Common Budgeting Risks with AI and How To Solve

When the canary stops singing, it's because there's danger ahead.

For product executives, managing & monitoring AI investments is a black box and if your budget isn't translating into higher quality and velocity within your SDLC, it's a clear signal your team is struggling with operationalizing AI.

Common AI budgeting risks, what they mean for your business, and some actions to course-correct.

1. Your AI Pilot is Stuck in R&D

• 🐤 You are spending heavily on API credits, model access, and data science exploration, but months have passed without deploying a user-facing feature or internal workflow.

• IRL: You are burning cash on experiments and while your internal team is stuck building agents in an isolated sandbox, your competitors are actively shipping AI features to their customers.

• Unblock: Start with an audit of your current AI pilots, select the one with the highest immediate business value, and bring in an experienced implementation team to wrap it in enterprise-grade architecture, then push it into production.

2. The Integration Bottleneck: Buying Tools, Not Solutions

• 🐤 Your budget is going toward expensive, off-the-shelf AI vendor licenses, but your core product or operational processes haven't actually changed.

• Risk: Your core engineering team is too busy keeping the lights on to wire new tooling into your process and your AI investment is wasted or siloed.

• Action Item: Shift your focus (and budget) from purchasing software to systems integration. Partner with an execution team that specializes in connecting AI models directly into your existing databases, APIs, and user interfaces so the technology actually does the work within your current ecosystem.

3. Shadow AI: Underfunding the Cause

• 🐤 You haven't allocated budget to officially build secure AI features, yet you are demanding higher velocity and output from your teams.

• Why This Matters: Your engineers and employees will inevitably find workarounds, pasting proprietary company code or customer data into public, consumer-grade AI tools to get their work done. You are trading security for speed and risking severe IP leakage.

• Action Item: Fund a secure, custom-built AI environment. Engage an external partner to rapidly build private, compliant AI workflows (like secure internal LLM wrappers) that your team can use safely without exposing your intellectual property.

If any of these sound familiar, Dom & Tom can help you change course

Having an AI strategy is only part of the solve for faster teams and high-quality output. The other is execution, adoption, and methods. At Dom & Tom, we specialize in unblocking AI initiatives that have stalled in the R&D phase or hit an integration wall.

When digital leaders realize their internal teams are stretched too thin to deploy complex AI features, they bring us in to cross the finish line.

We have successfully helped organizations in exactly your position by:

• Moving from Sandbox to Shipped: Migrate proof-of-concept AI models to a robust backend architecture, intuitive front-end interfaces, and scalable infrastructure needed to scale.

• Seamless Legacy Integration: Our engineering teams ensure your new AI capabilities integrate flawlessly with your existing tech stack, APIs, and legacy databases.

• Ensuring Enterprise-Grade Security: We help companies eliminate the "Shadow AI" risk by building SOC2-compliant, custom AI environments and secure LLM implementations that keep your proprietary data strictly in-house.

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