Through 2025, at least 30% of Generative AI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. While generative AI holds immense transformative potential, scaling it effectively comes with significant challenges. CTOs and roles responsible for the deployment of GenAI projects must prioritize business value, focus on AI literacy, nurture cross-functional collaboration, and stress continuous learning in order to successfully deploy and scale these projects.
Gartner analyst Arun Chandrasekaran shared his latest research at an Incorta-hosted event: addressing the current state of GenAI, its key use cases/technology landscape, and emerging best practices to safely deploy it in the enterprise. Read our key highlights below, or access the full report.
AI adoption is increasing - but balancing risk is a hurdle
Arun’s research showed that around six out of ten customers have deployed generative AI in either pilot or production environments: a significant increase from March 2023 when only about two out of ten had done so. Customer service, software development, and marketing roles all show a significant uptick in interest in GenAI tools to boost productivity. However, many enterprise clients abandon generative AI projects after the pilot stage due to four main hurdles:
- Data Quality and Accessibility. Poor data quality and difficulties integrating data into GenAI workflows, like Retrieval-Augmented Generation (RAG), often lead to project failure.
- Data Privacy & Risk: AI models run the risk of generating hallucinations or false information, while the “black-box” nature of these models also complicates reliability and repeatability.
- High Cost of Ownership: With few proven use cases, it can prove difficult for organization leaders to get sustainable funding for GenAI initiatives.
- Determinable ROI With so many GenAI products saturating the market, the clear ROI and clear, tangible value of many of these products is still to be determined.
Scale GenAI effectively with tested steps & best practices
Arun’s research covers a detailed 10-step strategy along with emerging best practices to overcome these hurdles.
- Develop Frameworks to Prioritize GenAI Use Cases: Using Gartner's research, you can prioritize GenAI use cases and regularly update your framework to stay current with market evolution.
- AI Engineering Practices: Adopt agile practices, create sandbox environments for experimentation, and ensure scalable, composable platform architectures.
- Responsible AI: Put responsible AI at the forefront, promoting human-machine collaboration.
- Data and AI Literacy: Implement personalized training programs to foster a culture of continuous learning and innovation.
By implementing these best practices, CTOs can navigate the complexities of scaling GenAI, ensuring it drives business value while managing risks effectively.Access the full report below to learn more about the 10 Best Practices for Scaling Generative AI Across the Enterprise:
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