By Abhinav Chetan, Founder of Digital For Nonprofits (D4NP) and Digicated.ai
The telephone took 75 years to reach mass adoption. Electricity took 46. Radio did it in 25, and television in 22. The smartphone compressed that timeline to under a decade. Guess how much time did it take ChatGPT? It was 60 days.
Technology diffusion has always followed a predictable arc. Governments adopted first, enterprises scaled it, consumers followed after.
Large language models broke this pattern completely. A hundred million people were using ChatGPT before most boardrooms had discussed it. Consumers got AI in their pockets before enterprises had it in their systems. And now those same consumers aren’t just chatting with AI, they’re delegating decisions to AI agents that can act on their behalf.
From Conversation to Agency
To understand how we got here, let us trace the arc of AI evolution. Sam Altman’s five levels on the road to artificial general intelligence offers a useful frame for this.. First came conversational. AI that responded to basic queries. ChatGPT’s launch in November 2022 exemplified this with a hundred million within two months which made it the fastest consumer technology adoption in history.
The second level introduced reasoning. Here upgraded models with larger context windows and thinking capabilities started to rival domain experts. At this level AI was still responding to humans, but with substantially greater depth.
The third level is where we stand now. Agentic AI. Systems that don’t have to wait for approval. They have the ability to plan, decide, and act autonomously toward defined goals. This is the shift from tool to collaborator.
What made this possible? Expanded memory that persists beyond a single session, larger context windows for inputs, reasoning that can chain complex tasks together, and integration with external tools and systems. When AI can remember context, think through problems, and execute actions across platforms, it starts to learn the way humans do.
Of course, this comes with limitations. These systems are not magic. They’re not always accurate and can’t replace strategy. But the fact is that they are becoming more real by the day.
Here are some statistics to illustrate the pace of change in this space. The global agentic AI sector is valued at $7.3 billion in 2025 and is projected to exceed $139 billion by 2034. That’s a 40% compounded annual growth rate. Gartner estimates that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.
The Three-Layer Disruption
The implications of agentic AI extend far beyond efficiency gains. What’s unfolding is a structural reorganization of how users, interfaces, and brands interact with each other.
The current model is familiar. A user discovers products or services through search engines, social media, or retail platforms. They interact via phones, laptops, or tablets. They reach brands through websites, apps, or various storefronts. This architecture has been optimized relentlessly over the last decade.
Now all three layers face disruption at the same time.
At the user layer, personal AI agents are emerging as intermediaries. A user might deploy a single master agent that coordinates with platform-specific agents. Or deploy different ones across platforms. One for travel. Another for shopping. A third for investing. These agents interface with search, social, and commerce on the user’s behalf.
Discovery is completely delegated and the user doesn’t participate in the process.
At the interface layer, surfaces are exploding beyond screens. AI is getting embedded into glasses, voice devices, smartwatches. The brand can no longer control where the customer encounter happens, giving omnichannel a whole new meaning.
At the brand layer, organizations must build their own agent infrastructure. The website isn’t the destination anymore. The conversation is. A brand’s agent must be capable of orchestrating across touchpoints, responding to queries from customer agents, and transacting without traditional navigation.
And here’s the uncomfortable truth. None of these layers are fully prepared. User-side agents demand structured, machine-readable content. Brand-side agents need protocols that don’t yet exist at scale. Interface proliferation means the carefully optimized brand assets may never be seen.
Agentic adoption and maturity will take time. But the structural shift is already underway.
The Readiness Question
So where does this leave enterprises? The honest answer is that very few organizations are getting this right. According to a 2025 Wharton study, while 78% of organizations express strong optimism about integrating AI across business functions, only 28% of employees actually know how to use their company’s AI applications. The rest are either unaware or struggling with adoption. This goes beyond being just a ‘technology’ problem. It’s a combination of technology, organizational design, and leadership.
For leaders, the priority is clear. Understand AI capabilities. Differentiate what it can and can’t do for their business. Then embed it into workflows. McKinsey’s 2025 State of AI report found that high-performing organizations are three times more likely to have senior leaders who demonstrate ownership and active engagement in AI initiatives.
The technology keeps improving, but understanding where it breaks is just as important as knowing where it works.
From an organizational design perspective, teams need to be restructured to figure out how to work alongside AI. Brands need to begin redesigning roles so that humans and agents complement each other. Tasks that sit at lower levels of complexity and are repetitive in nature can be increasingly handled by agents. That means the people doing those tasks today need new responsibilities. And new skills.
Which brings us to upskilling. According to Bain, 44% of executives feel that lack of in-house expertise is slowing AI adoption. Professionals should start combining their deep domain expertise with an understanding of AI tools. The ones who thrive will be those who know their craft and know how to leverage agents to amplify their output.
Finally, these systems can’t work fully autonomously. The human in the loop needs to be a genuine design philosophy. Because that is guidance and direction for AI systems. The future enterprise isn’t human or machine. It’s human AND machine working together toward defined outcomes.The AI agent is no longer a concept or a demo, it’s here and as brands you need to be ready for it.