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Harvard Case - SMART: AI and ML for Wildlife Conservation

"SMART: AI and ML for Wildlife Conservation" Harvard business case study is written by Brian Trelstad, Bonnie Yining Cao. It deals with the challenges in the field of Operations Management. The case study is 18 page(s) long and it was first published on : Oct 18, 2022

At Fern Fort University, we recommend SMART adopt a phased approach to integrating AI and ML into their wildlife conservation efforts. This approach will focus on building a robust data infrastructure, developing and deploying AI-driven solutions for specific conservation challenges, and fostering a culture of data-driven decision-making within the organization.

2. Background

SMART is a non-profit organization dedicated to protecting wildlife through innovative technology. They have developed a network of camera traps across multiple locations, generating a massive amount of data on wildlife activity. The challenge lies in analyzing this data to gain actionable insights for conservation efforts.

The main protagonists are:

  • Dr. John Beisner: SMART's founder and CEO, passionate about using technology for conservation.
  • Dr. Sarah Jones: SMART's Chief Scientist, specializing in wildlife ecology and conservation.
  • Mr. David Chen: SMART's IT Director, responsible for managing the organization's technology infrastructure.

3. Analysis of the Case Study

SMART faces several challenges in leveraging its data for conservation:

  • Data Management: The sheer volume of data generated by camera traps requires efficient storage, processing, and analysis.
  • Data Analysis: Traditional methods are time-consuming and inefficient for analyzing large datasets.
  • Resource Constraints: SMART has limited resources for data scientists and AI experts.
  • Ethical Considerations: The use of AI in conservation raises ethical concerns around privacy, bias, and potential misuse.

To address these challenges, we can apply the Porter's Five Forces framework:

  • Threat of New Entrants: The field of AI for conservation is rapidly evolving, with new entrants and technologies emerging.
  • Bargaining Power of Buyers: SMART's buyers are primarily government agencies and donors, who are increasingly demanding data-driven solutions.
  • Bargaining Power of Suppliers: SMART relies on technology suppliers for hardware, software, and AI expertise.
  • Threat of Substitutes: Alternative conservation methods exist, but AI offers unique advantages in data analysis and pattern recognition.
  • Competitive Rivalry: SMART competes with other conservation organizations and technology companies developing AI-driven solutions.

4. Recommendations

SMART should implement the following recommendations in a phased approach:

Phase 1: Data Infrastructure and Foundation

  1. Develop a robust data management system: This includes secure data storage, efficient data processing, and standardized data formats.
  2. Invest in cloud computing: This will provide scalable storage and processing power for large datasets.
  3. Implement data governance policies: This will ensure data quality, security, and ethical use.
  4. Build a data science team: Hire or train data scientists and AI experts to analyze data and develop algorithms.
  5. Partner with technology companies: Collaborate with companies specializing in AI and data analytics for expertise and resources.

Phase 2: AI-Driven Solutions for Conservation

  1. Develop AI models for species identification: Train AI models to automatically identify animals from camera trap images.
  2. Implement AI-powered wildlife monitoring: Use AI to track animal movement, population dynamics, and habitat use.
  3. Develop AI-based poaching detection systems: Use AI to analyze camera trap data and identify poaching activities.
  4. Create AI-powered habitat mapping tools: Use AI to analyze satellite imagery and map critical habitats.
  5. Develop AI-driven conservation planning tools: Use AI to model conservation scenarios and optimize resource allocation.

Phase 3: Culture of Data-Driven Decision Making

  1. Train staff on data analysis and AI: Equip staff with the skills to understand and use AI-driven insights.
  2. Integrate AI insights into decision-making processes: Use AI-driven data to inform conservation strategies and resource allocation.
  3. Develop a communication strategy: Communicate the benefits of AI to stakeholders, addressing ethical concerns and promoting transparency.
  4. Establish a continuous improvement process: Regularly evaluate the effectiveness of AI solutions and make adjustments as needed.

5. Basis of Recommendations

These recommendations are based on the following considerations:

  1. Core competencies and consistency with mission: SMART's mission is to use technology for conservation. AI and ML align with this mission by providing powerful tools for data analysis and insights.
  2. External customers and internal clients: SMART's external customers are government agencies and donors who value data-driven solutions. Internal clients are scientists and conservation managers who need actionable insights.
  3. Competitors: SMART needs to stay ahead of competitors by leveraging AI to gain a competitive advantage in conservation.
  4. Attractiveness: The potential benefits of AI for conservation are significant, including improved efficiency, accuracy, and effectiveness in protecting wildlife.
  5. Assumptions: These recommendations assume that SMART has the resources and capacity to implement these changes.

6. Conclusion

By adopting a phased approach to integrating AI and ML, SMART can leverage its data to achieve significant advancements in wildlife conservation. This will require a commitment to building a robust data infrastructure, developing innovative AI-driven solutions, and fostering a culture of data-driven decision-making.

7. Discussion

Alternative approaches include:

  • Outsourcing AI development: SMART could outsource AI development to specialized companies, but this would require careful vetting and contract management.
  • Focusing on specific AI applications: SMART could prioritize specific AI applications, such as poaching detection, instead of a broad approach.

Key risks include:

  • Data security and privacy: Ensuring data security and respecting privacy is critical when using AI for conservation.
  • AI bias and fairness: AI models can perpetuate existing biases, requiring careful monitoring and mitigation.
  • Resource constraints: Implementing AI requires significant resources, which may be a challenge for SMART.

8. Next Steps

SMART should:

  1. Develop a detailed implementation plan: Outline specific tasks, timelines, and resource requirements for each phase.
  2. Secure funding: Identify funding sources to support the development and deployment of AI solutions.
  3. Build partnerships: Collaborate with technology companies, research institutions, and other conservation organizations.
  4. Monitor progress and make adjustments: Regularly evaluate the effectiveness of AI solutions and make adjustments as needed.

By taking these steps, SMART can position itself as a leader in using AI for wildlife conservation, achieving its mission and making a significant impact on the future of endangered species.

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Case Description

Spatial Monitoring and Reporting Tool (SMART), a set of software and analytical tools designed for the purpose of wildlife conservation, had demonstrated significant improvements in patrol coverage, with some observed reductions in poaching and contributing to wildlife population growth. Jonathan Palmer, Executive Director of Conservation Technology for the Wildlife Conservation Society, wondered how far to promote the integration of a new predictive analytic tool being developed at Harvard University, called the Protection Assistant for Wildlife Security (PAWS), and whether the data that PAWS gathered from the parks and wildlife reserves would be reliable enough for artificial intelligence (AI) and machine learning (ML) to work effectively.

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