How fast is GenAI changing what “productive” looks like? By the time you finish reading this sentence, LILT’s AI has translated the equivalent of a novel. LILT’s AI platform has the capacity to power law enforcement translations at 150,000 words per minute across multiple languages. For comparison, a human translator might handle 2,500 words a day, meaning one AI-assisted translator could do the work of 60.
TurboTax also uses GenAI to streamline tax returns for 40 million users a year. The AI speeds things up, but most importantly, it helps spot deductions most people miss, applies tricky tax rules based on your situation, and surfaces credits you didn’t even know existed, like write-offs for freelancers or education costs. A professional tax preparer can complete 300 returns a year, so TurboTax’s AI can do the work of 145,000 professionals. It takes the headache out of decoding the tax code and delivers speed, accuracy, and personalized efficiency at scale.
To move GenAI from pilot to real business impact, companies need to focus on practical use cases within core functions. Treat it like a plug-in, and you’ll get flashy demos with little impact. But when GenAI is trained on the right data, embedded into real workflows, and supported by human oversight, it delivers results. Used wisely, it works.
In this article, we’ll look at where it’s already making a difference.
The quick difference with LLMs? Not all GenAI talks back.
You might think of GenAI and LLMs as the same thing—especially if your first exposure was through ChatGPT. It’s a common assumption, but the two aren’t interchangeable. Here’s how they connect and where they differ.
Generative AI (GenAI) refers to a class of AI models designed to create new content, such as text, code, images, audio, or even decisions. You prompt them with natural language, like you would a colleague, and they respond based on patterns learned from massive datasets.
Large language models (LLMs) are a specific type of GenAI focused on understanding and generating human language. All LLMs are GenAI models, but not all GenAI models are LLMs, since some are built to generate images, music, or other types of content beyond text.
Here’s a clear comparison of GenAI and LLMs—covering their scope, business outcomes, and key considerations.
Comparing Generative AI and LLMs for Business Impact
Now that the difference is clear, let’s look at how businesses are already putting both to work.
Optimizing Operations and Security with GenAI
GenAI can support data clean-ups, governance, and workflows, like a self-cleaning oven for operations. Once configured, it scans through bad inputs, rogue entries, and broken processes. You barely notice it, but everything runs more smoothly.
That same behind-the-scenes power can also drive efficiency in more complex operational challenges, such as security. GenAI can help triage thousands of security alerts, reducing them to under 10 a day. In Deloitte’s State and GenAI report 2025, they cited that at a major bank, developers spent 80% of their time fixing security alerts instead of building features. This led to dev frustration and lost productivity.
To solve it, the organization built a GenAI platform that translated regulations, policies, and standards into specific security controls—preventive, detective, responsive, and corrective. They embedded these into the development cycle, successfully filtering out thousands of false alerts and shrinking the manual review queue dramatically.
Improving Data Quality and Governance with GenAI
GenAI doesn’t just generate content. It can also clean, label, and enforce structure across sprawling datasets. GenAI makes sense of complex data by automatically organizing and correcting it. This ensures that your systems stay aligned with your standards, without the need for manual intervention. That might mean flagging a mislabeled entry, routing a record to the right workflow, or detecting inconsistencies in sensitive customer data. Through intelligent, rule-aware decisions, GenAI helps enforce data governance policies at scale.
We saw this in action with an e-commerce and payments client, where we optimized data workflows and significantly reduced infrastructure costs. Our team built a hierarchical classification model using Amazon labels and Gemini. If you’re wondering what that is, think of it like sorting files into folders and subfolders. First, you decide if a document belongs in “Finance” or “Marketing.” If it’s “Finance,” you then ask: is it “Invoices,” “Budgets,” or “Tax”? That’s how hierarchical classification works: step-by-step, from general to specific.
By implementing a more efficient hierarchical model, we were able to drastically lower processing costs and speed up performance. For instance, what once cost $30,000 per million classifications is now just $300, and we’ve reduced response times from 40 seconds to just 1.5 seconds. Plus, our model’s accuracy in tax classification has improved to 95%, ensuring more reliable and faster results for businesses.
This kind of system doesn’t just save time and money. It shows how GenAI can bring structure, accuracy, and consistency to data governance at scale.
Turning Unstructured Data into Insights with GenAI
Ever spent 20 minutes digging through Slack threads trying to find a product launch timeline? GenAI can fetch that instantly by understanding unstructured data, like emails, docs, support logs, and even buried Slack messages. One method it uses is vector search, which finds information based on meaning rather than exact words. Instead of matching keywords, it looks for semantic similarity, more like how your brain searches than how a search bar does. Ask about “space movies,” and it might return Interstellar instead of Space Jam. That deeper understanding is where GenAI shines.
