Artificial intelligence, first coined by John McCarthy in 1956, lay dormant for decades before exploding into a cultural and business phenomenon post-2012. From predictive algorithms to chatbots and creative tools, AI has evolved rapidly. Now, two powerful paradigms are shaping its future: generative AI, which crafts content from text to art, and agentic AI, which acts autonomously to solve complex tasks. But should businesses pit generative AI against agentic AI or combine them to innovate? The answer isn’t binary, because these technologies aren’t competing forces. In fact, they often complement each other in powerful ways, especially when it comes to transforming customer engagement.

The rise of generative AI: Creativity meets scale

Generative AI is all about creation; it represents the imaginative side of artificial intelligence. From producing marketing copy and designing campaign visuals to generating product descriptions and chat responses, generative AI has unlocked new possibilities for enterprises looking to scale content and personalisation like never before.

Fuelled by powerful models like ChatGPT, DALL·E, and MidJourney, these systems have entered the enterprise stack at speed. Marketing teams are using them to brainstorm ideas and accelerate go-to-market efforts. Customer support teams are deploying them to enhance chatbot interactions with more human-like language. Product teams are using generative AI to auto-draft FAQs or documentation. And sales teams are experimenting with tailored email pitches generated from past deal data.

At the heart of this capability is the model’s ability to learn from massive datasets, analysing and replicating patterns in text, visuals, and code to produce new, relevant content on demand. This has made generative AI a valuable tool in customer engagement workflows where speed, relevance, and personalisation are paramount. But while generative AI can start the conversation, it rarely finishes it. That’s where its limitations show up.

For instance, it can draft a beautifully written response to a billing query, but it can’t resolve the issue by accessing the customer’s account, applying credits, or triggering workflows across enterprise systems. In other words, it creates the message but not the outcome. This creative strength makes generative AI a powerful enabler of customer engagement but not a complete solution. To drive real business value, measured in resolution rates, retention, and revenue, enterprises need to go beyond content generation and toward intelligent action. This is where agentic AI comes into play.

How agentic AI is redefining enterprise and consumer engagement

As the need for deeper automation grows, agentic AI is taking centre stage. Agentic AI is built to act; it makes decisions, takes autonomous actions, and adapts in real time to achieve goals. For businesses, this marks a transformative shift. Generative AI has empowered enterprises to accelerate communication, generate insights, and personalise engagement. Agentic AI, on the other hand, goes beyond assistance to autonomy. Imagine a virtual enterprise assistant that doesn’t just draft emails but manages entire customer service workflows — triggering follow-ups, updating CRM systems, and escalating issues when needed.

In industries like supply chain, finance, and telecom, agentic AI can dynamically reconfigure networks, detect anomalies, or reroute deliveries—all with minimal human input. It’s a new era of AI-driven execution. On the consumer front, agentic AI takes engagement from passive response to proactive assistance. Think of a digital concierge that not only understands your intent but acts on your behalf — tracking shipments or negotiating a better mobile plan based on usage patterns.

A new layer of intelligence — with responsibility

The increased autonomy of agentic AI raises important questions around trust, governance, and accountability. Who’s liable when an agentic system makes an error or an ethically questionable decision? Enterprises adopting such systems will need to ensure alignment with human values, transparency in decision-making, and robust fail-safes.

Generative and agentic AI are not rivals — they’re complementary forces that, together, enable a new era of intelligent enterprise and consumer engagement.

When generative meets agentic AI

Generative AI and agentic AI may serve different functions. However, rather than operating in isolation, these technologies frequently collaborate, enhancing both communication and execution.

Take, for example, a virtual customer service agent. The agentic AI manages the flow of interaction, makes decisions, and determines next steps, while generative AI crafts clear, personalised responses tailored to the conversation in real time.

This collaborative dynamic also plays out in robotics. Imagine a robot chef: generative AI could invent creative recipes based on user tastes and available ingredients, while agentic AI would take over the cooking, executing the recipe with precision and adapting to real-time conditions in the kitchen.

Summing Up

As AI continues to evolve, the boundaries between generative and agentic systems will become increasingly fluid. We’re heading toward a future where AI doesn’t just imagine possibilities but also brings them to life, merging creativity with execution in a seamless loop. This fusion holds immense promise across industries, from streamlining healthcare operations to revolutionising manufacturing workflows.

However, with such transformative power comes great responsibility. Ethical development, transparency, and accountability must remain non-negotiable, especially when it comes to safeguarding consumer data. As these systems take on more autonomous roles, ensuring privacy, security, and user consent will be critical to building trust.

By understanding the distinct roles and combined potential of generative and agentic AI, we can shape a future where technology enhances human capability responsibly, meaningfully, and with integrity at its core.

This article is authored by Harsha Solanki, VP GM Asia, Infobip.

Disclaimer: The views expressed in this article are those of the author/authors and do not necessarily reflect the views of ET Edge Insights, its management, or its members