Top AI Hacks for Formulators

Artificial intelligence (AI) is here to stay, but what it looks like will continue to evolve—and it will likely do so at an increasing pace. AI awareness and usage continue to increase, and companies are looking to incorporate AI into day-to-day workflows for added efficiencies, diagnostics, and predictive tools. The technology allows food scientists to be more efficient and can highlight data early that may be indicative of future challenges, but AI will not take the food scientist out of the loop, contrary to fears held by some in the food industry.

Knowledgeable scientific oversight remains essential. With a food scientist in the loop, AI can increase the efficiency of a team or an individual. It is important to regard AI for what it really is: not something to fear, but a tool. Like all tools, the efficiency it provides is directly related to understanding and appropriate usage.

To use AI as efficiently as possible, there are several important considerations. We’ll look at three hacks for using AI more effectively in product formulation.

Choosing an AI Tool

First, it is important to recognize that not all AI tools are the same, and not all AI approaches fit every application. When selecting an AI tool, it is essential to understand which type of tool is best suited for the job. This foundational understanding can help food scientists better evaluate AI as a tool rather than a one-size-fits-all solution.

Here is a high-level overview of common AI approaches and their strengths:

• Machine learning is a type of AI that uses algorithms to make inferences about data and data patterns, allowing predictions to be made without explicit programming. This approach can be particularly useful when working with large datasets and identifying trends that may not be immediately obvious.
• Computer vision uses imagery or video to extract patterns or data. In food science, this could be useful for visual quality checks, manufacturing inspections, or identifying product defects.
• Fuzzy logic allows systems to evaluate information in degrees rather than in simple true-or-false values, which enables more flexible decision-making. This can be valuable in situations where formulation decisions rely on balancing variables rather than meeting absolute thresholds.
• Natural language processing focuses on understanding and generating human language, often resulting in conversational interactions. This is the type of AI many users encounter through chatbots or technical support systems, and it can be useful for technical questions, formulation brainstorming, or research support.

Each of these approaches functions differently and offers specific advantages depending on the application. They also have limitations, and some may not be suitable for certain use cases.

It is important to recognize that not all AI tools are the same, and not all AI approaches fit every application.

Another critical building block is understanding the datasets available to an AI tool. AI outputs are only as strong as the data they are trained on. If an AI tool can search the internet, misinformation may influence outputs. Errors or bias within training data can also shape responses. Perhaps most importantly, users should consider whether the AI tool is inclined to simply agree with prompts, potentially reinforcing false assumptions.

Hack #1: Understand the Data Behind the AI

The first hack is understanding the data an AI tool incorporates and ensuring it aligns with your standards. Users need to know both what resources AI uses and how those resources are incorporated into responses.

It is important to remember that AI often sounds highly confident regardless of whether its answers come from peer-reviewed papers, blogs, incomplete sources, or hallucinations. Confidence does not equal accuracy. Because of this, understanding data sourcing is one of the most critical elements of successful AI use.

Within this concern, users also need to understand whether the AI tool is collecting and training on their own data. If the tool is using user data, how is it training? Is the training happening in aggregate? How much user data is being incorporated? Is there a chance the AI tool could train on your proprietary inputs and use that information to improve answers for future users asking similar questions?

These questions matter greatly for food scientists and companies working with sensitive intellectual property. Use the answers to these questions to determine your comfort level with entering proprietary information into an AI platform. Food scientists need to know that their proprietary data is not being released and should be aware of how the AI tool may use that information before entering sensitive data. Understanding where data comes from, how it is curated, how current it is, and how your own information may be used should be one of the first checkpoints before relying on any AI system.

Hack #2: Match the Tool to the Task

This second hack may seem intuitive, but it is often overlooked: Choose the right AI tool for the task that you are trying to accomplish.

Understanding what an AI tool is capable of and how it generates responses is crucial to selecting the right platform. To choose the right tool, it is important to create a clear list of expectations and needs, then use that list to evaluate which tools may be the best fit.

Too often, individuals or companies choose AI tools quickly without fully understanding what those tools can do—or whether they are actually the right fit for their operational needs. Asking realistic questions about team use cases before selecting a tool can prevent frustration later.

There are many excellent AI tools that perform well for their intended purpose but have limitations outside that intended purpose. A great AI tool used incorrectly, or for functions it was not designed to handle, is not a good tool for the application. For example, using multiple tools for different parts of a process can create complexity and may require bridges between systems so tools can communicate effectively. However, attempting to force one tool to perform functions outside its scope can create misinformation, incomplete answers, and operational inefficiencies. For simplicity’s sake, it can be tempting to rely on one tool for more than its intended capacity, but long-term this can backfire and create more frustration.

This is a common frustration point. A team adopts an exciting AI tool, uses it successfully, and improves efficiency—until a new bottleneck emerges. The team may then attempt to force the same tool into solving a different problem it was never designed to address. When results disappoint, users may lose faith in the platform entirely, even though the issue is misuse rather than tool quality.

Hack #3: Give AI Complete Context

The third hack is straightforward: Give AI all relevant information. In this case, more is more. AI often works with the information provided and fills in missing gaps by making assumptions. These assumptions are typically based on common or default parameters, but those assumptions may not align with your actual needs.

For example, if a user asks a chatbot, “How many calories does a slice of pizza have?” the AI may assume a pepperoni slice, a medium pizza, a restaurant purchase, or perhaps even a New York–style slice. It is unlikely to assume a less common regional style, a BBQ chicken pizza, or another specific variation unless additional context is provided. By giving AI the missing information upfront, users can dramatically improve the relevance and specificity of the answer.

Successful AI use depends on giving the system the information it needs to succeed.

This principle is especially important in formulation, where small details matter. Ingredient type, processing conditions, intended claims, nutritional constraints, regulatory considerations, target market, and technical limitations can all influence outcomes. The more context AI has, the more likely it is to produce useful guidance. When prompts are vague, AI often fills in blanks with generalized assumptions. Providing complete details enables AI to generate more accurate, tailored responses.

Human Expertise Matters

These three hacks can help set food scientists up for success with AI tools. Understanding the functions AI must perform will help determine which tools are best for the job—and it may be beneficial to use more than one. Once potential tools are identified, one of the most important considerations is understanding data parameters, including where information originates and whether the platform trains on user data.

Finally, successful AI use depends on giving the system the information it needs to succeed. AI tools are exactly that: tools. They are not replacing scientists, but they can strengthen expertise and improve efficiency when the right tool, the right data, and the right application are aligned.