Your CIO Has Had It with “Let’s Try AI”

Your CIO Has Had It with “Let’s Try AI”

By Steven Blumberg, VP of Client Operations

A Smarter Approach to Next-Gen Technology Deployment

AI adoption is everywhere—but not every AI initiative delivers real value. Too often, companies jump in without a clear strategy, leading to runaway costs, wasted resources, and minimal impact.

AI Isn’t Free—And the Costs Go Beyond Dollars

AI requires more than just budget—it demands time, talent, and ongoing maintenance. Custom solutions can quickly turn into six-figure investments, diverting high-value resources from core business priorities.

Hidden costs add up fast. AI-driven automation can flood cloud storage with unstructured data, driving up costs. Employees may spend hours building or troubleshooting AI tools that will never be adopted. Without careful planning, what starts as an efficiency play can become a resource drain.

AI Without a Clear Outcome Is Just a Science Project

Deploying AI for the sake of innovation is risky. Every initiative needs a well-defined business outcome—whether it’s improving efficiency, reducing risk, or driving growth.

Recently, we helped a client implement AI automation to extract insights from contracts and invoices. Before our client invested in a specific tool, we guided them through a manual process (that would eventually be automated), conducted a Proof of Concept, and built a backlog of clean, structured inputs for testing. Without this groundwork, AI would have been an expensive distraction rather than a powerful accelerator.

Identifying the Right Team is Critical

Like any critical initiative, AI success depends on having the right team in place. There’s a wide gap between signing a service order and actually achieving meaningful outcomes. Based on our experience, here are a few key roles organizations should identify before kickoff:

• Product Manager – Defines requirements, prioritizes outcomes, and ensures AI projects avoid scope creep while delivering real value.
• Prompt Engineer – AI needs context to function effectively. This role fine-tunes prompts, mitigates errors, and ensures AI-generated outputs align with business goals.
• Enterprise Architect – Oversees security, data pipelines, storage, and integrations to prevent AI from becoming a liability. Uncontrolled AI deployments can expose sensitive data, clutter data warehouses with low-quality outputs, and create long-term risk.

Going Beyond Surface-level AI Testing

The AI boom isn’t slowing down, but smart businesses are digging deeper. That’s where Bluewave’s Assess, Advise, and Advocate approach comes in:

• Assess: Identify the right technologies to solve real business challenges.
• Advise: Build high-ROI use cases and connect you with the best-fit partners.
• Advocate: Simplify contracts, align stakeholders, and ensure long-term value.

We take a holistic approach, factoring in data center impacts, security, and cost control to keep AI investments on track.

So next time you’re at a conference hearing about the latest AI breakthroughs—give us a call.

Your CIO will thank you.

Book a free consultation.