Why Nearly Half of Enterprise AI Projects Fail and How to Make Them Work

1. Executive Summary 

While enterprises continue to invest heavily in artificial intelligence (AI), many projects fail before they even get off the ground, often because foundational data systems are not ready to support them. 

According to Accenture, 61% of business leaders report that their data assets are not yet ready for generative AI, and 64% continue to struggle with building a robust data foundation. ⁽¹⁾ Furthermore, 70% of organizations find it challenging to scale AI initiatives that rely on proprietary data, indicating that data quality, integration, and accessibility remain significant barriers. 

At the same time, McKinsey reports that more than 80% of companies still see no material impact on earnings from their generative AI initiatives, despite broad experimentation and investment. ⁽²⁾ 

For manufacturers, where efficiency, precision, and uptime are critical, these failures are more than just technical issues; they are significant. They directly threaten competitiveness and growth. The question is no longer if AI will transform manufacturing, but how manufacturers can avoid becoming yet another failed AI case. 

2. Why Many AI Projects Fail Even Before They Start

The root cause of AI failure is not a lack of ambition or funding; it’s poor data readiness.

As Joe Peppard of UCD Smurfit Executive Development explains, “Poor data quality—whether incomplete, biased, or unstructured—affects AI performance just like it would impact any other technology.” In manufacturing, this leads to missed forecasts, inefficient operations, and stalled production lines. 

Modern factories generate massive volumes of data from sensors, machines, ERP systems, MES platforms, IoT devices, and legacy equipment. But this data is often scattered across silos, incompatible formats, or systems that lack real-time connectivity. Without seamless integration, AI systems cannot access the data needed to produce accurate insights. 

As a result, instead of accelerating innovation, teams spend time cleaning, stitching, and debugging fragmented data, which delays or even derails AI initiatives. 

3. The Hidden Costs of Poor Data Readiness: Missed Revenue and Rising Costs

When AI projects stall or fail due to data issues, organizations incur tangible financial losses: 

• A Financial Times analysis warns that despite substantial AI investments; the revenue impact has so far been “negligible” relative to spending. This highlights a widening gap between ambition and ROI. ⁽⁴⁾ 
• A 2024 survey by Digital Route found that 71% of CFOs report struggling to effectively monetize AI initiatives, signaling widespread challenges in turning AI into measurable financial value.⁽⁵⁾
• According to Gartner, at least 30% of generative AI projects will be abandoned after the proof-of-concept stage by the end of 2025, often due to poor data quality and unclear business value. These abandoned initiatives represent lost development time, sunk costs, and missed revenue opportunities.⁽⁶⁾

These breakdowns often result in: 

• Missed revenue opportunities from failed predictive maintenance models or delayed AI-enabled product launches 
• Higher operational costs due to reliance on manual processes when AI systems underdeliver 
• Damaged customer trust from quality lapses or missed delivery deadlines linked to underperforming AI systems 

Each of these failures chips away at the reliability and consistency that customers expect. And in manufacturing, where reputation and precision are everything, losing that trust can be far more damaging than any short-term setback. 

4. What Sets Successful AI Implementations Apart

So, what separates those who struggle from those who scale AI with confidence? It’s not about budget; it’s about the approach. 

Automated Data Preparation: Rather than relying on manual processes, successful organizations invest in automated pipelines that ensure AI models receive clean, structured inputs. When data engineering teams allocate less time to maintenance, they can devote more effort to developing AI solutions that drive business value. 

Start Small and Scale: Rather than attempting enterprise-wide AI transformation, successful manufacturers begin with focused use cases that deliver value. Predictive maintenance for equipment, quality control for high-value products, and demand forecasting for product lines provide clear ROI while building confidence in AI capabilities. 

5. A Roadmap for AI Success in Manufacturing

To avoid joining the growing list of failed AI initiatives, manufacturers need a solid foundation. Here’s how to make AI not just possible, but sustainable and impactful. 

1. Automate the Data Foundation 

Prioritize automation in data management to free up engineering resources for innovation rather than maintenance. For manufacturers, this means deploying solutions that can handle machine, production, and supplier data simultaneously.  

2. Prioritize Data Quality 

For manufacturing, where split-second decisions impact yield and safety, the quality of input data directly impacts AI effectiveness. Start with validation and standardization tools to clean and structure incoming data.  

3. Break Down Departmental Silos 

Data silos are one of the most significant barriers to AI success. 

According to McKinsey, the fragmented data across departments and systems costs businesses an estimated $3.1 trillion annually in lost productivity and revenue.⁽⁷⁾ 

For manufacturers, this means valuable AI initiatives can stall when data from production, supply chain, and customer systems remains disconnected. 

By unifying these data sources into a single integrated view, companies can unlock advanced capabilities such as predictive maintenance, accurate demand forecasting, and real-time inventory optimization, converting hidden inefficiencies into tangible business gains. 

4. Build a Data-First Culture 

Manufacturing leaders must invest in data literacy programs and foster cross-functional collaboration to ensure that AI is understood, trusted, and actionable across all departments.

6. The Path Forward

The manufacturers that will thrive in the AI-driven future aren’t those with the most significant AI budgets or the most ambitious plans. They’re the ones who recognize that AI success starts with data readiness. By automating data preparation, prioritizing data quality, breaking down silos, and building a shared understanding of their data, manufacturers are laying the foundation of scalable and impactful AI adoption. 

Because success with AI requires more than a well-crafted strategy, it depends on execution. Organizations that invest in automation, integration, and data readiness are the ones that turn AI ambitions into measurable business outcomes. 

The choice facing manufacturers today isn’t whether to invest in AI, but whether they’re ready to build the data ecosystem that makes AI scalable, trusted, and enterprise wide. 

7. Resources

1. Accenture, New Accenture Research Finds that Companies with AI-Led Processes Outperform Peers
   https://newsroom.accenture.com/news/2024/new-accenture-research-finds-that-companies-with-ai-led-processes-outperform-peers
2. McKinsey & Company, Seizing the agentic AI advantage
   https://www.accenture.com/us-en/insights/strategic-managed-services/reinvent-operations-with-genai
3. Wall Street Journal, Why Most Companies Shouldn’t Have an AI Strategy, Joe Peppard, UCD Smurfit Executive Development (Quoted in reference to the impact of poor data quality on AI performance) 
   https://www.wsj.com/business/c-suite/ai-strategy-mistakes-5db90efa?gaa_at=eafs&gaa_n=ASWzDAgzhlq1zu1S2SfZDJfEPXlfISIVxr5Cet51P7oZZxx42tssSx8-QsVT&gaa_sig=qWjw8Uwrlr0s8pjs4Fn5LJM2Vs4yYcW7e4IRdbkrbgo6lhtWrTnR1OC1rlm0zw6xa385nHrNzPWlT3t93Ye0Eg%3D%3D&gaa_ts=685e62be&
4. Financial Times, AI returns have not yet justified investment mania
   https://www.ft.com/content/eb1f7a80-6a4f-436d-8578-61020fa4b216
5. Digital Route, World at brink of “second digital gold rush“
   https://www.digitalroute.com/press-releases/world-at-brink-of-second-digital-gold-rush/
6. Gartner, Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025
   https://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
7. McKinsey & Company, as cited in Forbes, “Why Removing Data Silos Is Key To Unlocking AI Value,” Feb 3, 2025
   https://www.forbes.com/sites/sap/2025/02/03/why-removing-data-silos-is-key-to-unlocking-ai-value/