Imagine Julia, an AI teammate who autonomously analyzes marketing tactics, optimizes advertising budgets, and creates and publishes engaging content—turning weeks of painstaking human effort into hours. In this example, Julia is an AI agent — an autonomous program that performs tasks and makes decisions on behalf of users.
Business software has evolved from monolithic systems to cloud-based, API-driven tools, but most applications still depend on static workflows, manual inputs, and limited dashboards. While automation via rule-based bots and RPA has boosted efficiency, it hasn’t fundamentally transformed operations. That’s about to change with the rise of AI agents.
As a senior product leader in AI-driven product innovation, Prashant Tomar shares insights on the evolving impact of AI agents in business. He specializes in AI-driven product innovation, enterprise software, and driving product-led growth. He is also a mentor, investor, and frequent contributor on the future of work and emerging technologies.
How do AI agents work?
At the core of AI, agents are large language models (LLMs) that enable them to understand context, reason, and act. AI agents are distinguished by their ability to use tools and create action plans. These tools can include external datasets, web searches, and APIs, allowing AI agents to not only analyze information but also execute decisions. Like humans, AI agents can update their “memory” as they learn new information, continuously improving their performance over time.
Unlike chatbots or RPA tools that follow a predefined logic, AI agents don’t just assist humans — they act. They can interpret context, make autonomous decisions, and dynamically optimize business processes. If traditional automation was about scripting workflows, AI agents are about rewriting them in real-time. According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, with at least 15% of daily work decisions made autonomously.
How are businesses using AI agents today?
Early adopters are already seeing value in deploying AI agents across multiple functions. Some early use cases include:
● Research and development: Duolingo increased 25% in its developer productivity with GitHub Copilot. Copilot independently iterates on code, implements fixes, and reduces the need to manually produce boilerplate code to help developers stay focused on solving complex business challenges.
● Marketing: “A leading consumer packaged goods company used intelligent agents to create blog posts, reducing costs by 95% and improving speed by 50x (publishing new blog posts in a single day as opposed to four weeks).”
● Sales: Kitch, an e-commerce player, leverages Meta Business AI to drive more sales by providing personalized recommendations to its customers and helping them discover their perfect hair care match.
● Customer Service: Formula 1 speeds up service response by 80% with Salesforce Agentforce. By blending advanced AI with intuitive self-service, F1 is making it easier than ever for fans to get help on their own through their portal.
The road(map) ahead
The market for AI agents is expected to grow at a 45% CAGR to $50B over the next five years. However, AI agents won’t replace traditional enterprise software overnight. Businesses are expected to move through a few key phases as they transition from automation to fully AI-driven operations:
Phase 1: intelligent copilots (now)
In this phase, AI will act as an intelligent copilot deployed to scale repetitive tasks focused on driving productivity. Businesses will primarily leverage AI to boost productivity, with use cases like coding assistance, report generation, meeting summarization, and routine task automation.
Phase 2: workflow orchestrators (next 2 years)
As AI agents evolve, they will start orchestrating multi-step workflows across different tools and platforms. Rather than working within one app, these agents will integrate across systems like Slack, Workday, and ServiceNow to automate broader tasks, such as employee onboarding, status reporting, or approval processes. Businesses begin using these agents for more complex use cases, enabling smoother operations and improved coordination across functions.
Phase 3: domain-specific autonomous operators (in 2–5 years)
By this stage, it is anticipated that AI agents will start to manage entire workflows with minimal human intervention. Functions like software development and back-office operations will be disrupted first, with organizations starting to experiment with AI first teams. Employees will need to be upskilled to manage and work alongside AI teammates.
Phase 4: AI-first organizations (2030+)
By 2030, business operations are expected to be AI-native, with organizations functioning through AI-first systems rather than traditional enterprise software. AI agents will dynamically create and execute business strategies, with humans playing a more strategic role in oversight, governance, and ethics. Workforce structures will start looking completely different. Instead of teams spending time on execution, they’ll focus on AI management and continuous improvement. Traditional management structures will transform when AI can dynamically assign, monitor, and optimize workflows in real time, and the need for multiple layers of middle management will start to fade.
Preparing for the agentic shift
AI agents bring potential, but they also introduce critical risks. Poor-quality data is the most fundamental challenge — AI agents rely on data to make decisions, and if that data is incomplete, biased, or incorrect, they can automate bad processes, make flawed recommendations, or expose businesses to compliance risks.
Lack of visibility and privacy is another major issue. As companies deploy more AI agents, they risk losing oversight of what each agent is doing and why, leading to inefficiencies, governance failures, and unpredictable outcomes. Without strong management frameworks, organizations could face a chaotic, unregulated expansion of AI-driven operations.
Advanced cybersecurity threats will be a growing concern. As AI agents gain more autonomy, they become prime targets for malicious actors. Attackers could manipulate AI systems, launch AI-driven malware, or exploit vulnerabilities to gain unauthorized access to sensitive data.
Finally, there will be a significant shift in talent management and HR functions to enable a hybrid workforce of humans and AI agents. To prepare, organizations will need to redesign roles and workflows, viewing AI agents as collaborators. Reskilling employees, building trust, and redesigning processes to support this hybrid workforce will be essential.
What can be done now?
It is theorized that there will soon be more AI agents than people in the world. The early adoption of AI agents will allow companies to shape the next generation of industry leaders. The following steps outline how businesses can start preparing today for the integration of AI agents:
● Identify high-impact opportunities: Identify workflows that involve repetitive tasks, high volumes of data, and a need for scalability. These are prime candidates for AI agent integration.
● Prioritize data accessibility: AI agents thrive on data. Prioritize use cases where clean, high-quality data is readily available and accessible.
● Reimagine workflows from the ground up: As businesses mature, they should start rethinking entire workflows with an automation-first mindset. Consider how AI agents can handle data collection, analysis, and decision-making, and then strategically reintroduce human involvement where critical judgment, creativity, or empathy are required.
● Phased implementation: It is recommended to begin with small-scale pilot projects to test and refine AI agent integration and gradually expand to more complex workflows as experience and confidence are gained.
● Plan for the future: Businesses should lay the groundwork. Think of AI agents as digital teammates with evolving responsibilities. Define how they’ll fit into team structures, what systems they’ll need access to, and how your organization can evolve to support scalable, agent-driven collaboration, strategy, and execution.
Source: Digital Journal / Digpu NewsTex
