How businesses of all sizes are using intelligent digital assistants to save time, reduce costs, accelerate workflows, enhance customer experiences, while keeping humans at the center.
Whether you are an online clothing retailer fielding a flood of customer queries, a financial firm tailoring services to clients, or a startup with bright ideas but limited resources – the challenge is universal: how do you meet rising demand, accelerate workflows, and improve customer satisfaction without the burden of complex technology?
The answer for a growing number of companies – enterprise AI agents working alongside your team.
What are enterprise AI agents?
Enterprise AI agents are intelligent digital assistants that automate business processes. Unlike basic chatbots or traditional AI tools that operate on pre-set scripts or limited tasks, enterprise AI agents integrate directly into your business systems to generate reports, analyze complex data, draft personalized content, and assist developers with code reviews and testing.
Powered by large language models (LLMs) and connected to enterprise data, they deliver secure, context-aware, and scalable support that adapts to your specific business needs. Most importantly, they handle repetitive tasks so your team can focus on creative problem-solving and strategic initiatives.
AI agents are already making a difference
Enterprise AI agents are already proving valuable across industries. They are deployed to reduce wait times and improve resolution rates in customer service, effectively handling complex interactions—including those that require empathy. Marketing teams use them to tailor messaging and scale campaigns, and HR and operations teams are using agents to handle routine employee queries and streamline onboarding processes.
According to McKinsey, enterprise LLMs could unlock up to $4.4 trillion in annual value. Boston Consulting Group predicts the AI agent market’s average annual growth rate will be 45 percent through to 2030.
No-code tools are making AI easier to adopt
Until recently, deploying enterprise AI required specialist teams and complex infrastructure. Today, modern platforms have changed that.
Low-code and no-code AI platforms allow business teams to build and manage intelligent agents using drag-and-drop visual workflows – with no manual coding required. With Retrieval-Augmented Generation (RAG), these agents pull answers from approved internal sources, keeping responses accurate and compliant.
One example is the Tencent Cloud Agent Development Platform (TCADP). It helps companies quickly build agents that integrate with tools like internal chat, databases, and document systems, supporting functions like marketing, analytics, and customer service.
And for technical teams, CodeBuddy – Tencent’s AI agent for developers – automates tasks like code generation, bug detection, and testing. It has helped reduce development bottlenecks and improve product delivery timelines. In fact, 85 percent of Tencent engineers now use it daily.
AI agent success cases in five sectors
Here are five examples of how enterprise AI agents are solving real business challenges, from simplifying operations to personalizing customer engagement.
1. Healthcare: managing complexity, improving care
At Peking Union Medical College Hospital, rising patient demand and disconnected services were overloading doctors and staff. By introducing an AI-powered Q&A agent, the hospital unified 15 services – from appointment scheduling to billing – into a single interface. It also enabled full-cycle patient management from admission to post-op.
Result: Reduced admin burden, smoother patient experiences, and more time for clinical care.
2. Auto: enhancing support at scale
FAW Toyota, a leading car maker, needed to improve service quality across a complex support network reliant on large volumes of brand manuals, illustrated guides, and technical documents.
To address this, the company deployed an AI agent using Optical Character Recognition (OCR) and RAG to interpret information and generate accurate, real-time responses. This enabled end-to-end knowledge processing and automated responses.
Since launch, resolution rates have risen from 37 percent to 84 percent, with the AI agent now handling about 17,000 monthly inquiries.
Result: Faster, more accurate responses and improved customer satisfaction.
3. Finance: cutting through complexity
For Huaxing Bank, due diligence reports were essential but time-consuming tasks that once took over a week to complete.
Using an AI credit assistant, 95 percent of that work is now automated with 93 percent accuracy. Operational efficiency is up tenfold, and report generation time slashed from 10 days to just one.
Result: Staff can focus on higher-value analysis while accelerating compliance.
4. Retail: personalizing at scale
DaShenLin is one of China’s largest pharmacy chains, with more than 16,000 stores nationwide. It had accumulated a vast amount of enterprise knowledge including drug information, sales support and customer feedback across their stores.
The company used a Hunyuan-powered AI agent to consolidate that knowledge base. In addition, their in-store Q&A support agent can now help over 50,000 employees answer questions instantly, reducing staff response times by 80 percent.
Result: More confident front-line staff and faster service.
5. Gaming: elevating player experiences
Game studios are under constant pressure to evolve and upgrade their offerings to keep players engaged.
Giant Network, makers of hit game Among Us, used Tencent’s Hunyuan Turbo S model to introduce AI-powered non-player characters (NPCs) into its impostor challenge playing mode. These intelligent agents mimic human reasoning and conversation, creating deeper, more strategic player engagement.
Result: More immersive gameplay and increased player activity.
Getting started: key considerations
- Choose the Right Foundation: Look for platforms that integrate securely with existing data and have proven track records in your industry.
- Start Small, Scale Smart: Begin with high-volume, routine tasks before expanding to more complex processes.
- Measure What Matters: Track metrics like time savings, accuracy improvements, and employee satisfaction to demonstrate ROI.
- Focus on Human Enhancement: Position AI agents as tools that amplify human capabilities rather than replace them.
Ready to scale smarter?
Enterprise AI agents aren’t future technology. They’re already working alongside teams today to reduce costs, speed up operations, and deliver better customer and employee experiences.
What part of your business could run smarter with AI agents on your team?