Intrenion

Decision Backbone: ChatGPT Implementation, Rollout, and Adoption Project

Christian Ullrich
January 2026

Info
This decision backbone lists the explicit decisions that must hold as an initiative approaches an irreversible commitment. Each sentence clarifies scope, ownership, and accepted downsides, ensuring contracts, plans, and slides remain consistent under scrutiny and that any decision that cannot survive review is corrected or removed before commitment.

Table of Contents

Scope

  1. We decide to structure the project solely around rollout and first-level user support because this gives us a narrowly scoped delivery with limited coordination overhead instead of adding integration, development, or optimization workstreams and accept that broader value creation is postponed.
  2. We decide to rely solely on ChatGPT as a standalone tool without workflow redesign activities because this gives us a tightly bounded project scope and predictable effort instead of redesigning business processes alongside rollout and accept that efficiency gains depend entirely on user initiative.
  3. We decide to make the rollout applicable to all business areas without exception because this gives us organizational clarity and avoids boundary discussions instead of limiting access to selected departments and accept that some areas may see limited short-term relevance.

Vendors

  1. We decide to rely on OpenAI as the sole vendor for this project because this gives us a single contractual and technical counterpart instead of managing multiple AI providers and accept that vendor dependency increases.
  2. We decide to accept OpenAI’s standard Enterprise contract without customization because this gives us fast procurement and legal simplicity instead of negotiating bespoke terms and accept that specific organizational preferences are not reflected.
  3. We decide to limit vendor deliverables strictly to providing ChatGPT Enterprise as contracted because this gives us clear responsibility boundaries instead of expecting advisory, training, or change support from the vendor and accept that internal teams carry those efforts.
  4. We decide to escalate issues to OpenAI enterprise support only when internal resolution fails because this gives us a filtered and efficient escalation path instead of direct user vendor interaction and accept that issue resolution may take longer.

Technology

  1. We decide to rely exclusively on ChatGPT Enterprise as the sole technology platform because this gives us a minimal and controllable technical footprint instead of combining multiple generative AI tools and accept that alternative model capabilities are unavailable.
  2. We decide to include the full standard ChatGPT Enterprise feature set from day one because this gives us immediate functional completeness with minimal configuration effort instead of defining a reduced feature subset for initial rollout and accept that some features may be underused early.
  3. We decide to accept the native feature set and limitations of ChatGPT Enterprise without modification because this gives us predictable behavior and vendor responsibility instead of customizing or extending functionality and accept that some user needs remain unmet.
  4. We decide to exclude all system integrations and custom applications from this project because this gives us a fast and low-risk technical rollout instead of building APIs or embedded solutions and accept that automation and orchestration use cases are delayed.
  5. We decide to avoid connecting ChatGPT to any internal data sources because this gives us immediate security clarity and reduced implementation effort instead of enabling internal knowledge access and accept that responses lack company-specific context.
  6. We decide to configure ChatGPT only through standard Enterprise configuration options and single sign-on because this gives us centralized access control and low operational complexity instead of deploying additional configuration layers and accept that fine-grained customization is not possible.
  7. We decide to postpone any changes to legacy systems or workflows until after full rollout because this gives us a stable baseline environment instead of coordinating parallel system changes and accept that integration readiness is deferred.

Licensing

  1. We decide to cap license spending and expand only after observed adoption because this gives us financial risk control tied to real usage instead of pre-purchasing licenses for all employees and accept that early adoption rates constrain growth.
  2. We decide to align license volume strictly with the approved project budget because this gives us cost control tied to governance decisions instead of scaling licenses opportunistically and accept that rollout speed is financially constrained.
  3. We decide to link any license expansion to observed usage after each rollout batch because this gives us evidence-based spending decisions instead of committing to full organizational coverage upfront and accept that adoption growth can stall if usage signals are weak.
  4. We decide to increase the total number of licenses in clearly defined blocks because this gives us predictable financial planning and rollout effort instead of adding licenses incrementally per request and accept that some demand waits for the next block.

Governance

  1. We decide to reserve final decision authority for the governance board because this gives us a single accountable approval body instead of distributing decision rights across multiple committees and accept that approvals may slow execution.
  2. We decide to treat the project team as empowered to decide within defined scope without intermediate approvals because this gives us fast operational progress instead of requiring frequent governance check-ins and accept that some stakeholders feel insufficiently involved.
  3. We decide to enforce a single escalation path directly to the governance board because this gives us a lean resolution mechanism instead of layered escalation structures and accept that minor issues may consume senior attention.
  4. We decide to run bi-weekly project meetings and bi-monthly governance board meetings because this gives us rhythm and visibility instead of on-demand or irregular meetings and accept that meeting load is high.

Project

  1. We decide to limit formal milestones to rollout completion and regular use validation because this gives us simple governance checkpoints instead of detailed phase gate structures and accept that progress visibility is less granular.

