And What You Can Do About It
The Alarming Statistics
AI Projects Fail
According to Gartner, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams managing them (2020)
Never Reach Production
VentureBeat reports that 87% of AI projects never make it to production
Failure Rate
CIO.com highlights that 80% of AI projects fail
If we had a dollar for every AI project that ended up in a forgotten Jupyter notebook, we'd be able to fund the next OpenAI compute cluster. The AI world is brimming with ambitious demos and snazzy dashboards that never see the light of production.
Issue: No Clear Business Case
AI can solve a lot of problems - but not every problem is worth solving with AI. Projects often begin with excitement over a cool model rather than a clearly defined business need. Without a strong use case, it's hard to justify continued investment or measure ROI. Be sure you have the following before diving head first into POC.
Business Outcome
Clearly defined need and ROI
Strategic Alignment
Connected to organizational goals
Technical Feasibility
Practical implementation path and talent
Issue: Data Quality and Accessibility Issues
Messy Data
Incomplete, inconsistent, or poorly structured data that requires extensive cleaning
Siloed Information
Data trapped in disconnected systems making it difficult to access or integrate
Training vs. Production Gap
Significant differences between data used for training and data available in production environments
Bias Introduction
Data that inadvertently introduces or amplifies biases in model outputs
AI is only as good as the data it's trained on. Many teams encounter messy, incomplete, or siloed data that slows down progress or introduces bias. Worse, the data needed for training may differ dramatically from the data available in production. Do you really need AI or do you need to invest in Data Engineering first?
Issue: Infrastructure and Organizational Challenges
Lack of Infrastructure and Tooling
Many organizations aren't equipped with the cloud infrastructure or MLOps capabilities needed to deploy and monitor models at scale. This makes it difficult to go from notebook to production pipeline.
Organizational Silos
AI success requires cross-functional collaboration between data scientists, engineers, and business stakeholders. When these groups are disconnected, models often get stuck in the "proof of concept" phase.
Gone should be the days of data science working in a silo on creating lagging, batch, or asynchronous insights—where they hand off a model like a message in a bottle and hope engineering can somehow deliver it to shore.
Talent Gaps
There's a shortage of skilled ML engineers and platform teams that can operationalize AI. Even when organizations have strong data science talent, they often lack the support needed to deploy and maintain models efficiently.
What You Can Do About It
Start With Impact
Define the business outcome you want to drive. Then work backwards to design a solution that aligns with your data and goals.
Invest in MLOps Early
Treat model deployment like software deployment. Build (or buy) the tooling to support CI/CD, monitoring, and retraining.
Build Bridges, Not Silos
Encourage collaboration between teams. Pair data scientists with engineers from day one to ensure production-readiness.
Simplify the Path to Production
Leverage platforms and tools that make it easy to deploy and manage models in real-world environments.
The Takeaway
Strategic Vision
Align AI with business goals
Operational Excellence
Build robust deployment pipelines
Cross-Team Collaboration
Break down organizational silos
Continuous Improvement
Monitor and refine models in production
Building an AI model is no longer the hard part. Deploying it, maintaining it, and proving its value in production—that's where the real work begins. Organizations that succeed at operationalizing AI treat it not just as a technical experiment, but as a strategic capability.
As AI adoption matures, the companies that win won't be the ones with the most models. They'll be the ones that can reliably get models into production and keep them there.
Avoid Becoming Part of the 85%
Gartner Research Findings
Gartner. "Top 10 Data and Analytics Technology Trends for 2020." Reports that 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams managing them.
McKinsey Insights
McKinsey & Company. "The State of AI in 2021." Notes that few companies succeed in scaling AI across the enterprise, highlighting the gap between experimentation and production.
CIO.com Analysis
CIO.com. "Why 80% of AI Projects Fail." Examines the organizational and technical barriers preventing AI initiatives from reaching production environments.
VentureBeat Report
VentureBeat. "Report: 87% of AI projects never make it to production." Provides statistics on the high failure rate of AI initiatives across industries.
So before you launch your next AI initiative, ask yourself: is this going to be a production system—or just another very expensive science fair project?
Want to avoid becoming part of the 85%?
Reach out to learn how we're helping teams deploy and maintain AI models with confidence - and yes, we promise, no more graveyards of abandoned models. You have nothing to lose — well, you've already lost plenty, actually.
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