Where Does AI Fit Into Your 2026 Budget?

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.

1. Know Where You Stand

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:

• Where your company currently stands regarding AI adoption readiness
• A customized roadmap your company can follow for successful AI integration.

Once you have that baseline, the rest of this guide will help you think through how to move forward.

2. Identify Use Cases & Frame Them Around Gains

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:

• Operational efficiency: Workflow acceleration, automated processes, fewer hours spent on repetitive tasks, etc.
• Customer experience: Improved marketing that drives more revenue, in the form of smarter personalization, faster response times, improved engagement, etc.

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.

3. Understand the Real Costs

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:

• Implementation costs: Whether you build internally or work with a partner company, implementation takes time and money. If you pull internal resources, those team members won’t be doing their normal jobs while they build AI workflows (and if you’re working with an agency or consultant, factor that into your budget from the start).

• Adjustment period: New processes cause temporary productivity dips you should plan for when building a timeline. If you implement a new workflow in June, don’t expect to see full gains until at least a few weeks later. We recently published an article centered around preparing your team (and tools) for AI that can help with this.
  
  

4. Choose the Right Type of Solution

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:

• Point solutions are products that do one specific AI-powered thing. They’re faster to deploy and often cheaper upfront. But customization is pretty limited, and you may outgrow the tool as your requirements evolve. These align with Levels 1-2 of the adoption framework from our AI Assessment tool: where individuals or teams are using AI tools but without deep integration into their processes.

• Custom implementations are built specifically for your stack and workflows. They’re more flexible and tailored to your environment. But they require more investment upfront, a longer implementation timeline, and ongoing maintenance. These represent Levels 3-4 in the adoption framework: where AI is embedded into your operations with custom integrations designed for your specific needs.

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.