Rollout

  1. We decide to treat budget and rollout capacity as fixed constraints for this project because this gives us planning stability instead of continuously renegotiating scope and timelines and accept that opportunities requiring additional resources are deferred.
  2. We decide to roll out licenses in staged user batches rather than all at once because this gives us budget control and manageable support demand instead of a big bang deployment and accept that some employees wait longer for access.
  3. We decide to sequence rollout by prior ChatGPT experience rather than role criticality because this gives us higher initial usage and peer support capacity instead of targeting senior or high-impact roles first and accept that some key roles are onboarded later.
  4. We decide to roll out complete teams when a critical mass of experienced users exists because this gives us localized peer-to-peer support within daily work instead of granting access to isolated individuals and accept that some high interest individuals are delayed.
  5. We decide to base rollout sequencing on self-reported frequency of prior ChatGPT use because this gives us a fast and low effort segmentation method instead of running skills assessments or capability tests and accept that self-reports may be imprecise.
  6. We decide to assign employees to rollout stages strictly based on reported ChatGPT usage frequency because this gives us a simple and scalable batching rule instead of combining multiple qualitative criteria and accept that some users are misclassified.
  7. We decide to roll out access to complete teams when experience density is sufficient because this gives us local peer support structures instead of onboarding individuals across many teams and accept that some interested individuals wait longer.
  8. We decide to enforce team-level rollout boundaries rather than department-wide waves because this gives us operational simplicity instead of coordinating large organizational units and accept that coverage appears fragmented.
  9. We decide to require users to switch fully to the corporate ChatGPT account upon rollout because this gives us controlled usage data and security instead of tolerating parallel personal account use and accept that some users resist the transition.

Adoption & Measurement

  1. We decide to rely on peer-to-peer support rather than formal adoption programs because this gives us low operational overhead and faster informal learning instead of designing structured onboarding and training formats and accept that learning quality varies by team.
  2. We decide to avoid incentives for ChatGPT usage at all rollout stages because this gives us a signal of intrinsic adoption instead of driving usage through rewards or targets and accept that some users remain passive.
  3. We decide to measure success primarily through actual ChatGPT usage data because this gives us an objective and readily available signal instead of attempting to quantify productivity improvements directly and accept that business value remains partially inferred.
  4. We decide to rely exclusively on the ChatGPT Enterprise metrics dashboard for technical usage data because this gives us a single authoritative source instead of combining multiple tracking systems and accept that metric granularity is limited.
  5. We decide to complement usage metrics with periodic user surveys because this gives us qualitative insight into perceived value instead of depending only on system data and accept that survey responses are subjective.
  6. We decide to report metrics on a fixed monthly cadence with bi-annual surveys because this gives us predictable reporting effort instead of continuous monitoring and accept that emerging issues may surface late.
  7. We decide to delay corrective actions until after full rollout is completed because this gives us stable baseline data across all user groups instead of reacting to early cohort fluctuations and accept that adoption problems persist longer before intervention.
  8. We decide to accept declining average usage metrics as rollout expands to less experienced users because this gives us freedom to scale access gradually instead of pausing rollout to stabilize metrics and accept that short-term statistics look weaker.
  9. We decide to accept slower-than-expected adoption after full rollout as a tolerable defect because this gives us continuity of the rollout strategy instead of reopening rollout design decisions and accept that usage growth may plateau.

Organizational change

  1. We decide to rely on designated experienced users within each team to carry out organizational change execution because this gives us decentralized influence without adding formal change roles instead of staffing a dedicated organizational change management function and accept that support capacity varies by team.
  2. We decide to communicate primarily through mass emails and a single internal wiki page because this gives us consistent and low-effort messaging instead of running workshops, roadshows, or tailored communications and accept that some messages are overlooked.
  3. We decide to defer creation of a formal ChatGPT governance framework until tensions emerge because this gives us speed and avoids premature policy debates instead of establishing a comprehensive governance model upfront and accept that ambiguities are resolved reactively.
  4. We decide to assume low initial resistance by focusing on experienced users because this gives us momentum in early stages instead of investing in resistance management planning and accept that later cohorts raise more objections.
  5. We decide to require departments to accept ChatGPT as a default work tool because this gives us a common baseline for new working habits instead of allowing optional or opt-out adoption and accept that some teams feel forced into change.
  6. We decide to avoid planning formal role changes before rollout completion because this gives us flexibility to react to real effects instead of redesigning roles upfront and accept that temporary role ambiguity persists.

