Top 8 failings in delivering value with generative AI and how to overcome them

Generative AI (GenAI) is rapidly emerging as a game changer for enterprises, 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 are conducting an average of 37 GenAI proofs of concept (POCs), with only five advancing to production. Of those, just three are considered successful. This stark contrast between experimentation and execution underscores the difficulties in harnessing AI’s transformative power. To bridge this gap, CIOs and technology leaders must not only identify the barriers but also adopt strategic approaches to improve the success rate and deliver real business value from GenAI initiatives. Let’s discuss the barriers and solutions for them.

Data privacy and compliance issues

• Failing: Mismanagement 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 like 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 repercussions.

Bias and hallucinations

• Failing: GenAI models often produce biased or inaccurate outputs, leading to misinformation and potential legal issues.
• Solution: Regularly audit and retrain models using diverse and representative data sets. Implement bias detection and mitigation tools.
• Key takeaway: Continuous monitoring and updating of AI models are essential to minimize bias and improve accuracy. Provide transparency back to the original data source to allow verification of information.

High costs

• Failing: The infrastructure and computational costs for training and running GenAI models are significant.
• Solution: Optimize models for efficiency, leveraging cloud-based solutions. But don’t forget to assess whether a private cloud option or a small language model will address your concerns.
• Key takeaway: Cost management strategies are crucial for sustainable AI deployment. We’ve already seen people struggle with cloud budgets; we are seeing a similar pattern with GenAI.

Integration challenges

• Failing: Integrating AI into existing systems can be technically and operationally challenging.
• Solution: Develop a clear integration road map, 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

• Failing: AI solutions that work in controlled environments may struggle to scale effectively in real-world conditions.
• Solution: Conduct thorough scalability testing and use modular architectures to facilitate easier scaling.
• Key takeaway: Scalability should be a core consideration from the outset to ensure long-term success.

Lack of clear use cases

• Failing: Difficulty in identifying specific business needs that GenAI can address.
• 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 essential for demonstrating AI’s value. Look for super use cases that address multiple opportunities rather than point solutions.

Trust and oversight

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

Intellectual property risks

• Failing: GenAI can inadvertently use copyrighted material, leading to legal complications.
• Solution: Implement strict content sourcing policies and use AI tools that can verify the originality of generated content.
• Key takeaway: Protecting intellectual property is essential to avoid legal issues and maintain ethical standards.

Conclusion

GenAI offers transformative possibilities, but unlocking its true value demands more than just enthusiasm; it requires strategy, foresight, and resilience. To move from potential to impact, organizations must confront its unique challenges head-on with well-thought-out solutions. By zeroing in on critical lessons and proactively managing risks, businesses can not only mitigate the pitfalls but also position themselves to fully capitalize on the immense power of GenAI, driving innovation and delivering sustained value.

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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 tech media, data, and marketing services company. Recently voted Analyst Firm of the Year for the third consecutive time, IDC’s Technology Leader Solutions provide you with expert guidance backed by our industry-leading research and advisory services, robust leadership and development programs, and best-in-class benchmarking and sourcing intelligence data 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 practice. This practice provides support to boards, business leaders, and technology executives in their efforts to architect, benchmark, and optimize their organization’s information technology. IDC’s end-user consulting practice utilizes 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 contextualized benchmarking capabilities.