Implementation playbooks
60-Day AI Implementation Playbook: connect context and harden execution
A 60-day operating plan for expanding a proven AI workflow without losing governance or observability.
Executive summary
Measure the operating outcome, not the AI activity.
Sixty days starts where the first proven workflow ends. This plan assumes one outcome already completes in production and spends the additional time on the things a pilot deliberately skipped: broader authoritative context, the exception tail, resumable failure handling, permission structure that survives a security review, and a second workflow that shares the same spine.
The problem
What 60-Day AI Implementation Playbook is trying to fix.
The second phase of an AI implementation fails differently from the first. The workflow works, so confidence is high, and the natural instinct is to add more workflows quickly. What that instinct misses is that the first workflow succeeded partly because its scope excluded the hard cases, and every new workflow built on the same foundation inherits the same unhandled tail. Breadth added at this point multiplies fragility instead of value.
A pilot also carries deliberate shortcuts that become liabilities at this stage. Credentials are often broader than they should be because narrowing them was slower. Failure handling is thin because failures were being watched by the person who built it. Observability is a person checking each morning. None of that is wrong for thirty days, and all of it breaks the moment a second team depends on the workflow or the builder takes leave.
The third pressure is context. The first workflow used the minimum data required, which is correct for proving feasibility and insufficient for reliability. Cases that were exceptions in the pilot are frequently exceptions only because the workflow could not see enough. Sixty days is the window in which broadening authoritative context, hardening execution, and adding exactly one more workflow can be done deliberately, before scale makes each of them a project.
There is an organisational dynamic in the second phase that is easy to underestimate. The first workflow's success creates demand from other teams, and that demand arrives as specific requests rather than as a prioritised queue. Saying yes to three of them consumes the entire sixty days in parallel builds, each one shallow, none of them hardened. The phase requires an explicit decision to disappoint some requesters in order to make the first workflow durable, and that decision is considerably easier to make if it is stated at the beginning rather than negotiated repeatedly.
The second pressure is that pilot-era shortcuts are usually undocumented. Nobody wrote down that the credentials were broad, that failure handling assumed someone was watching, or that one data condition was handled by a manual step nobody mentions. Sixty days spent hardening a workflow whose shortcuts have not been inventoried tends to harden the parts that are visible and leave the ones that matter. An explicit inventory of what the pilot deliberately skipped is the first artefact this phase should produce.
Architecture
How UbiVibe supports this implementation.
Each stage below is something the platform does during delivery, not a suggested project phase to staff separately.
Step 01
Exception tail analysis
Start from the exception reasons the first workflow recorded in production and rank them by frequency and cost. This ranking, not a roadmap written in advance, determines what the sixty days actually build, because real exceptions are the most reliable specification available.
Step 02
Context broadening from authoritative sources
Connect the additional systems the ranked exceptions require -- entitlement, billing state, historical activity, related entities -- with provenance recorded. A meaningful share of pilot exceptions disappear entirely at this step, because the workflow was failing on incomplete context rather than on genuine edge conditions.
Step 03
Resumable execution and idempotency
Rework steps so that a mid-run failure resumes rather than restarts, and so that repetition does not duplicate side effects. This is the specific hardening that lets the workflow run without someone watching, which is the practical definition of leaving pilot status.
Step 04
Permission narrowing and review readiness
Replace the pilot's broad credentials with scoped permissions per workflow, verify tenant and data isolation, and assemble the audit evidence a security review will ask for. Doing this before the second team arrives is materially cheaper than doing it after, and it is the most common reason expansions stall.
Step 05
Observability without a human watcher
Set thresholds and alerts on completion, exception rate, and stalled runs so that a degradation surfaces automatically with a plain-English explanation. The pilot's morning check becomes an exception feed, which is what allows the original builder to move on to the second workflow.
Step 06
One adjacent workflow on the same spine
Add a second workflow that reuses the same connectors, permission model, and state handling rather than one that opens a new system boundary. Adjacency is the constraint that makes the second workflow take a fraction of the first one's effort and keeps the platform from fragmenting.
Step 07
Pilot shortcut inventory
Before any hardening begins, list what the first workflow deliberately skipped: broad credentials, assumed supervision, unhandled data conditions, manual steps that were never documented. Hardening without this inventory reliably improves the visible parts and leaves the load-bearing shortcuts in place.
Step 08
Explicit demand deferral
Requests arriving on the back of the first success are captured with a stated outcome and owner, and deferred deliberately rather than absorbed. The phase depends on the team having permission to disappoint some requesters, and making the queue visible is what makes that permission possible to obtain.
Methodology
Rule 1
Define the target outcome and owner.
Rule 2
Document the current workflow and baseline.
Rule 3
Connect only the authoritative systems required for the first outcome.
Rule 4
Implement bounded permissions and exception paths.
Rule 5
Run acceptance evidence before expanding scope.
Measurement framework
Five dimensions worth measuring repeatedly.
Outcome completion
Qualified intents that reach the expected business outcome
Activity counts do not prove that the workflow delivered value.
Cycle time
Elapsed time from trigger to completed outcome
Faster completion is one of the clearest benefits of connected execution.
Human intervention
Manual touches, approvals, retries, and escalations per completed outcome
Automation should reduce avoidable work without removing appropriate oversight.
Exception rate
Runs that leave the expected path or require recovery
Exception frequency exposes brittle workflows and poor context.
Data provenance
Share of material decisions supported by current authoritative sources
AI output quality depends on trusted operating context.
Examples
60-Day AI Implementation Playbook in practice.
Concrete situations this framework is designed to resolve. Scenarios are illustrative operating patterns, not customer case studies.
Exceptions that were really missing context
A renewal workflow logged a steady exception stream for accounts with multiple subscriptions. Connecting the billing source resolved the entire category without any workflow logic change, which is the most common and most under-anticipated finding of the second phase.
