Most enterprise AI pilots never reach production. Discussing the reasons why, across the industry, is useful, since it highlights what typically is the most challenging facet of any enterprise AI adoption initiative - that the pilot itself, by its very nature, is intended to be a proof of concept, demonstrating feasibility in a controlled environment. Meanwhile, the challenge is ensuring everything necessary to adopt the technology into the business in a way that realizes demonstrably viable and sustainable outcomes. This article details the approach MonoSpear uses to transition from proof of concept to production-ready enterprise AI.
Choosing the Right Pilot
A pilot that is appropriately limited in scope to demonstrate value but meaningfully constrained to a relevant business process is crucial to moving forward - and more challenging than it may seem, since a pilot that is too large may take too long to deliver demonstrable value and generate too broad of a set of dependencies to troubleshoot at a later stage. Meanwhile, a too-insulated pilot, disconnected from actual business processes and utilizing only carefully prepared training data within an isolated sandboxed environment, while useful as a proof of concept, is significantly less useful in terms of delivering actionable feedback on whether the capability can actually work with production data and operational realities.
The right pilot scope is often one that targets a relevant but narrowly constrained process that will deliver demonstrable value within a reasonable timeframe and utilizes actual production data and systems, albeit within a limited scope, from the outset. Such an approach will maximize the amount of meaningful information delivered by the pilot about its potential viability at a larger scale. The pilot’s limitations will have a smaller impact on its results, which in turn provide more reliable information about the potential outcomes of a wider adoption.
Planning Production Requirements Early
The requirements for production integration, governance, and monitoring are established from the outset of the pilot rather than being planned for a later date after the pilot has been completed and production readiness assessed - this is often the largest determining factor in an otherwise successful pilot being unable to reach production. Perhaps uniquely counterintuitive, this consideration involves designing the pilot around the requirements it will need to meet to be accepted for production, rather than designing production readiness requirements around the pilot.
This is a crucial step because the gap between “this capability works” and “this capability is ready for production” is in many cases defined almost entirely by the set of concerns that must be addressed for any capability to be formally brought into production. These concerns, including appropriate data and system integration, comprehensive governance, and production-grade monitoring, are often deferred to a later date after “proof of concept” has been established, which in practice often leaves such capabilities in limbo, unable to reach production due to lacking these essential supporting elements.
In many ways, this is a fundamentally rational approach - if a capability has already demonstrated itself to be useful, why waste time on concerns that would be relevant to it being supported longterm when there is no such support yet? However, it greatly increases the risks that any retrofitting and changes made to the capability to bring it into production will be done under unnecessary time pressure once it reaches that stage, with stakeholders expecting to see it transition to production after a successful pilot. This is especially true when it comes to addressing the technical requirements for production-readiness.
Designing production requirements early, even if only in an aspirational sense that considers the pilot’s smaller scale and less complex requirements compared to a wider rollout, ensures that the transition from a successful pilot to production readiness involves fewer changes being made at once. This reduces the amount of time needed to complete these changes while reducing the risks associated with making them - it becomes primarily an exercise in expanding and accelerating an existing deployment rather than reworking an isolated prototype.
Demonstrating ROI to Enable Scale
The establishment of clear ROI baselines and performance metrics relevant to business outcomes, rather than technology-specific measures, enable budget allocation and stakeholder support to scale the AI transformation beyond just the pilot use case. While technical performance metrics are useful within the context of the team building the AI solution, they rarely resonate with the wider business beyond the technical leadership. A model accuracy baseline may be encouraging for the data science team, but it will not directly drive business outcomes, whereas a reduction in processing time, error rates, and other relevant metrics will have a direct impact on the business and motivate its stakeholders.
This is another consideration that is often overlooked when it comes to designing a pilot, often due to the pressures of designing it around quickly demonstrating value. As a result, many pilots end up being unable to utilize the technical performance gains they have made to drive actual business outcomes due to a lack of appropriate metrics, baseline measurements, and even stakeholder engagement at the business level. In many ways, it is similar to the issue of pilots being unable to scale due to being sandboxed - if the pilot is designed to only prove that the technology works, rather than to achieve specific business outcomes, it will not have the infrastructure in place to actually utilize these outcomes to scale.
Learning From AI Transformations That Didn’t Scale
An appropriately designed AI transformation methodology will recognize that not every use case is necessarily a good fit for initial adoption and will have mechanisms in place to encourage the pilot to be treated as a learning opportunity rather than a failure. Some use cases, due to the nature of their data, being less standardized across the business than expected, have more complex operational realities, or require additional governance considerations to be taken into account, simply cannot scale at least at the near-term horizon. Even in these cases, however, the pilot serves its purpose by demonstrating these concerns and ensuring that they are taken into account before wider adoption is pursued. In these situations, an organization using a more holistic approach to its AI transformation will have better outcomes simply due to being aware of such pitfalls and having considered them ahead of time rather than being blindsided by them at a later date.
Organizations that have processes in place to reward and encourage such “successful failures” rather than only recognizing “failures that succeeded” tend to see significantly less frustration among their staff and project stakeholders, leading to a higher overall degree of engagement and participation in future AI adoption efforts
The MonoSpear Approach
MonoSpear’s approach to enterprise AI transformation emphasizes the importance of production readiness throughout the pilot phase, rather than only focusing on the proof of concept. This manifests in integration architecture, governance frameworks, and monitoring being planned from the beginning of the pilot, with appropriate adjustments being made based on its scope and potential production scale. At the same time, these considerations are designed around the same principles and often shared infrastructure that will be utilized during the wider rollout rather than being scaled back to only support the needs of the pilot.
Along with designing appropriate production requirements for the pilot, MonoSpear also works with the client stakeholders to establish the required business-outcome metrics and baseline measurements to ensure that a successful pilot will have the evidence necessary to be approved for a wider adoption with minimal additional requirements once it has been completed.
FAQs
1. Why do most enterprise AI pilots fail to reach production?
Often due to the requirements for production integration, governance, and monitoring being inadequately considered at the design stage, creating situations where the production readiness of a capability has to be assessed after a pilot has already proven its conceptual viability.
2. What makes a good pilot use case?
A pilot that is appropriately constrained within a relevant business process and utilizes actual production systems and data rather than being sandboxed and isolated. It should also be narrowly focused enough to demonstrate value within a reasonable timeframe rather than attempting to tackle an insurmountable task.
3. How is AI transformation ROI measured?
By establishing appropriate business outcome metrics rather than focusing on technology-specific performance indicators, which requires having baseline measurements established prior to implementing the pilot solution rather than waiting for it to finish.
4. How does MonoSpear help scale AI from pilot to production?
By building production requirements into the design of any AI solution at the earliest possible stage while working with stakeholders to establish the business outcomes that the pilot would need to demonstrate to justify scaling its adoption.