Introduction

AI agents have the potential to transform enterprise operations by automating complex workflows and enhancing decision-making capabilities. The key to unlocking AI’s full potential lies in a phased approach that aligns deployment with corporate strategy, prioritizes high impact use cases, ensures seamless integration, maintains strong data governance, and fosters workforce adoption. This article explores the essential steps for organizations to transition from strategy to scalable AI agent deployment.

Overview

“ AI agents will transform the way we interact with technology, making it more natural and intuitive. They will enable us to have more meaningful and productive interactions with computers.”

-Fei-Fei Li, Professor of Computer Science at Stanford University

As a pioneer in AI and computer vision, Fei-Fei Li highlights the transformative potential of AI Agents. 

We’re still in the early stages of AI agents, but the pace of progress is accelerating. A 2024 survey by LangChain, featured in their State of AI Agents article, reveals that 51% of respondents are already using AI agents in production, while 78% have plans to deploy them in the near future.

AI Agent: Definition

An AI agent is an autonomous or semi-autonomous system that perceives its environment, processes information, and takes actions to achieve specific goals. 

AI agents leverage ML, NLP, and reasoning algorithms to interact with users, systems, or other agents. They are designed to automate tasks, optimize decision-making, and elevate user experiences across various industries and business functions. 

What separates types of AI agents is their level of sophistication in perceiving and interacting with their environment, as well as their decision-making capabilities. Simpler reflex agents, like chatbots, follow basic if-then rules, while more complex AI agents can employ advanced ML models, engage in long-term planning, learn and adapt over time, and collaborate with other agents and/or humans. The goal of AI Agent development is to create systems that can effectively assist or automate tasks while demonstrating intelligent, goal-directed behavior.

There are six types of AI agents, categorized based on their capabilities, described in Table 1 below:

These AI agents vary in complexity, with more advanced models incorporating deep learning and reinforcement learning to improve autonomy and adaptability in dynamic environments.

AI Agent Landscape

The landscape is rapidly evolving, with both established tech giants and new players leading the way in innovation. Tech giants like Microsoft (Copilot Studio), Salesforce (Agentforce), and ServiceNow (AI Agent Studio) provide powerful AI agent-building tools and pre-configured agents across various business functions. Meanwhile, emerging players such as Harvey, Moveworks, Sierra, and kore.ai are carving out niches by developing AI agents tailored to specific industries, business functions, or cross-functional needs.

The Chinese start-up company, The Butterfly Effect, made waves with the launch of its multi-agent platform, Manus, touted as the “first general AI agent” capable of autonomously managing complex tasks. Reactions from early testers have been mixed—some praise Manus as a breakthrough in autonomous multi-agent systems, while others remain skeptical about its true capabilities. This launch underscores the intensifying global competition in the race for AI agent supremacy.

1 ServiceNow announced its plans to acquire Moveworks for $2.85 billion on 3/10

A key distinction in this space is between companies offering vertical-specific AI agents and those providing horizontal, function-based solutions.  Companies developing vertical AI agents leverage domain-specific data and workflows in markets like healthcare, professional services, and financial services. In contrast, horizontal solutions focus on specific business functions or are cross-functional, streamlining tasks like customer service, sales & marketing, and talent management. As AI capabilities advance, the competition between these approaches will shape how businesses adopt and integrate AI agents into their operations.

Examples of key players in the Agentic AI landscape are listed in Figure 1 below:

The AI Agent Journey: From Strategy to Enterprise-Wide Adoption

JLA Advisors helps at every stage of the AI journey, spanning from conceptualization to implementation or commercialization. This includes support in strategy development, proof of value, technology stack assessment, integration, implementation, governance, and hardware (if needed) as shown in Figure 2 below:

1. Strategy

Before scaling AI agents, businesses must establish well-defined goals and objectives and identify key use cases that align with their strategic priorities. Are AI agents being deployed to reduce operational costs, enhance customer experience, improve decision-making, or automate specific workflows? What type of AI agent will be deployed? Defining clear strategic priorities and setting measurable KPIs—such as improved response times, increased automation rates, and revenue growth—ensures that AI initiatives remain focused and accountable.

2. Business Case

Developing a business case is essential to determine if the right use case or use cases have been selected. This involves reviewing market opportunities evaluated during the Strategy phase, estimating cost savings and/or revenue potential, and analyzing competitive advantages AI agents can provide. The business case also includes a build vs. buy vs. hybrid approach evaluation to determine the AI agent deployment cost. A well-documented business case ensures leadership commitment and resource allocation.

