Generative AI is making headlines, but applying it effectively is a challenge. With 51% of companies facing budget constraints, executives aren’t asking, “What can GenAI do?” They’re asking, “What works and where’s the ROI?”
Many AI projects fall short of what businesses hope to achieve. Failures often occur when chasing hype overshadows focusing on specific areas where AI can make a measurable difference.
If 2023 was about experimentation, 2025 is the “prove-it-or-cut-it” moment. Companies need tools that can be built in months and plugged into existing infrastructure.
Here, the experts at Coherent Solutions explore nine proven GenAI use cases and the relevant metrics to measure their value. These use cases have been tested, are applicable across industries and can be applied now to augment human effort and expertise.
1. RAG or Fine-Tuned Copilots for Large Knowledge Base
Imagine giving AI a library card to access your company’s documents. A retrieval-augmented generation model sources validated answers from internal repositories, pulling information from contracts, protocols and PDFs, and weaves this information into accurate responses. Both Microsoft Azure and Amazon Bedrock now support out-of-the-box tools or frameworks to build and deploy RAG-based applications.
Knowledge-based industries and teams with extensive documentation benefit most: hospitals, law firms, insurers and enterprise IT teams. Metrics for these AI models are productivity and accessibility. Some businesses are using gamification to track productivity, quantify employee output and get a clearer picture of ROI.
This is also true for AI copilots. They are “GPTs, but with context”, for example, for HR platforms and insurance claims processors, as they’re trained on company-specific workflows, policies and tone.
Another example here is a network of healthcare companies struggling to make sense of their ever-growing repositories of clinical notes, research papers and protocols. With the help of RAG—less like a search engine, more like a second brain—their system can surface context-aware summaries and recommended actions. Clinicians spend less time on paperwork and make sharper diagnoses.
2. LLM-Powered Data Labeling for Computer Vision and Natural Language Processing
AI can build its own training wheels using data that it has previously labeled (for example, marking images, classifying documents and tagging examples).
Modern large language models (LLMs) are skilled at creating and labeling training data to save hours of manual work. Although AI doesn’t show up in the final product, it enables cleaner data that improves product performance.
For example, MedTech companies require trained computer vision models to recognize subtle anomalies in diagnostic scans. LLMs help engineering teams generate structured annotations in weeks instead of months of manual tagging. A better model’s performance leads to higher confidence in predictions.
You can track labeling speed and data coverage to measure a model’s value. Over time, you should see fewer misclassifications, sharper predictions and faster retraining.
3. Synthetic Data for Computer Vision & Natural Language Processing Model Training
Testing software typically involves inputting data from real users or systems in a live business environment. Production data is often off-limits due to privacy laws, customer confidentiality, or commercial sensitivity. Synthetic data offers a faster, lower-cost way to create realistic and comprehensive test sets without constraints. This is especially useful in computer vision (CV) and natural language processing (NLP) environments. Vendors like Mostly AI, Gretel and Snorkel Flow provide commercial tools to generate such data.
Retailers, logistics providers and fintech firms can simulate edge cases and benchmark AI models. The key metrics to evaluate are the accuracy and efficiency of models tested on synthetic datasets.
For instance, fintech teams may use synthetic data to test fraud detection systems under extreme conditions (rare patterns or staged attacks) and uncover vulnerabilities before they reach production. Computer vision is another case where synthetic data is used to train warehouse robots by generating large amounts of annotated images of packages under different lighting, angles and occlusion scenarios.
4. Agentic AI Chatbots: Beyond Scripted Response
Customer service chatbots typically follow rigid scripts, offering canned responses that frustrate users more than they help. This friction can have damaging effects on both customer retention and brand perception. Agentic AI steps in, making a system “think” before replying.
AI-enabled chatbots understand intent, adapt to context, and remember conversation history when users switch between chat, email, phone, or other support channels. When building such a solution, focus on metrics including first resolution rate, average handle time and callback rate.