Across industries, employees lose hours each week searching for critical information, like procedures, compliance details, or product guidance, buried in PDFs, wikis, or clunky systems. Bayer tackled this problem with a custom LLM trained on 160 years of proprietary agronomic data. Their GenAI assistant, E.L.Y. (Expert Learning for You), serves as an on-demand expert for agriculture-related questions, covering everything from protective equipment requirements to application rates for specific crops.
The results speak for themselves. Over 1,500 frontline employees use E.L.Y., saving up to 4 hours a week. In benchmark trials, it delivered answers 40% more accurately than ChatGPT. In a margin-sensitive sector like agriculture, that accuracy translates directly to better decisions and profitability.
This same shift, from reactivity to foresight, is also taking hold in logistics and field operations, industries known for high-stakes decisions, complex environments, and limited room for error. In environments like nuclear plants or large-scale industrial facilities, GenAI can process raw, unstructured signals. It turns them into actionable insights, like defect summaries or predicted equipment failures. As organizations unlock this buried data, they’ll be able to move from reactive troubleshooting to proactive planning, boosting uptime, safety, and operational efficiency.
Enhancing Customer Experience at Scale with GenAI
Customer service is one of the most natural places for GenAI to shine. It’s never fun to call a support line and find yourself 15th in the queue. GenAI-powered chatbots offer a scalable alternative. They handle high volumes of requests, responding instantly and staying composed no matter how tense the situation. As an impartial team member, GenAI helps manage conversations without getting flustered or fatigued. It can also scan customer feedback for product teams or rewrite responses to strike the right tone on sensitive topics. And since call center agents often follow scripts, GenAI can do the same, just more consistently and without burnout.
GenAI doesn’t just keep its cool when conversations get tense. It also helps your brand stay consistent across every channel. With standardized prompts and smart workflows, it makes sure tone, facts, and intent line up whether someone’s reaching out by chat, email, or social. That kind of consistency builds trust. It makes your brand feel like itself everywhere. And because GenAI can track conversations across channels, it helps avoid repetition or disconnects, keeping the experience smooth and the relationship strong.
Another way GenAI improves the customer experience? Breaking down language barriers. Take Airbnb’s message translation feature. It uses Meta’s open-source GenAI model, NLLB (No Language Left Behind), to translate host and guest messages in real time. In a globalized world where economic and social exchanges happen across languages and time zones, that kind of seamless connection has become essential and expected.
Scaling Content Creation and Personalization with GenAI
GenAI is simplifying how companies create content. What once required studios, cameras, and entire creative teams can now be generated from a simple text or image prompt. That opens up new possibilities—especially for teams that need visuals, videos, and messages delivered fast and at scale. Every company needs to communicate and promote itself, but not every team has the time or budget to keep up. GenAI levels the playing field.
Global advertising firm WPP partnered with Nvidia to build a GenAI content engine that creates photo-realistic ads from simple product prompts. The goal was to make ad creation faster while still feeling local. In one example, the system helped tailor Coca-Cola ads for different regions. The meal shown with the drink changes based on cultural context, so a Coke ad in Nepal features momo, not tacos.
At BairesDev, we worked with a client in marketing automation to integrate GenAI video generation into their HubSpot campaigns. The goal: create personalized videos faster and at lower cost. Our engineers are building a solution that connects the AI video platform with HubSpot, so videos are generated automatically and linked to each contact. Once ready, the videos are stored and sent through personalized email campaigns—fully automated, no manual steps required.
Of course, when content is this easy to produce, the legal side gets trickier. Not every GenAI-generated image or video is free to use without restrictions. Studio Ghibli, for example, has raised concerns about GenAI models being trained on their iconic animation style without permission, blurring the lines between homage and infringement. It’s a reminder that while GenAI can create incredible content, organizations still need to consider copyright, originality, and brand integrity.
Predicting Failures and Reducing Downtime with GenAI
GenAI is helping operations teams cut costs, reduce downtime, and deliver more reliable service. It takes raw system data and turns it into insights you can act on. This way, teams can make faster decisions, plan ahead, and avoid surprises. GenAI can spot issues early, flag unusual patterns, and suggest fixes before small problems become big ones. It does so by learning from past performance, sensor data, and maintenance records
At BairesDev, we supported a client’s move toward smarter infrastructure by using GenAI to help prevent unnecessary plumber callouts. Our team built the systems needed to enable real-time monitoring and predictive maintenance, critical for avoiding service disruptions. This included connecting AI-powered inspection tools with existing platforms and making issue detection faster and easier. As a result, the client cut down manual review work by 90%, improving response times and reducing operational costs.
This kind of predictive capability means fewer emergencies, more efficient maintenance schedules, and lower operational costs. It also sets the stage for more advanced GenAI use cases, like generating inspection reports automatically or flagging issues before they escalate.
