/
If you're a technology leader tasked with delivering on AI, you know the pressure is immense. You also know the data is unforgiving: a staggering 42% of AI initiatives are abandoned, and nearly half of all proofs-of-concept never make it to production.
Budgeting for AI as if every project is a guaranteed success is a recipe for credibility gaps and painful budget shocks.
In the first of three articles, we'll provide a new financial framework to de-risk your AI roadmap. This can be your guide to building a more defensible and effective AI investment strategy.
We'll cover:
The "sure bet" approach—where you calculate an impressive ROI for a single project and request funding—fundamentally misunderstands the nature of AI innovation. Unlike predictable software rollouts, AI is fraught with uncertainty. When these projects fail, the damage goes far beyond the initial budget.
The real costs of a flawed AI strategy are often hidden and far more corrosive.
Before proposing a new model, you need to recognize the actual cost of the current one.
To fix this, you must stop funding individual projects and start managing a balanced portfolio. The Strategic Experimentation Fund (SEF) is a disciplined framework that allocates capital across different risk levels, giving you a protected space for innovation while safeguarding the bulk of your budget for predictable returns.
Your goal: Allocate 15-30% of your total AI investment to an SEF.
The remainder of your AI budget is then split between two other buckets:
This portfolio approach allows you to innovate safely, predictably, and defensibly.
Where your organization falls in that 15-30% range for your SEF depends on a few key factors.
The SEF operates using a disciplined stage-gate model. An experiment must prove its worth at each gate to receive further funding.
This structure provides the discipline needed to avoid the sunk-cost fallacy.
The real power of the SEF comes from its disciplined termination criteria. By defining these rules upfront, you permit your teams to stop projects that aren't working.
Technical Kill Rules:
Business Kill Rules:
This framework does more than mitigate financial risk. It fundamentally changes the conversation around innovation in your organization.
"The most successful technology leaders don't think about AI project terminations as failures—they think about them as validated learning with capped downside."
This mental shift from "failure" to "validated learning" is the core of the strategy. Every experiment—success or failure—generates insights that make your next investment smarter. This marks the beginning of a Compound Learning Effect, which we'll detail as the ultimate competitive advantage in Part 3.
You now have the strategic framework and the benchmark numbers to justify a new approach to AI budgeting. Your next step is to ground this in your reality.
Before reading Part 2, do this exercise: calculate the hidden costs your organization has paid over the last 12 months.
That number is the most powerful tool you have for starting this conversation with your leadership team.
With the "why" and the financial framework established, your next logical step is to build the operational plan.
Read Part 2: Your 90-Day Implementation Playbook →
Part 1: Related Resources
More insights from the best-practices category
Get the latest articles on AI automation, industry trends, and practical implementation strategies delivered to your inbox.
Discover how Xomatic's custom AI solutions can help your organization achieve similar results. Our team of experts is ready to help you implement the automation strategies discussed in this article.
Schedule a Consultation