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Research Tracks

Our research investigates how digital technologies, especially AI are reshaping innovation, decision-making, and business. We partner with organizations to test ideas in the field, scale systems through experimentation, and translate insights into impact.

Research Track 1: AI in Business and Society

Lead Faculty: Karim Lakhani 

In today’s landscape, organizations across industries are embracing tools like large language models and other generative AIs reshaping how businesses lead, innovate, and compete. Our research bridges the gap between AI’s potential and its practical application, helping organizations strategically deploy AI to unlock productivity, improve decision-making, and design innovation ecosystems that are diverse, connected, and resilient. 

 Key Focus Areas:

  • Human-AI collaboration
  • AI and the future of work
  • Responsible AI deployment

Representative Work:

Research Track 2: Rethinking Innovation Evaluation

Lead Faculty: Jacqueline Lane

Led by Professor Jackie Lane, this research explores the dynamics of technological innovation, with a particular focus on creative problem-solving. It seeks to understand how novel ideas are evaluated, refined, and brought to life. A central theme in her work is leveraging artificial intelligence to enhance human-AI collaboration in the evaluation and selection of innovative ideas. 

Key Focus Areas:

  • Innovation, evaluation and selection
  • Creative Problem Solving 
  • AI in entrepreneurship 

Representative Work:

Research Track 3: Designing Intelligent Innovation Systems

Lead Faculty: Karim Lakhani& Jacqueline Lane

The lab’s previous successes with open innovation competitions highlight the value of reaching beyond traditional organizational boundaries. Building on past success with crowd-based contests, we began exploring whether generative AI could replicate or replace key crowd-based functions. This led us to investigate how intelligent systems and inclusive platforms can complement and enhance human capabilities.

Key Focus Areas:

  • Intelligent system design and deployment
  • Agent-based modeling and simulation
  • Inclusive innovation through digital platforms

Representative Work:

Research Track 4: Data Science & AI Operations Lab (D^3) Sub-Lab Spotlight https://d3.harvard.edu/labs/data-science-and-ai-operations-lab/

Lead Faculty: Iavor Bojinov, Edward McFowland III, Michael Lingzhi Li


The Data Science & AI Operations Lab explores how AI can be integrated into core business operations to drive performance, accountability, and innovation. We design and test intelligent systems that enhance organizational decision-making and automate critical functions, emphasizing experimentation and cross-disciplinary methods. 

Key Focus Areas:

  • Applications of AI and its development 
  • Experimentation & Causal Inference in the age of AI 

Representative Work:

Research Track 5: Science in the Age of AI

Lead Faculty: Kyle Myers 

Conventional mechanisms for resource allocation in science may not remain viable in settings where generative AI technologies can produce seemingly high-quality hypotheses. To prepare for these challenges, this track aims to develop novel methods for identifying productive scientists and allocating them resources for their research. Emerging projects involve the use of price-based mechanisms to elicit scientists’ beliefs about themselves and their ideas.

Key Focus Areas:

  • Resource allocation with market-based mechanisms
  • Productivity measurement in science
  • Commercializing science and the “Valley of Death”

Representative Work:

” AI is undoubtedly a transformative force, but its impact will depend on how we choose to use it. By understanding AI’s capabilities and limitations, we can unlock its potential to enhance human productivity without overlooking the value of human insight and creativity. It’s not about replacing humans but augmenting our abilities to achieve more together” KARIM LAKHANI