Introduction
“Everyone’s building AI agents. No one’s building adoption.”
From pitch decks to product roadmaps, AI agents are everywhere.
The talk is big. The expectations, bigger.
But beneath the surface, most enterprises are still grappling with the basics:
- Where do we start?
- How do we scale?
- What’s actually working?
It’s not a tech problem. It’s a reality check.
- 62% of enterprises exploring AI agents lack a clear starting point.
- 41% still treat them as a side project.
- 32% stall after pilot – never reaching production.
At Lyzr, we’ve spent the past year knee-deep in this space – across 200,000+ user interactions, 3,000+ demo requests, and 2,000+ conversations with business and tech leaders.
This report distills what’s real, what’s stuck, and what’s next.
Not just where AI agents are headed.
But how to actually make them work, now.
If you’re building for the enterprise, this is your field guide to:.
- Where AI agents are driving real value
- How some enterprises are scaling, and why others are stuck
- How to architect AI success
Let’s set a new benchmark for enterprise AI.
Where do our insights come from?
Unlike traditional survey-based reports, our insights are built on real customer interactions, real data, and real adoption trends. This is not a speculative outlook—it’s an inside look at how enterprises are actually deploying AI Agents.
The Data Behind This Report:
Over the past year, we’ve gone beyond surveys and speculation—digging into real-world data from those building and using AI agents at scale.
We analyzed 200K+ user interactions to understand engagement patterns, AI adoption signals, and behavioral trends. With 7,000+ AI agent builders, we tracked how both developers and business teams design, test, and deploy real AI workflows.
From 3,000+ demo requests, we mapped industry-level interest and key enterprise pain points. 2,000+ deep-dive conversations gave us front-row access to what’s working, what’s breaking, and what’s missing in AI adoption.
And through 200+ Fortune 500 CIO chats, we gathered strategic insights on enterprise priorities, compliance needs, and the future of AI in business.
Key Insights from the Report
1. Enterprises have started betting on AI Agents.
Over 70% of AI adoption efforts focus on action-based AI Agents, not just conversational AI.
2. Top industries leading AI Agent adoption:
Technology, Financial Services, Banking, and Insurance are investing the most in AI-driven automation.
3. The biggest AI adoption barriers?
Security, compliance, and integration complexity are preventing enterprises from scaling AI Agents faster.
4. ROI is driving AI adoption.
Enterprises that deploy AI Agents are estimating up to 50% efficiency gains in customer service, sales, and HR operations.
5. The AI Agent roadmap is clearer than ever.
Most enterprises start with pilots and scale AI Agents across workflows in 5 phases of adoption.
6. AI must be private, secure & enterprise-controlled.
SaaS-based AI models create compliance risks – 80% of enterprises prefer AI hosted inside their AWS cloud.
7. The future? AI that learns & improves.
2025 will see the rise of AI Agents with memory & reasoning, allowing AI to act independently.
AI Agents Adoption Across Business Functions
AI is no longer just an experiment – it’s actively reshaping critical business functions across industries. Our data shows that 64% of AI agent adoption is centered around business process automation, enabling enterprises to optimize workflows and enhance efficiency.
Customer Service (20%)
AI chat & voice agents handle up to 80% of L1/L2 queries, slash resolution time, and improve CSAT.
Sales (17.33%)
AI SDRs research leads, personalize outreach, and boost meeting conversions – 4x faster than manual efforts.
Marketing (16%)
From blogs to LinkedIn to videos – AI agents run content, email, and distribution workflows end-to-end.
Research & Analytics (12%)
AI agents surface competitor insights, analyze customer data, and turn natural language into SQL queries.
HR (6.67%)
AI assistants screen resumes, automate onboarding, conduct exit interviews, and boost employee engagement.
Project Management (6.67%)
Smart agents manage risk analysis, resource allocation, and track delivery – keeping projects on track.
Procurement & Legal (4%)
Agents scan for RFPs, draft proposals, review contracts, and follow up with vendors automatically.
The Takeaway? AI agents aren’t just an emerging technology – they’re becoming a necessity for modern enterprises looking to scale efficiently.
AI Agents Adoption Across Business Segments
AI adoption isn’t one-size-fits-all – different business segments are embracing AI Agents based on their unique challenges and priorities.
- SMBs (65%) are leading the charge, leveraging AI to automate operations, reduce costs, and scale efficiently without heavy IT overhead.
- Mid-Market Companies (24%) are adopting AI to streamline workflows, enhance customer engagement, and drive revenue growth while balancing scalability and cost.
