Why do over 90% of Generative AI PoC projects fail to transition into actual projects?

Introduction

Artificial intelligence, particularly generative AI, is at the forefront of technological innovation. While many companies are attempting to adopt this revolutionary technology, the journey appears to be more challenging than anticipated. According to a recent Forbes report, approximately 90% of Generative AI Proof of Concept (PoC) projects fail to reach the actual production stage. This suggests a significant gap between the potential of AI technology and its practical implementation.

This blog will delve into the causes of this high failure rate and examine the specific challenges faced in the actual AI adoption process. We will also propose practical solutions to overcome these obstacles, helping companies realize the true value of AI technology.

5 Major Causes Identified by Forbes

According to Forbes' analysis, the main reasons why Generative AI PoC projects fail to transition into actual adoption are as follows:

1. Immaturity of Gen AI technology: While generative AI technology is rapidly evolving, it still does not guarantee perfect reliability and stability in many areas.

2. Complexity and cost of change management: AI adoption involves changes across the entire organization, beyond simple technology implementation. The process of managing these changes is more complex and costly than expected.

3. Unexpected risks, security, and intellectual property issues: AI systems can raise new legal and ethical issues related to data security, privacy, and intellectual property.

4. Difficulty in demonstrating ROI (Return on Investment): It is not easy to quantitatively measure and prove the performance of AI projects, leading to difficulties in securing continuous investment.

5. High cost of Gen AI technology adoption: Initial costs associated with AI adoption, such as high-performance hardware, securing specialized personnel, and data management, are quite high.

In addition to these general issues, we have identified additional obstacles that AI solution companies and their clients experience during the PoC stage. The next section will examine these additional challenges in detail.

3 Additional Challenges

1. Lack of Active Customer Participation

Active participation from the entire organization is essential when developing AI systems, especially AI chatbots or knowledge systems based on internal knowledge. However, many companies struggle with this, and it becomes a major factor hindering project success.

Case Study: Manufacturing Company A's Internal Knowledge-Based AI Chatbot Project

Company A decided to develop an internal knowledge-based AI chatbot to improve employee efficiency. However, they faced the following problems during the project:

• Production Department: Reluctant to provide information due to concerns about their know-how being disclosed.

• Research and Development Team: Passive in organizing necessary technical documents due to busy schedules.

• HR Team: Refused to share employee-related information due to privacy concerns.

As a result, the AI chatbot had limited practicality due to incomplete information and failed to gain user trust, preventing its actual adoption.

Solutions:

• Implement a company-wide program to raise awareness of the need for AI adoption.

• Introduce a departmental participation incentive system.

• Establish clear guidelines for data sharing.

• Form cross-functional teams to promote inter-departmental collaboration.

• Continuously share success stories and benefits of AI projects.

• Encourage participation by establishing test cases.

2. Lack of Trust in Gen AI

While the capabilities of generative AI are improving day by day, it is still difficult to guarantee 100% accuracy. Especially in fields requiring critical decision-making, such as finance, healthcare, and law, the reliability of AI is a key issue.

Case Study: Financial Company B's Customer Service AI Chatbot PoC

Company B attempted to introduce an AI chatbot to improve customer service but encountered the following problems during initial testing:

• The AI occasionally provided inaccurate information about complex financial products.

• It presented biased views on financial product recommendations, posing a risk of leading some customers to make wrong investment decisions.

• It provided inconsistent answers to questions related to customer privacy protection.

Due to concerns about legal risks and reputational damage, Company B postponed the actual adoption of the AI chatbot indefinitely.

Solutions:

• Apply enhanced RAG (Retrieval-Augmented Generation) technology.

• Establish a system for continuous learning and updates of AI models.

• Introduce a verification process by human experts.

• Adopt AI model evaluation and supervision solutions.

• Build a system to automatically evaluate the accuracy, consistency, and appropriateness of answers.

• Automate the process of human expert review for critical decisions.

• Apply explainable AI (XAI) technology to ensure transparency in the AI decision-making process.

3. Burden of Continuous Maintenance and Updates

AI systems require continuous management and updates, not just one-time development. This demands additional costs and resource allocation that many companies do not anticipate.

Case Study: Global Logistics Company D's AI-Based Prediction System

Company D developed an AI-based prediction system for logistics optimization as a PoC, but faced the following problems during actual adoption:

1. Maintaining Model Performance The AI model, which initially showed excellent performance, started to experience a relative decline in accuracy over time. This was due to changes in logistics patterns, the introduction of new regulations, and global events. Maintaining the model's accuracy required large-scale retraining at least quarterly, demanding significant computing resources and specialized personnel.