The builder took two weeks off
A pilot that depended on a daily manual check stalled silently for three days during annual leave. Threshold alerting and resumable steps were built specifically in response, and the workflow subsequently ran a full quarter without a human watcher, which is the real graduation criterion.
Security review at the second team
Extending a support workflow to a second region triggered a review the pilot credentials could not pass. Narrowing permissions and assembling audit evidence took most of two weeks. Sequenced before expansion, that work would have been routine rather than blocking.
A second workflow in a fifth of the time
A team added order-status responses on top of the connectors, permissions, and state handling already built for order confirmations. The second workflow reached production in days. Choosing adjacency over a new department was the entire reason for the difference.
Three parallel builds and nothing hardened
A team accepted requests from three departments after a successful pilot. At day sixty, four shallow workflows existed and none could run unsupervised. Consolidating on one hardened spine took a further month that an explicit deferral decision at the outset would have made unnecessary.
The shortcut nobody had written down
A pilot depended on an analyst manually correcting one data condition each morning. It had never been mentioned because it felt like normal work. Hardening proceeded around it for three weeks until a holiday exposed it. A shortcut inventory in week one is a cheap insurance policy.
Permissions narrowed in the wrong order
A team narrowed credentials for the workflow that was easiest rather than the one facing security review first. The review still blocked expansion. Sequencing permission work by which team is next to onboard, rather than by convenience, is what keeps the hardening aligned with the constraint.
An exception category deliberately left manual
Analysis showed one exception type occurred rarely and required significant judgement. The decision was a documented, well-routed manual path rather than automation. That is a legitimate outcome, and recording it prevents the same case being re-analysed every quarter.
What to do next
Recommended actions.
Action 01
Start with the smallest useful outcome.
Action 02
Keep rollback and export paths explicit.
Action 03
Instrument completion, cycle time, exceptions, and intervention.
Action 04
Expand only after the first workflow remains healthy without recurring manual rescue.
Limitations and evidence standard
What this playbook does not claim.
- This plan assumes a first workflow already completes in production with a recorded baseline. Applied without that, sixty days becomes an unbounded build with no evidence of feasibility to anchor it.
- Hardening work is largely invisible to stakeholders. Expect the second phase to look slower than the first even when it is the phase that makes the first one durable, and set that expectation before starting.
- Not every exception is worth resolving. Some categories are rare and expensive to handle, and the correct decision is a permanent, well-routed manual path rather than more automation.
- Security review timelines are outside the team's control and vary widely between organisations. Where review is likely to be slow, start the permission work in the first days rather than at the point of expansion.
- Adding a second workflow sharing the same spine improves speed and concentrates risk. A defect in the shared connector or permission layer now affects both, so the observability work is a prerequisite rather than a parallel task.
- The phase depends on having permission to defer new requests. Where that permission is not granted, the realistic output is several shallow workflows rather than one durable one, and stating that trade-off early is better than discovering it at day sixty.
- A shortcut inventory relies on the original builders being available and candid. Where they have moved on, expect to rediscover some shortcuts through failure, and budget for that rather than assuming the inventory is complete.
- Hardening reduces visible output. Stakeholders comparing this phase's demo to the first month's will see less, and setting that expectation in advance is part of the work rather than a communications afterthought.
FAQ
Questions about 60-Day AI Implementation Playbook.
How is this different from just running the 30-day playbook twice?
The thirty-day plan proves feasibility by deliberately excluding hard cases. This phase does the opposite: it starts from the excluded cases, broadens context, and hardens execution so the workflow survives without supervision. Running the first plan twice produces two fragile workflows rather than one durable platform.
Why prioritise the exception tail over new workflows?
Because the exception tail is a free, evidence-based specification generated by real traffic, and because every new workflow built on an unhardened foundation inherits the same tail. Resolving it once benefits everything built afterwards, which is rarely true of adding breadth.
What does "hardened" actually mean here?
The workflow resumes rather than restarts after failure, repeated steps do not duplicate effects, permissions are scoped per workflow, degradation raises an alert automatically, and no individual person needs to check it each morning. Those five conditions together are what let it run unsupervised.
How do you choose the second workflow?
By adjacency, not by importance. Pick the workflow that reuses the most existing connectors, permission structure, and state handling. It will ship in a fraction of the time and it keeps the implementation converging on one spine instead of fragmenting into parallel stacks.
What should be done with requests arriving after the first success?
Capture them with a stated outcome and owner, and defer them explicitly. Absorbing three requests turns sixty days into four shallow workflows with no hardened foundation, which is the most common way a promising first month fails to compound into a platform.
Why inventory the pilot's shortcuts first?
Because the load-bearing shortcuts are usually undocumented and invisible. Teams harden what they can see and leave the manual correction someone does each morning, the broad credential, or the assumed supervision, which are precisely the things that break when the workflow scales or the builder takes leave.
Is it acceptable to leave some exceptions manual?
Yes, and recording that decision is important. Rare, judgement-heavy cases can cost more to automate than they save. A documented, well-routed manual path is a legitimate design outcome and prevents the same analysis being repeated every planning cycle.
Start with ARIA
Ask ARIA to act on 60-Day AI Implementation Playbook.
Reading it is one thing; running it is another. Tell ARIA the outcome you want from this and it works out which capabilities, systems, and data the work needs — then executes inside the permissions you set.
- ARIA acts only through the systems and permissions you connect.
- Connections use scoped credentials you can change or revoke.
- Actions are recorded, and consequential ones can require approval.
Continue
Turn 60-Day AI Implementation Playbook into a working result.
ARIA can take this from framework to running work — building the surface, connecting the systems that stay authoritative, and operating the loop afterwards.