Fintech companies, travel brands and retailers are in a prime position to replace scripted chatbots with an agentic AI system trained on their specific customer interaction data. Instacart and AT&T have all moved past scripted chatbots, launching AI agents that resolve real issues in production.
5. Internal Summarizers for Faster Decision Making
Decision-makers often don’t have enough time to wade through meeting transcripts or investor updates, especially those working in compliance-sensitive industries, including insurance, healthcare and finance. This applies to product managers, legal teams, operations leaders and analysts who might normally spend hours sifting through legal or other complex documents.
Internal summarizers act like priority sorters: they surface what’s new, flag what’s urgent and suppress the noise. For example, Microsoft 365 Copilot’s “Catch-Up” feature is a built-in internal summarizer that scans emails, documents and chats to surface key updates and priorities.
Summarizers save teams time reviewing documentation, shorten decision timelines, reduce escalation of missed issues, and improve regulatory tracking—these are the metrics to observe.
6. Multimodal Asset Generators for Content Creation
Content generation is probably the most familiar use of AI because it has been highly popularized and hyped in consumer circles. However, in a business context, content created with the help of AI designers, video editors and voiceover tools might require several rounds of review, editing and approval to meet brand guidelines and align messaging.
Multimodal AI tools such as Adobe Firefly Services and Runway Gen3 offer production-ready multimodal generators that create images and video from text prompts. They can help marketing teams move faster while maintaining brand consistency across channels and formats. We’re not suggesting replacing creative teams or their original ideas but augmenting their work and speeding up A/B testing.
Marketing and research departments use AI generators to test new product visuals and localize campaigns. Retail brands can use them to prepare promotional materials in different seasonal styles and formats (email banners, in-app cards, paid ads and so on). The impact metrics are content production time per campaign, engagement lift from localized or variant content and the time saved for creative teams.
7. Compliance and Risk Copilots
AI copilots can be more accurate and precise than humans while tracking regulatory changes, compliance with internal policies, and preparing for audits. Thomson Reuters and Intapp have launched compliance-focused AI tools into general availability, showing that in risk-sensitive sectors, businesses are investing in Gen-AI systems.
In the case of health insurance businesses, AI copilots, trained on HIPAA and state-specific rules, will check new policy forms for missing disclosures or noncompliant language. Key metrics to measure a copilot’s impact involve the number of compliance errors caught before submission and the time to revise regulatory documents.
8. Scientific-Research Copilots
When pharma R&D teams are developing new therapies, they can use a copilot to summarize studies, extract data tables and generate literature reviews or grant sections. AI helps scan thousands of PDFs and convert them into structured tables that may include conflicting findings, observed side effects, delivery success rates across studies and more.
For example, BenchSci’s AI platform, ASCEND, is actively used in production by 16 of the world’s 20 largest pharmaceutical companies. AI’s impact is measured by hours saved on literature reviews per project, the quality of AI-generated summaries as rated by domain experts, and the reduction in copy-paste or formatting time.
9. Financial-Planning Copilots
B2B SaaS businesses are a natural fit for financial planning copilots because they rely on accurate recurring revenue forecasts and typically operate across fragmented systems (CRM, billing and ERP). They can integrate AI copilots with Salesforce, NetSuite and Google Sheets and generate reports for finance and executive teams. Anaplan PlanIQ offers finance teams AI copilots that forecast scenarios, flag anomalies and support planning decisions.
The impact of implementing a copilot can be seen in reduced time to assemble a forecast, fewer corrections needed after the first AI draft and the accuracy of predicted versus actual trends.
From Hype to Return
Achieving ROI as intended becomes possible when you have a solid starting point. Like with any technology, AI requires planning and effort for proper implementation.
Before capitalizing on the described use cases, clarify whether stakeholders clearly understand the goals they wish to achieve with AI. Make sure there is a solid data strategy in place, while quality practices and AI governance are set up. Also, provide employees with an appropriate amount of training, promoting secure and ethical AI use.
