Over the next decade, Artificial Intelligence (AI) will revolutionize the world of work. According to PwC, AI could potentially contribute over USD 15 Trillion to the global economy by 2030 through enhanced productivity, enabling businesses to solve problems and innovate in novel ways.1

We’ve already seen the likes of ChatGPT mainstream conversations around AI and its impact on society. For leading hi-tech and professional services firms, AI will prove integral to driving accelerated business outcomes and superior customer experiences in an ever-evolving technology and consumer landscape.

Research from Forrester Consulting and WNS reveals that almost 60 percent of hi-tech firms plan to deploy top AI technologies, including AI-enhanced Business Intelligence (BI) platforms, text analytics, Natural Language Understanding (NLU) and computer vision.

However, the success of these platforms ultimately relies on the quality of the data they are fed. The business imperative to create high-quality data for supervised Machine Learning (ML) is such that the global data collection and labeling market size is expected to reach USD 12.75 Billion in value by 2030, registering a compound annual growth rate of 25 percent (from 2022 to 2030).2

In doing so, businesses will find themselves empowered by AI platforms capable of uncovering unprecedented insights. With these insights, companies can make more informed decisions, develop innovative products and services and enhance operations. When combined with domain expertise, data privacy measures and merchant compliance, advanced AI technologies can help hi-tech firms achieve optimal performance, driving innovation and executing their strategic visions well into the future.

High-quality Data for Objective AI Models

Currently, hi-tech and professional services firms need help executing their visions for data, analytics and AI. Research shows that the top challenges include data silos, lack of budget and legal and compliance issues, all of which impede the enterprise’s ability to innovate at speed. Fortunately, solutions to these issues are emerging quickly.

Take data silos, for instance. Whereas enterprise functions may have previously worked from different data sets, with swathes of insights left buried in unstructured data, data extraction and contextualization platforms can now automate the extraction and ingestion of data from disparate sources and create harmonized datasets devoid of bias. With Gartner estimating that more than 80 percent of enterprise data today is unstructured, the benefits of harnessing this data, in particular within AI training models and ML, are significant.3

Furthermore, data annotation and labeling are crucial when developing AI systems. Those trained on optimally labeled examples learn quickly to distinguish between different kinds of data and categorize correctly, with AI taking over after initial human-guided examples. Data annotation represents a manual and intensive process, but new solutions are accelerating this with transformational data engineering capabilities.

Domain Expertise for Optimal Business Application

Although the creativity and intelligence of AI and ML solutions may be widely celebrated, it is crucial to maintain a proper equilibrium between human employees and technology. To achieve optimal results, it is vital to cultivate a team of individuals with both data science training and relevant industry experience, as this combination represents the ideal approach for building domain expertise. By doing so, AI models can flourish, and insights can be correctly applied to the business, ultimately leading to success.

Naturally, some enterprises and industries are better equipped than others to build this expertise. With intensive work required upfront from data scientists and engineers concerning data labeling, identifying anomalies and re-thinking categorizations, many hi-tech firms are turning to third-party solutions for assistance.

External partners can help plug gaps in skills and capabilities and accelerate critical data and analytics implementation aligned with business objectives. Those embracing such partnerships are realizing the advantages, with hi-tech firms that leverage external support expecting a 43 percent average increase in the engagement of third-party service providers over the next year. By forging the right partnerships, hi-tech firms can secure the resources and intelligence they need to thrive in a highly competitive landscape.

Working with partners this way can enable companies to co-create with AI developers and ensure models are optimally applied to the specific business. Custom elements that can be collaborated on include everything, from data captured and rules followed to workflows and user interfaces.

Ensuring Compliance to Limit Risk

Hi-tech firms are constantly seeking supportive partnerships beyond their domain expertise. As the number of platform aggregators in the market continues to grow, ranging from social media platforms to payment apps and digital wallets, the demand for third-party providers who can assist with merchant compliance is on the rise.

Increased digitization is seeing critical risks around privacy, data integrity and cybersecurity magnified – and leading service providers are stepping in to help. From offering ongoing support and guidance to help firms stay updated with changing regulations and requirements of different geographies to providing training and education to ensure the right talent pools, partnerships can help hi-tech companies save time, money and resources and avoid costly penalties or legal issues.

Enabling a Culture of Innovation to Flourish

With the focus on quality data, domain expertise and compliance, hi-tech businesses are primed for innovation to flourish. These businesses already exhibit a comparatively high level of data and analytics maturity. With these three crucial elements in place, AI models can function optimally, enabling enterprises to uncover new insights quickly and embrace experimentation.

These capabilities result in concrete outcomes that allow businesses to harness third-party platforms, capabilities and resources to solve several business problems. By embracing an AI-led platform-centric approach, companies can drive faster time to market within research and development. By leveraging this approach, one business realized an estimated USD 50 Million in estimated lifecycle savings, roughly translating to 10 times ROI. Similarly, another company, a risk solutions provider, deployed an AI-led platform to automate data extraction and improve productivity by a staggering 75 percent.

Progressive organizations should similarly implement a continuous iterative process of identifying gaps in their key competencies while experimenting with newer ML and AI models. Doing so will allow leading hi-tech firms to successfully execute their top business priorities: improving customer experience (80 percent), building topline growth (79 percent) and accelerating responses to market changes (79 percent).

To know about how WNS is helping hi-tech companies unlock innovation by harnessing AI, data and domain, visit Hi-Tech Business Process Management | WNS.


  1. https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html

  2. https://www.prnewswire.com/news-releases/global-data-collection-and-labeling-market-to-reach-12-75-billion-by-2030--301633931.html

  3. https://www.geekwire.com/sponsor-post/unstructured-data-organizations-best-kept-secret/

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