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LabelboxOctober 10, 2022

How a software-first approach to labeling AI data gets you a better ROI than tech-enabled BPOs

AI teams typically require a vast amount of high-quality labeled data, quickly. Traditionally, AI teams would outsource their data labeling to business process outsourcing (BPO) companies and the engagement would be project-based and transactional in nature. In recent years, many of these BPOs have evolved to be ‘tech-enabled’ meaning, in effect, that they now train their own machine learning models on the customer’s data in order to increase their margins, leading to AI teams losing control of their most important intellectual property (IP) for their AI systems. This also means that they are often paying a significant premium for their labeled data as well.

To alleviate the misalignment of incentives and inefficiency, a software-first approach has emerged, which allows these AI teams needing labeled data to use any labeling service or vendor with full transparency, and collaborate easily with all stakeholders throughout the labeling process, and to train their own models to automate labeling thereby significantly reducing their unit costs for labeling.

In this blog post, we’ll outline the three primary reasons that enterprises should strongly favor a software-first approach, instead of relying on tech-enabled BPOs to handle their data and models. From working with hundreds of AI teams across Fortune 500 organizations to leading AI-first startups, we have also seen that this is the single linchpin to dramatically reducing costs, increasing team agility to experiment more, and ensuring data quality across your organization.

Reap the full benefits of labeling automation by sending residual labeling tasks to humans only when absolutely needed

It’s no surprise but bears repeating: your model understands your data better than any other model. Your team and your model are the foremost experts of what you’re labeling, which is why we recommend using automation to pre-label, and only sending labeling tasks to humans when necessary. When speaking with ML teams who have worked with tech-enabled BPOs, we want to understand if their goal is to build the best performing model for their domain. The answer is typically a resounding yes, which is why it’s unreasonable to expect any third-party BPO/vendor to have a better model for your application than you do. (If they did, then you would just buy their model and be off to the races).

This is why a software-first approach differentiates itself through better technology. As an example, of one of the main benefits of a software-first approach is the ability for AI teams to import model predictions as pre-labels. This allows their labelers to be able to just review and correct, instead of labeling all data from scratch. By utilizing model pre-labeling (otherwise known as model-assisted labeling), this approach has been proven to reduce labeling time and costs by up to 50%-60%. In addition, model-based pre-labeling decreases labeling costs as the model gets smarter with every iteration, leaving teams more time to focus on manually labeling edge cases, or areas where the model might not be performing as well. It’s not only faster and less expensive, but delivers better model performance and lets you keep the gains from automation, as opposed to relying on tech-enabled BPOs that are not offering you the ability to use your own models to get more efficient.

Pay only for productive screen activity time from your human labeling service

Labelbox’s software-first approach means that we are motivated to provide a product that allows our customers to pay only when actual labeling work is happening because our focus is on improving the software itself. This is in stark contrast to tech-enabled BPOs that price based on fixed label cost models.

This difference in approach means that we are aligned on reducing the time it takes to label each asset, as well as entirely focused on increasing the value of each label and completing it more rapidly. Our customers tell us that charging based on labeling hours in which activity is happening is what they prefer because they only pay for productive screen activity time. This helps us keep your costs controlled and our financial incentives aligned with yours as projects inevitably grow in value and scope so that you get the highest payoff for the fixed amount that you spend.

In addition, we’ve noticed a recurring pattern having seen the work of hundreds of labeling teams. When your human labelers are motivated to be fast and adept at completing their projects through the right performance metrics and incentives, there is a skill proficiency gained from mastering certain tasks, which in turn helps reduce your overall cost of labeling spend.

Get complete transparency on your labeling metrics when working with external labeling service vendors

Customers have told us time and time again that they have had less than ideal experiences working with tech-enabled BPOs. It’s a common pattern that emerges, where the customer feels like they are flying blind because they lack the in-depth performance metrics needed to gauge the success of their project, as well as understand the productivity of the people doing the work. In contrast, Labelbox provides the anti-black box, where we’ve committed our product to showing you at the workspace, project, and individual labeler level what’s happening.  By giving your team visibility into the value chain every step of the way, you get transparency and accountability to better forecast your needs and to ship on time. This leads to major reductions in wasted spend, duplicate data, and the need for less pilots.

We consult our customers to do their own due diligence before engaging with tech-enabled BPOs that lack quality dashboards, individual labeler insight, and customizable review queues - all which leads to serious difficulties in scaling your labeling needs. Because Labelbox takes a software-first approach, we give you visibility coupled with actionable metrics that drive efficiency. Plus, we’ll work with you to find the most efficient workflows and drive down labeling costs over time.

Final takeaways

Organizations that choose to adopt Labelbox’s data engine save months of lost time and keep the automation gains for themselves, allowing them to build a long-term competitive advantage. We advise against passing off your critical training data workflows to tech-enabled BPOs who are less motivated to be efficient with your resources, and even less committed to product innovation.

If you’re interested to learn more about how a software-first approach is currently helping enterprises who have already made the switch from tech-enabled BPOs, download the complete guide to data engines for AI or you can try Labelbox for free today.