Three Foundational Questions to Ask Before Launching an AI Solution
Three often-overlooked factors that can dramatically affect an AI solution’s ultimate success
By Emily Reitman
AI programs rarely fail because the technology does not work. They fail because key requirements are assumed rather than designed: validation effort, data reliability, and adoption.
Before selecting a vendor, building internally, or launching a pilot, leadership teams should pressure test three fundamentals. This creates realistic plans for resourcing, cost, and time to value.
1. How Much Ongoing Effort Will Model Training and Validation Require?
Key Takeaways
Validation is an ongoing capability that requires clear ownership, realistic timelines, and sustained attention.
Data readiness is often the limiting factor in AI performance and should be tested early and deliberately.
Adoption determines value, making change management as critical as the technology itself.
AI performance does not remain static. Some models require limited retraining, while others continuously evolve based on new data and usage patterns. These differences materially affect timelines, staffing needs, and operating costs.
As a best practice, organizations should:
Assess model maturity and understand whether the solution is static or continuously learning
Explicitly define ownership for validation across business, data, and technology teams
Build sufficient time into delivery plans for validation, retraining, and refinement, including a buffer beyond vendor estimates
Implement monitoring mechanisms to support systematic validation and automated flagging of outliers where appropriate
Establish a clear cadence for ongoing validation after go‑live to account for changes in data, processes, or user behavior
Validation should be treated as an ongoing operational responsibility rather than a one‑time project activity.
2. Can the Existing Data Be Trusted?
Data quality remains one of the most common and underestimated constraints on AI success. Even well‑designed models produce unreliable outputs when foundational data is inconsistent, incomplete, or poorly governed.
As a best practice, organizations should:
Evaluate how sensitive the selected solution is to data gaps and anomalies
Make an explicit decision on whether to address data quality issues before launch or through phased improvement
Use a pilot or proof of concept to expose data challenges early with a limited user group
Define clear data quality thresholds to maintain user trust and confidence in outputs
Upfront investment in data readiness typically leads to stronger performance, faster adoption, and more sustainable returns.
3. What Is the Plan for Adoption and Behavior Change?
AI implementations introduce new ways of working. They change how decisions are made, how work flows, and how people allocate their time. Without a deliberate adoption strategy, even technically sound solutions struggle to deliver value.
As a best practice, organizations should:
Define clear adoption outcomes, such as phased rollouts or targeted super‑user models
Clarify expectations for how freed‑up capacity will be used, whether through higher throughput or higher‑value work
Design incentives, feedback loops, and support mechanisms for early adopters, particularly when launching a minimum viable product
Enable managers and early users to reinforce new behaviors and normalize the new operating model
Effective change management turns AI adoption into a measurable business outcome rather than an aspirational goal.
By pausing to answer these questions up front, organizations will help them launch smarter, move faster, and deliver real value from AI initiatives from day one.
About Forum Solutions
Forum Solutions works with executives to translate that choice into practice. Through strategy, operating model design, governance, and change enablement, we help organizations move beyond experimentation to sustained impact while protecting the human judgment AI ultimately depends on.
About the Author
Emily Reitman is a Senior Consultant at Forum Solutions, where she leads critical business initiatives in high-visibility, fast-paced environments, while adeptly centering teams and leaders to deliver business value.