Operations & User support

  1. We decide to treat external service downtime as an accepted operational risk because this gives us a simple run model instead of building contingency tools or backups and accept that users experience temporary interruptions.
  2. We decide to document operations in a lightweight internal handbook because this gives us fast maintainability instead of producing detailed runbooks for every scenario and accept that edge cases are handled ad hoc.
  3. We decide to limit project support scope strictly to user account initialization issues because this gives us a clearly bounded support workload instead of handling all ChatGPT-related questions centrally and accept that usage questions remain unresolved by the project team.
  4. We decide to transition ongoing account issue handling to internal IT after initial rollout because this gives us a clean exit from operational support instead of keeping the project team engaged long-term and accept that handover friction occurs.
  5. We decide to enforce a 48-hour response time for account-related issues during business days because this gives us a realistic service level instead of promising immediate responses and accept that user frustration increases during delays.
  6. We decide to route prompt engineering and feature questions to peer-to-peer support because this gives us scalable help through experienced users instead of building an expert support desk and accept that advice quality is inconsistent.
  7. We decide to introduce additional support or learning measures only when peer support breaks down because this gives us a lean people model by default instead of proactively assigning training resources and accept that intervention happens later than some users would prefer.
  8. We decide to escalate technical issues to OpenAI enterprise support only through the project or IT teams because this gives us controlled vendor communication instead of allowing direct user escalation and accept that resolution cycles are longer.

Risks

  1. We decide to accept lower than expected ChatGPT usage as a tolerable project risk because this gives us a realistic rollout without forcing artificial adoption measures instead of delaying rollout until guaranteed usage levels and accept that license value may be questioned. (DOUBLE)
  2. We decide to accept employee frustration from later rollout placement because this gives us a sequencing model aligned with adoption likelihood instead of prioritizing fairness or equal access and accept that morale complaints increase.
  3. We decide to rely on intensive communication as the primary risk mitigation because this gives us a low-cost and fast response mechanism instead of structural changes to rollout or licensing strategy and accept that communication alone may not resolve dissatisfaction.
  4. We decide to allow rollout adjustments only within the existing rollout logic because this gives us consistency and planning stability instead of redesigning the rollout mid execution and accept that some risks cannot be fully mitigated.
  5. We decide to escalate risks only when stakeholders explicitly request governance involvement because this gives us a lean escalation threshold instead of proactive risk escalation routines and accept that some issues surface late.

Stakeholders

  1. We decide to treat anyone who claims stakeholder status as a stakeholder because this gives us open participation without gatekeeping instead of defining a fixed stakeholder list and accept that stakeholder volume increases coordination noise.
  2. We decide to require no active contribution from stakeholders beyond opinions because this gives us execution speed instead of assigning stakeholder work packages and accept that alignment relies on limited engagement.
  3. We decide to run the project with minimal stakeholder commitments because this gives us low dependency on availability instead of formal involvement models and accept that buy-in is thinner.
  4. We decide to lock the rollout approach once decided and treat it as non-negotiable for stakeholders because this gives us planning stability instead of reopening sequencing debates and accept that dissatisfaction cannot be resolved structurally.
  5. We decide to reject expectations of immediate measurable productivity gains from stakeholders because this gives us realistic evaluation framing instead of promising short-term returns and accept that some stakeholders remain unconvinced.

Documentation

  1. We decide to maintain only a decision backbone, project schedule, and rollout plan as official records because this gives us low documentation overhead instead of producing extensive reports, playbooks, or manuals and accept that historical detail is limited.
  2. We decide to treat the decision backbone as the single source of truth for scope and direction because this gives us clear reference points instead of spreading decisions across presentations, emails, and meeting notes and accept that informal context is lost.
  3. We decide to update all project artifacts on a fixed monthly cadence because this gives us predictable maintenance effort instead of ad hoc or continuous updates and accept that some information becomes outdated between updates.
  4. We decide to avoid documenting informal adoption practices and peer learning patterns because this gives us a clean and manageable documentation set instead of capturing evolving team behaviors and accept that successful local practices are not formally replicated.

Learning

  1. We decide to deliver no formal learning formats as part of this project because this gives us zero training design and delivery overhead instead of creating courses, workshops, or onboarding sessions and accept that some users struggle longer without structured guidance.
  2. We decide to rely on rollout sequencing by prior ChatGPT experience as the primary learning mechanism because this gives us embedded peer learning in daily work instead of scheduled learning interventions and accept that knowledge transfer is uneven.
  3. We decide to track skill gaps only among highly experienced users because this gives us insight into advanced usage patterns instead of mapping skills across the entire workforce and accept that beginner gaps remain unmeasured.
  4. We decide to postpone decisions about advanced skill development such as post ChatGPT practices until after rollout because this gives us real usage signals to react to instead of speculating upfront learning paths and accept that capability building beyond basic use is delayed.

Process

  1. We decide to allow information research, idea generation, and text creation workflows to change without formal process redesign because this enables rapid adoption through experimentation instead of documenting and enforcing new standard workflows and accept that practices diverge across teams.
  2. We decide to accept fundamental workflow disruption as intentional project impact because this gives us meaningful change leverage instead of preserving existing process stability and accept that some established routines break.

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