external expert review on certain decisions.
• Nice-to-Have: Automated decision outcome monitoring (AI evaluator); Meeting facilitation role (could be AI) to enforce process; and future role for knowledge sharing/training about decisions.

By implementing the above, our one-human-many-AI pipeline will better mirror the essential checks and balances found in larger organizations, without unduly sacrificing the efficiency that our small scale affords. We will cover the roles that should exist (addressing our third risk of not knowing what we’re missing) by comparing against industry baselines, and we’ll intelligently compress them where needed, always adding compensating controls when one person (or one AI) is doing double duty (wheat-biz.com). This approach will make our decision-making process more robust, auditable, and ready to scale as the team grows.

References

• Atlassian Team Playbook – DACI Decision Making Framework (roles: Driver, Approver, Contributors, Informed) – via Centercode (www.centercode.com) (www.centercode.com).
• Bain & Company – RAPID Framework (Recommend, Agree (veto), Perform, Input, Decide) – via Centercode (www.centercode.com) (www.centercode.com).
• Centercode Blog – RACI vs DACI vs RAPID: How to Pick the Right Responsibility Framework (explains differences; RACI lacks a clear decider role) (www.centercode.com) (www.centercode.com).
• Jeff Bezos – 2015 Shareholder Letter – Type-1 vs Type-2 Decisions (one-way door vs two-way door, and need for agility) – via Nobl.io (nobl.io).
• Holacracy Constitution – Integrative Decision-Making (group consent decision process, facilitator and no single approver) – via ReinventingOrganizations Wiki (reinventingorganizationswiki.com).
• Spotify Engineering Culture – Squads and Autonomy (squads have authority to decide within their domain, high alignment via sharing) (www.scribd.com).
• PwC Japan – J-SOX Internal Controls Guidance (importance of segregation of duties and independent monitoring in even small companies) (wheat-biz.com).
• Fitsmallbusiness – Segregation of Duties in Small Business (no single person should control a transaction from start to finish) (fitsmallbusiness.com).
• OpenAI – Swarm multi-agent framework (exploration of agent orchestration with handoffs, illustrating multi-agent roles) .
• Cognition AI – “Devin” AI Software Engineer (AI agent can plan, code, test autonomously, highlighting AI as implementer role) (brixo.com) (aws.amazon.com).
• Regulation and Standards: ISO 27001, SOX 404 – requirements for change management and approval processes (segregation of duties, audit trails) (fitsmallbusiness.com) (wheat-biz.com).
• Internal Wheat.com Blog (2026) – Preventing Fraud with Internal Controls in Small Companies – emphasizes combining minimal role separation with multi-layer monitoring and external oversight (wheat-biz.com). (contains note on AI risks requiring logs and double-checks).