The AI ecosystem feels chaotic because it is. Upheaval is constant; there is no high ground. OpenAI, once lauded as AI’s best brand, is navigating leadership churn, communications resets and increasing competition.
No one is safe in this environment.
That’s what Michael Nuñez, VentureBeat’s editorial director, and I recently sat down and discussed, along with trends from the last year and why the next 12 months will separate real enterprise builders from generative wannabes.
One of the biggest trends to emerge: Horizontal, one-size-fits-all generative AI is rapidly losing its luster, while verticalized AI, built for specific industries, workflows and enterprise realities, is showing potential for long-term viability.
This dynamic is playing out in companies, funding decisions and customer adoption.
Over the next 12 months, the gap will widen dramatically between companies that truly understand their industries and those still riding the generic generative wave.
GPT Wrappers Lose Their Shine
In 2023, the market rewarded speed. If you slapped a UI on top of a frontier generative AI model and told a good story, you could raise money and grab headlines.
Gen AI was the poster child for that moment. It was hot, it was buzzy, it was everywhere.
And then…it wasn’t.
Gen AI startups were pegged as “GPT wrappers,” a negative tag that meant they lacked a durable technical moat, differentiation, deep enterprise customers or leadership that could shape the narrative.
Funding still kept them afloat, but their perceived viability began to fade.
By 2025, many generative AI platforms were showing high failure rates because they didn’t deliver ROI, suffered from poor data quality, lacked integration into workflows and had no clear monetization paths.
The market is maturing. Enterprises want more than clever demos and are asking the hard questions:
- How does this integrate with my workflows?
- How does this handle my proprietary data?
- What’s the ROI?
- What happens when the underlying model changes?
If your answer is “We’ll figure that out later,” later has arrived.
Why Verticalized AI Is Pulling Ahead
Verticalized AI changes how and why AI is employed. Instead of “What can this model do?” it starts with, “What does this industry actually need?”
That distinction matters.
In regulated, complex, high-stakes environments, such as law, healthcare, finance and supply chain, AI only has value if it understands its customers’ ecosystem. It must understand:
- Domain-specific data
- Specialized documents and workflows
- Risk tolerance and compliance
- How decisions are actually made
Verticalization is why companies like Harvey (legal) or enterprise-focused platforms in banking and healthcare are gaining traction. They’re embedding operational intelligence about those industries into the workflow.
It’s also why enterprise AI is now outpacing consumer AI. Consumer tools can be easily swapped, especially if they lack a durable moat, i.e., proprietary models, unique data or deep integrations that can not be easily replicated or commoditized.
How Writer Beat the Odds
Writer, an enterprise AI platform for agentic work, is an example of a success story in the tumultuous AI landscape. “The risk for Writer,” Nuñez noted, “is whether marketing, unlike law or finance, can sustain long-term differentiation without collapsing into commoditization.”
“While Writer built its own foundation model, that’s not its secret sauce,” Nuñez said. Instead, he pointed to its early focus on the enterprise, reformatting its UI to make adoption easier for large organizations and differentiate Writer from simple AI tool providers.
Company leadership is also a decisive factor. May Habib, the CEO of Writer, has emerged as a credible spokesperson for the enterprise. Habib was part of AWS CEO Marc Garman’s keynote at AWS re:Invent 2025 in December, marking Writer’s credibility and reach in the enterprise space.
Writer is already working with top Fortune 500 companies, including Accenture, Intuit, Salesforce, Uber and Vanguard. The more it specializes and invests in these relationships, the stronger its foothold in the enterprise sector.
Industry verticalization is especially valuable in the marketing arena, a tricky vertical. Unlike law or accounting, there are infinite “right” answers, and hundreds of AI marketing solutions. But, as Nuñez noted, “the longer Writer works directly with enterprises, the more durable it becomes. Workflow knowledge compounds, integration deepens and switching costs rise.”
That’s how moats are built in AI now, not with features, but with context.
Pay Attention to the Next 12 Months
2026 will be critical for AI companies.
The ones that survive are ignoring the hype and digging in:
- Embedding into enterprise systems
- Rethinking workflows, not just automating them
- Building trust with customers who may move slowly but stay forever
- Turning AI from a tool into infrastructure
By the beginning of 2027, we’ll know which companies have staying power.
The Future of Enterprise AI
The generative wave is already receding.
Over the next three to five years, one-size-fits-all AI solutions will become even more commoditized. Most horizontal AI platforms will become feature layers inside enterprise stacks. Foundation models will be table stakes; workflow intelligence will determine valuation. Companies will bring on AI firms, not for models, but for embedded institutional knowledge.
Companies that understand their industries with tailored AI solutions will deliver the ROI enterprises now demand.