- Enterprises (11%) focus on AI-driven compliance, security, and large-scale automation, ensuring AI seamlessly integrates into their existing infrastructure.
While SMBs are early adopters, enterprises are steadily scaling AI – prioritizing security, compliance, and custom workflows. The AI wave is sweeping across businesses of all sizes, reshaping how work gets done.
Which personas are building AI Agents?
AI adoption isn’t just for developers – business teams are taking charge. While 70% of AI Agent builders on Lyzr Agent Studio – come from developer backgrounds, a significant 30% are business users from Product, Marketing, Sales, Customer Service, and HR.
This shift signals a major transformation – AI is no longer just a technical tool; it’s becoming a business enabler. With intuitive, no-code solutions, teams across functions are now leveraging AI Agents to automate workflows, improve decision-making, and enhance customer interactions.
The takeaway? AI isn’t just for coders anymore – it’s for anyone looking to drive impact. 🚀
Who’s Leading the AI Agent Adoption Race?
AI Agents are making waves across industries, but some sectors are moving faster than others. Technology leads the way, accounting for a massive 46% of AI Agent demo requests, followed by Consulting & Professional Services (18%) and Financial Services (11%).
Why? These industries thrive on efficiency, automation, and data-driven decision-making—areas where AI Agents excel.
However, adoption isn’t limited to tech; sectors like Healthcare, Education, and Manufacturing are also ramping up interest, proving that AI is not just a trend but a necessity for businesses looking to scale and optimize operations.
The message is clear: AI Agents are transforming workflows across industries—the question is, how fast will the rest catch up?
The AI Agents Stack: Top Choices for Building Intelligent Agents
Most Popular LLMs by AI Agent Use Case
Not all LLMs are built the same. Different AI agents use cases demand specialized models, each optimized for its own domain. Based on real-world AI agent deployments, here are the top-performing LLMs in their respective categories:
- Most Popular Research Model – Perplexity R1 177B
When AI agents need deep research capabilities, Perplexity R1 177B is the go-to model, offering advanced document comprehension and synthesis. - Most Popular Reasoning Model – Groq Deepseek Distil Lama 17B
AI agents built for complex decision-making and reasoning tasks rely on Groq Deepseek for structured thought processes. - General Purpose Model – GPT-4o
Versatile and powerful, GPT-4o serves as the backbone for a variety of AI-powered applications across industries. - Most Popular Coding Model – Claude 3.5 Sonnet
AI agents writing, debugging, and optimizing code leverage Claude 3.5 Sonnet for superior programming assistance. - Most Popular Low-Cost Model – Gemini Flash 1.5 Lite
For businesses optimizing cost without compromising performance, Gemini Flash 1.5 Lite delivers efficiency at a lower price. - Most Popular Open-Source Model – Llama 3.1
AI developers looking for flexible, open-source solutions turn to Llama 3.1 for its adaptability and strong community support. - Most Popular Small Language Model (SLM) – Phi 3.5
Lightweight yet powerful, Phi 3.5 is perfect for running AI agents with limited compute resources without losing effectiveness.
Most Popular Vector Databases Preferred by AI Agent Builders
AI agents need to retrieve, store, and process vast amounts of structured and unstructured data. These are the most preferred vector databases for optimizing retrieval-augmented generation (RAG) and semantic search:
- Qdrant – A high-performance vector database known for its efficient indexing and real-time search capabilities. Access our case study here.
- DocumentDB – AWS-backed document database enabling seamless AI integration with enterprise data sources.
- PGVector – A lightweight and Postgres-native vector database extension that makes it easy to add semantic search to existing enterprise applications without needing separate infrastructure.
Most Popular Cloud Hosting Platforms for AI Agent-Powered Applications
AI agents require scalable, secure, and enterprise-grade hosting solutions. These are the top choices for organizations deploying AI-powered applications:
- AWS – The #1 cloud platform for AI workloads, offering best-in-class security, scalability, and ML tooling.
- Azure – A strong choice for enterprises already leveraging Microsoft’s cloud ecosystem.
- GCP – Preferred by AI-heavy enterprises looking to leverage Google’s deep learning capabilities.
- On-Prem (NVIDIA) – For businesses prioritizing on-prem AI deployments, NVIDIA-powered infrastructure remains a top choice.
Most Popular Voice Models for AI Voice Agents
AI voice agents are redefining customer interactions across industries. Here’s how different voice models are leading the way:
- Automated Reminder Calls – Vapi.ai
AI-powered call agents efficiently handle appointment reminders, payment follow-ups, and automated alerts. - Customer Support Agents – ElevenLabs
AI-driven voice agents improve customer experience with realistic, human-like interactions. - Voice-Powered Applications – OpenAI Realtime API
Enabling real-time conversational AI, OpenAI’s Realtime API powers interactive voice-based applications.
