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

by Paul W. Bennett 
Originally published on Policy Options
September 5, 2025

by Paul W. Bennett 
Originally published on Policy Options
September 5, 2025

Consider these everyday experiences in today’s digitally dependent world rich with artificial intelligence (AI). A convenience store cashier struggles to make change. Your Uber driver gets lost on his way to your destination. A building contractor tries to calculate the load-bearing capacity of your new floor. An emergency-room nursing assistant guesses at the correct dosage in administering a life-saving heart medication.

All of these are instances of an underlying problem that can be merely an irritant or a matter of life and death. What happens when brains accustomed to backup from phones and devices must go it on their own?  

Increasingly we are relying upon technology to do our thinking for us. Cognitive offloading to calculators, GPS, ChatGPT and digital platforms enables us to do many things without relying on human memory. But that comes with a price.   

Leading cognitive science researchers have begun to connect the dots. In a paper entitled The Memory Paradox, released earlier this year, American cognitive psychologist Barbara Oakley and a team of neuroscience researchers exposed the critical but peculiar irony of the digital era: as AI-powered tools become more capable, our brains may be bowing out of the hard mental lift. This erodes the very memory skills we should be exercising. We are left less capable of using our heads.

Collective loss of memory

Studies show that decades of steadily rising IQ scores from the 1930s to the 1980s — the famed Flynn effect — have levelled off and even begun to reverse in several advanced countries. Recent declines in the United States, Britain, France and Norway cry out for explanation. Oakley and her research team applied neuroscience research to find an answer. Although IQ is undoubtedly influenced by multiple factors, the researchers attribute the decline to two intertwined trends. One is the educational shift away from direct instruction and memorization. The other is a rise in cognitive offloading, that is, people habitually leaning on calculators, smartphones and AI to recall facts and solve problems. 

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Surveying decades of cognitive psychology and neuroscience research, Oakley and her team show how memory works best when it involves more than storage. It’s also about retrieval, integration and pattern recognition. When we repeatedly retrieve information, our brains form durable memory schemata and neural manifolds. These structures are indispensable for intuitive reasoning, error-checking and smooth skill execution. But if we default to “just Google it,” those processes so fundamental for innovation and critical thinking may never fully develop, particularly in the smartphone generation.

A key insight from the paper is the connection between deep learning behaviours in artificial neural networks (consider “grokking” in which patterns suddenly crystallize after extensive machine training) and human learning. Just as machines benefit from structured, repeated exposure before grasping deep patterns, so do humans. Practice, retrieval and timed repetition develop intuition and mastery.

Atrophy of mental exercise

The researchers sound a cautionary note. Purely constructivist or discovery‑based teaching, starting with assumptions that “students know best” and need little guidance, can short‑circuit mental muscle‑building, especially in our AI world. The team found that when students rely too early on AI or calculators, they skip key steps in the cognitive sequence: encoding, retrieval, consolidation and mastery of the brain’s essential building blocks. The result is individuals whose mental processes are more dependent upon guesswork, superficial grasp of critical facts and background knowledge and less flexible thinking.

Even techno skeptics see a role for digital tools. Oakley and her colleagues argue for what they term cognitive complementarity — a marriage of strong internal knowledge and smart external tools. ChatGPT or calculators should enhance — not replace — our deep mental blueprints that let us evaluate, refine and build upon AI output. That’s the real challenge that lies ahead.

The latest cognitive research has profound implications for educational leaders, consultants and classroom teachers. Popular progressive and constructionist approaches, which give students considerable autonomy, may have exacerbated the problem. It’s time to embrace lessons from the new science of learning to turn the situation around in today’s classrooms. This includes reintegrating retrieval practice (automatic recall of information from memory), spaced repetition and step-by-step skills progression in Grades K-12.

Using your head

What are the new and emerging essentials in the AI-dominated world? Oakley and her team deliver some sound recommendations, including:

• Teaching students to limit AI use and delay offloading.

• Training teachers to design AI‑inclusive but memory‑supportive curriculums, demonstrating that effective AI use requires prior knowledge and the ability to distinguish fact from fiction

• Guiding institutions to adopt AI in ways that build upon, not supplant, the human brain, such as editing original prose or mapping data.    

Using our heads and tapping into our memory banks must not become obsolete. They are essential mental activities. Access to instant information can and does foster lazy habits of mind. British education researcher Carl Hendrick put it this way: “The most advanced AI can simulate intelligence, but it cannot think for you. That task remains, stubbornly and magnificently, human.”

The most important form of memory is still the one inside our heads.

*Composed in a fierce dialectical encounter with ChatGPT.

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