Choose a Credible Baseline for an AWS AI Pilot

Compare an AI candidate with the real incumbent workflow, preserve task difficulty and expose case-mix false wins before making an investment decision.

Use the workflow the organization can realistically continue operating as the primary baseline for an AI pilot. Keep the task contract, evidence access, workload mix and observation cutoff comparable. Add a feasible non-AI improvement when it could resolve the same problem. Comparing an easy AI demonstration with a difficult historical queue can manufacture a win even when the candidate takes longer on the work you need it to handle.

This article helps an evaluation lead and workflow owner agree the comparator before testing a Bedrock-backed application. The output is a baseline selection record with a declared population and a claim it can support. The examples below use stipulated task counts and active minutes. They are arithmetic illustrations, not AWS measurements, customer results or evidence of savings. No model requests or evaluation jobs were executed.

Start with the incumbent people would actually use

Describe the current process from intake through the accepted outcome. Include its search interface, templates, validation, reviewer visits and unresolved work. If operators already use a rules engine, comparing the AI candidate with an unaided person invents a different incumbent. If people use an older AI system, that operating system is a comparator, including its review and recovery requirements. Preserve differences that the proposed replacement would have to absorb.

Give every baseline a purpose. The current operating process answers whether replacing it is attractive. A cleaned-up template or deterministic lookup answers whether generation is necessary. A previous model configuration answers whether a model change improves the same product. A no-capability baseline can be legitimate for a genuinely new task, but it cannot support a claim that the organization saves existing labor. Identify what people do instead: defer the request, buy a service, accept a delay or leave the need unmet.

Do not force a single comparator to answer every question. A sponsor may need the best feasible operating alternative for an investment decision while an engineer needs the current release for regression checks. Retain both names and scopes. Avoid comparing the candidate with an imagined perfectly staffed process if that process cannot be operated, or with an intentionally broken one if a modest supported improvement would repair it.

Freeze the unit before timing either path

A shared unit might be one internal policy question resolved with current permitted evidence and a source-linked answer by the deadline. Producing a fluent draft is a different unit. If the incumbent resolves the question but the candidate only drafts text, include the remaining candidate review and correction work before comparing completion. Record a correct deferral separately when the task permits it; do not count it as an answered question by changing the definition after a run.

Capture the starting information and evidence rights. Giving the AI path a curated answer packet while the incumbent must find documents changes the intervention. That can still be a valid comparison of two complete workflow designs, but it does not isolate the benefit of a model. State that narrower attribution limit. Conversely, removing familiar search tools from the incumbent can create artificial reviewer effort that would disappear outside the pilot.

Amazon Bedrock's evaluation overview describes programmatic, human-worker and judge-model evaluation approaches. Those approaches help assess outputs; selecting one does not define your business completion rule. A model score cannot observe an operator's later correction time unless the study explicitly captures it. Keep answer evaluation and workflow observation linked through permitted task references without treating them as the same evidence.

Preserve difficulty before comparing averages

Choose slices that change handling or consequence: routine versus conflicting evidence, short versus long documents, supported versus unsupported languages, familiar versus new subjects, and complete versus missing sources. Use the task inventory to decide which slices matter. Do not label a case difficult solely because the candidate failed it. Set the classification before looking at the candidate result and retain disagreements for adjudication.

A representative operating total uses a declared workload mix. A deliberately balanced diagnostic set can expose failures in uncommon slices, but its overall average does not automatically represent production. Report slice results first. If a population-weighted estimate is useful, state the weights, their source and the date or planning assumption. An empty slice has no estimate. Do not fill it with the average of easier slices or redistribute its weight invisibly.

NIST's randomized block design guidance explains controlling important nuisance factors within comparable blocks. For an AI workflow, reviewer experience and task difficulty can affect measurements. Blocking is a design choice, not a guarantee that all confounding disappears. Record assignment and analysis decisions; a post hoc table alone does not turn an observational pilot into a randomized study.

