As artificial intelligence reshapes industries, its transformative potential remains out of reach for many. While AI is revolutionizing healthcare, education, and financial services, access to these advancements is often skewed toward large enterprises and developed economies. Bridging this digital divide is not just a technical challenge—it’s a societal imperative.
Few understand this better than Sulakshana Singh, a Senior Software Engineer at Equifax Workforce Solutions, bringing more than 14 years of experience to the role, and an AI ethics advocate committed to ensuring AI’s benefits reach underserved communities. Her work at Equifax Workforce Solutions has been pivotal in developing scalable microservices architectures that enhance workforce analytics by optimizing data processing and decision-making frameworks. By ensuring transparency, security, and accessibility in enterprise solutions, she has championed responsible technology adoption—a commitment that earned her recognition as the IEEE St. Louis Section Outstanding Woman-in-Engineering Award recipient.
“AI should not just be powerful—it should be equitable,” Sulakshana emphasizes. “We have a responsibility to make AI work for everyone.”
The Digital Divide in AI Adoption
Despite the rapid growth of artificial intelligence, access remains limited for many businesses and individuals due to infrastructure gaps, high costs, and a lack of digital literacy. A report by The Wall Street Journal highlights that the surge in AI usage is heavily straining data centers and power grids, as well as the nation’s network capabilities, due to the high bandwidth and low latency requirements of AI workloads. This indicates that many organizations may lack the necessary infrastructure to effectively implement AI solutions. For enterprises in developing economies or small businesses lacking AI expertise, integration remains an uphill battle.
Sulakshana has worked extensively in democratizing AI adoption, particularly through cloud-based AI models that lower the cost of implementation. By advocating for multi-cloud architectures, she ensures AI systems are scalable and accessible beyond tech giants, making them viable for mid-sized enterprises, nonprofits, and emerging markets.
“Cloud and AI should work together to lower entry barriers,” she explains. As a published author on Hackernoon, she has explored the role of log analysis in cloud environments, highlighting how structured debugging and real-time monitoring are essential for ensuring reliable AI deployments. In microservices-based AI architectures, log analysis plays a crucial role in detecting failures, optimizing system performance, and maintaining transparency which are key factors in democratizing AI access for enterprises of all sizes. By improving observability and troubleshooting in AI-powered applications, she reinforces the importance of building resilient, accessible, and efficient digital ecosystems that support equitable AI adoption.
As AI systems make more decisions that affect human lives, bias, data privacy, and accountability have emerged as major concerns. If AI is built on skewed data or trained without diverse representation, it risks reinforcing inequalities rather than solving them.
Sulakshana is a strong advocate for Explainable AI (XAI)—ensuring that AI decisions are transparent, interpretable, and free from bias. As a technical peer reviewer for IEEE IoT, she plays a key role in evaluating AI and IoT research to uphold security, fairness, and transparency in emerging technologies.
“AI is only as good as the data it learns from,” she notes. “We need to audit models constantly to ensure fairness and eliminate bias.”
Solutions to Bridge the Digital Divide
Bridging AI’s accessibility gap requires affordable adoption, workforce training, and ethical AI development. Cloud-based AI solutions and open-source frameworks like TensorFlow Lite lower costs, enabling startups and small businesses to integrate AI without heavy infrastructure investments.
Beyond affordability, AI must address real-world challenges. In healthcare, AI-powered telemedicine platforms connect rural patients with doctors through AI-driven diagnostics. In finance, AI-driven lending expands credit access to underserved communities, bypassing traditional banking barriers.
Sulakshana actively champions these initiatives, advocating for AI ethics, cloud computing, and automation in upskilling programs. In a recent interview, she discussed how AI is revolutionizing the software development lifecycle, emphasizing the need for transparency in AI-driven automation and the ethical deployment of generative AI tools. Her work in improving CI/CD pipelines, bias mitigation, and AI-assisted testing underscores her commitment to building scalable, responsible AI solutions that support both enterprises and developers in adapting to the future of work.
Looking ahead, Sulakshana envisions a future where AI isn’t just an enterprise tool, but a fundamental enabler of economic and social mobility. Whether through AI-driven education platforms, microfinance solutions, or public-sector AI initiatives, she believes that AI can empower communities, not just corporations—but only if it is built and implemented responsibly.
“AI’s success isn’t measured by what it can do—it’s measured by who it benefits,” she concludes. “We have the opportunity to make AI a tool for global progress, not just a luxury for a select few.”
With leaders like Sulakshana driving change, AI is poised not just to transform industries, but to narrow the digital divide and create a more inclusive technological future.