Managing Compliance and Reducing Risk with GenAI
GenAI is like a co-pilot during turbulence. It helps you monitor all the instruments, catch anomalies, and stay on course, even as conditions shift. In the world of compliance and risk, that means keeping up with evolving data privacy laws across multiple jurisdictions, such as GDPR updates or new U.S. state-level regulations. This technology can help spot irregularities in vast datasets, track policy changes, and surface potential red flags before they become costly problems. You’re still in charge, but you’re not flying solo.
Prudential is piloting Google’s MedLM, an LLM trained on healthcare data. The model simplifies and summarizes medical claim-related documents, including reports and invoices. In trials, MedLM doubled the automation rate of claim reviews and assessments. Faster approvals and payouts solve one of the biggest pain points for patients and providers alike. At the same time, standardized reviews and traceable decision outputs help reduce compliance risk, ensuring that automation doesn’t come at the expense of accuracy or accountability.
For a client in the legal services industry, BairesDev developed a GenAI tool that summarizes 10,000 legal transcripts per day, saving an estimated 80,000 hours of work. The tool processes 200 to 300 pages in just 3 to 4 seconds, inserts hyperlinks to the original document to preserve traceability, and anonymizes transcripts to meet privacy requirements. Manually reviewing this kind of volume drains resources and increases the risk of human error, inconsistent redactions, and audit gaps. Automating the process helps legal and compliance teams standardize outputs, reduce exposure to privacy breaches, and maintain a clear record of what was reviewed.
But AI still needs guardrails. A sharp reminder came in early 2025, when two Morgan & Morgan attorneys faced sanctions after submitting a legal brief with fabricated case citations. The citations were hallucinated by a GenAI tool. GenAI is often too eager to please. It would rather make something up than admit it doesn’t know. Even with automation gains, teams must stay accountable for high-stakes decisions.
Accelerating Onboarding and Talent Growth with GenAI
Onboarding is often slow, inconsistent, and overly focused on paperwork instead of real preparation. Because nothing says “ready for the job” like yet another phishing awareness video. GenAI helps fix this by delivering personalized, role-specific guidance at scale. Think of it like a GPS for new hires, guiding each person based on their role. A backend engineer might get help with security protocols, architecture, and API docs, while a product manager sees guidance on roadmap tools, meetings, and compliance. It’s tailored support that helps people ramp up faster.
What about reducing the time it takes for a new hire to contribute? Or avoiding early mistakes that snowball into bigger issues? GenAI can do both by personalizing onboarding. Say a new software engineer at a payments company needs to understand encryption standards before pushing code. Instead of digging through outdated PDFs or waiting on a teammate, they ask a GenAI assistant. It retrieves the policy and walks them through it. If they make a mistake, the system flags it and offers a quick refresher—reducing confusion, improving compliance, and speeding up the learning curve.
Traditional onboarding often overwhelms employees, which leads to mistakes. GenAI can gate tool access until critical modules are completed, like identity checks or secure code policies. It could also flag misunderstandings. If an engineer flubs a question about role-based access, GenAI can pause and send them back to the right explanation. It’s onboarding with feedback loops, not just a rabbit hole of training videos. Over time, employees can use the same tools to reshape their roles by automating repetitive tasks and building smarter workflows that match how they actually work.
Supporting Strategic Planning and Growth with GenAI
When used well, GenAI can support strategic planning by simulating outcomes, identifying risks, and extracting patterns from sources too large or fragmented for humans to process quickly. Instead of digging through slide decks and financial filings, leaders can prompt GenAI to compare market entry options, model revenue impact from pricing changes, or discover recurring concerns across investor calls.
For example, a strategy team evaluating a new product line might ask GenAI to compare historical launch data from similar SKUs. It could then pull competitive positioning from analyst reports and model various scenarios. One scenario could consider the current supply chain capacity, another one a regional vendor swap, and an additional one assuming a 15% increase in demand. The results won’t be perfectly predictive, but they highlight variables and trade-offs faster than traditional methods.
Similarly, a business development lead could ask a GenAI to synthesize public comments from a regulatory hearing, distill trends across five competitor earnings transcripts, and suggest implications for a planned expansion. When the stakes are high, GenAI sharpens visibility into key variables and assumptions, enabling faster decisions, stronger risk mitigation, and fewer surprises. It gives leaders a head start on the questions worth asking.
Ready to move GenAI from pilot to production?
All of these examples show what GenAI looks like beyond the prototype stage. From tax returns to translation, predictive diagnostics to personalized marketing, GenAI isn’t coming for the edge cases; it’s coming for the repetitive, time-draining work. It quietly rewrites how teams handle data, documents, alerts, summaries, and content creation.
But behind every smooth AI-powered workflow is a real team making it happen. To unlock GenAI’s full business value, you’ll need prompt engineers, data scientists, full-stack developers, and ML ops specialists who understand your industry’s edge cases and risks. You need partners who understand your business. That’s how this technology becomes a real asset, not just another tool. Our AI developers and machine learning engineers are ready to help you turn GenAI into lasting business value.
