AI Proliferation in Enterprises: addressing the need for optimal data labeling
From modest beginnings around the turn of the millennium, global retail eCommerce sales had reached USD 4.28 trillion by 2020, with estimated projections of USD 5.4 trillion, by 2022. But far from being limited to an expanding footprint and escalating bottom line, the real story behind the meteoric rise of eCommerce is the several iterations of technology, which the industry has enthusiastically adopted.
It can be justifiably argued that the most significant of these tech-based evolutions in eCommerce has been the ability to leverage data. And with the advent of AI, this capability is set to transform eCommerce like never before.
The Impact of AI in eCommerce Entreprises
The typical eCommerce business generates many different kinds of datasets. Daily transactions, browsing behavior, catalog data, inventory records involving product SKUs, and many more details result in an ever-increasing volume of data. Unlocking the value, which this information can add to customer experiences, is a constant challenge. And this is where AI is impacting eCommerce most significantly.
eCommerce enterprises are looking to use this data to build AI models that generate value to shoppers.
All seven of the imperatives put forward in McKinsey & Company's recent Retail Speaks report can be addressed more effectively by using AI-enabled technologies. For example, research estimates that Chatbots – a single capability enabled by AI - will lead to cost savings of more than $8 billion per annum by 2022. Most critically, AI allows eCommerce enterprises to build AI-driven products that make sense of the large amounts of data being generated and adds value to customer experiences daily. Such solutions include:
- Empowering personalized merchandising and autonomous stores, etc.
- Enabling image recognition and digital searches
- Stock management and video surveillance
- Identifying and monitoring the Customer Journey path
- Streamlining supply chain logistics
- Enabling virtual digital assistance in eCommerce
A recent report issued by Omdia estimates that AI implementation spend will expand from a budget of 1.3Bn USD in 2019 to a projected 9.8Bn in 2025 across the major economic regions in the world. And given the benefits and value being unlocked for customers, this is a figure that will continue to grow even further with the continuing emergence of new tech-enabled services and capabilities.
But the game-changing AI-driven innovations are capabilities like Competitive Intelligence, which is currently deployed by more than 90% of Fortune 500 companies. AI has played a pivotal role in managing and generating valuable insights from the volumes of data being generated. Some data-driven use cases include:
- Competitive Intelligence, where algorithms are tuned to track price changes in real-time for a decisive competitive advantage
- Product Catalog Curation and Classification where product attributes are manually created for relevant search results
- Search Relevance to ensure that users get the most accurate search results
- Attribute Extraction to enrich product catalog by automatic extraction from product images
The Formula to Create an AI-ready eCommerce Enterprise
As Ray Dalio, founder of Bridgewater Associates, aptly puts it, "Failing to consider second-and third-order consequences is the cause of a lot of painfully bad decisions, and it is especially deadly when the first inferior option confirms your own biases."
AI isn't a magic potion. eCommerce Enterprises shouldn't expect anything without tertiary infrastructure. From the development of full-stack AI solutions to hiring the right personnel, eCommerce businesses need to take a holistic approach to leverage this technology. The tertiary infrastructure includes both ML Ops and Labeling Ops which work together to form successful production AI.
Labeling Ops + ML Ops = Successful Production AI
Optimizing Data Labeling is a fundamental building block. When enterprises focus only on ML Ops, the other pillars of AI – Computing Power and Algorithms become redundant.
Labeled Data enhances Production AI, unlocking its full potential to create an AI-ready eCommerce Enterprise.
The effectiveness of any AI solution is limited by the quality of data used to train it. A typical data labeling use case may include tagging, classification, data annotation, and other related processes. AI-ready and dynamic eCommerce enterprises understand the second-order effects of AI proliferation and have taken steps towards strengthening supporting infrastructure for data labeling such as orchestration of labeling applications, quality control workflows, labeler performance management, labeling process optimization, which dramatically enhance the AI creation process, and the capabilities it enables.
However, in most enterprises, it is ML ops infrastructure that has the highest investment in contrast to the labeled-data procurement platform (labeled data) and management of the labeling partner network. In fact, most enterprises will under-invest in labeled data despite how fundamental it is to AI creation.
The lack of investment can be contributed to the lack of control enterprises have over the value chain - from Labeled Data procurement to training to deploying AI models.
Modern eCommerce enterprises understand the value of AI in eCommerce. It's why they've introduced specialized new AI-centric roles - such as a VP of Data Science, Head of Search and Recommendations, Head of Pricing, etc. However, even though enterprises are now implementing roles focused on enhancing their AI development, the question remains: do they have a Centralized Labeled Data Procurement Strategy to make better Retail AI, faster?
Most enterprises have an AI strategy. Do they have a labeled-data procurement strategy ?
The Urgent Need for a Labeled-Data Procurement Strategy
Managing and labeling data for eCommerce AI brings with it a specific set of challenges. The sheer complexities of product taxonomies and the challenges that arise due to multiple data types - ranging from images to audio and video, etc. – are compounded by the need for substantial labeling teams across geographies with highly defined domain expertise, and many use cases.
According to Nasscom's 'Data Annotation - Billion Dollar Potential Driving The AI Revolution' Report, the data labeling market serviced by India alone is set to exceed USD 7 billion in value. It has the potential to employ a workforce of up to 1 million by 2030. The global labeling spends on third-party solutions is estimated to escalate to 7X of its value in 2018 by 2023. Despite such a dramatic escalation, this figure will only account for about 1/4th of the total projected spend on data annotation at that time.
The two building blocks needed for effective data annotation services are a trained workforce and an efficient labeling platform to operationalize data labeling. Failure to do this adequately has severe negative consequences, directly impacting the quality of the AI - often leading to suboptimal utilization of resources and the creation of bottlenecks within an organization. In effect, enterprises not only need an AI strategy but also a labeled-data procurement strategy,
McKinsey & Company's 'Notes from the AI Frontier' report estimates that AI could add $13 trillion in global economic activity by 2030. Unlocking this staggering potential will depend on an effective data labeling solution to help Machine Learning and cognitive technology leverage and accurately understand the systems they are supporting. Considering this, the global data labeling and annotation tools market's projected growth has been estimated at a CAGR of 27.1% between 2021 and 2028.
Data Labeling shouldn't be as watered down as it has become. What has been missing all this while is centralization and optimization.
TaskMonk is a centralized labeled-data procurement platform that is labeler-centric and accommodates the needs of a labeler. The platform unifies labeling infrastructure, to cater to the demands and necessities of retail AI.
As new products and technologies emerge, seeking to gain a foothold within a rapidly evolving customer base, personalization, interpretation of data, and leveraging intelligent insights will separate successful eCommerce businesses from the rest of the crowd. Developed on the basis of experience gained over more than a decade of processing raw eCommerce data, Taskmonk's all-pervasive solution can be integrated with any data source and is configurable for any data type.
Used by some of the biggest eCommerce Retailers in the world with highly demanding data annotation needs, TaskMonk has been purpose-built to handle the nuances of an eCommerce enterprise labeling team.
TaskMonk is the easiest way for AI-forward enterprises to have a labeling infrastructure that results in faster and more optimal Production AI. Taskmonk can evolve as the AI matures, the volumes and complexity of data increase, and the size of labeling teams grow.
Solutions like Taskmonk are not simply a 'good-to-have advantage; they are quickly becoming a default technology to help retailers navigate the constantly shifting sands of eCommerce, now and into the future.