By Michael Bevilacqua, Vice President of AI Product Management at Adeptia
For three years, the enterprise AI conversation has centered on what models can generate. In 2027, it shifts to everything around the generation: deciding what to ask for, paying to run it, defending what you sell when buyers assume software is replaceable, and knowing who an agent is acting for. Here are four predictions from where I sit, building AI into an integration platform that runs inside heavily regulated enterprises.
1. The bottleneck moves to both ends of the code.
Agents already produce working code faster than a team can review it. The constraint has moved to the two ends of the work: specifying precisely what to build before any code exists, and proving that what was built is correct afterward. Teams that keep organizing around coding throughput will find that their review queue is the new backlog.
The teams that pull ahead will treat the specification as a contract. A person writes what the software must do in testable terms, an agent builds it, and a separate check that the building agent never sees judges the result.
2. Gross margin becomes an engineering decision.
In 2027 CFOs will read inference spend the way they read cloud spend a decade ago: a line that grows with usage and lands directly on gross margin. Where the model runs, and how often, stops being an architecture detail.
3. “Couldn’t an LLM just do this?” becomes the first question in every software deal.
That question costs a buyer nothing to ask and a vendor a great deal to answer. In 2027, it becomes the default opening in procurement reviews: software is assumed replaceable until the vendor proves otherwise. Thin applications that wrap a workflow around a database will struggle with it.
What survives is that a general model can’t regenerate on demand deep connections into the systems that run a business, accumulated knowledge of how messy real-world data behaves, and accountability in workflows where a wrong answer has legal consequences. A model can write a connector in an afternoon. Keeping hundreds of connections running correctly while partner formats change without notice, is a different job. As building gets easier, companies build more, and the hard part shifts to running it all reliably.
4. Agent identity becomes the security perimeter.
By the end of 2027, the most important security question about an AI agent will be simple: who is it acting for? Many agents today reach business systems through a shared service account with broad permissions. That detaches every action from a person and turns the audit log into a list of things “the bot” did. As agents move from reading data to changing it, that shortcut becomes the next breach report.
Enterprises will require every agent action to resolve to two identities, the person who delegated the work and the agent that performed it, and will enforce access where the agent calls a tool rather than inside the model. Prompt injection will remain a risk to contain, so the tool boundary is the control that actually holds.
The common thread
None of these predictions depends on a better model. Each is about the work around the model: the specification before it, the cost of running it, the systems it connects to, and the identity it acts under. The companies that do well in 2027 will be the ones that treat that surrounding work as the product.
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ABOUT THE AUTHOR
Michael Bevilacqua is Vice President of AI Product Management at Adeptia, where he leads the company’s AI product strategy and its move to AI-native software delivery. Adeptia’s platform connects enterprises with their customers, partners and internal systems, and is used by Fortune 500 companies in financial services, insurance and healthcare.
