How Glass.AI Uncovers Adoption of Emerging Technologies and Augments the Data from Surveys.
In a rapidly evolving tech landscape, understanding how new technologies are adopted across sectors isn’t just valuable—it’s essential, especially for policymakers. But getting accurate data on adoption and diffusion has traditionally been a costly and slow process. Think massive surveys of many thousands of firms, and often high-level and outdated insights by the time the analysis is ready.
At Glass.AI, we’ve developed an innovative way that can augment or replace in some instances the need for surveys or interviews.
Beyond the Survey: A Web-Scale Approach
Traditional studies rely heavily on direct surveys to assess adoption rates of technologies like AI, virtual reality, quantum or blockchain. While valuable, these methods are time-consuming, expensive and often lack the granularity needed for timely strategic decisions. Moreover, in some cases, the survey results don’t make any sense. Take, for example, these recent figures on AI adoption published by McKinsey. It’s hard to take the numbers seriously — unless respondents believe that using Microsoft Office qualifies as being on the cutting edge of AI. 78% of organisations in the world using AI is not realistic. Yet another reminder that tech adoption surveys can be deeply misleading.
We take a different approach. Instead of asking companies what they’re doing, we go directly to the digital universe—company websites, press releases, job postings, news coverage, social media, and more. We use our advanced AI in language understanding to crawl, parse, and understand public web content at scale. This lets us surface real-world indicators, use cases and case studies of tech adoption in real-time. Recently, various governments have applied our AI capability to gather data and insights that complement traditional surveys. Corporations are also using this approach.
Example: Virtual & Augmented Reality Adoption
In collaboration with PwC, we conducted a large-scale mapping of virtual and augmented reality adoption across the UK, US, and selected regions in Europe and the Middle East. Using our web intelligence capability, we identified not only developers but also adopters—businesses deploying these technologies across diverse industries. These insights helped PwC showcase technology diffusion in ways that would have been nearly impossible with surveys alone.
What We Look For
Our AI doesn't just track mentions of technology—it understands the context. For example, we look for the following indicators or “signals’:
Mentions of emerging tech themes in company content.
Press coverage linking firms to specific technologies.
Leadership roles tied to innovation or tech strategy.
Job postings related to emerging tech skills.
Notable hires in relevant technical domains.
New products or services are launched using emerging technologies.
Awards or recognition tied to innovation.
Each insight is backed by a provenance of the evidence, offering transparency and traceability for deeper analysis.
Researching the Emerging Tech Landscape Globally
In our ongoing work mapping emerging technology sectors, we’re using our AI capability to track not only the development but also the deployment of selected emerging technologies. Our AI scans the digital footprints of companies across the countries to identify who is not just talking about innovation—but actually doing it.
This approach delivers scalable, evidence-backed insights into technology diffusion, turning the vast and messy web into a powerful lens for understanding real-world adoption.