LogoLogo
  • Introduction
    • Machine-to-Machine Learning White Paper Introduction
    • What is M2ML?
    • 3 Waves - Overcoming Limitations of Human Performance
  • Wave 1
    • Wave 1 – Vision & Identification
    • Wave 1 – Archetypes
    • Wave 1 – Opportunity Areas
      • Automating & Roboticizing Stodgy Industries
      • Accelerating Serendipitous Discovery
  • Wave 2
    • Wave 2 – Building an MVP
    • Wave 2 – Archetypes
    • Wave 2 – Opportunity Areas
      • Automating & Roboticizing Stodgy Industries
      • Accelerating Serendipitous Discovery
    • Wave 2 - The North Star: Building for the J-Curve
  • Wave 3
    • Wave 3 – Building a Platform
    • Wave 3 – Archetypes
    • Wave 3 – Opportunity Areas
      • Automating & Roboticizing Stodgy Industries
      • Accelerating Serendipitous Discovery
  • Insights
    • Identifying Winners Across Waves
    • Case Study: Rapid Robotics
    • Go-to-Market Playbook
      • Product
      • Sales
      • Platform
    • Waves in Motion
  • Conclusion
    • Why Now?
    • Get in Touch
Powered by GitBook
On this page
  1. Wave 1
  2. Wave 1 – Opportunity Areas

Automating & Roboticizing Stodgy Industries

Optimizing service-heavy processes

Robotic Process Automation (RPA)

RPA represents metaphorical software robots, fueled by data efficiencies that result in optimization of repetitive processes, like Bee portfolio company Glide Health automating back-office claim coding.

Optimizing an Existing Workflow to more efficiently Acquire and Normalize Data

This expedites the production of digital twins to support diverse uses cases across QA, speed, safety, flexibility and predictive maintenance. Bee portfolio company, Skycatch, offers turnkey digital-twins as a service leveraging off-the-shelf drones via a partnership with renowned drone-maker DJI.

Mechanization of Prevalent, Procedural and Time-Consuming Tasks

Error-prone tasks (e.g. machine tending, labeling) performed by humans can lead to a defensible, eminently transferable lego-like library of workflows (for example, Bee portfolio company Rapid Robotics leverages a highly extensible task library - learn more in our Case Study).

“First Principles” System Design Thinking

CHEP pallets is a perfect example - they built a slightly pricier pallet and switched to a rental model to create step change improvements in efficiency beyond any one task and instead across an entire network. This is a compelling approach due to the associated outsized financial returns.

Augmenting Manual Tasks with Technology

Augmenting manual tasks to boost productivity or accuracy enables startups to frame solutions seamlessly into existing high ROI workflows and avoid the build vs. buy conundrum. Examples include identifying shop floor risks using computer vision on camera feeds, worker safety using exosuits from Verve Motion, AR for immersive tutorials, and voice-based documentation for doctors like Bee portfolio company DeepScribe.

PreviousWave 1 – Opportunity AreasNextAccelerating Serendipitous Discovery

Last updated 3 years ago

Page cover image