By Keith Schlosser
AI ambition has outpaced enterprise readiness. In this article, Keith Schlosser explains why many business-led AI pilots are faltering and what CIOs must do next. You will learn how to replace fragmented experimentation with governed platforms, stronger architecture, and a structured recovery framework that turns unstable initiatives into scalable advantage.
Forrester’s Predictions 2026: Tech Leadership report says one in four CIOs will be asked to step in and fix failed, business-led AI projects. It’s not a hypothetical; it’s already happening.
Across industries, teams launched AI pilots without the enterprise backbone to sustain them. Many organizations didn’t have robust architectures, data governance, or even security boundaries in place before spinning up a dozen experiments. Some projects included IT input, but many didn’t—and as a result, the technical work never happened or was incomplete. What looked like innovation on paper quickly became a tangle of shadow integrations, brittle prompts, and ungoverned agents.
Now those projects are landing on the CIO’s desk with a familiar mandate: make it work—and make it safe.
From Chaos to Architecture
This isn’t the first time technology spread faster than its scaffolding. In the 1990s, departments rushed to deploy their own CRM systems. Pockets of value appeared, but the enterprise became fragmented and risky until IT stepped in to standardize and scale. The same pattern is playing out with AI.
CIOs are well positioned to stabilize what others started. The job now is to replace scattered experimentation with an architecture that provides context, control, and transparency across every AI initiative.
Why Platforms Are the Turning Point
When early AI pilots launched, there simply weren’t platforms to build on. Every team had to wire together its own stack—data pipelines, connectors, governance layers—from scratch. It was the only way to experiment, but it wasn’t sustainable.
That era is over. Purpose-built agentic AI platforms now exist to handle the heavy lifting: multi-model orchestration, observability, document preparation, and security. They let IT regain control of fragmented efforts without starting from zero.
As Eric Barroca, CEO of Vertesia, recently wrote, “Wiring stacks together isn’t innovation—it’s plumbing.” Platforms like this give CIOs the foundation for the turnaround. They’re designed to wrap existing AI efforts with guardrails—central security, evaluation harnesses, and orchestration—so CIOs can skip the plumbing and focus on what matters: getting business outcomes from the systems already in motion.
This isn’t about slowing innovation. It’s about putting it on rails. The new job of IT leadership is to bring discipline to what’s already out there using the capabilities modern platforms provide:
- Governance at scale. Centralize security, authentication, and observability across every agent and model.
- Multi-model orchestration – The ability to use, compare, and switch across models (open or proprietary) as cost, speed, or performance shift.
- Document and content preparation – Structuring long-form, multimodal content into retrievable knowledge for more accurate results from LLMs.
- Context preservation. Ensure systems can retain and apply business knowledge securely, so results are grounded in enterprise reality.
- Workflow integration – Agents that span documents, APIs, and systems to complete multi-step work.
A Practical Six-Step Rescue Framework
Most rescue efforts start out messy. Inherited agents behave inconsistently, data pipelines are brittle, and no one knows what’s in production. The framework below gives CIOs a structured way to re-establish order and move from firefighting to sustained control.
While these steps can be executed manually, modern AI platforms make much of the groundwork—monitoring, orchestration, and evaluation—faster and safer to implement.
- Triage – Benchmark every existing agent’s accuracy, cost, and reliability.
- Govern – Eliminate shadow projects, define access controls, and enforce audit trails.
- Re-ground – Improve retrieval pipelines and tool constraints to stabilize outputs.
- Route – Add model rotation and A/B testing to balance speed, cost, and compliance.
- Observe – All agent actions, outputs, and applications across all departments.
- Scale – Template what works and promote it safely from pilot to production.
What Comes Next
The fix doesn’t require ripping and replacing every project that went sideways. It requires giving IT the tools, structure, and authority to govern what’s already been built—and the clarity to advise what should continue.
AI doesn’t fail because the models are bad; it fails because the systems around them aren’t ready—and because teams are fragmented, each working on their own siloed initiatives. Now, CIOs have both the technology and the mandate to correct that.
This is more than rescue work. It’s a strategic opening for IT to reset the enterprise AI agenda—moving from scattered, business-led pilots to a governed, outcome-driven platform model. The CIOs who seize that moment won’t just stabilize AI in their organizations—they’ll define how it’s run for the next decade.
Keith Schlosser
