MELISSA DREW

A Front-Seat leader is an individual who takes an active role, enabling everyone to embrace organizational change, challenges, and decision-making. To evolve into an effective modern-day leader, an individual must encompass three primary virtues or risk becoming an obstacle rather than an enabler for organizational growth. 

Accountability – “A Front Seat Leader is not someone who appears when things go well and disappears when they do not”, Melissa Drew. They recognize that active engagement is necessary rather than governance from a distance. This is echoed in a 2020 Harvard Business Review article highlighting that ethical accountability is directly correlated with perceived leadership trustworthiness and the team’s psychological safety. 

This is important because modern-day leaders operate with a level of fluidity that did not exist in the traditional leadership model.  Hybrid workers, more frequent disruptions, technological advancements, and fickle consumers require leaders to be multi-directional.  Their presence or lack thereof has become more visible than before, linking their lack of commitment and unwillingness to be held accountable.

Transparency – “Often, when a leader says nothing, they are really saying a lot.”, Melissa Drew. Remaining silent is not the same as remaining neutral. Silence will be misinterpreted, which can generate confusion and fear. It allows others to struggle with decisions, often without understanding the bigger picture and risks.

A leader who communicates freely allows others to understand not just the what and how, but the why behind ethical decisions (Deloitte, 2022). There has been more organizational change in the last decade than in the past century.   Staying silent may have been a traditional virtue richly rewarded, but in modern-day organizations, silence breeds mistrust.

Courage – “It is the front-seat leader who is aware of who is making decisions, why those decisions are made, and how those decisions are impacting the future of the organization.”  Melissa Drew. Ethical leadership is not leading when it is convenient; it is about doing what is right at a point in time when it matters the most.  Courageous leadership is a key trait to building resilient, adaptive cultures during ethical crises (Korn Ferry Institute, 2023).

This has become increasingly critical with the rise of AI-powered recommendations in goods and services, where biases and uncertainties are not easily recognized. Traditionally, making decisions that favor operational speed was encouraged and often celebrated. The courageous leader understands that exposing process limitations or taking the time to determine whether a decision will increase negative impacts is crucial. This is what is needed to build sustainable trust within the organization.

Could AI Help the Egg Shortage in the US?  The answer is Yes!

The United States has been experiencing periodic egg shortages due to avian flu outbreaks, supply chain disruptions, and rising feed costs. These challenges have led to fluctuating egg prices and difficulty maintaining a steady supply. Artificial intelligence (AI) is a tool that can mitigate these issues, helping optimize supply chains, prevent disease outbreaks, and improve egg production efficiency. This article explores the key ways AI can be leveraged to address the egg shortage crisis effectively.

1. Predictive Analytics for Supply Chain Management

AI-driven predictive analytics can help producers, distributors, and retailers forecast egg production and demand more accurately. By analyzing historical data, weather patterns, disease trends, and consumer behavior, AI can generate real-time insights to adjust production and logistics accordingly. This can help prevent overproduction or underproduction, reducing waste while ensuring a steady supply.

For instance, AI models can predict seasonal demand spikes and suggest preemptive stock adjustments to prevent shortages. Furthermore, retailers can use AI-based inventory management systems to track egg sales in real-time, optimizing restocking schedules to ensure availability without excessive stockpiling.

2. AI-Powered Disease Detection and Prevention

Avian flu is one of the biggest threats to egg supply, and it can rapidly decimate poultry populations. AI-powered computer vision and IoT (Internet of Things) sensors can monitor bird behavior, detect early signs of illness, and alert farmers before a full-scale outbreak occurs. By analyzing data from sensors tracking temperature, air quality, and bird movement, AI can identify anomalies that indicate disease presence, allowing for immediate intervention.

Additionally, AI can assist in biosecurity measures by analyzing movement patterns within farms to suggest improvements in sanitation and isolation protocols. This can significantly reduce the risk of disease transmission and improve overall poultry health.

3. Smart Farming for Increased Production

AI-driven automation in poultry farms can improve efficiency and egg production rates. Intelligent farming systems equipped with AI can monitor environmental factors like temperature, humidity, lighting, and feed intake to ensure optimal conditions for hens. These systems can automatically adjust in real-time, ensuring hens remain healthy and productive.

