For three weeks, a sales operations team produces the Monday forecast twice.
One analyst follows the familiar routine: export the pipeline, repair the spreadsheet, chase regional updates, and build the slides. At the same time, the team runs a new AI-assisted workflow that gathers the same inputs and drafts the same forecast. The parallel run was supposed to make the pilot safer.
By week eight, both versions still exist. People compare them, investigate every mismatch, and attend an extra review meeting. The new workflow saves production time, but the team has turned its safety check into a second operating model.
Parallel runs are useful because they let a team learn without betting the business on an untested process. They become costly when nobody defines what the comparison is meant to prove or when it will end. Every AI workflow pilot needs an expiry date for the overlap, plus a decision rule for cut over, extend, or stop.
Adoption is not the same as redesign
Microsoft's 2026 Work Trend Index describes a gap between people's willingness to use AI and their organizations' ability to change the system around it. In a survey of 20,000 AI-using knowledge workers across 10 markets, 45% said it felt safer to focus on current goals than to redesign work, and only 13% said reinvention was rewarded even when results were not met. Microsoft calls this the Transformation Paradox.
A permanent parallel run is that paradox in miniature. The team adopts the new tool while protecting every old expectation. It still produces the legacy report, follows the legacy approval chain, and measures the legacy activity. The AI step arrives as additional work because nothing has been allowed to leave.
This can hide in a positive pilot update. Usage is rising. The draft appears faster. Reviewers catch errors. Yet the real workflow now contains duplicate preparation, reconciliation, and approval. If the team measures only the new tool, it may report a time saving while everyone doing the work feels busier.
The purpose of a parallel run is not to prove that two processes can coexist. It is to collect enough evidence to choose one operating path.
Give the overlap a written contract
Before the first live cycle, write a short parallel-run contract. Keep it in the project brief or meeting record where the people doing the comparison can challenge it.
Decision owner: Name the person who can authorize the cutover, extension, or rollback. A pilot without a decision owner tends to continue by inertia.
Comparison window: Use a fixed number of real cycles, not a vague period such as "until we are confident." Four weekly forecasts or 30 customer follow-ups may reveal more than 30 calendar days with uneven volume.
Protected outcome: State what must not get worse. This might be an approved forecast by 10 a.m., correct handling of conditional commitments, or a response that reaches the customer within one business day.
Evidence to collect: Choose a few signals that expose the whole workflow. Useful candidates include elapsed time, hands-on time, material corrections, missed exceptions, reviewer effort, and downstream rework.
Stop conditions: Identify failures that pause the pilot immediately, such as leaking restricted data, inventing a financial figure, or sending an unapproved customer commitment.
Expiry meeting: Put the decision on the calendar before the pilot begins. The date matters because operational work will always produce one more edge case worth watching.
The contract makes the overlap a controlled experiment rather than a new permanent layer of work.
Compare the workflow, not just the output
Two polished forecasts can look equally good while imposing very different costs.
Map the work from trigger to consequence. For the old forecast, the trigger may be Friday's pipeline close; the consequence is a number leaders use for staffing and cash planning. Between those points sit data cleanup, regional follow-up, judgment calls, slide preparation, review, publication, and later corrections.
Now map the AI-assisted route at the same level. Do not erase human review by calling it "oversight." Record who checks which claims, how long reconciliation takes, what happens when sources conflict, and who repairs the workflow after a data field changes.
NIST's 2026 report on deployed AI monitoring explains why real-world observation matters: controlled evaluations cannot fully reveal variable inputs, unexpected outputs, and downstream consequences. It separates monitoring into areas including functionality, operations, human factors, security, compliance, and large-scale impacts. The report also notes practical barriers such as fragmented logging and the difficulty of scaling human review alongside rapid rollouts. NIST AI 800-4
A small team does not need a six-part monitoring program for every forecast. It does need to notice when the new route transfers effort rather than removing it. Ten minutes saved in drafting can be outweighed by 25 minutes spent reconciling two versions. A faster handoff can still fail if the next team must reconstruct the reasoning behind it.
Make the expiry meeting produce one of three decisions
The expiry meeting is not a general retrospective. It should end with one of three explicit outcomes.
Cut over
Choose the new workflow when it protects the required outcome, keeps risk within the agreed boundary, and improves the end-to-end process. Name the exact cycle when the legacy route stops. Archive the old instructions, remove duplicate calendar reminders, and tell downstream users which output is now authoritative.
Keep a rollback path for a limited period, but distinguish a fallback from a parallel production requirement. A documented way to restore the old process is cheaper than running it every week just in case.
Extend with a question
Extend only when the team has a specific uncertainty that another cycle can resolve. For example: "We have not yet tested a quarter-end volume spike" or "The workflow has seen no multi-currency deals."
Set a new end date and collect evidence for that question. "We want more confidence" is not a test plan. Without a bounded question, extension usually means the organization is avoiding the decision.
Stop or redesign
End the pilot when the new workflow does not improve the full process, creates unacceptable risk, or solves the wrong problem. This is a useful result, not an embarrassment.
Sometimes the comparison reveals that the old report itself should disappear. If nobody changes a decision after reading either version, automating its production is less valuable than retiring it. The best cutover may be from two workflows to none.
Record what actually leaves
At the decision meeting, capture the subtraction as carefully as the launch:
New operating path: AI drafts the regional forecast from approved pipeline sources; the sales operations lead reviews exceptions and publishes the final version.
Retired work: Manual slide assembly, separate regional spreadsheets, and the Tuesday reconciliation call.
Authority: The published forecast is the single operating version.
Fallback: Restore the archived manual checklist if source ingestion fails for two consecutive cycles.
Review date: Reassess after the first quarter-end close.
This record prevents the old process from returning through habit. It also gives managers an honest account of the gain. The benefit is not that an AI draft exists. It is that a reliable result now requires fewer handoffs, fewer duplicate artifacts, or less avoidable effort.
Work redesign becomes real at the moment a team decides what it will stop doing. Until then, the organization may simply be asking people to carry the future and the past at once.
Caspi supports live recap, suggested questions, contextual chat, proactive flags from connected tools, post-call action items, and persistent meeting memory. Those capabilities can help a team preserve pilot exceptions, surface unresolved questions, and record the cutover decision. The expiry date still belongs to the people responsible for the workflow, and so does the choice to retire work that no longer earns its place.