IT teams at midsized businesses moving AI projects from pilot to production are running pilots that look promising on paper. Maybe a chatbot handles support tickets cleanly in testing, or Microsoft Copilot drafts solid first-pass documents for a small adopter group. Then the project stalls, caught somewhere between “working” and “live,” while the rest of the organization waits.
This gap between a successful pilot and a scaled production deployment has earned a name among IT professionals — “pilot purgatory.” Recent research backs up how common it is. A recent study found that only about 5% of enterprise AI pilots deliver measurable business value.
For a midsized business, closing that gap starts with readiness, not better technology.
Pilot purgatory sets in when a model performs well in a handpicked demo, then fails to translate into daily operations. The transition exposes everything the pilot never had to account for, and a stalled pilot drains capital and morale that a large enterprise absorbs more easily.
Fewer than one in five organizations track key performance indicators (KPIs) for AI solutions, and less than a third follow prevalent adoption and scaling practices. A few structural issues show up again and again:
These gaps point to what production-readiness actually requires.
Production-readiness is a specific, verifiable threshold. These three factors determine whether an AI project has crossed it.
Success criteria must be defined in writing, with a specific metric, target and timeline, before any technology is evaluated — or a pilot can stay in evaluation indefinitely. Someone also needs to own production outcomes, not just the project, and that person should be involved in pilot design from day one.
For a midsized business, budgets are tighter than at a large enterprise. Without a named owner who holds real authority, decisions stall exactly when they matter most — during edge cases, workflow changes and budget defense.
A pilot often runs on data that has been manually cleaned or exported for testing. Production pulls from live systems instead, which is where governance gaps surface.
Microsoft Copilot inherits whatever access structure already exists across a Microsoft 365 (M365) environment, so overly broad permissions may be exposed to end users rather than created by the tool itself.
Shadow AI use compounds this same insider risk, as employees pasting client records into unapproved tools can lose control of that data. Strong access controls limit systems and users to the information they’re authorized to see.
Midsized businesses face the same technical risks as larger enterprises without the financial runway to absorb a major failure. A few elements need to exist before you go live — a documented governance, risk and compliance framework, a tested rollback plan and compliance alignment with the regulatory requirements relevant to your industry.
That rollback plan matters most. Turning off the AI feature, reverting to an earlier version or returning to human-only review should be rehearsed, not improvised mid-incident.
Moving an AI project from pilot to production is a structured process. These four steps apply to almost any AI pilot, Copilot included.
The AI deployments most likely to reach production start with one narrow, high-volume workflow, not a broad initiative. Before selecting any technology, define the workflow AI will support, the KPI it needs to improve and where that metric stands today. Having a baseline is necessary to tell whether production is working.
Favor operational, repetitive use cases over initiatives that span the whole organization. A single, well-scoped workflow lets your team validate data access and security requirements before a company-wide rollout, keeping IT’s role in governing production AI workflows manageable from the start.
Before building anything, audit the specific data sources the AI will use, identifying owners, access permissions and any oversharing or permission sprawl as part of building an AI-ready data foundation.
In a Microsoft 365 environment, that means reviewing SharePoint, OneDrive and Teams access before Copilot moves beyond a small test group. For a focused scope, this process is more of a sprint than a blocker. Skipping this step is a common reason pilots stall at the production threshold.
Full deployment should take place gradually from pilot to full team. Roll out to a small, engaged group first, then set a formal decision gate before expanding:
Launch is just the beginning. Monitoring needs to cover business performance, KPI improvement and system behavior, including override rates and cost per workflow. A system that saves time but leaves risks unreviewed still falls short of the production-ready standard.
A practical cadence is weekly reviews for the first 30 days, then monthly, paired with a standing feedback channel for surfacing edge cases. Production is an ongoing state, and a steady technology partner can help maintain it.
For many midsized businesses, Microsoft Copilot marks the starting point of the AI pilot journey, and the point where many stall. A Copilot pilot often fails to reach production because the underlying M365 environment isn’t ready. Permissions may be too broad, and access gaps may remain invisible in a small pilot, only to become real liabilities at scale.
A structured Copilot readiness assessment closes that gap before deployment. The assessment evaluates:
The result is a prioritized roadmap for closing specific gaps, not just a list of findings.
Closing the gap between a promising AI pilot and a secure production deployment starts with knowing exactly where your Microsoft 365 environment stands today. Morefield’s Copilot Readiness Assessment evaluates data governance, identity security and access controls across SharePoint, OneDrive and Teams, giving you a clear, prioritized roadmap.
From there, Morefield’s expertise in IT infrastructure, cybersecurity and cloud gives your organization the technical foundation to execute the remediation steps, build real governance and deploy with confidence. For more than 80 years, organizations across central Pennsylvania have relied on that expertise to make smarter technology decisions.
Call Morefield today at (717) 761-6170 to find out exactly what stands between your AI pilot and a production-ready future.
