AI’s nascence means the world is learning how to operationalize and scale a new technology in real time.
It’s exciting. But it’s also expensive.
Complex tasks have a certain token burn rate, and when building and running agents, these costs can quickly become unwieldy. Agents need to reason through complex business logic, make intelligent assumptions about context, read results, and decide the next best action, sometimes multiple times in a single instance.
Expensive one-off costs are not the main barrier to operationalization, though. The problem is what happens when attempting to run the same process, only to get a different answer each time.
While the token costs add up when restarting a process or refiguring important business logic, unpredictability is a nonstarter.
You cannot scale a business on randomness.
Business is not roulette
LLMs are probabilistic. They generate outputs from a probability distribution, which means the same request can produce different responses each time.
Some business tasks, like writing, don’t mind this variability. There are many ways to write a good email (and of course, many ways to write a bad email). As long as you have an effective email, AI will likely save you time.
Other tasks, however, require very specific answers.
Business-critical calculations, like those that get presented to auditors and board members, must be visible, understandable, repeatable, and auditable, what we refer to as VURA. Given the same inputs and business rules, they need to produce the same result every time. They must survive contact with reality, proving accurate under the highest levels of scrutiny.
Many business leaders I speak with are spending exorbitant amounts of time and money trying to turn something probabilistic into something deterministic, or in other words, turning something with high degrees of variance into something repeatable that will always give a certain output given the same input.
This isn’t impossible with LLMs. AI models can write code for deterministic tasks. The problem is that, for most business users, AI-generated code isn’t accessible. Finance leaders may understand the revenue-recognition rules better than anyone, but they often can’t validate a Python script.
Business users understand your business processes better than anyone else. They must be the ones to verify AI-driven processes. But if they can’t understand what’s happening, then you’re trying to operationalize a black box.
Why one-off AI solutions don’t scale
Imagine you want to build an AI agent to calculate revenue recognition for your finance team.
If you ask Gemini or Claude to look at your data from your enterprise systems, it can draw out high-level metrics in minutes. It may even write code to help you perform the right deterministic analytics. But if you show your finance leader what you built, they’re going to take a look at all the code on your laptop and ask you how it works.
You can walk them through your prompts, but if they can’t parse through Python and can’t understand the key logic steps, it’s impossible for them to trust this new function you’ve built, let alone operationalize it.
One option is to create a Git repository; put everything related to your new workflow in one place and share it with your finance team so they can verify the code and iterate on it. However, the chances that your finance team is suddenly going to start working in Git repositories and implement Git-based version control and reviews to verify your AI-generated Python scripts are low.
This is the single-player problem in enterprise AI: AI models can help you do incredible things, but if you’re not able to share those outputs with business experts who can verify the business logic and iterate on an AI-generated workflow, then the utility of whatever you built becomes a one-off artifact that’s difficult for the business to govern or scale.
Solving the single-player problem
Even if it’s not realistic to have your finance team start working with Git repositories, you can still share AI solutions in a central place for your business experts to review and iterate on.
Ask your AI model to build that solution as an Alteryx workflow instead of code. Business users can inspect the resulting workflow and see what’s happening at every step: what data was used, the logic that was applied, and the calculations performed, something no one can do with a Python script.
Then, every workflow becomes a shareable, verifiable artifact. Business users can review and see what’s happening. They can even have AI help them make tweaks to the workflow if changes are needed.
In addition, every Alteryx workflow can become a source of trusted business logic and an executable tool for AI.
If you have an Alteryx workflow called “Revenue Recognition,” you have a formalized, machine-readable record of how your company recognizes revenue. All of the relevant formulas and internal business logic are baked in.
Your AI agents can access these workflows to understand and use the business logic that defines how your company operates. You don’t need to burn tokens asking AI to refigure your business logic every time you want to calculate something.
Second, your workflows become “tools” for your AI models, executable functions that your AI models can call to perform specific actions. Whenever you need to calculate revenue recognition, you can simply ask your LLM to tap into that Alteryx workflow via MCP, right from your LLM interface.
Building a foundation of trust
Some tasks are perfect for probabilistic AI models. Others, like running important calculations and analytics, need to be done the same way, every time. Every business has standardized rules, logic, metrics, and calculations. Without these, you cannot scale.
By using Alteryx workflows for these calculations, you no longer need AI models to reinvent the calculations and business logic every time. The validated business rules live in the workflow, where they can be inspected, tested, governed, and reused. Every Alteryx workflow becomes a reusable business asset that powers your AI processes with trusted logic.
Suddenly, the black box is a whole lot clearer.