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If you’re currently planning your 2026 budget, AI is probably somewhere on the list.
And if it’s not, it should be. This past year has shown us that AI tools are more accessible than ever, use cases are clearer, and implementations are delivering measurable results.
The organizations planning for it now will be further along, while others will still be figuring out where to start.
But where exactly does AI fit into next year’s budget, and how do you make a case for it to leadership?
These are the questions we’ll be answering in today’s article. Let’s get into it.
One thing we want to make clear from the get-go is that AI budgeting isn’t about building a roadmap. It’s about identifying specific use cases, understanding the infrastructure they require, and being clear about both the costs and the gains.
If you’re not sure where your organization currently stands in regards to AI usage and implementation, start with our AI Assessment Tool.
After completing a short questionnaire, the tool will automatically generate a personalized report that details:
Once you have that baseline, the rest of this guide will help you think through how to move forward.
This is where you get specific. Using the AI Assessment report as a guide, look for AI use case opportunities that fall into two categories:
And don’t try to solve everything at once. Pick one or two high-impact opportunities where AI can make a measurable difference, then expand later as needed.
Once you have your use cases, leadership needs to understand the value. Before you talk cost, focus on tangible outcomes. Leadership approves AI projects when they see clear gains. Know which category of “gains” your use case falls into and frame your ask accordingly. The clearer you are about the outcome, the easier it is for leadership to say yes.
We see AI costs are often underestimated. A single-user subscription to something like ChatGPT Enterprise might run $25/month, but that doesn’t enable robust automation. Automation requires tokens, and tokens come with an entirely different pricing structure. Each major LLM handles this a bit differently, but most of them will provide a rate per 1 million tokens. Whether you’re using OpenAI, Claude, Gemini, etc., check the rates that apply to you.
Aside from usage rates, there are other important factors to consider:
It’s important to remember that not all AI solutions are created equal. The type of solution you choose has real implications for cost, flexibility, and long-term viability. The nature of your AI use case opportunities (that you identified in tip #2 above) will guide which solution type is necessary. We’ll break these solutions into 2 categories as well:
There are some solutions that sit in the middle of these two categories. Our AI teammate for Marketing Ops, Otto, is built on a structured foundation, but it’s assembled bespoke for each client’s unique tech stack and business needs. It knows how to behave inside your unique platform, which steps to take, and what the limitations of certain API calls are.
As we head into 2026 at full speed, it’s important to think strategically about where AI fits into your operations and how that is reflected in your budget. Use our AI assessment tool to get you started, and follow these tips to build a framework that makes sense for your organization (and leadership team).
If you’re still not sure where to begin or want to talk through what this might look like for your specific situation, we’re here to help.
This is exactly the kind of conversation that’s worth ASAP, rather than six months from now when you’re stuck playing catch-up.