6 Ways Generative AI Has Solved Intractable Problems Across Industries

In SEO, one problem that always felt impossible to fully solve was understanding search intent at scale — especially when dealing with thousands of keywords across multiple markets.

Generative AI completely changed that. Instead of manually classifying keywords into "informational," "commercial," or "transactional," we now feed them into AI models that analyze context, SERP patterns, and related entities to predict intent automatically.

What used to take days of manual sorting now takes minutes — and it's surprisingly accurate. The breakthrough wasn't just automation; it was the model's ability to interpret meaning instead of just matching words. That unlocked faster strategy building, better content mapping, and way more precise targeting.

Generative AI resolved a persistent challenge in product imagery for compliance-heavy medical catalogs. Historically, creating consistent, regulation-ready visuals across thousands of SKUs required extensive manual editing to meet standards for lighting, labeling, and orientation. Even small inconsistencies could delay approvals or cause discrepancies across distributor listings.

The breakthrough came from training a diffusion-based model on our internal image library annotated with compliance attributes—barcode clarity, device angle, and reflection control. The model could generate or correct product renders automatically while adhering to FDA visual documentation standards. Its capability to synthesize photorealistic textures with embedded metadata replaced weeks of retouching with a few minutes of automated validation. What once depended on repetitive human review became a verifiable, traceable workflow. The result wasn't just efficiency but reliability, as every generated image met audit requirements from the first render.

Generative AI has helped us overcome one of the toughest challenges in primary care—communicating complex medical information in a way that patients truly understand. We began using AI tools to draft simplified explanations of chronic conditions like hypertension and diabetes, customized to each patient's visit summary. The technology's ability to generate clear, natural language content allowed us to translate medical jargon into everyday terms without losing accuracy. Patients left with written instructions that felt personal and approachable, which led to higher treatment adherence and fewer follow-up clarifications. What once required significant staff time and effort now happens seamlessly, allowing our team at Health Rising DPC to focus more on patient relationships while maintaining a consistent educational voice across every interaction.

The biggest challenge we faced as a managed IT services provider was finding enough hours to perform preventive system maintenance.

Our team was constantly drowning in log files and system alerts, spending countless hours sifting through noise to find actionable issues. By the time we identified real problems, they'd often already impacted our clients and it was difficult to keep up.

We solved this using AI workforces built in Relevance AI that automatically process log files and system alerts.

Relevance AI allowed us to build micro workers aka AI agents that performed very specific but important tasks.

Now our AI workforce reviews thousands of alerts on a monthly basis and shows us only the actionable items that need human intervention.

This freed up a lot of hours per week per technician that we previously spent on manual reviews. Those hours now go directly into fixing actual client problems instead of just reviews.

The entire process has saved us a ton of time and allowed us to do more with less.

One of the most impressive uses of generative AI I've seen was during a billing and claims modernization project for a large property and casualty insurer. In the past, our teams had a hard time processing and reconciling unstructured billing correspondence like emails, claim notes, and documents from various brokers, carriers, and regional offices. Each source used its own formats and terminology, which made it almost impossible to automate reconciliation or extract consistent financial data.

Traditional OCR and rules-based systems could capture text, but they couldn't interpret context such as identifying whether a statement was about a premium adjustment, a coverage change, or a subrogation recovery. This limitation led to week long delays and required people to step in for validation.

Gen AI changed all of this. We used a LLM that we trained on past billing narratives and transaction patterns. Because it could understand natural language and create structured summaries, it completely changed how we managed these documents. The Model could read a long broker email, figure out key financial points, like a premium refund due to a midterm policy change, and automatically create a standardized billing adjustment entry which was not possible with legacy old systems.

Within three months, we reduced manual review effort by over 60% and cut resolution times nearly in half. More importantly, the system continually improved as it processed more data, allowing finance and billing teams to focus on strategic tasks like trend analysis and predictive cash flow forecasting.

This experience taught us an important lesson. The real strength of generative AI isn't just in automating processes. It's in understanding complex information, turning it into action, and finding value in data that used to be too unstructured to use well.

One of the biggest breakthroughs we've seen is in content production. What used to cost a few hundred dollars and take several days now happens in a single afternoon. We use generative AI tools to help us write blog content and design visuals that feel original, not automated. The process is faster, but the standard hasn't dropped. The writing still sounds like our clients, and the images still match their brand. The difference is that we can now deliver more work, at a higher quality, in a fraction of the time. The real leap wasn't just speed — it was quality control. AI can now analyze a client's brand tone, audience, and local market data before writing a word. That capability turned what used to be a slow, manual, creative process into something that's fast, consistent, and measurable. For our agency, it changed content from a cost center into a growth engine. And most importantly, SEO and GEO have never been more affordable for small businesses to compete and win.