2. Data Pipeline Management Accurate predictions from the AI system required a vast amount of data updated in real-time. To achieve this, Company D had to build a complex data pipeline connecting logistics centers, transportation vehicles, and customs data worldwide. Maintaining this pipeline alone required a dedicated team.

3. Regulatory Compliance and Explainability

   To comply with varying AI regulations across different countries, Company D had to continuously develop and update features that could explain the AI's decision-making process. This was a complex task that went beyond technical issues, requiring collaboration with legal and regulatory experts.

4. User Training and Change Management

   Whenever the AI system's functions were updated, retraining was necessary for employees at branches worldwide. This was a significant burden in terms of time and cost and sometimes even involved changes in work processes.

5. Technical Debt Management Due to the rapid advancement of AI technology, Company D had to review its system architecture every six months. Introducing new AI models or technologies often led to compatibility issues with existing systems, resulting in unexpected additional development costs.

As a result, Company D had to invest more than three times its initial budget in maintenance and updates, leading management to question the ROI of the AI system.

Solutions:

• Design a flexible system architecture.

• Build an automated system for AI model performance monitoring and updates.

• Utilize cloud-based scalable infrastructure.

• Adopt AI model evaluation and supervision solutions.

• Continuously monitor and evaluate model performance.

• Build a system for automatic notifications and improvement suggestions in case of performance degradation.

• Establish data quality management and automated data pipelines.

Importance of AI Model Evaluation and Supervision Solutions

To effectively address the challenges mentioned above, it is crucial to continuously evaluate and monitor AI systems through evaluation and supervision layers rather than directly using AI applications. This approach offers the following benefits:

1. Improved Reliability: By continuously monitoring and evaluating the output of AI models, inaccurate or biased results can be quickly detected and corrected.

2. Regulatory Compliance: Provides the ability to track and explain AI decision-making processes, meeting regulatory requirements.

3. Performance Optimization: Real-time monitoring and analysis of model performance enable early detection of performance degradation and timely updates.

4. Cost Efficiency: Prevents unnecessary retraining or excessive resource usage and updates the model only when necessary, reducing costs.

5. Building User Trust: Increases trust among end-users by informing them that the AI system's decisions are continuously monitored and validated.

6. Implementing Ethical AI: Continuously evaluates whether AI decisions comply with ethical standards and makes adjustments when necessary.

Specific implementation methods for these evaluation and supervision solutions include:

• Automated Performance Metric Tracking: Build a system to automatically measure and report key performance indicators such as accuracy, consistency, and response time.

• Sampling Review by Human Experts: Automate the process of randomly selecting a portion of AI decisions for review by human experts.

• Anomaly Detection System: Implement a mechanism to automatically detect and notify abnormal behavior or decisions of the AI model.

• A/B Testing Framework: Build an environment for safe testing and comparison of new model versions.

• Explainability Tools: Integrate tools to visualize and interpret the AI's decision-making process.

Conclusion

Generative AI technology undoubtedly possesses innovative potential, but the journey from PoC to actual adoption is more complex and challenging than expected. The reality that over 90% of projects fail to reach the actual implementation stage highlights the magnitude of this challenge.

However, these obstacles are not insurmountable. Successful AI adoption requires a comprehensive approach, including:

1. Company-wide Participation and Cultural Change: AI projects should be perceived as an organization-wide change management process, not just a technology adoption.

2. Phased Approach and Continuous Improvement: A gradual approach that builds on small successes can be more effective than attempting to build a perfect system all at once.

3. Setting Realistic Expectations: Acknowledge the limitations of AI and consider hybrid models that combine human expertise with the strengths of AI.

4. Long-term Investment Perspective: AI adoption should be approached from the perspective of securing long-term competitiveness rather than short-term ROI.

5. Integrating Ethical Considerations: Efforts are needed to ensure fairness, transparency, and accountability in AI systems.

6. Establishing a Robust Evaluation and Supervision System: Implement a system to continuously monitor and evaluate AI models to ensure reliability and performance.

To increase the success rate of actual Generative AI implementation, a holistic approach encompassing not only technical aspects but also organizational, cultural, and ethical aspects is essential. Furthermore, the adoption of AI model evaluation and oversight solutions will play a key role in effectively managing and overcoming these complex challenges.

Through these multi-faceted efforts and systematic approaches, businesses can realize the true value of Generative AI technology and drive innovative business transformations. It is time to recognize and prepare for AI adoption as a strategic journey that redefines the future of an organization, not just a mere technology project.