Editorial
The first wave of GenAI came with big ambitions — and an even bigger learning curve. Enterprises spent millions wiring together their own stacks, hoping to turn pilots into production. We were promised transformation. What most got instead was technical debt. In hindsight, the mistake was clear: everyone tried to build the infrastructure themselves.
Back then, it was understandable. In 2022, there were no real platforms — just a pile of APIs and raw models. To experiment, you had to build it all yourself: data pipelines, connectors, security, governance. But that phase was always going to be temporary — just like the early days of cloud or mobile.
The lesson now is simple: wiring stacks together isn’t innovation. It’s plumbing. The real advantage lies in focusing on outcomes — the work AI can actually do for businesses.
Why the Early GenAI Efforts Failed
MIT’s 2025 research found that 95% of organizations saw no measurable ROI from GenAI. Gartner predicts that by 2027, nearly half of agentic AI projects will be canceled — usually because costs balloon, governance lags or no one can prove the business value.
These numbers don’t mean GenAI is in decline — they mark the next phase. Every major tech shift goes through it: the move from experimentation to realism. The first wave was always going to be inefficient. Most of it wasn’t doing real work yet — it was demo theatre.
Related Article: Why Only 5% of Companies Are Seeing Real AI ROI
What Agentic AI Really Means
That shift — from generative AI to agentic AI — happened for a reason. Early chatbots could talk, but they couldn’t act. They produced answers, not outcomes. They didn’t connect to systems, complete processes or adapt to change. And often, they weren’t even right.
Scripted bots and RPA tools follow rigid rules. They don’t reason. They don’t evolve. Agentic AI introduces something new: an operating model where systems can act with intent — within guardrails, but with the freedom to decide how best to achieve the goal.
Agentic AI is different. An agent can perceive context, plan and execute multi-step tasks — sometimes alongside other agents. It remembers. It learns. It connects to your systems and adapts as conditions shift. It behaves less like a calculator, more like a capable colleague.
Why Platforms Matter Now
If agents are how work gets done, the question becomes: how do you operate them at scale? That’s where early efforts broke down. The enthusiasm wasn’t the problem. It was the assumption that home-grown infrastructure would scale. The result: impressive demos and disappointing results.
The projects that did scale shared one thing: they were built on platforms. MIT found that the few success stories leaned on structured platforms instead of bespoke builds. IDC echoes this — composable platforms are the connective tissue that lets models, workflows and governance operate as one system.
That’s the turning point we’re in now. Platforms exist. Building your own infrastructure isn’t bold anymore — it’s wasteful.
What a Real Platform Must Provide
For agentic AI to deliver measurable value, platforms need a solid foundation — one that turns experiments into operational systems. Six essentials define it:
- Multi-model orchestration. The ability to use, compare and switch across open or proprietary models as costs, speed and performance shift.
- Multi-persona tooling and governance. Interfaces built for analysts, product owners and developers alike — with audit trails, monitoring and observability built in from day one.
- Business context as a foundation. Outputs should be grounded in your organization’s own data — its documents, systems and guardrails.
- Enterprise-grade security. Data boundaries, permissions and compliance reporting that protect every interaction.
- Document and content preparation. Tools that transform long-form content — PDFs, contracts, images, video — into structured, retrievable knowledge that powers accurate reasoning.
- Workflow integration. Agents that can span documents, APIs and systems to complete multi-step work without manual intervention.
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These aren’t add-ons. They’re the minimum requirements for deploying agentic AI at scale.
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What Comes Next
GenAI’s story isn’t a bubble deflating — it’s maturing. The first phase spent time and money building infrastructure because there was no other choice. Now there is. The DIY era is over.
The advantage ahead won’t come from wiring different pieces of technology together — it’ll come from using AI for real work. Platforms provide the foundation. You decide where to focus. Those who make that shift will move agentic AI beyond hype — into systems that deliver measurable, lasting value.
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