Nine questions are the key to AI success in building resilient supply chains

Artificial intelligence (AI) is a transformative technology that can reinvent industries, streamline operations, and give businesses a competitive edge. However, implementing AI in your enterprise is not without challenges. From understanding the underlying technology to ensuring alignment with business goals, leaders must ask the right questions to make informed decisions and smart investments.

Before embarking on any AI investment or implementation, it’s crucial to understand the technology's potential and mitigate risks. Here are some key questions that every CEO and supply chain leader should ask when evaluating AI investments for their enterprise.

1. What problem are we trying to solve?

The first and most critical question is identifying what problem your business needs to address. AI should be a solution to a specific challenge or opportunity. Whether it’s improving operational efficiency, enhancing customer experience, or predicting market trends, having a clear understanding of the problem helps define the scope and goals of the AI initiative.

Considerations:

• Are we looking at AI for incremental gains or a moonshot breakthrough?
• Is the problem data-driven and conducive to an AI solution?
• What value will solving this problem bring the business (e.g., cost savings, improved performance, increased productivity, customer satisfaction)?

2. How will AI align with our business strategy?

AI implementation must be aligned with your overall business strategy and goals. This involves integrating AI into key business processes and ensuring all stakeholders understand how it contributes to the company’s growth.

Considerations:

• Will this AI solution scale with our business needs?
• Does this align with our digital transformation roadmap?
• How will this technology influence other parts of the business, such as marketing, sales, or product development?

3. What data do we have, and is it ready for AI?

AI systems are only as good as the data they are trained on. Before you can implement AI, you need to assess the quality, quantity, and availability of your data. Many organizations need to invest in cleaning, organizing, or even acquiring new data to make AI implementations feasible.

Considerations:

• Is the data accurate, complete, and relevant?
• Is the data structured or unstructured, and can it be used effectively by AI models?
• Are there any data privacy concerns, especially when dealing with customer information?

4. What technology or AI model best suits our needs?

Not all AI models or technologies are the same. Depending on the problem you’re trying to solve, you may need different types of AI, such as machine learning (ML), natural language processing (NLP), generative AI, or symbolic AI. You’ll need to determine the type of AI that fits your needs, how it will be implemented, and what tools or platforms are required.

Considerations:

• Should we build a custom AI solution or use an off-the-shelf product?
• Do we want to undertake this as a collaboration with a partner and does our potential partner have the AI experience required?
• How will this AI integrate with our existing technology stack?

5. How will AI impact our workforce and processes?

AI can improve efficiency and automate certain tasks, but it will also change how your team works. It’s important to consider the impact of AI on employees and operational processes. This includes retraining employees, shifting responsibilities, and managing change.

Considerations:

• What roles or tasks will AI automate, and how will that affect our workforce?
• How can we upskill or reskill employees to work alongside AI?
• Will AI complement or replace existing business processes?

6. What are the ethical and legal implications?

AI introduces new ethical and legal concerns, especially around data usage, privacy, and algorithmic bias. Companies need to be proactive in addressing these issues and ensuring that their AI implementation complies with regulatory standards and aligns with the company’s ethical values.

Considerations:

• Are there any biases in the data or the AI model that could lead to unfair or unethical outcomes?
• How will we ensure transparency and fairness in AI decision-making processes?
• Are there any data protection laws or industry regulations we need to comply with?

7. How will we measure success?

Defining key performance indicators (KPIs) before implementing AI is essential to gauge its effectiveness. Metrics such as increased revenue, improved customer satisfaction, or operational efficiency should be used to evaluate whether the AI initiative is achieving its intended goals.

Considerations:

• What KPIs will we use to measure the success of our AI implementation?
• How frequently will we assess the performance of the AI solution?
• How do we ensure ongoing optimization and improvement of the AI system?

8. How will we manage change and drive adoption?

Adopting AI is more than just a technical transformation; it’s also a cultural shift. Ensuring the organization is ready for this change is critical to success. Proper change management will help reduce resistance, ensure the technology is used effectively, and promote widespread adoption.

Considerations:

• How will we communicate the benefits of AI to our employees and customers, and do we have feedback loops to utilize employee and customer input?
• What training and support will be needed to ensure smooth adoption?
• Who will champion AI initiatives within the organization?

9. Are we ready for continuous learning and adaptation?

AI is not a “set it and forget it” technology. It requires continuous learning and adaptation. You’ll need a strategy for updating your AI models, retraining the algorithms, and evolving the system as your business grows and the market changes.

Considerations:

• How frequently will the AI system need updates or retraining?
• How will we keep up with new AI developments to maintain our competitive edge?
• Do we have the right internal resources to continuously improve the AI system?

AI has the potential to bring transformative benefits, but it also presents unique challenges that must be carefully navigated. Each question outlined above provides a foundation for making well-informed decisions that align with your business strategy, workforce, and long-term objectives. The Center for Global Enterprise (CGE) is launching a new non-profit applied research institute to bring world class AI expertise and resources to business leaders so they can feel confident they are making the right AI investments and decisions for their company and its stakeholders. When AI is thoughtfully implemented, it can unlock remarkable levels of innovation, efficiency, and growth. CGE welcomes collaboration with companies around the world to ensure this potential is realized.

About the author

Christopher G. Caine is president of the Center for Global Enterprise (https://www.thecge.net/), a New York-based non-profit organization dedicated to the study of the contemporary corporation in the era of global economic integration. He is also president & CEO of Mercator XXI, LLC (www.mercatorxxi.com), a professional services firm helping clients engage the global economy.

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