Kincy Clark

this is an essay I wrote in March 2025. We’ll see how well it stands up to reality

SCALING HUMAN POTENTIAL FOR AI-POWERED TIER THREE SUPPORT

Technical support isn’t just about solving problems—it’s about scaling problem resolution. Some issues are structured and have clear pathways to resolution. Unstructured problems require deep technical expertise, the ability to synthesize multiple inputs, and comfort with ambiguity.

The key to scaling support is quickly turning unstructured problems into structured ones, creating opportunities for process, automation, and better product outcomes. AI is going to accelerate this process by helping support organizations recognize patterns faster and move more problems into structured, repeatable solutions. Here’s an illustration of the concept.

Tier Three Support is the team responsible for taking down the Godzilla problems—the unstructured challenges that don’t come with a playbook. We don’t know how Godzilla got to Tokyo, and we don’t know how to get rid of him—but it’s T3’s job to figure it out.

The organizational challenge with solving unstructured problems is that it doesn’t scale. Every new Godzilla is a fresh crisis that requires deep technical expertise, cross-functional coordination, and time-consuming troubleshooting. Traditionally, the only way to solve more unstructured problems was to add more people—a strategy that quickly hits its limits.

This is where AI changes the game. Instead of just hiring more engineers to increase capacity, AI allows Tier Three teams to scale the unstructured problem resolution process—helping engineers recognize patterns faster, connect seemingly disparate issues, and push more problems into structured pathways that scale.

AI as an Augmentation Tool, Not an Answer Machine

While many structured problems should be automated, unstructured problem-solving will remain a human craft, at least in the short/medium term. AI isn’t there to “do the thing”—it’s there to power the team, not be a replacement for human expertise.

AI is a probabilistic tool, not an all-knowing oracle. A key risk is relying on it to do all the thinking for us. If engineers treat AI as a shortcut to answers rather than a tool for refining their understanding, they don’t become better problem-solvers. Instead, they remain reactive troubleshooters rather than evolving into problem architects who can scale effectively.

Generative AI plays a critical role in this evolution by helping Tier Three engineers recognize patterns, define pathways for ambiguous issues, and more quickly convert them into structured problems. This shift moves Tier Three engineers beyond reactive troubleshooting and enables them to build solutions that drive scale instead of just resolving individual cases.

As the focus of Tier Three shifts from troubleshooting to problem architecture, the leadership challenge isn’t simply about leveraging AI—it’s about building a team that can thrive in this evolving role. Instead of managing a team of soloists, I will lead deeply technical generalists who use AI to integrate customer interactions, business processes, and technical infrastructure to solve problems, structure them, and drive scale. To succeed, this team must be made up of people who are naturally curious, adaptable, and comfortable working in ambiguity—engineers who don’t just execute solutions but define frameworks for solving future problems.

To scale Tier Three ’s ability to tackle unstructured problems, I’ve organized some of my ideas for practical techniques into three areas of impact: Scaling Problem-Solving, Developing Talent, and Driving Better CX. Each of these areas supports a fundamental shift in technical support—moving toward scalable, structured problem-solving that compounds over time.

1 - Scaling Problem-Solving: Expanding technical capabilities to structure problems faster

• Debugging & Optimization. I want to equip Tier Three engineers with expertise in AI model behavior analysis, prompt engineering, and fine-tuning to ensure reliable, trustworthy outputs. My focus is on identifying and mitigating biases, hallucinations, and inconsistencies through ongoing training, real-time observability tools, and stronger collaboration with research teams to continuously refine AI behavior.
• Tooling & Observability. Enhance AI tooling and observability by equipping support engineers with advanced internal tools for model introspection, real-time API monitoring, and automated log analysis. This will improve pattern detection, anomaly identification, and issue resolution speed, allowing Tier Three engineers to move beyond troubleshooting and act as AI consultants, helping customers optimize AI implementations.
• Codebase and Product Awareness. I will leverage AI for source code analysis to give engineers deeper product insights and a clearer understanding of system behavior. By continuously analyzing source code and commit histories, AI will serve as a real-time assistant to support engineers—accelerating debugging, preventing regressions, and improving system performance.

2 - Driving Better Customer Outcomes: Ensure the product scales by aligning it with real user needs.

• Engagement Assistance. AI-powered language assistance can help engineers refine clarity, tone, and structure in customer interactions. AI will optimize responses while preserving individual style, enhancing empathy, curiosity, and consistency. It will also guide engineers in asking better, more precise questions, leading to faster issue resolution and a stronger shared understanding of customer needs.
• Classification & Intelligent Routing. AI-driven automation can enable precision routing, ensuring issues reach the right engineer the first time. AI-powered classifications will also uncover patterns and insights that can improve product outcomes and customer experience.

3 - Developing Talent: Elevating the team’s ability to grow, adapt, and lead in an AI-augmented world.

• Real-Time Coach. AI Agents can be active mentors who can help sharpen debugging strategies, ask better troubleshooting questions, and learn from top performers. By using AI as a driver of continuous improvement, engineers can deepen their technical expertise, refine critical thinking, and improve problem-solving skills.
• Performance Management. Engineers can take ownership of their growth by using summarization to track achievements across emails, tickets, and contributions. Gone are the days of trying to recall accomplishments from the most recent cycle. This offers more control with much less work and shifts the conversation from a vague overview to a holistic discussion around impact and opportunity.
• Unlocking Talent. AI can remove barriers for high-potential engineers who have been overlooked due to physical limitations, geographic constraints, or non-traditional career paths. By enhancing communication, automating workflows, and providing adaptive tools, I will expand the talent pool and bring in new perspectives that drive innovation and better problem-solving.

Risk Management

Leaders must be aware of the risks when aggressively transforming an organization. In addition to the standard change management risks—resistance to change, operational disruption, misalignment with business goals, etc.—I would prioritize the following:

Overreliance on AI can weaken critical thinking if engineers defer too much to automated solutions instead of sharpening their own expertise.

Misinformation and hallucinations will remain challenges, requiring strong validation processes to keep AI-generated responses accurate and trustworthy.

Over-automation can remove too much human oversight, leading to blind spots in complex, unstructured problem-solving.

Thoughtful implementation is key—AI must be a force multiplier, not a substitute for human expertise.

Final Thoughts

The future of Tier Three support isn’t just about solving problems—it’s about scaling problem-solving itself. AI gives us the ability to transform unstructured problems into structured, repeatable solutions that create opportunities for scale. I will focus on building a team that thrives in this AI-powered environment—engineers who don’t just troubleshoot issues but architect scalable solutions. With the right people, processes, and AI-driven insights, Tier Three can raise the bar for what AI-augmented technical support can be.