The phrase “AI-powered” is everywhere now, and in many conversations, it’s used as a catch-all term that describes a wide range of technologies. But in practice, the systems behind the label work in fundamentally different ways.

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In industries like finance, energy, and infrastructure, the distinction is vital. These sectors in particular, increasingly rely on AI-driven analysis to guide their decisions, from emissions calculations to financial risk modeling. When AI plays that role, the characteristics of the system behind the analysis become critically important.

Two Very Different Types of AI

Most of the AI tools being discussed today fall into two broad categories:

The first is deterministic analytical modeling, the type of machine learning and statistical modeling grounded in established theory, tested rigorously, and designed to produce consistent, repeatable results. These systems behave predictably, and given the same inputs, they will always produce the same outputs. Analysts have the ability to inspect how the model works, understand the assumptions involved, and trace how results were generated. In other words, these models can show their work.

The second category is generative AI, including large language models. These systems are extraordinary tools for interacting with information. They can summarize complex documents, explain technical topics, and help users explore data conversationally. Their outputs are probabilistic. That means the same input may produce different responses at different times, which isn’t necessarily a flaw; it’s actually how these systems are designed.

What is important for users to know is that these two types of AI serve different roles.

Why Distinction Matters

In many industries, repeatability is vital. That is, the guarantee that the same inputs will always produce the same outputs. Banks rely on analytical models to evaluate financial risk. Utilities use modeling systems to forecast infrastructure performance. Energy and climate programs increasingly use digital tools to estimate building performance and emissions outcomes.

At Climative, our automated climate model (ACM) serves as the analytical engine behind the platform, using deterministic analytical models to produce building-level energy performance and emissions estimates. When financial institutions, utilities, or governments use those estimates to guide investments or policy decisions, the underlying calculations must be transparent, reproducible, and defensible.

In these contexts, decision-makers need to answer basic questions:

• What are the inputs, and what is the data quality?
• How was this result generated?
• Can the calculation be reproduced?
• What assumptions are built into the model?

These are crucial to transparency, accountability, and regulatory compliance.

The Risk of Treating All AI the Same

As generative AI tools become more common, there is a growing tendency to treat each of them as universal analytical engines. Generative systems were not designed to replace the deterministic analytical models.

Ask a large language model the same question multiple times, and you’ll likely receive different responses. For tasks like summarization or explanation, that variability can be acceptable. For analysis, however, stakeholders need results that are consistent, explainable, and auditable.

The solution isn’t necessarily choosing one type of AI over another. It’s simply about understanding the strengths of each. Generative AI excels at helping people break down and understand complex information. Deterministic analytical models excel at using established methods and subject-matter expertise to produce structured, repeatable, and auditable analysis. When used together thoughtfully, these technologies are complementary, combining meticulous analysis with accessible communication.

Responsible AI Starts With Clarity

As AI becomes embedded in systems across industries, clarity about what these technologies do (and how they work) is essential to understand at every level. Not all AI systems operate the same way. Understanding those differences helps individuals and organizations use each tool effectively while maintaining the transparency and accountability that infrastructure systems require.

AI is transforming how we analyze and interact with data, but like any powerful technology, its value depends on using the right tools for the right problems.

Read the original article on LinkedIn.