The Backbone of the AI Industry
Every industry has a backbone: a business or a group of businesses that provide the essential services for the whole industry to function. In the case of AI, this backbone is comprised of Data Labeling Service Providers: companies whose sole business model is providing human workforce to others, helping them label huge, complex training datasets used to build working ML applications.
Striking the perfect mix of labeling expertise, workforce management, and customer satisfaction can itself be a daunting task. But LSPs face additional challenges that are very specific to their business model and the level of maturity of their organization. Your company may be people-based or tech-based, use crowdsourcing or professional workforce, may be a big corporation or a small business, but regardless of your place on this spectrum, competition is always fierce. New players enter the market all the time, making the industry a very competitive field where actors need to constantly maintain their edge.
One huge differentiator is a standardized labeling stack. Today, only a handful of LSPs are equipped with it, with most still unaware of its massive benefits. It is still common to see LSPs switch labeling tools to whatever a given customer uses, which basically means retooling for every project. In today’s harsh economic climate, this will no longer be sustainable or financially viable. Going forward, having a solid software stack will have a massive impact on the productivity and overall success of every LSP and the first players to standardize their processes will grow faster, be more profitable, and lead the future market, with others struggling to catch up.
The Great Tooling Shift
Under constant pressure for competitiveness and quality, the LSP market has been evolving and reinventing itself at a rapid pace, with leader positions up for grabs for determined and daring challengers. Big players entering the market tend to be equipped with a solid labeling stack because they are usually tech companies expanding their offer to the labeling realm. Some of the other players are tech-first, meaning their first investment has been on cutting-edge labeling technology to empower their workforce. The rest of the industry have either already identified the need for an established stack, or are quickly realizing that a company without a strong labeling tool will not be able to follow the current growth curve. Of course, labeling software comes with a price tag. But so does building or provisioning a new tool (or a set of tools) for every new task and having to retrain your workforce to use it. In fact, the latter may end up costing way more.
Let's start with the fact that each new project requires dedicated technical and human setup: from onboarding on the tool, through integration with data sources (cloud-based or located on-premises) and other tools, to determining the correct export format, establishing a quality review, iteration flow, etc. To make matters worse, using open-source tools may mean additional integration development work that complicates matters even more.
Reduce supervision costs
Juggling many tools at once drives up supervision costs: the less familiar the tool, the harder it will be for you to assess productivity in the long term, create efficiency benchmarks on a specific task or data format, and generally manage your error rates and productivity in a granular-enough manner. What’s more, using different tools means using sets of different features, with each solution having its own specific strengths and limitations. Finally, constantly switching labeling platforms means never using the same capabilities, which means permanent context switching for your labelers. All in all, this may increase your transition workload, both technical and human, by at least 40% driving up the total cost of workforce and, in the end, impacting the business proposal for your prospect.
Improve output quality
And then there’s quality: more and more customers require a quality commitment of 95%, which means that more than 95% of assets must be correctly labeled. This translates to a near-to-perfect application of labeling guidelines, spotless annotation precision, complete lack of bias, etc. If this quality threshold is not reached, teams have to rework the dataset for free until the quality rate is achieved. Today most LSPs rework up to 30% of labels in their annotation projects. If your labelers do not strive for highest quality from day 1, you run the risk of massive rework, costing your company time and money and possibly ruining your rapport with the customer.
Let's face it: the pioneering days are over. Labeling has evolved and become a complex business that has to handle lots of tasks (object detection, classification, relations, etc.) and then skillfully apply them to numerous asset types (image, text, documents, video, conversations, geospatial imagery, medical imagery, etc.) while at the same time ensuring the overall usability and ease of use of the user interface.
To maintain competitiveness and boost your growth, having a tool that brings down all those pesky setup, supervision and context switching costs we've already mentioned, while at the same time offering a substantial boost to project quality is becoming essential. A tool that helps you identify quality problems at the beginning of the project, and then lets you quickly establish strict labeling guidelines, enforce review manually with in-app class filtering & message threads, and programmatically through quality plugins. In a nutshell, imagine a tool that walks you through all the essential steps to successfully deliver a customer project, reduce your costs, increase your profit margin, and ensure high customer satisfaction. In the current state of affairs, only professional labeling tools have a change to deliver on this promise.
Is there a perfect labeling tool? Of course not. Some of them focus on one or two specific tasks or asset types and try to do that as well as possible. Others try to cover a larger spectrum, with the possible tradeoff being lower performance levels. Choosing the right software that checks all the boxes is all about strategy. If healthcare use cases drive 80% of your revenue, you want to look for a different tool than an LSP whose main focus is defense and geospatial analysis. Apply the Pareto rule: get a tool that covers 80% of your revenue and then manage the rest with ad-hoc solutions.
The benefits of having a stable technical stack clearly outweigh the extra cost involved. This means that the technical stack is and will be an essential part of your competitive edge in the years to come, improving your productivity and quality, and securing solid business. The whole LSP market is super-competitive in the best of times and in the current economic climate the decision to invest in a labeling tool may simply prove to be a sink-or-swim moment for many companies. Don’t lose any time wondering whether or not to invest in your labeling stack, as the market definitely will not.