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An AI pilot can prove that a model handles a carefully chosen task; it does not prove that the task will produce measurable value across real workflows, users, data, exceptions, and operating conditions. Scaling requires connecting that promising demonstration to business ownership, integration, evaluation, security, ongoing operations, and people’s day-to-day work.
What the demo proved—and what it did not
A demo usually answers a narrow question: can this system produce a useful result in a selected scenario? Production asks a broader one: can the organization deliver that result reliably, safely, and economically as part of real work?
The difference is not necessarily a failure of the model. A demo may use clean inputs, a limited user group, human oversight, and a workflow that avoids unusual cases. In live use, data may be incomplete, users may ask different questions, connected systems may be unavailable, and exceptions may require judgment. Those conditions can expose gaps that a successful demonstration never tested.
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A separate McKinsey workplace survey found that 1 percent of C-suite respondents described their generative AI rollouts as mature. In that report, “mature” meant AI fundamentally changing how work is done and driving substantial business outcomes. The survey was conducted in October and November 2024, covered 238 C-level executives and 3,613 employees, and primarily concerned US workplaces. It is not directly comparable with the Global Survey’s scaling figure because the surveys and definitions differ. McKinsey, “AI in the workplace: A report for 2025”
Diagnose the gap before choosing a fix
Use these questions to locate the distance between a successful demo and a viable operating capability. More than one answer may point to a blocker: scaling problems often involve interacting business, technical, and organizational conditions.
Is there a business outcome and an accountable owner?
Name the outcome the system is meant to improve, such as reducing a defined processing delay or improving the quality of a particular decision. Identify who owns that result and how it will be measured against the existing process. A compelling output is not, by itself, evidence of business value. If there is no accountable owner or meaningful outcome measure, the pilot has not yet established why the organization should scale it.
Does the system fit the actual workflow?
Map what happens before and after the AI step: who supplies information, who reviews the result, what decisions remain with people, and what happens when the system is uncertain or wrong. Check handoffs, exceptions, and the time users spend correcting or verifying outputs. A tool can perform well in isolation while adding friction to the end-to-end process.
Can it reach the right data and systems?
Determine whether the application can access the information and internal systems needed for the task, under appropriate permissions. Check data quality, freshness, and relevance, as well as how the AI component interacts with other software. McKinsey’s 2024 scale-up guidance emphasizes making components work together securely and targeting useful data—not treating model selection as the whole implementation. McKinsey, “Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale”
Has the evaluation gone beyond the happy path?
Ask which users, input variations, edge cases, and failure conditions were actually tested. A demonstration built around a few successful examples says little about performance across the range of situations the workflow will encounter. Evaluation should match the consequences of error and the conditions of intended use.
NIST’s ARIA 0.1 pilot evaluation report offers one example of layered assessment: five participating organizations submitted seven AI applications, which were examined through model testing, red teaming, and field testing. The report also discusses dialogue annotation, tester questionnaires, and measurement trees. This is an example of evaluation methods, not a certification or a required process for every deployment. NIST, “Assessing Risks and Impacts of AI (ARIA): Pilot Evaluation Report”
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Clarify who responds to incidents, reviews performance, maintains logging, and manages changes to the model or connected components. Establish how the service should behave when a dependency fails or the system’s output is not usable. A pilot supported by close attention from its creators may not reveal the operational work required when that attention is no longer constant.
Are risk controls and user responsibilities clear?
Check whether security, compliance, and human oversight are appropriate to the application. Users need to know when they can rely on an output, when they must verify it, and how to report a problem. Risk controls should be considered as part of the workflow and ongoing operation, not just as a pre-launch approval.
Are the people and processes ready to change?
Identify the skills users need, what training they will receive, and whether roles or procedures must change. McKinsey’s scale-up guidance treats broad team capabilities and change management as part of scaling. If people do not understand how the system fits their responsibilities, even technically sound tools may go unused or be used in unintended ways.
Turn the diagnosis into a scale decision
Do not treat “scale” as a single leap from demo to organization-wide rollout. Use the diagnosis to decide whether to improve the pilot, run a more representative field evaluation, or proceed toward deployment with explicit operating responsibilities.
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- Define the target: document the business outcome, its owner, and the measure that will show whether the process improved.
- Describe the real workflow: include users, handoffs, human decisions, exceptions, and recovery paths—not only the AI interaction.
- Verify the dependencies: confirm required data access, permissions, integrations, security controls, and expected operating costs.
- Test representative conditions: evaluate normal use and relevant edge cases; use red-team or field testing where appropriate to the risks and context.
- Assign ongoing ownership: name the people responsible for reliability, incidents, logging, review, and changes to connected components.
- Prepare users and processes: train affected teams, set clear expectations for verification, and adjust procedures where the new capability changes work.
Then make a decision against the evidence: proceed only when the outcome is worth pursuing and the workflow, dependencies, evaluation, and operating plan are credible for the intended use. If a key condition is missing, treat the next step as resolving that specific gap rather than assuming a better demo will solve it.
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Design for the work after launch
Deployment is not the end of evaluation. NIST’s March 2026 summary of deployed-AI monitoring work groups monitoring into six categories: functionality, operations, human factors, security, compliance, and large-scale impacts. It also identifies challenges such as detecting performance degradation and drift, fragmented logging across distributed infrastructure, and policy complexity. These categories provide a useful way to organize questions, but they do not mean monitoring is a solved or fully standardized practice. NIST, “New Report: Challenges to the Monitoring of Deployed AI Systems”
Plan what will be observed after launch, who will review it, and what action follows if performance or conditions change. NIST’s announcement notes: “Given that AI systems have novel properties that introduce variability and manifest in unpredictable ways, post-deployment monitoring – from incident monitoring to field studies – is a crucial practice for confident, wide-spread AI adoption.” The practical implication is to keep checking the live system in the context where people use it, rather than relying on a one-time demo or pre-launch test.
Reuse what works without assuming scale is automatic
Where teams have validated useful components, documentation, and evaluation methods, reuse can reduce duplicated effort. McKinsey’s 2024 guidance estimates that reusable code can increase generative AI use-case development speed by 30 to 50 percent. That is an attributed estimate, not a guaranteed saving for every organization; reuse still depends on whether an asset fits the new workflow and its requirements.
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