Excitement around AI and machine learning is high, and with good reason. Evidence shows AI can significantly enhance processes and outcomes. In many instances, however, users conflate its ability to augment human effort with the ability to completely eliminate it. As a result, context, industry knowledge, accessibility, and data privacy are taking significant hits.
The Consortium for Information & Software Quality's 2022 report, The Cost of Poor Software Quality in the US, found that poor software quality cost the U.S. economy $2.41 Trillion in 2022.
Source: CISQ 2022 Report
The WebAIM Million - 2025 report found that only 5.2% of websites are fully compliant with WCAG standards.
Source: WebAIM Million - 2025
known data breach victims in 2024
In 2024, the Identity Theft Resource Center tracked 3,158 data compromises that resulted in more than 1.3 billion notices going to individuals
Source: ITRC 2024 Data Breach Report
The widespread adoption of AI and machine learning (ML) tools has accelerated software delivery and cut costs. As a result, these technologies have become deeply embedded across the tech industry. However, speed without oversight brings risks. At ObsidianIQ, we don’t just move fast—we move with purpose, precision, and impact. We make certain that we're not just delivering something, but the right thing, by continuously soliciting and incorporating feedback from stakeholders.
AI/ML shouldn’t replace human effort—it should augment it. Treating LLMs like virtual team members risks exposing sensitive data and eroding institutional knowledge. At ObsidianIQ, we advocate for a human-in-the-loop approach, where expert oversight ensures AI is used responsibly, with context, care, and compliance in mind.
When AI/ML tools are used without accessibility in mind, inclusion takes a hit. The result? Inaccessible content—missing alt text, broken structures, or unusable experiences for screen reader users. For public-facing sectors especially, this risks legal and reputational harm. At ObsidianIQ, we believe accessibility must be built in from the start. Inclusion isn’t optional—it’s standard.
Privacy and security are at risk when AI/ML are not used with proper safeguards. Sensitive data gets exposed and regulations get violated. At ObsidianIQ, we maintain that real intelligence should guide artificial intelligence. Our human-in-the-loop approach ensures responsible use through expert oversight and a deep respect for privacy and quality. We help you test early and often and embed privacy by design.