MELISSA DREW

Across industries, leaders are racing to embed AI usage into performance reviews. Microsoft is asking managers to track employee AI adoption. Amazon requires certain employees to demonstrate AI proficiency before they can be promoted. Google is signaling that AI literacy is now a baseline expectation.

On the surface, this approach seems logical: tying AI usage to performance can accelerate adoption and keep your workforce competitive. But executives must pause and consider the risks beneath the surface.

The first risk is erosion of trust. If employees feel AI metrics are being imposed without clarity or fairness, they may comply out of fear rather than curiosity. That creates shallow adoption—box-checking instead of true transformation.

The second risk is inequity. Not every role, team, or demographic group starts at the same level of digital readiness. If AI literacy isn’t paired with robust training, coaching, and access, performance reviews can unintentionally punish those already at a disadvantage, reinforcing inequality in the workplace.

The third risk is the misuse of AI itself. When performance reviews reward “more AI,” employees may turn to shortcuts, feeding sensitive data into unapproved tools, automating tasks poorly, or ignoring ethical and compliance guidelines.

However, perhaps the most significant risk stems from executive literacy itself. Leaders who lack a grounding in data and AI fundamentals may set unrealistic expectations, misinterpret outputs, or over-index on activity rather than outcomes. Without AI literacy at the top, organizations risk measuring the wrong things, rewarding the bad behaviors, and stalling the very innovation they seek to encourage.

AI can be a powerful accelerator—but without balanced, literate, and responsible leadership, it can also deepen silos, undermine culture, and expose the enterprise to reputational and regulatory harm.

3 Takeaways for Executives

1. Balance Adoption with Equity

Mandating AI use in performance reviews risks leaving behind employees who lack access to training, or those in roles where AI has less immediate application. Leaders must ensure that AI literacy programs are inclusive, accessible, and tailored to specific roles to avoid widening skill gaps.

2. Prioritize Responsible Data & AI Education

More AI isn’t always better. If performance reviews reward AI usage without context, employees may misuse tools, compromising data security, confidentiality, or compliance. Establish clear guardrails and emphasize the responsible and ethical application over blind adoption.

3. Measure Outcomes, Not Just Inputs

An employee who saves time by using AI may have just as much impact as one who builds an advanced AI-powered workflow. Performance reviews should assess the quality of outcomes and innovation, rather than just the number of AI tools employed.

Final Thought

AI will define the next era of competitive advantage. If executives approach AI metrics as a blunt instrument, they risk creating a culture of fear, inequity, and shallow adoption.

The real opportunity isn’t just in mandating AI usage—it’s in building a workforce that is confident, responsible, and future-ready.

The workplace is changing faster than ever. Microsoft, Amazon, Google, and even companies like Duolingo and Shopify have one clear message for their employees: AI skills are no longer optional.

What used to be a "nice-to-have" tech curiosity is now becoming a career requirement. Several companies are already integrating AI usage metrics into performance reviews. That means how well you adapt, experiment, and incorporate AI tools into your day-to-day work could directly influence your next promotion, bonus, or even job security.

For employees, this shift represents both a challenge and an opportunity. The challenge is real—those who ignore AI risk falling behind. This means your career trajectory may soon hinge on your ability to demonstrate AI literacy—not only knowing the buzzwords, but actively applying AI tools to create value, improve efficiency, and spark innovation in your role.

But the opportunity is even greater: AI can help you work smarter, free up time for creative problem-solving, and showcase your ability to innovate

For employees, this creates a new kind of urgency. AI is no longer an abstract “future of work” conversation—it’s today’s reality, and the organizations that are embedding AI into performance management are setting the pace for every other industry to follow.

Now is the time to stop viewing AI as “the future” and start treating it as today’s professional survival skill. If you’re waiting for a memo from HR before you begin building your AI skills, you’re already behind. The time to act is now.

3 Takeaways Every Employee Should Act On

1. Experiment with AI Daily

Don’t wait for your company to mandate it—start small. Use AI to draft emails, summarize meeting notes, brainstorm new ideas, or analyze data trends. The more familiar you become, the more confident you’ll be when managers look at your AI adoption.

2. Showcase AI Wins in Your Work

Keep a running list of how AI has improved your productivity or creativity—whether that’s saving hours on a task or sparking a new idea. Highlight these in your performance review to demonstrate tangible value.

3. Invest in Your AI Literacy

Take courses, attend workshops, or join peer groups focused on AI. Knowing how to use AI responsibly, spot risks, and integrate it ethically will set you apart—not just as a good employee, but as a future-ready professional.

Final Thought

AI isn’t here to take your job—it’s here to transform it. The real risk isn’t that AI will replace employees, but that employees who embrace AI will replace those who don’t.

If your company hasn’t yet included AI in performance reviews, chances are it will soon. Don’t wait for that moment—start building your AI literacy today. 

Artificial intelligence is no longer just a buzzword—it's reshaping how companies evaluate performance and drive employee development. Let’s explore which organizations are at the forefront of this shift and how they’re doing it.

Who’s Using AI in Performance Management?

Microsoft

Microsoft has taken a bold step: managers are now instructed to consider employees' usage of internal AI tools—like GitHub Copilot—as a core part of performance reviews. The message is clear: "Using AI is no longer optional—it's core to every role and every level."The Washington Post

Google

Echoing Microsoft, top executives at Google are signaling that AI proficiency is essential—not a luxury—for career growth. AI adoption is being written into employee evaluations and future advancement discussions.The Times of India

Duolingo, Meta, Shopify, Box

These companies are embedding AI-first strategies organization-wide. In particular:

• Duolingo is transitioning toward AI-generated content and incorporating AI performance insights while training employees to adapt.Financial Times

• Shopify expects employees to justify hiring decisions by proving that AI cannot fulfill a task—setting a new performance benchmark.The Washington Post

• Meta and Box are also embedding AI into performance workflows, automating routine tasks and assessments.Axios

Oracle

Back in 2023, Oracle rolled out AI-assisted review tools, finding striking approval: 89% of employees favored AI-supported self-assessments.Business Insider

Rippling (Talent Signal)

Rippling is enabling AI-generated performance evaluations by tapping into data from GitHub, Salesforce, Gong, Zendesk, and more. The system scores performance as "on‑track," "high‑performing," or "needs attention."JOSH BERSIN

Culture Amp

Used by companies like Airbnb, Slack, and Warby Parker, Culture Amp utilizes AI-driven sentiment analysis to decode employee feedback and identify strengths, areas for growth, and cultural trends.SuperAGI

Other Tools & Platforms

• AI tools are stepping into review meetings too—some analyze communication patterns, tone, interruptions, and sentiment from recorded review sessions to guide better feedback delivery.Kolabtree

• Platforms designed for fairness and learning—like Workhuman—use AI to enhance peer recognition and surface promising talent.The Washington Post

Why This Matters

A July 2025 LinkedIn survey of over 2,000 U.S. workers revealed that more than one-third of executives plan to include AI skills in performance reviews and hiring decisions within the following year. Axios AI is rapidly shifting from a supporting tool to a foundational business expectation.

AI is no longer optional in performance management; it’s becoming integral. From input tracking to sentiment analysis and proactive feedback, companies are redesigning reviews for objectivity, speed, and strategic insight.

That said, successful implementation demands transparency, fairness, and a balance between data-driven evaluation and human judgment. AI should enhance—not replace—the meaningful performance dialogue between employees and organizations.

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.