Most Mid-Market Companies are Running AI They Can’t Actually Observe

By Corey Beck, VP of Service Delivery at DataStrike

‍

There's a pattern that keeps showing up in mid-market AI conversations. A company picks an AI use case, runs a pilot, gets something working, and moves on. Somewhere along the way, the question of whether what they built is performing reliably gets set aside, usually because it's not obvious who owns it and there's always something more urgent. That gap tends to stay invisible until something breaks, an AI agent producing confidently wrong answers, token costs growing in a direction nobody is tracking, a model update changing output behavior that nobody catches for months. By the time the problem surfaces, it's bigger than it needed to be.

‍

Most of the conversation in the AI market is organized around getting to production. For a lot of mid-market organizations, the more pressing problem is what happens next.

‍

The Missing Operational Layer

‍

Once an AI system is running, it behaves like any other piece of critical infrastructure. It needs to be monitored, and its outputs need to be evaluated against quality benchmarks. It needs controls to prevent it from operating outside the boundaries set for it. Someone needs to be accountable when it degrades and needs to watch the spend, because LLM costs have a way of growing quietly until they become a line item that's hard to explain.

‍

Specialized tools like Arize, Langfuse, and Braintrust have built platforms specifically for this problem, and they're serious products that do what they say. The challenge is that they're designed for organizations with dedicated platform teams and ML engineers. Most mid-market companies don't have a dedicated team, and buying a tool doesn't solve the problem of who runs it, what benchmarks they evaluate against, or who's accountable when the numbers aren’t right.

‍

Solving the Infrastructure Problem First

‍

Before observability even applies, there's a more fundamental issue. AI runs on data, and if the infrastructure underneath isn't solid, the AI system isn't solid either. Poor data quality produces unreliable outputs. Fragmented pipelines create gaps that agents can't fill. Database environments that aren't properly maintained become the single point of failure for everything built on top of them, and when something breaks it's hard to know whether the problem is in the AI layer or the layer below it.

‍

Most AI implementation partners don't cover this. They build the system, hand it over, and leave. According to Gartner research published in April 2026, based on a survey of 782 IT infrastructure and operations leaders, only 28 percent of AI use cases fully succeed and meet ROI expectations while 20 percent fail outright. A significant share of that failure happens at the infrastructure layer, before the AI itself has a chance to work properly.

‍

Why Both Sides Rarely Get Solved by the Same Partner

‍

Managed service providers know infrastructure. They understand how to keep complex, mixed-technology environments running reliably. What most don't have is deep expertise in AI implementation, RAG pipeline optimization, or LLM evaluation. AI consultancies know implementation and can assess readiness, build agents, and get to production quickly. What most hand off when the engagement ends is the ongoing operational accountability.

‍

That gap between infrastructure operations and AI execution is where a lot of mid-market AI initiatives quietly stall. The two layers get managed separately, by different people with different tooling, and nobody owns the space between them.

‍

What DataStrike and Brainforge Cover Together

‍

DataStrike provides 24x7 managed services across every major database and cloud environment, including SQL Server, Oracle, MySQL, PostgreSQL, MongoDB, SAP HANA, MariaDB, Redshift, Snowflake, Databricks, AWS, Azure, and Oracle Cloud, under one contract with senior onshore engineers. More than 200 clients across North America rely on DataStrike to keep their data environments running, without preference for any single vendor or platform.

‍

Brainforge works at the AI execution layer, helping organizations assess readiness, build governance frameworks, implement agents and RAG pipelines, and put in place the tracing, evaluation, and guardrail frameworks that tell teams whether those systems are performing reliably after go-live. Brainforge helped a national pool supply retailer get a working AI assistant live in under two weeks, handling more than 90 percent of customer queries and saving 20 hours per week in customer service time. They helped a North American steel fabrication company cut quote turnaround by 60 percent and hit 95 percent cost estimation accuracy in under a month.

‍

For a mid-market IT leader, the partnership means a single relationship covering the full stack, from the database layer through AI deployment and ongoing observability, without managing two separate vendors or wondering who's responsible when something in between breaks.

‍

Getting Started

‍

DataStrike and Brainforge are offering a joint AI Discovery Sprint, a four-week fixed-scope engagement that maps current AI usage, scores and prioritizes use cases, assesses the data infrastructure underneath them, and delivers a 90-day implementation roadmap that leadership can fund and procurement can act on. For organizations already running AI in production, the partnership offers a more direct path to the monitoring, evaluation, and governance frameworks that make production AI something a leadership team can trust.

‍

Learn more about our joint  AI Discovery Sprint here.

‍

FAQ

‍

What is AI observability and why does it matter for mid-market organizations?

‍

AI observability refers to the ability to monitor and evaluate AI systems once they're running in production. It covers output quality, model drift, system uptime, cost per query, and whether guardrails are holding. Without it, there's no reliable way to know whether an AI system is performing as intended, degrading over time, or producing outputs that create risk.

‍

Why do so many AI projects stall after go-live?

‍

Usually, a few things happen at once. The team that built the system moves on. Nobody owns monitoring and evaluation. The data infrastructure underneath gets managed separately from the AI layer, making problems hard to diagnose. And without established quality benchmarks, it's hard to know whether performance is degrading or was always at that level.

‍

What's the difference between AI observability tools and a managed observability service?

‍

Tools like Arize, Langfuse, and Braintrust are platforms that engineering teams implement and operate themselves. They require setup, configuration, and internal expertise to get value from. A managed observability service delivers the monitoring, tracing, and evaluation as an ongoing service, which is more realistic for mid-market organizations without a dedicated platform engineering team.

‍

What is a RAG pipeline and why does it need monitoring?

‍

RAG stands for Retrieval-Augmented Generation. It connects a language model to an external knowledge base so the model can retrieve relevant information before generating a response. RAG pipelines have multiple steps where things can go wrong, and without tracing across those steps it's very hard to find where a pipeline is breaking down when outputs start to look wrong.

‍

What databases and platforms do DataStrike and Brainforge support?

‍

The combined partnership covers SQL Server, Oracle, MySQL, PostgreSQL, MongoDB, SAP HANA, MariaDB, Amazon Redshift, Snowflake, Databricks, Microsoft Power BI, Microsoft Fabric, dbt, AWS, Microsoft Azure, and Oracle Cloud. Neither company is aligned with any single vendor.

‍

What is the AI Discovery Sprint?

‍

A four-week fixed-scope engagement designed for organizations that have experimented with AI and are ready for a structured plan. It maps current AI usage, assesses the data infrastructure, scores and prioritizes use cases, and delivers a 90-day implementation roadmap along with a decision package that leadership can approve and procurement can act on.

‍

How quickly can an organization get AI observability in place?

‍

For organizations starting from scratch, the Discovery Sprint establishes the baseline in four weeks, with observability frameworks built into the implementation phase that follows. Organizations with AI already in production can move directly into an observability and evaluation engagement. DataStrike's infrastructure monitoring can typically be onboarded within one to two weeks.

‍