AI Feasibility Assessments: The Missing Step Between Ambition and ROI
- Michael Pascu, Michelle Niazi
- Jan 21
- 5 min read
by Michael Pascu, Senior Manager, Artificial Intelligence and Michelle Niazi, INQ Law Intern
This blog post explores how to conduct an AI feasibility assessment and what to consider at each stage. Download INQ’s AI feasibility assessment checklist here.
What is an AI Feasibility Assessment and Why Does It Matter?
Organizations are racing to adopt AI, but urgency often produces pilots that never scale, wasting resources and eroding confidence. By some estimates, more than 80% of AI projects fail; twice the failure rate of traditional IT projects.¹ The costs are staggering. Custom generative AI models require $5-20 million just to build or fine-tune, yet nearly a third are projected to be abandoned before the end of the year.² These failures stem from predictable patterns:
unclear business objectives with no measurable link to value;
poor data readiness, as organizations underestimate the effort required for quality, accessible, well-governed data;
infrastructure gaps including insufficient compute, integration challenges, and missing MLOps capabilities; and,
regulatory blind spots where compliance constraints emerge too late.
The consequences extend beyond cost; repeated failures undermine trust in AI, discourage leadership investment, and divert talent from broader transformation efforts.
Before investing in an AI initiative, organizations face a key question: “Can we actually do this here?” A feasibility assessment provides a structured, evidence-based way to answer this question by evaluating technical feasibility, business viability, and governance readiness before resources are committed.
Feasibility assessments evaluate an AI initiative across three dimensions:
Business: Does it solve a real problem with clear ownership, change readiness, and a realistic path to ROI?
Technical: Can we build, integrate, and run it at production quality, given the data, infrastructure, and operational capacity required?
Governance: What risks and obligations apply, and do we have the controls needed to deploy safely and compliantly?
When executed rigorously, these assessments surface three critical outcomes: whether the solution will be adopted across the organization, deliver measurable ROI, and improve productivity in real workflows.
Ultimately, feasibility assessments help organizations “fail fast on paper” rather than in production; in doing so, they reduce risk, save time, and increase the likelihood of meaningful AI impact.
When Should You Conduct a Feasibility Assessment?
Feasibility assessments prevent costly failures and wasted resources in four critical scenarios:
Before purchasing AI solutions. They provide structured analysis of organizational capabilities, alignment, and risk, preventing the common mistake of buying based on vendor demos alone. AI pilot projects fail at rates between 70-90%, often due to unclear objectives, poor data quality, and insufficient talent.³ Many organizations struggle with unrealistic expectations and lack of change management, leading to initiatives that fail to deliver intended benefits.⁴
When scaling pilots to production. Controlled pilot environments differ materially from enterprise deployment, exposing hidden gaps in workflow integration, data quality, and governance. Even successful pilots face significant challenges during production rollouts, with 91% of deployed models experiencing performance degradation due to data drift and changing business contexts.⁵
When evaluating build-versus-buy-versus-partner decisions. Assessments ensure choices reflect organizational strengths and resource constraints, critical given that many firms lack in-house AI capabilities. Organizations face considerable risks when relying on external providers, as vendors may alter pricing structures or discontinue services entirely.⁶
When regulatory or compliance stakes are high. Early integration of compliance and risk considerations prevents expensive retrofitting after deployment and reduces legal exposure. Understanding AI's specific limitations, such as token length constraints and potential for hallucinations, builds rather than reduces trust by helping users develop realistic expectations.⁷
Feasibility assessments at these decision points shift AI initiatives from speculation to strategic execution.
How Should You Conduct a Feasibility Assessment?
An effective AI feasibility assessment evaluates five critical dimensions. Each addresses a common failure point that derails AI initiatives, even those with strong initial promise.
Business Alignment: ensures the initiative solves a clearly defined problem tied to measurable outcomes. Without this foundation, AI projects lack the sponsorship and clarity needed to move beyond pilots.
Data Readiness: determines whether your data is accessible, reliable, and fit for purpose. Overestimating data quality is a leading cause of AI failure, as data quality issues degrade model performance by 20-50% across algorithms, even for technically sophisticated models.⁸
Technical Feasibility: validates whether your organization can realistically build, deploy, and maintain the system. Infrastructure gaps and capability constraints surface here, before they become costly issues post deployment.
Risk and Responsibility: identifies privacy, security, bias, and compliance risks early. High-impact systems require governance assessment upfront to avoid costly rework and regulatory issues downstream.
Economic Feasibility: ensures the business case withstands realistic projections, where data preparation accounts for 15-25% of budgets and model complexity takes 30-40%, with total costs often doubling with scaling and compliance requirements, avoiding the overruns that sink most AI initiatives
Use these five dimensions to decide: Proceed, Pilot, Defer, or Reject. Do not treat the assessment as a formality or guaranteed path to approval. Use it to surface constraints early and choose the lowest-risk next step. When potential is high, but unknowns remain, run a targeted proof of concept to validate feasibility with real data and working code before committing to full deployment.
For detailed evaluation criteria and questions for each dimension, download our AI Feasibility Assessment Checklist here.
Conclusion
In conclusion, organizations that embed feasibility discipline into their AI governance consistently achieve higher success rates and faster time-to-value, because decisions are grounded in evidence rather than urgency. By treating feasibility as a core governance practice, leaders create a shared standard for evaluating opportunity, risk, and return. In doing so, reducing costly missteps and building trust in AI as a scalable capability rather than a series of stalled pilots. In this context, the real competitive advantage is not being first to deploy AI but being right: investing in systems that can scale responsibly, comply with constraints, and deliver measurable value over time.
Sources
¹Ryseff, James, et al. The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI. RAND Corporation, 2024, www.rand.org/pubs/research_reports/RRA2680-1.html.
²Sallam, Rita. "Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept by End of 2025." Gartner, 29 July 2024, www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025.
³Makinani, S., and M. B. Nagaraja. "Scaling AI from Project Pilots to Program-Wide Transformations."International Journal of Engineering Research and Emerging Trends, vol. 6, no.3, 13 July 2025, pp. 41–46, https://ijeret.org/index.php/ijeret/article/view/242.
⁴Übellacker, Thomas. "Making Sense of AI Limitations: How Individual Perceptions Shape Organizational Readiness for AI Adoption." arXiv, 21 Feb. 2025, arXiv:2502.15870v1.
⁵Makinani, Supra note 3.
⁶Michalak, Russell, and Devon Ellixson. "Buy versus Build: Navigating Artificial Intelligence (AI) Tool Adoption in Academic Libraries." Information Services and Use, vol. 44, no. 4, 2024, pp. 316–26, doi:10.1177/18758789241296755.
⁷Übellacker, Supra note 4.
⁸Mohammed, Safi, et al. "The Effects of Data Quality on Machine Learning Performance." International Journal of Information Management Data Insights, vol. 5, no. 1, 2025, pp. 1–15.
Gartner, Inc. A Strategic Guide to Maturing AI. Gartner, 2025.
