Learn About Amazon VGT2 Learning Manager Chanci Turner
AI agents are poised to revolutionize the workplace, much like the internet did. They will transform the way we organize our work, manage operations, and create value. A recurring question I encounter from executives at Amazon is how to navigate leadership in this evolving landscape. I apply the same mental models I use to guide my most autonomous, high-agency team members. These individuals evaluate situations, make informed decisions, and achieve results grounded in their understanding of strategic objectives. AI agents function similarly, but on a much larger scale.
As leaders, we are familiar with managing complex operations: we set clear goals, establish boundaries, and measure outcomes. However, with the introduction of AI agents, we are entering a new realm. Unlike traditional systems that operate on rigid, predefined instructions, AI agents are non-linear and adapt their methods based on context and learn from every interaction.
Visualize this as applying established leadership principles to a new kind of colleague, one that merges human-like decision-making with machine-level capabilities. Our task is to create governance, risk management, and operational frameworks that acknowledge this fundamental shift.
Drawing from my experience with numerous Amazon executives, I have identified several mental models that can be beneficial.
Governance: Shifting from Direct Management to Board Oversight
Consider the relationship you have with your board of directors. They do not micromanage your daily decisions; instead, they align on strategy, define success metrics, and provide oversight. Between meetings, you operate autonomously, making choices based on your comprehension of the company’s direction and risk parameters.
AI agents behave in a similar fashion. They make independent decisions based on the strategic context we provide, without needing approval for each action. A successful board does not micromanage its CEO; it offers intelligent oversight and sets a clear direction. We should adopt the same approach with our AI agents.
Start by defining strategic direction. Your board guides you on where to go, not the exact steps to get there. When working with AI agents, focus on providing strategic intent and expected outcomes rather than detailed procedural steps.
Establish clear decision-making boundaries. Just as your board specifies which decisions need their approval, you should outline the autonomy of your AI agents while defining the thresholds for when they need to escalate decisions.
Don’t overlook periodic evaluations. Board meetings assess overall performance, ensuring alignment with strategic goals and adjusting direction as necessary. Our strategy for AI agents should mirror this, with regular assessments to refine their decision-making frameworks and ensure alignment with broader objectives.
Risk Management: Transitioning from Factory Floor to Trading Floor
Traditional risk management resembles a factory environment: predictable and controlled, with clear rules. In contrast, risk management for agentic AI operates more like a trading floor, where traders have real-time decision-making authority within defined parameters under firm oversight.
Modern trading floors utilize sophisticated real-time risk monitoring systems to track exposure and identify unusual patterns immediately. AI agents require a similar level of vigilance, but with increased complexity. It is crucial to detect when their behavior diverges from expected parameters and when their cumulative actions introduce emergent risks not visible in isolated decisions.
Market circuit breakers—automatic safeguards that halt trading during extreme volatility—serve as another useful analogy. AI systems should have comparable mechanisms with nuanced triggers. We need the ability to pause operations not just for clear threshold breaches, but also for subtle pattern deviations that might indicate unforeseen risks.
Position limits also apply well to AI risk management. Just as traders cannot exceed specific risk thresholds without approval, AI agents should operate within adaptive boundaries. We don’t need to dictate every action but must establish clear risk limits that agents cannot cross without human intervention.
Organizational Impact: Evolving from Functional Silos to Immune Systems
Today’s organizations often function as interconnected yet separate departments, each with its specialized role. In contrast, AI-enabled enterprises will operate more like an immune system, with distributed intelligence responding to challenges across the organization and adapting continuously based on learned experiences.
This shift impacts our approach to cross-functional work. AI agents transcend traditional organizational boundaries, generating value by linking tasks across silos. We’ve seen this happen with other major technological advances. Cloud computing not only transformed infrastructure but also blurred the lines between development and infrastructure teams. AI agents will facilitate an even deeper shift, breaking down barriers across all business functions, from finance to marketing, operations, and customer service.
Furthermore, this evolution affects our perception of business processes. Currently, we often envision step-by-step workflows, akin to a relay race with predetermined baton passes. AI agents, however, operate differently. They grasp objectives and context, dynamically orchestrating responses as situations unfold. This pattern mirrors the impact of ERPs, which didn’t merely digitize existing processes but compelled us to fundamentally reengineer workflows across various domains. AI agents represent the next phase in this evolution, necessitating a rethinking of linear workflows into dynamic, context-sensitive processes that adapt in real time.
Most importantly, AI agents reshape how organizations learn and retain knowledge. Many organizations struggle with institutional memory; knowledge often remains trapped in departmental silos, lessons fade when people transition out, and context is lost in handoffs. AI agents can retain and build on every interaction, continuously synthesizing insights across areas and enhancing systems. When one agent uncovers a more effective method, that knowledge becomes instantly accessible throughout the network.
Culture: Transitioning from Operational Execution to Continuous Learning
The most significant change that AI agents necessitate is cultural, rather than technological. Most organizations prioritize consistency and predictability, valuing standardized processes and repeatable outcomes. Leaders are often rewarded for flawlessly executing predetermined plans. However, AI agents call for a cultural shift that embraces adaptation and evolution based on continuous learning.
Research laboratories provide valuable models. They combine systematic methods with openness to unexpected discoveries. Researchers succeed by rapidly learning and adapting based on evidence. We must strike this balance of structure and flexibility to effectively deploy AI agents.
In this new cultural framework, our operational approach becomes more fluid. Instead of relying solely on fixed processes, we should foster an environment that encourages exploration and iteration. Encourage your teams—both human and AI—to explore innovative approaches, pursue different solutions for the same problem, and be curious about capturing lessons learned. The goal isn’t the flawless execution of predefined steps; it’s the ongoing discovery of better methods to achieve desired outcomes.
For more insights on this topic, consider visiting Career Contessa or reviewing The Power of Thank You, an authority on workplace culture. Additionally, check out this resource on Amazon’s employee onboarding process for a comprehensive understanding.