ServiceNow, in collaboration with Oxford Economics, published the Impact AI: Enterprise AI Maturity Index 2024 report, drawing insights from a global survey of approximately 4,500 executives using AI in their organizations. The survey categorized respondents into Pacesetters and Others, with Pacesetters excelling across five AI Maturity Index parameters1. A key finding from the report mentions that Pacesetters are more likely to adopt a hybrid AI strategy, combining both purchased tools and proprietary AI models (47% vs. 31% for Others).

1 Parameters are AI Strategy & Leadership, Workflow Integration, Talent & Workforce Components, AI Governance, and Realizing Value in AI Investment

For organizations developing their own agents or adopting a hybrid approach, a growing set of best practices is emerging from early adopters. For instance, Anthropic’s Building Effective Agents provides lessons learned based on its work with numerous companies and its own internal efforts to develop AI agents.

3. Proof of Value

After deciding on the build, buy, or hybrid approach, the vendor evaluation and selection process is carried out if the buy or hybrid options are chosen. Next, enterprises validate the feasibility of the AI agent through a proof of value phase, where they create a prototype either internally or with a partner to showcase the AI agent’s potential in a controlled environment. Collecting early feedback and performance data during this phase helps fine-tune the models, reducing risks and ensuring they align with business requirements before full-scale implementation.

4. Data & Integration

A major challenge in AI deployment is integrating AI agents with existing enterprise systems like CRM, ERP, and legacy databases. Adopting a well-designed API-driven approach can enable smooth communication between AI agents and core business applications, ensuring real-time data exchange and informed decision-making. Furthermore, maintaining high-quality, structured data is essential for the accuracy and performance of AI models. 

According to MuleSoft’s 2025 Connectivity Benchmark Report, produced in partnership with Vanson Bourne and Deloitte Digital, “integration remains the most significant barrier to AI implementation, with 95% of organizations facing challenges when integrating AI into their existing processes.”

5. Implementation

Once validated, AI agents are deployed in a phased manner. Businesses should start with small-scale rollouts, monitor performance metrics, and iteratively optimize before company-wide implementation. Change management, user training, and ongoing support play a crucial role in successful implementation.

6. Policy & Governance

As AI agents process vast amounts of enterprise data, ensuring security and regulatory compliance is critical. Organizations must implement robust data governance policies, address AI bias, and maintain transparency in AI-driven decision-making. Compliance with industry regulations such as GDPR and HIPAA and regular security audits are also essential to safeguard sensitive data and maintain customer trust. Establishing an AI governance framework helps maintain oversight and ensures AI models remain aligned with corporate policies.

While not specific to AI agent deployments, the ServiceNow and Oxford Economics report also identified the progress made by Pacesetters vs. Others in establishing data governance frameworks as shown in Figure 3 below: 

The chart emphasizes the urgency for companies to accelerate the development of their data governance frameworks to achieve successful deployments. Pacesetters must advance by an additional 40%, while Others require a 58% increase to establish frameworks for mapping both structured and unstructured data.

The Future of AI Agent Deployment in Enterprises

The next phase of AI agent deployment will bring significant advancements in autonomous decision-making, enabling agents to analyze complex scenarios, predict outcomes, and take actions with minimal human intervention. Additionally, hierarchical and multi-agent collaboration will evolve, allowing AI agents to work together dynamically, whether coordinating tasks within a single enterprise function or collaborating across departments to drive efficiencies and innovation. These developments will not only enhance operational capabilities but also reshape how organizations structure workflows and make strategic decisions.

Summary

Successfully deploying AI agents is more than just improving capabilities and productivity –  it represents a fundamental business transformation. By following JLA Advisor’s AI Transformation Journey, enterprises can navigate the complexities of AI integration, from identifying high-value use cases to ensuring seamless system interoperability. Addressing key challenges, such as establishing data governance frameworks and ensuring workforce adoption, are critical for successfully scaling AI agents and unlocking their full potential.

JLA Advisors (www.jlaadvisors.io) is a boutique consulting firm dedicated to empowering businesses with transformative strategies and customized solutions for success. JLA’s AI consulting practice helps clients leverage the power of AI to enhance existing products, unlock new opportunities, and deliver innovative solutions.

Contact us at ai@jlaadvisors.io to explore how we can assist with your AI needs.