Work the false win caused by different case mixes

Fictional task family T31 contains routine questions with complete evidence and exception questions with conflicting evidence. Both methods are stipulated to produce the same acceptable outcome for every task after all included handling. This assumption isolates effort arithmetic; real correctness is unknown. The incumbent needs two active minutes for a routine question and twelve for an exception. Candidate A needs one and fourteen respectively, including its checking and correction visits.

The unmatched totals suggest an 835-minute difference, or 83.5 percent of the incumbent total. That percentage describes the supplied totals, not the effect of adopting A. Under the same exception-heavy mix, A requires 140 more minutes, or 14 percent more than the incumbent. The apparent win disappears because the demonstration avoids most of the work where A adds effort. No provider price or measured speed is involved.

For a routine-heavy planned population of 95 routine and five exception tasks, the incumbent requires 250 minutes and A requires 165. That bounded difference is 85 minutes, or 34 percent of 250. It could motivate studying that population if the mix is credible and safety conditions pass. It cannot justify applying A to the exception-heavy queue. A task-routing policy that changes the exposed population needs its own operating evidence and cannot quietly retain the original broad claim.

An opposing result can support further investigation

Keep the exception-heavy 20/80 mix and the same outcome assumption. Candidate B needs one routine minute and eight exception minutes. Its total is 20 × 1 + 80 × 8 = 660 minutes, a 340-minute difference from the incumbent's 1,000, or 34 percent. This is a matched arithmetic improvement across both slices, unlike A's unmatched demonstration. It supports a fictional recommendation to investigate B's observed quality and effort under that scope, not a deployment verdict.

Suppose the comparison later finds that B's exception answers omit a prohibited policy restriction. The effort finding remains in the record, but expansion is held under the task's safety gate. Removing the affected exceptions would define a new population requiring a new comparison. If B merely pushes verification to a downstream team, add that work to both relevant boundaries and recalculate. A shorter visit in one interface does not establish lower end-to-end effort.

A matched design also has limits. Stipulated slice means do not capture variation, waiting, learning or clustered users. A few exceptionally long tasks can dominate staffing. Repeated handling of the same case can teach reviewers the answer, making the second method appear easier. Counterbalance order or use separately assigned comparable cases where practical, and explain the trade-off between pairing and carryover. Qualified study design and actual evidence are needed before causal claims.

Make collection comparable without hiding disruption

Observe the incumbent contemporaneously when policy, source material or staffing has changed enough to make historical records unrepresentative. Retained historical evidence can still diagnose a problem. Record what makes it transferable, what differs and which claim stays held. Avoid assuming that last quarter's queue is comparable because its tickets have the same category label. A changed reviewer team or source-search system can alter effort without any candidate change.

Use the same maturity cutoff for outcomes. Keep timed-out, abandoned, deferred and unfinished tasks in the eligible population with their current dispositions. A fast candidate draft compared with an incumbent's final resolution introduces unequal observation. If outcomes mature later, preserve pending status and update the cohort under the original rule. Distinguish active handling from elapsed waiting; a lower active total can coexist with missed service deadlines.

Evidence permission precedes replay. Bedrock evaluation data management describes temporary evaluation copies in an AWS-owned S3 bucket and encryption choices. Its documented deletion of that temporary copy does not establish deletion of your original dataset or exported report. Obtain appropriate processing, evaluator-access and retention authority. Controlled references often suffice in a comparison record; do not paste customer prompts or restricted documents into a public worksheet.

Use a baseline selection record before collecting scores

The filled record below preserves the decision reached from T31. Its aliases and populations are fictional. The blank prompts alongside it tell an owner what evidence must replace them. No checkbox authenticates a workload mix, grants data access or certifies an outcome.

Before a run, the workflow owner should challenge whether the comparator is a real alternative. The evaluation lead should challenge slices, assignment and outcome maturity. The operations owner should identify work that crosses team boundaries. Retain the agreed revision before seeing favorable scores. If the baseline changes, preserve the old comparison and open a new one rather than editing its denominator.

Use the production evaluation workbook to execute the resulting study, and the completed-task cost article to reconcile operating expenses separately. The next action is to select one actual incumbent task, reconstruct every included handling step and agree its target slices with the workflow owner. If the team cannot defend that comparator, resolve it before commissioning a model comparison.