AI for Growth

AI productivity tools are most useful when they support human judgment, reduce cognitive friction, and fit naturally into real workflows. My interest is in designing and evaluating AI systems that help people move from information to action without losing agency, context, or quality.

Research Focus

AI productivity systems should do more than generate faster outputs. My interest is in how AI can reduce cognitive friction, support better judgment, preserve user agency, and help people move from complexity to meaningful action.

Productivity and Work

Studying how AI tools can improve the quality, speed, and clarity of knowledge work while reducing cognitive load and maintaining human judgment.

closeup photography of plant on ground
closeup photography of plant on ground
Org Level Decision-Making

Using AI and data science to help organizations convert fragmented behavioral, operational, and textual data into clearer decisions.

Developing evaluation approaches for AI systems with attention to usefulness, fairness, behavioral effects, and unintended consequences.

Responsible Evaluation
people sitting on chair inside building
people sitting on chair inside building
A ruler measuring text on a page.
A ruler measuring text on a page.

WHY THIS MATTERS

Most AI productivity tools are evaluated through speed: how quickly they summarize, draft, or automate a task. However, speed is only one part of productivity. For knowledge work, the more important questions are whether the system improves clarity, judgment, quality, follow-through, and confidence.

A useful AI productivity system should help people act better, not only produce more.

Evidence Mapping Research

A research direction for evaluating whether AI productivity tools in specific domains actually improve work quality, decision-making, and long-term efficiency.

Problem

Many domains are rapidly adopting AI tools to improve productivity, especially in knowledge-intensive work such as legal tech, education, healthcare, consulting, and research. However, the value of these tools is often assumed rather than carefully evaluated.

EXPLORATORY DIRECTION

Gap

Most AI productivity claims focus on short-term speed, automation, or user excitement. This does not show whether the tool improves quality, reduces cognitive load, supports better decisions, fits real workflows, or remains useful after the novelty effect fades.

Proposed Approach

This project would review recent AI productivity work in a specific domain, map the proposed use cases, and analyze what appears to work, what does not, and why. The focus would be on mechanisms: task fit, workflow integration, user trust, error risk, adoption friction, and measurable outcomes.

Potential Results

The project could produce an evidence map of AI productivity interventions in one domain, a framework for evaluating long-term usefulness, and practical design principles for deciding when AI support is likely to create real value rather than superficial efficiency.