Moreover, AI-powered robotic systems can be employed for egg collection, sorting, and packaging, reducing human error and increasing operational efficiency. AI can help farmers maximize egg production while reducing resource wastage by optimizing feeding schedules and identifying individual hens with lower productivity.

4. Supply Chain Optimization

AI-driven logistics platforms can enhance egg distribution efficiency, ensuring eggs reach consumers faster and fresher. Machine learning algorithms can optimize delivery routes based on traffic patterns, weather conditions, and demand fluctuations. This reduces delays, minimizes spoilage, and enhances overall supply chain resilience.

AI can also streamline warehousing and cold storage management. Sensors and AI-powered monitoring systems can track egg storage conditions and provide real-time alerts if temperatures fluctuate beyond safe levels. This prevents spoilage and reduces losses within the supply chain.

5. Alternative Egg Production Solutions

Beyond traditional poultry farming, AI is crucial in the development of lab-grown and plant-based egg alternatives. Companies developing these alternatives use AI to analyze consumer preferences, optimize formulations, and scale production efficiently. Machine learning models can identify the best plant-based ingredients to mimic the taste and texture of eggs, improving the quality and acceptance of these products in the market.

By advancing the production of alternative eggs, AI can help reduce reliance on traditional poultry farming, offering consumers more choices and increasing supply resilience.

6. Market Price Optimization

Egg prices fluctuate based on supply and demand dynamics, sometimes leading to consumer panic and hoarding. AI-powered pricing models can help retailers and suppliers set fair, dynamic pricing strategies. AI can recommend price adjustments that balance affordability with profitability by analyzing demand trends, competitor pricing, and external factors like inflation and supply chain disruptions.

This approach can prevent price gouging during shortages and ensure a more stable market for consumers and producers.

Conclusion

The U.S. egg shortage is a complex issue driven by multiple factors. Yet AI technologies represent a range of solutions to enhance production, streamline distribution, and reduce risks associated with disease outbreaks. From predictive analytics and smart farming to alternative egg production and pricing optimization, AI can play a pivotal role in stabilizing the egg supply chain. By leveraging these technologies, the industry can build a more resilient and sustainable system to meet consumer needs efficiently.

Most of the ideas in the article remain either as concepts or ideas adapted from other industries or are currently in research and development. 

As AI continues to evolve, its integration into the poultry industry will likely become more sophisticated, further improving food security and supply chain efficiency. Technology will reshape the future of egg production, ensuring that shortages become less frequent and less disruptive to consumers and businesses.

The bias we attribute to memory recall is similar to AI bias, which shapes how information is retrieved, interpreted, and presented.

1. Data Degradation Bias – Just as human memories fade over time, AI models trained on older datasets may generate narratives influenced by incomplete or outdated information, leading to inaccuracies.

2. Sentiment Amplification Bias – AI may amplify nostalgic tones or negative framing based on patterns in historical data, exaggerating emotions associated with past events.

3. Selection Bias—The data an AI system relies on might not represent the full range of experiences, leading to an overemphasis on certain aspects while ignoring others.

4. Confirmation Bias in Training Data – AI models may reinforce dominant narratives from historical datasets, aligning with prevalent perspectives, even if they don’t reflect a more nuanced or updated understanding.

5. Hindsight Bias in AI Predictions – AI-generated retrospectives may present past events as more predictable or inevitable than they were, reflecting patterns learned from outcome-based training data.

6. Self-Preservation Bias—AI models may generate responses that favor consistency with previous outputs, reinforcing existing perspectives rather than questioning or revising past conclusions.

7. Anchoring Bias in Information Retrieval—AI may disproportionately weight early details when reconstructing past events, shaping the overall narrative around the most prominent or first-retrieved pieces of information.

8. Peak-End Rule in AI Storytelling – AI may emphasize the most dramatic or conclusive moments when summarizing or retelling events, mirroring human tendencies to focus on peaks and endings rather than the full event distribution.

To mitigate these biases, AI systems must be designed to cross-reference diverse data sources, adapt to evolving knowledge, and recognize the limitations of historical narratives.

Including team members with diverse perspectives will alls mitigate risks.

