Generative AI (GenAI) is rapidly emerging as a game changer for businesses, but turning its potential into measurable value remains a significant challenge. According to a recent IDC study (Future Enterprise Resiliency and Spending Survey, Wave 4, IDC, April 2024), companies conduct an average of 37 GenAI proofs of concept (POCs), with only five progressing to production. Of these, only three are considered successful. This stark contrast between experimentation and execution underscores the difficulties in harnessing the transformative power of AI. To bridge this gap, CIOs and technology leaders must not only identify the barriers but also adopt strategic approaches to improve success rates and deliver real business value from GenAI initiatives. Let’s discuss the obstacles and the solutions for them.

Data privacy and compliance issues

• Lack: Mishandling of internal data with external models can lead to privacy breaches and non-compliance with regulations.
• Solution: Implement robust data governance frameworks and ensure compliance with regulations such as GDPR and CCPA. Use anonymization and encryption techniques to protect sensitive data.
• Key takeaway: Prioritize data privacy and compliance to build trust and avoid legal consequences.

Bias and hallucinations

• Lack: GenAI models often produce biased or incorrect output, leading to incorrect information and potential legal issues.
• Solution: Revise and retrain models regularly using diverse and representative datasets. Implement bias detection and mitigation tools.
• Key takeaway: Continuous monitoring and updating of AI models is essential to minimize bias and improve accuracy. Provide transparency back to the original data source to allow verification of information.

High costs

• Lack: The infrastructure and computational costs to train and run GenAI models are significant.
• Solution: Optimize models for efficiency, leverage cloud-based solutions. But don’t forget to assess whether a private cloud option or a small language model will solve your problems.
• Key takeaway: Cost management strategies are critical to sustainable AI deployment. We’ve already seen people struggle with cloud budgets; we see a similar pattern with GenAI.

Integration challenges

• Lack: Integrating AI into existing systems can be technically and operationally challenging.
• Solution: Develop a clear integration roadmap, invest in middleware solutions and ensure cross-functional collaboration.
• Key takeaway: A well-planned integration strategy can smooth the transition and maximize AI benefits.

Scalability issues

• Lack: AI solutions that operate in controlled environments may struggle to scale effectively in real-world conditions.
• Solution: Conduct rigorous scalability testing and use modular architectures to facilitate scaling.
• Key takeaway: Scalability should be a key factor from the start to ensure long-term success.

Lack of clear use cases

• Lack: Difficulty identifying specific business needs that GenAI can handle.
• Solution: Engage stakeholders to identify pain points and opportunities where AI can add value. Pilot projects can help validate use cases.
• Key takeaway: Clear, well-defined use cases are critical to demonstrating AI’s value. Look for super use cases that address multiple capabilities rather than point solutions.

Trust and overview

• Lack: Lack of transparency and explanatory possibilities in AI models can erode trust among users and stakeholders.
• Solution: Use explanatory AI (XAI) techniques and maintain clear documentation of AI decision processes.
• Key takeaway: Transparency and explainability are key to building and maintaining trust in AI systems.

Intangible risks

• Lack: GenAI may inadvertently use copyrighted material, leading to legal complications.
• Solution: Implement strict content source policies and use AI tools that can verify the originality of generated content.
• Key takeaway: Protecting intellectual property rights is important to avoid legal problems and maintain ethical standards.

Conclusion

GenAI offers transformative opportunities, but unlocking its true value requires more than enthusiasm; it requires strategy, foresight and resilience. To move from potential to impact, organizations must meet their unique challenges head-on with thoughtful solutions. By zeroing in on critical learnings and proactively managing risk, companies can not only mitigate the pitfalls, but also position themselves to fully leverage the enormous power of GenAI, drive innovation and deliver sustainable value.

Learn more about IDC research for technology leaders OR subscribe today to get industry-leading research straight to your inbox.

International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services and events for the technology markets. IDC is a wholly owned subsidiary of International Data Group (IDG Inc.), the world’s leading technology media, data and marketing services company. Recently named Analyst Company of the Year for the third year in a row, IDC’s Technology Leader Solutions provides you with expert guidance backed by our industry-leading research and advisory services, robust leadership and development programs, and best-in-class benchmarking and purchasing information. from the industry’s most experienced advisors. Contact us today to learn more.

Daniel Saroff is Group Vice President of Consulting and Research at IDC, where he is a senior practitioner in the end-user consulting business. This practice supports boards of directors, business leaders, and CTOs in their efforts to design, benchmark, and optimize their organization’s information technology. IDC’s end-user consulting practice uses IDC’s extensive international IT data library, robust research base, and tailored consulting solutions to deliver unique business value through IT acceleration, performance management, cost optimization, and contextual benchmarking capabilities.