Unlocking AI’s Potential with Next-generation Data Annotation

Across industries, Artificial Intelligence (AI) is already driving accelerated business outcomes and opening new avenues to innovation. PwC forecasts a 14 percent increase in global GDP by the end of the decade, attributing this growth to AI.¹ However, the success of AI and Machine Learning (ML) models – and the ability to realize this value – hinges on accurate data classification and annotation. Precise labeling is essential for optimal model training, fueling a global data collection and labeling market expected to reach USD 12.75 Billion by 2030.²

Hi-tech businesses are at the forefront of this challenge, tasked with creating groundbreaking digital solutions that leverage AI and handle large volumes of data, often with short turnaround times. Traditional businesses, too, are increasingly impacted by AI as they implement digital storefronts, generating unprecedented levels of data.

Despite their efforts, many businesses face significant hurdles in managing and processing this influx of data. Research from Forrester Consulting and WNS reveals that 53 percent of hi-tech enterprises struggle with processing big data fast enough, and 59 percent cite a lack of maturity in data management as a significant challenge.

The Significance of Annotation-led AI

Annotation-backed AI is revolutionizing data handling across industries. It is enhancing product categorization in retail and e-commerce, improving document verification in financial services and advancing medical imaging in healthcare, among other applications.

Harnessing this potential requires the right combination of domain expertise, process excellence and AI-powered digital tools. This enables enterprises with data that is clean and contextualized, optimally annotated and can be seamlessly ingested into existing platforms. In doing so, businesses will be empowered with AI capabilities that can fuel next-generation innovation.

Preparing Data for AI and ML Optimization

Faced with a burgeoning volume of data that needs to be validated and synthesized, leading hi-tech firms are implementing new approaches to optimally power AI and ML models and unlock new routes to growth. The initial step involves data cleansing and classification. Organizations often face inconsistencies, silos and a lack of standardization, rendering digital transformation efforts futile. KPMG analysis shows that 51 percent of digital transformation investments fail to impact performance.³

AI-led data extraction and contextualization platforms offer a solution, automating the extraction, organization and cleansing of data from disparate sources, including structured categories as well as unstructured categories like user-generated data. This creates harmonized datasets for enhanced decision-making, setting the stage for the next step: Data annotation. For instance, a leading insurer struggling to manage escalating claims leveraged an AI and ML platform to mine critical information from documents by extracting, contextualizing and annotating claims-related data. This resulted in a 50 percent increase in data labeling productivity, resulting in faster claims processing.

Precise labeling is vital for training models and improving capabilities like computer vision and speech recognition. However, annotating vast datasets is a manual and intensive process. Next-generation data annotation services provide comprehensive and seamless solutions across a breadth of capabilities for hi-tech firms to tap into, ensuring that all kinds of data are labeled accurately and efficiently.

Text annotation services, for instance, can handle tasks like sentiment analysis or spam detection, enhancing search engine performance and customer chatbots. Audio annotation can empower virtual assistants or voice search capabilities, while image annotation can supercharge visual intelligence. Tagging video footage can improve computer vision products and object detection, with Light Detection and Ranging (LiDAR) and geospatial annotation offering numerous use cases in the hi-tech space.

Essentially, optimally annotated data enables ML models to accurately understand what is being processed and make better, faster decisions in response. This can drastically change the operating dynamics of various industries, from e-commerce marketplaces and legal services through to the entertainment and security industries.

Tangible impacts across these industries include improved user experiences through personalized recommendations or intuitive search, optimized location accuracy for context-specific services or deep learning capabilities that can predict demand and enhance service quality.

Partnering for End-to-End Accuracy, Agility and AI Success

While some organizations tackle annotation challenges internally, the intensive and specialized nature of processes is seeing many collaborate with third-parties. By forming the right partnerships, hi-tech organizations gain access to deep domain expertise, process knowledge and a scalable, experienced workforce. This ensures accuracy, reliability and flexibility to handle high-volume projects or support smaller companies. It also means access to next-generation AI-powered tools that streamline workflows and reduce timeframes for data generation. Crucially, the right partners can seamlessly ingest and integrate data within existing models.

Such partnerships can deliver faster time-to-market, a significant reduction in cost per label and a drastic decrease in time per label created – all with precise annotation accuracy. In essence, they can unlock transformation at speed and scale, enabling hi-tech firms to drive accelerated business outcomes.

Click here to learn more about how next-generation data annotation services can fuel AI and ML success for hi-tech businesses.

References

1. Global Artificial Intelligence Study | PwC

2. Global Data Collection and Labeling Market Size | PR Newswire

3. KPMG US Tech Survey Report Findings