Most Popular Horizontal AI Use Cases
AI agents are transforming operations across multiple business functions. Here are the most widely adopted horizontal AI applications:
- Customer Service Automation – AI-powered chatbots and voice agents handling L1 & L2 support.
- Marketing Automation – AI-driven content creation, campaign optimization, and hyper-personalized messaging.
- Lead Enrichment & CRM Updates – Automating prospect research and updating CRM records in real-time.
- AI SDR (Sales Development Representative) – AI-driven outbound sales engagement and follow-ups.
- HR Operations Automation – Automating hiring workflows, employee onboarding, and performance tracking.
- Company Research (Competitor, Customer) – AI agents continuously monitoring industry trends and competition.
Most Popular Banking Use Cases for AI Agents
AI agents are revolutionizing banking, reducing fraud, and improving customer engagement. Top use cases include:
- Customer Onboarding Automation – AI verifies documents, streamlining KYC and onboarding processes.
- Regulatory Monitoring Automation – AI ensures real-time compliance with evolving financial regulations.
- Customer Support Automation – AI chat and voice agents reduce operational load and improve CX.
- KYC Processing Automation – AI-powered verification accelerates customer identity authentication.
- AML Processing (Anti-Money Laundering) – AI detects suspicious activities and prevents financial fraud.
- Refund Processing – AI-driven automation speeds up claims processing and settlements.
- Retirement Planning Assistant – AI helps customers with financial advisory and personalized investment strategies.
- Personalized Wealth Manager Agent – AI-powered financial advisors offer personalized portfolio insights.
Most Popular Insurance Use Cases for AI Agents
From claims processing to customer interactions, AI agents are reshaping the insurance landscape:
- Claims Processing Automation – AI expedites claims validation and settlements.
- Document Extraction for Litigation – AI automates legal document analysis for faster resolution.
- Policy Underwriting Support Agent – AI improves risk assessment and premium calculation.
- Voice-Powered AI Customer Support – AI-driven voice agents enhance policyholder experience.
- Voice-Powered Partner Quality Assurance Audit – AI audits and evaluates partner interactions for compliance.
AI adoption across segments is no longer about “if,” but “where”
Enterprises, mid-market firms, and SMBs are all building with AI agents – but the functions they prioritize reveal what each segment values most.
🔹 Enterprises are doubling down on operations and compliance-heavy workflows. 46% of adoption is centered on business functions – think procurement, HR, and finance – where scale and control matter most. Customer service and sales follow closely, with a growing interest in AI-powered engagement.
🔹 Mid-market companies are leaning into customer-facing automation, with 39% of adoption focused on core business functions, and a rising trend in AI for sales (18%) and marketing (16%). These firms want scale – but with agility.
🔹 SMBs, on the other hand, are going all-in on growth. Sales and marketing combined account for over 65% of their AI agent adoption, showing a clear intent to drive revenue and reach. AI is being used here not just for efficiency – but for acceleration.
The takeaway?
AI Agents are being shaped not just by industry – but by maturity, ambition, and operational priorities. Whether it’s scale, speed, or customer engagement, every segment is turning to AI agents to meet their most pressing needs.
And the divergence is just beginning.
Conclusion: From Possibility to Practice
AI Agents are no longer an experiment. They’ve moved from buzzword to boardroom – demanding real outcomes, reliable execution, and enterprise-grade scale.
Over the last year, we’ve seen a shift in how forward-thinking enterprises are approaching AI Agent adoption. There’s growing clarity – and urgency.
The prototyping phase is behind us.
Leaders aren’t looking to test ideas anymore—they’re looking to put AI agents into production. Proof of concept has been replaced by proof of impact.
Big-bang strategies are giving way to agile execution.
Rather than over-engineering a multi-year AI roadmap, the most successful organizations start with one high-impact use case and expand rapidly based on results.
Building AI agents is becoming a core skill.
Organizations aren’t outsourcing the entire problem—they’re enabling their own teams to experiment, deploy, and iterate. AI literacy is becoming table stakes.
So, where does your organization stand today?
If you’re navigating questions of AI readiness, integration complexity, or team enablement—it’s worth taking a moment to assess.
Take the AI Readiness Assessment
This is a lens into your current state, and a nudge toward where to focus next.
AI Agents aren’t a future initiative. They’re here. And the decisions you make in the next 6–12 months will define your competitive trajectory for the next 5 years.
The only real risk? Waiting too long to begin.