Encouraging transparency and human oversight can help AI-generated information balance accuracy and meaningful information.

The more you understand AI limitations, the better you will become an AI-literate citizen.

The Chips and Science Act of 2022, effective since November 2023, has major implications for U.S. procurement and supply chain operations to revitalize domestic manufacturing to create jobs in America. 

The plan is for US companies to become more competitive by strengthening American manufacturing, supply chains, and national security. Funding includes investments in research and development, science and technology, and the workforce of the future to keep the leader in nanotechnology, clean energy, quantum computing, and artificial intelligence. We are just now experiencing the full impact.

WHAT IS IT?

• The Act aims to boost advanced chip manufacturing domestically and reduce reliance on foreign suppliers.
• $280 billion allocated for funding, with $52 billion dedicated to research, including support for green manufacturing initiatives, R&D, workforce development, and communications & marketing. 

POLICY GOALS:

• Enhance silicon chip production within the US
• Strengthen Supply Chain resilience
• Foster research and development activities
• Mitigate national security concerns related to foreign chip manufacturing

IMPACT TO PROCUREMENT & SUPPLY CHAIN:

• Reshoring manufacturing operations to the US, diminishing dependencies on China and Taiwan, mitigating risks and future delays.
• Adapting Supply Chain structures to include new domestic suppliers.
• Initial rise in short-term costs (operational, labor, switching, and other overheads).
• More access to AI technologies for US suppliers leads to increased evaluation of suppliers by procurement entities.

NEXT STEPS

• 1. Review existing supplier networks to incorporate previously unconsidered suppliers.
• 2. Adjust supplier onboarding procedures for the influx of new suppliers.
• 3. Secure external compliance management resources compliant with US and EU regulations.
• 4. Evaluate and potentially modify contractual agreements.
• 5. Reassess supplier collaborations.
• 6. Conduct scenario analyses balancing cost and Supply Chain security.
• 7. Enhance team members' AI literacy concerning its impact.

CONCLUSION

This disruption, part of ongoing US and EU initiatives, necessitates proactive measures to adapt to the evolving landscape. It is not about transforming all over again. There should be steps to fold into the existing foundation that has already been developed. Expect more frequent changes shortly and have the foundation, resources, and skills necessary to adjust.

In a recent survey by Deloitte, 65% of procurement professionals believe that data-driven decisions are crucial for achieving procurement excellence. Yet many organizations continue to struggle with executing strategies to achieve these goals.

Procurement organizations are implementing profound transformations due to the rapidly changing business landscape; constant fluctuations in consumer demand, faster technological evolution, and frequent disruptions are no longer the exception. However, transformation efforts come with challenges, particularly in data management.

One of the underlying problems facing procurement professionals today is the vast amount of data generated throughout the procurement process. From supplier information and pricing data to market trends and performance metrics, procurement teams are inundated and often overwhelmed by the amount of data available.  

This data is dispersed across multiple systems and formats, making accessing, analyzing, and deriving meaningful and actionable insights difficult. Furthermore, the quality of this data is often compromised, leading to inaccuracies, inconsistencies, and inefficiencies in data-driven decisions.

It is no longer about having the right data at the right time. We need the right data at the point in time when it has the most impact on our decisions. According to research by The Hackett Group, poor data quality costs organizations an average of $9.7 million annually in missed savings opportunities and increased operational expenses.

So why is data quality essential? Because data drives everything, data underpins every aspect of the procurement process, from identifying cost-saving opportunities and mitigating risks to optimizing supplier relationships and enhancing operational efficiency.

High-quality data enables procurement professionals to make informed decisions, mitigate risks, and drive strategic value for their organizations. By improving data quality, procurement teams can unlock additional benefits, such as enhanced supplier performance, reduced process end-to-end cycle times, and strengthened compliance.

With advanced analytics and artificial intelligence, high-quality data lays the foundation for predictive and prescriptive insights that can further optimize procurement processes and drive continuous improvement.

In summary, in today's data-driven world, data quality is no longer a nice to have; it is a critical component of procurement success. By addressing the underlying problems of data management and prioritizing data quality initiatives within the Transformations, the organizations can empower their procurement teams to become real value creators, drive strategic impact, and deliver tangible results.