Why most AI projects fail: It's infrastructure and people

Years ago, he says, tooling decisions were primarily made top-down without developer input: “Then, starting 15–20 years ago, the developers won back, quite rightly, the power in being able to choose their own tools.”

The problem now, Merrick claims, is that developers make tooling decisions exclusively for prototyping environments, without anticipating production needs. Internal divisions mean developers are often only responsible for building prototypes before passing the baton to an entirely separate operations team for production:

“The upshot is you really don’t have, in some organizations, this throughline of understanding [of] what the production requirements are all the way back to the developer making the initial choices.”

For Merrick, this disconnect is where AI projects start to fall apart, as teams are left trying to move AI prototypes from accessible-but-inadequate vendor-managed cloud platforms to enterprise-grade data infrastructure that meets requirements for deployment flexibility, security, data sovereignty, and high availability.

“But if you make the right data infrastructure choice, you won’t have that disconnect,” he says, “because you’ll have this throughline from prototype to production.”

He names Postgres as the data infrastructure that best helps developers bridge this divide, calling it “the Swiss army knife of databases” due to its extensibility, fully open-source nature, and ability to address diverse data management problems, from unstructured data to vector embeddings to geospatial data.

Where and how Postgres is run matters too, Merrick points out, again drawing attention to the limits of many vendor-managed cloud environments that often lack the governance controls to meet data requirements and/or the deployment flexibility to shift to compliant on-premises or BYO cloud environments.

But picking the right data infrastructure only solves half the problem

Merrick says there’s another part of the equation most organizations are overlooking: people, or more precisely, database administrators (DBAs) and their growing workloads.

Per Stack Overflow’s 2025 Developer Survey, 84% of respondents use AI tools, up from 76% the year prior. Meanwhile, Supabase says over 60% of databases on its platform have been launched “by some sort of AI tool.” As Merrick points out, this explosion of productivity comes with a catch: there aren’t enough DBAs to keep up.

“You’ve had this massive, massive step shift in developer productivity,” he explains. “But you have to have some way of managing that on the production side; these databases can’t go unmonitored.” Operations and administration teams were already struggling to keep track of existing Postgres databases before agentic engineering added even more, he says: “Who’s going to manage them?”

He says it’s time for agentic operations to catch up with agentic engineering.

AI DBA agents can give humans “superpowers”

If it seems Merrick is proposing organizations look to fully autonomous DBA agents to take over, he says the industry isn’t there yet:

“The world is not ready for fully autonomous databases administered by AI DBA agents. But there is a massive resource shortage and productivity problem, and DBAs can only manage so many databases,” he explains. Meanwhile, new “AI applications require so many more databases to put in production.”

“The world is not ready for fully autonomous databases administered by AI DBA agents.”

So how can organizations increase their operational capacity?

Merrick says DBAs should look to new AI DBA agents, not to take over but to give them “superpowers” to monitor and manage more databases with less manual slog.

pgEdge’s Ellie is one example. Part of the pgEdge AI DBA Workbench, Ellie is an AI agent that has 21 MCP tools and can run EXPLAIN ANALYZE, inspect schemas, query historical metrics, and walk through multi-step diagnostic workflows. When a database falters, Ellie finds the problem, diagnoses it, and provides a solution in the form of working SQL code for the human DBA to review. “When you’ve reviewed it and agree that it’s the right course of action, you literally press the play button, and the agent plays that SQL code into the database, and you solve your problem,” explains Merrick.

In this way, Ellie should bring more capacity to operations teams, where Merrick insists organizations are starved for DBA expertise. To his point, some industry predictions say 41% of today’s database professionals intend to leave the industry in the next decade, half moving into retirement and the rest seeking other work.

“An agent … can actually respond to those alerts far more quickly and productively than a human can.”

Without AI agents, Merrick argues, DBA work is tedious, laborious, and time-consuming. As he explains it, a database may have been humming along just fine, but when there’s a snag, trouble can manifest across multiple applications; it’s then up to the DBA to comb through monitoring data to observe and diagnose the problem, essentially scouring for a needle in a haystack.

“An agent,” he says, “can actually respond to those alerts far more quickly and productively than a human can.”

Better infrastructure AND people: It takes two to improve AI prototype success rates

In nearly any context, AI raises questions about quality over quantity, and enterprise AI projects are no exception. Agentic engineering means developers can now produce more, but all those prototypes don’t just fly directly into production. Limitations in both infrastructure and operational human power are creating obstacles that cause many AI prototypes to fail.

For Merrick, easing the transition from prototype to production requires not only great AI tooling but production-ready data infrastructure, paired with agentic operations that can keep up with the agentic engineering boom.

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