Strategies to Combat GenAI Implementation Risks

The Urgency of GenAI Implementation

Organizations worldwide are gearing up to spend over $40 billion on Generative AI (GenAI) in 2024. This massive investment reflects the rapid adoption of GenAI technologies across various industries. However, it also raises significant concerns about the preparedness of IT departments to handle the complex requirements for successful implementation.

Notable GenAI Blunders

Recent incidents highlight the potential pitfalls of hasty GenAI adoption:

• ChatGPT falsely accused a law professor of harassment.
• Air Canada was ordered to compensate a customer misled by its chatbot.
• Google had to pause its Gemini AI model due to inaccuracies in historical images.
• Samsung employees leaked proprietary data to ChatGPT.
• A ChatGPT bug exposed user conversations to other clients.

These examples underscore the severe risks of data spills, brand damage, and legal issues that arise from the “move fast and break things” mentality. Mark Zuckerberg’s famous quote should serve as a cautionary tale rather than a directive.

Key Risks in GenAI Implementation

Organizations face a myriad of risks when deploying GenAI, including but not limited to:

• AI and data poisoning
• Bias and limited explainability
• Brand threat
• Copyright infringement
• Cost overruns
• Environmental impact
• Governance and security challenges
• Integration and interoperability issues
• Litigation and regulatory compliance

Vendor Understanding and Preparedness

A significant portion of CEOs (45%) and CIOs (66%) believe that technology vendors don’t fully grasp the risks associated with AI. This skepticism necessitates rigorous questioning of vendors about privacy, data protection, security, and the use of training data. Effective partnering requires transparency and clear documentation from vendors.

GenAI is increasingly being integrated into existing enterprise applications. Organizations must understand how these integrations work, their implications on data privacy, and the conditions under which they operate. Prepare a comprehensive checklist of questions to evaluate these integrations and discuss the implications with your vendors.

Five Components of GenAI Implementation

To mitigate GenAI implementation risk, consider the following five elements:

• Technology: Systems, services, networking, and platforms underpinning GenAI (could be cloud, on-premises, or hybrid).
• Processes: Bias mitigation, security, model transparency, data ingestion, and privacy.
• Talent: Technical, data, and model skills to be obtained through training, hiring, or partnering.
• Governance: Oversight, accountability, legal, statutory, and ethical expertise.
• Data: Good, known, understood data relevant to the use case.

Assessing AI Maturity

Understanding AI maturity is crucial for successful GenAI implementation. Organizations must realistically assess their AI maturity levels relative to the project requirements to avoid overcommitting resources. This maturity can be built, procured, or acquired through partnerships, with knowledge transfer being a critical component of any external collaboration.

Use Case Categories and Their Requirements

GenAI use cases fall into three main categories:

• Productivity or Efficiency: Low-risk tasks like summarizing reports or creating RFPs, requiring minimal customization and talent. These correlate to a low AI maturity and typically bring limited benefits.
• Functional: Medium-risk tasks like hyper-personalized marketing, necessitating good data and available in-house talent. These correlate to a moderate AI maturity and can bring moderate benefits.
• Industry or Transformational: High-risk, high-impact use cases like generative drug discovery, requiring significant investment in talent and data quality. These can be competitive differentiators or create a competitive moat. They correlate to a high AI maturity and can bring big benefits.

Build Versus Buy: A Balanced Approach

Organizations should adopt a mix of build-and-buy strategies tailored to their specific business and technology contexts. This balanced approach ensures that projects align with organizational maturity and value requirements.

Conclusion

Navigating the risks of GenAI requires a comprehensive understanding of an organization’s AI maturity, a balanced build-and-buy approach, and rigorous vendor evaluation. By addressing these challenges and leveraging the right infrastructure, organizations can harness the transformative potential of GenAI while minimizing risks.

Contact IDC for more advice on managing the risks of GenAI and build versus buy strategies for GenAI. Daniel Saroff, IDC Group Vice President of Consulting and Research, leads the CIO/end-user research practice at IDC, providing guidance to business and technology executives on leveraging technology for innovative and disruptive business outcomes.