In recent years, Artificial Intelligence (AI) has captured the imagination of businesses and individuals alike. Since the launch of ChatGPT in late 2022, millions of users have deployed generative AI chatbots for work or leisure. While  the AI 'sparkle' icon is appearing in more and more places, including in enterprise software services and apps, the coming months will be a good test of whether AI-enabled tools are proving beneficial enough for businesses to continue paying for them.

Some signs of caution are beginning to emerge. According to a Wall Street Journal report, anywhere between 70% and 90% of AI projects initiated by companies are not making it beyond the pilot stage into production.

As renewal time approaches for companies in paid contracts to use GenAI services, the big question is: what proportion of customers will decide to renew? In this article, we will look at some common pitfalls of AI and key considerations for businesses considering renewal or adoption of such services.

AI adoption: What could go wrong

AI undoubtedly holds revolutionary potential, offering possibilities that seemed like science fiction just a few years ago. However, the path to successful AI implementation is fraught with pitfalls that businesses must watch out for. In the fast-paced world of technology, the old Silicon Valley mantra of "move fast and break things" can lead to serious trouble, especially when it comes to AI.

Recent history provides us with several cautionary tales of AI implementations gone awry. A service provider’s chatbot began swearing at customers, turning what was meant to be an efficiency enhancer into a public relations problem. In another instance, a company’s AI feature that had to be rolled back after it was discovered to be inadvertently copying work by other creators.

Such examples illustrate the risks of damage to reputation and loss of customer trust when a new technology is not deployed correctly. The remedies for such situations are often expensive, time-consuming, and embarrassing for the companies involved.

It is therefore crucial to ensure careful planning, thorough testing and robust safeguards in AI implementation.

Balancing Innovation and Risk

Given these risks, how can organizations continue to innovate and move forward with AI without jeopardizing their reputation and resources? The answer lies in implementing proper processes and practices tailored to each organization's specific needs and objectives.

Understanding AI Objectives

Each organization will have different reasons for investing in AI, but in general, these objectives fall into three main categories:

1. Improve Service: AI can enhance customer experiences through personalized recommendations, 24/7 customer support and more efficient problem resolution.
2. Improve Efficiency: AI can automate routine tasks, optimise processes and provide insights for better decision-making.
3. Reduce Costs: By automating tasks and improving efficiency, AI can significantly reduce operational costs.

Some organizations may also have more specialised objectives, such as:

   4. Improved R&D Cycle Times: AI can accelerate research and development processes by analysing vast amounts of data and identifying patterns that humans might miss.

   5. New Product Development: AI can assist in creating innovative products or services, opening up new market opportunities.

Key Areas to Address for Safe AI Implementation

To achieve these objectives while mitigating risks, enterprises must prioritize several key areas:

1. Choose the Correct Use Case: Understand the limitations of AI. Remember it is not infallible, so ensure there are safety nets and systems in place to minimise and mitigate any mistakes.
    
2. Accuracy: Ensure that AI models are trained on high-quality, representative data and continuously monitored for performance. Regular testing and validation are crucial to maintain accuracy over time.
    
3. Fairness: Implement rigorous testing to identify and mitigate biases in AI systems. This includes examining training data for underrepresentation and regularly auditing AI decisions for fairness across different demographic groups.
    
4. Data Quality: Establish robust data governance practices to ensure that AI models are trained on accurate, relevant and up-to-date information. This includes data cleansing, validation and regular updates to reflect changing real-world conditions.
    
5. Regulatory Compliance: Stay informed about and adhere to relevant AI regulations and standards, such as GDPR in Europe or industry-specific guidelines. This may involve appointing compliance officers and conducting regular audits.
    
6. Ethical AI Practices: Develop and adhere to clear ethical guidelines for AI development and deployment. This includes considerations of transparency, accountability and the potential societal impact of AI systems.
    
7. Risk Management: Implement comprehensive risk assessment and mitigation strategies. This includes identifying potential failure modes, developing contingency plans, and establishing clear protocols for handling AI-related incidents.
    
8. Robust Testing: Conduct thorough testing of AI systems before deployment, including stress tests, edge-case scenarios and adversarial testing to identify potential vulnerabilities.
    
9. Human Oversight: Maintain human supervision and intervention capabilities in AI systems, especially for critical decisions or customer-facing applications.
    
10. Continuous Monitoring and Improvement: Implement systems for ongoing monitoring of AI performance and user feedback. Regular reviews and updates are essential to maintain system effectiveness and safety.

The Path Forward

Navigating the ever-changing AI landscape requires expertise, experience and a commitment to responsible innovation. While the potential benefits of AI are immense, the risks of hasty or ill-conceived implementation can be severe.

At Alvarez and Marsal, we specialize in guiding enterprises through the intricacies of AI implementation. Our team of experts understands the delicate balance between innovation and risk management. We work closely with clients to develop AI strategies that are not only cutting-edge but also safe, ethical and compliant with relevant regulations.