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Many machine-learning projects never reach production, but the often-repeated claim that models are “rarely deployed” should not be treated as a measured industry-wide rate. In a 2022 KDnuggets poll reported by Eric Siegel, most of 114 respondents said that only 0–20% of models created with deployment in mind had actually been deployed. That is a result from a small, self-selected reader poll—not a representative estimate of the current industry.
Siegel’s larger point is about leadership: deployment is not just the final technical step after a model is built. It changes how people make decisions and how work gets done, so decision-makers, users, integration needs, and post-launch operations need attention from the start.
What the poll does—and does not—show
Siegel’s Jan. 17, 2022 KDnuggets article reports on 114 responses to a question asking what share of models created with deployment intent had actually been deployed. The majority selected the 0–20% range. The article itself cautions that respondents may have self-selected and that the sample is too small for meaningful cross-tabulation. It therefore illustrates a problem respondents encountered; it does not establish a universal deployment rate or prove that models are rarely deployed across the industry.
The poll also asked about impediments. Integration challenges accounted for 35% of responses to that question, while the three most-selected impediments together accounted for 91%. Those percentages describe the poll’s answers, not population-wide shares. They suggest that technical integration is important, but they do not show that integration alone explains why projects stall.
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Siegel cites other figures in his article: an 11% Rexer Analytics 2020 survey result for respondents whose models were always deployed, and ESI ThoughtLab 2020 figures of a 1.3% average return on AI investments and 20% of AI projects in widespread deployment. These are secondary attributions in the 2022 article, not independently verified here against the original datasets; their definitions and survey methods should be checked before using them as industry benchmarks.
Why deployment is a leadership problem as well as an engineering problem
Siegel argues that deployment requires organizational change: “Deployment means radical change to existing operations.” A model may alter who makes a decision, what information they see, when they act, and how exceptions are handled. A technically sound model can still fail to enter routine use if the people responsible for the workflow do not trust it, do not see its value, or cannot incorporate its output into their work.
In Siegel’s framing, stakeholder buy-in and leadership can be more decisive than model-building. He writes that “The greatest bottleneck for deployment is usually gaining buy-in from human decision makers, even if the integration challenges are also impressive.” This is his interpretation of the problem, not a causal conclusion established by the poll. It does, however, point to a practical distinction: a project can have a functioning model and still lack an agreed operational decision about what to do with its predictions.
How to lead a project toward deployment
Define the operational decision first
Start with the business or service problem and specify what a successful deployment would change. Identify who will use the model’s output, what decision it informs, and what action follows. Define acceptable errors in the context of that decision rather than treating model accuracy as the entire success criterion.
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Involve decision-makers and future users during scoping
Bring the people accountable for the decision and the people who will use the system into the project before data preparation and modeling. Their involvement may change the target, workflow, acceptable error trade-offs, or even whether a model is the right intervention. Early participation also gives leaders a chance to address concerns before the model is presented as a finished product.
Plan data and integration alongside modeling
Assess whether the required data exists, is usable, and can be delivered at the needed frequency. Map where model inputs come from and where outputs must go, including the systems and processes that will consume them. Treat data access, integration, deployment responsibilities, and workflow changes as core project work—not as a handoff to solve after model development.
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The route to adoption may differ by project. A model that augments an established workflow may fit more readily than an exploratory project introducing a new capability. In the latter case, both the path to user adoption and the integration work may be longer than initially expected.
Use MLOps as part of the plan, not as a substitute for it
MLOps can support the technical work of operationalizing models, but Siegel’s argument is that it is not a standalone remedy for missing business ownership, stakeholder buy-in, or workflow design. Tooling cannot decide which operational problem matters, secure agreement on how a prediction should be used, or make an unsuitable workflow change acceptable.
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What responsible post-launch operation requires
A 2025 review in Applied AI Letters describes deployment as ongoing work, not a one-time release. A deployed system should continue to perform as expected, remain reliable, scalable, efficient, and robust to change, meet end-user expectations, and deliver business impact. Teams also need a way to detect problems and correct them promptly.
- Check data quality: monitor whether incoming data remains complete and appropriate for the model’s use.
- Monitor performance and change: watch for concept drift and other signs that real-world conditions no longer match the model’s assumptions.
- Match monitoring to use: high-frequency predictions may require real-time monitoring so issues are detected quickly enough to matter.
- Test deployment conditions: assess robustness in the operating environment, not only under development or evaluation conditions.
- Assess user and business outcomes: verify that users can work with the system and that deployment produces the intended operational impact.
- Assign response ownership: make clear who investigates alerts, corrects data or system issues, and decides whether use should be limited while a problem is addressed.
What leaders should take from the claim
The 2022 poll is a warning signal, not an industry census. Its more durable lesson is that a model’s path into use must be managed as an organizational and operational project from inception. Leaders should make deployment intent concrete, bring affected people into design, plan integration and data work early, and define how the system will be monitored after launch. Those steps do not guarantee adoption, but they address the barriers that model development alone cannot resolve.
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