What Our Most-Read Content Says About AI Adoption Today 

In 2025, the content that resonated most with our audience wasn’t about AI hype. It focused on the real problems businesses are facing, real industry needs and real-world constraints and how AI can address them responsibly and effectively. 

Across healthcare, media, insurance and financial services, the blog posts that were most read and shared by our audience have a common theme: organizations aren’t asking if they should use AI. They’re asking how to apply it in ways that deliver value, ensure trust and stand up to operational and regulatory scrutiny. 

Here’s what our top-performing content from 2025 tells us about the market and where AI adoption is headed. 

AI Is Moving Deep into Industry-Specific Workflows 

Our conversation on how AI is transforming clinical trials struck a chord with life sciences professionals dealing with overwhelming volumes of scientific, clinical and regulatory data. 

What resonated wasn’t the promise of automation alone, but the idea that AI can make complex, high-stakes processes easier to navigate. Readers engaged most with discussions around: 

• Using AI to index and analyze both structured and unstructured biomedical data at scale 
• Improving trial design and benchmarking by learning from thousands of historical studies 
• Addressing one of the industry’s biggest bottlenecks: patient recruitment, through better data-driven eligibility criteria 
• Gaining real-time competitive intelligence across global trial activity  

The takeaway is clear: in life sciences, AI is no longer an experiment. It’s becoming essential infrastructure for accelerating research while reducing risk, and it must be accurate, explainable and grounded in domain expertise. 

Trust and Human Oversight Aren’t Optional 

Another favorite post focused on human-centered AI for content teams, and its popularity reflects a broader shift happening across industries that relies on editorial integrity, regulatory compliance or knowledge-intensive work. 

Our readers responded to the idea that AI should amplify human expertise, not replace it. Key themes included: 

• The need for transparency and explainability to move beyond “black box” AI 
• Keeping humans in the loop through editorial oversight, validation, and governance 
• Preserving context, creativity and domain knowledge while scaling content operations 
• Demonstrating real operational impact, such as reducing content processing time, without sacrificing quality  

This interest signals a maturing AI market. Organizations want speed and scale, but not at the expense of control or trust. The future isn’t AI alone; it’s humans and AI working together, with clear accountability. 

Focus on Practical AI  

Our post on building an AI success story in insurance resonated with our audiences because it addressed a tension that many organizations are experiencing: pressure from the top to “do something with AI,” without identifying the problem to be solved nor understanding precisely where it will actually deliver value. 

Our practical guidance emphasized: 

• Starting with clearly defined business problems, not technology 
• Involving the right stakeholders beyond IT: underwriting, claims, operations, compliance 
• Balancing automation with human judgment in high-stakes decisions 
• Choosing partners with real industry experience who can challenge assumptions 

As AI adoption accelerates, organizations are becoming more disciplined. They’re prioritizing outcomes over optics and looking for solutions that are designed to fit the realities of their industry, data and workforce. 

Context Is the New Differentiator in Financial Crime Compliance 

One of the most widely shared posts tackled a persistent issue in financial services: alert fatigue in adverse media screening for AML teams. 

The response underscored a growing recognition that more data doesn’t automatically mean better detection. What matters is context. Some of the biggest discussions centered around: 

• Why traditional keyword-based screening creates noise and false positives 
• How ambiguity, outdated content and entity confusion undermine investigations 
• The role of hybrid AI—combining language models, machine learning, and knowledge graphs—to interpret meaning, relevance and relationships 
• Improving explainability, auditability and analyst effectiveness without automating judgment 

This reflects a broader shift in regulated industries. AI is expected to sharpen human decision making, not replace it. Precision, transparency and operational confidence are now baseline requirements. 

What This Tells Us About AI Adoption in 2026 

Taken together, our most popular content points to a clear evolution in how organizations think about AI: 

• AI is expected to work inside real workflows, not alongside them 
• Industry context and domain expertise matter more than generic capability 
• Explainability, governance and human oversight are central, not optional 
• Success is defined by business impact, not experimentation 

As we look ahead, these themes will continue to guide how organizations deploy AI responsibly and how expert.ai supports them—by turning unstructured information into actionable insight, while keeping people firmly in control.