Welcome to 2024, where everything is, or wants to be AI. With so much bluster, it can be hard to separate the hype from reality. For the ITAD Industry, specifically for E-Waste generators, the impact from AI is very real and already driving significant change to the traditional ITAD model.
Past vs Present
Most E-Waste generators have traditionally used a small cohort of partners to process their downstream material. This “limited partner” model is often used because it can be cumbersome to qualify and manage a larger partner pool where each partner has their own reporting format, capabilities, and backend systems.
The downside in this traditional ITAD model, is that when using a limited partner pool:
- Reduced Profitability – Material is not being brought to market in a competitive manner.
- Higher Emissions – Unnecessary emissions are being generated to due to longer transportation routes.
- Poor Data – Fragmented data flows from a variety of reporting types are hard to piece together.
Why is this traditional model changing now?
The three main factors driving a change from the traditional ITAD model for E-Waste generators
are:
- High Quality Data Requirements – Multiple stakeholders across E-Waste generator organizations now need high quality data about downstream activities to satisfy data security compliance and carbon accounting reporting requirements.
- GHG Scope 3 Emissions Reduction Requirements – It can no longer be justified to route material 2,000 miles away just because that’s where the nearest processing partner is.
- AI Tools that allow for E-Waste generators to manage a broader partner pool – New AI tools allow E-Waste generators to collect data and manage a diverse partner pool with greater efficiency, profitability, and accuracy.
What are these new AI tools for E-Waste Processing?
- Data Ingestion –The use of AI models trained to ingest, and index, structured and unstructured data.
Impact: Trained AI models can read and index hundreds of compliance certificates, settlement reports, and environmental metrics in minutes, regardless of their format. These models also rate themselves on how well they performed and can ask for human intervention if it senses a sub-optimal performance. Aggregating ITAD data into a single homogeneous database unlocks a variety of AI and analytical tools. - Small Language Models – The creation of small, highly customized, language models (SL models).
Impact: Customized language models built specifically for an E-Waste generator’s operations using their specific nuanced language about facilities, product categories, and emissions allows for better evaluation of historical activity and the ability to improve future performance. - Generative Analytics – On demand generation of reports and visuals using custom SL models. Impact: A generative AI assistant can build detailed, rich graphics, and trends using
homogeneous data and SL models and update those reports automatically. Making it easy to interpret E-waste data. - AI Skills – Machine Learning models focused on purpose built semantic models.
Impact: Think CHAT GPT where the user can have conversations with their specific E-Waste data. They can summarize activity and have a natural language conversation about everything they have processed including emissions, transportation, data compliance, commodity type, and activity by facility and geography.
Why Should You Care?
In the past, most E-Waste generators have not applied much rigor to how they processed their electronic material. If you are managing the downstream process for your company, your customers, colleagues, and stakeholders are depending on you to understand how your activities are driving carbon emissions, data security, and financial results. They also need you to ensure you are capturing high quality data about your activities, which is critical for today’s scope 3 carbon accounting requirements.
To learn more about Circularity Electronics and how we are helping E-Waste generators use AI to transition away from the traditional ITAD model, reach out for a demo today