Anyone with a keen interest in AI has probably heard of the term AI reasoning and is coming across it more frequently.

It’s become an AI buzz word, but there’s more to it than just hype. While still in its early days, reasoning could mark a significant leap forward in AI capabilities, helping business and other organizations tackle more complex, ambiguous, multi-step challenges.

One of AI’s great strengths — sifting through vast amounts of data to recognize patterns — can become a weakness when a model is asked to tackle problems requiring more flexible, context-aware, nuanced processing. Even advanced models perform quite badly at solving riddles and puzzles that humans can complete quite easily, for example.

Advances in AI reasoning are set to change that, enabling models to process tasks in a deeper, more sophisticated way. It can boost the value that businesses get from agentic AI systems, allowing them to run more complex, higher-level autonomous workflows. Reasoning’s advanced logical and mathematical abilities will also enable businesses to draw better insights from their data, while the ability for organizations to trace its “thinking” can help to build transparency and user trust in AI.

What is AI reasoning?

Put simply, reasoning LLMs work by exploring hypotheses, backtracking, and double-checking before committing to an answer. Instead of learning “what” to answer, they learn “how” to answer.

This enables models to provide better, more accurate answers to complex problems. If someone asks you a tricky math problem, you’re more likely to find the right solution if you take time to carefully consider multiple approaches and double-check your answers in different ways. The same goes for AI.

It also enables a model to tackle problems more thoroughly and creatively, refining its thinking, trying different strategies, and backtracking to correct errors.

This “thinking” process has led some to compare AI reasoning to what goes on in the human brain. But while it may mimic some aspects of human reasoning, AI reasoning is still based on data and algorithms rather than the vastly more complex interplay of intuition, logic, and contextual understanding that takes place in our brains.

"A common misconception is that normal models don’t do any reasoning, while special ones do all the thinking. Really, even a simpler model handles reasoning in many training stages. But you don’t always need those big chain-of-thought traces. If your task is straightforward, less elaborate “thinking” can be faster and still accurate." - Max Bartolo, Manager of Technical Staff, Cohere

Reasoning is better thought of as an incremental advance — albeit an exciting one — in which LLMs are designed to work out better answers when tackling new problems unseen during training.

An innovative shift in how models work

LLMs have been built on the basis that the more resources (parameters, tokens, and compute capacity) you put into pre-training (known as “train-time compute”), the more powerful the model will be.

Test-time compute, by contrast, refers to the resources needed during inference (when answering users’ prompts), after training is complete. This gives users a new lever to use when building models, and more control over the amount of compute resources used. Compared to traditional models, reasoning models use proportionately more resources during test-time compute. That's because they need to go through intermediate steps, backtrack on problems, or work through multiple scenarios before producing an answer.

Different techniques can be used in test-time compute to strengthen a model’s reasoning performance. The most common method is for the model to perform its step-by-step reasoning before creating a summarized final answer for the user. This helps surface its logic and often improves accuracy, especially for complex or multi-step tasks. Another method involves allowing a model to generate parallel reasoning paths spanning several attempts at a solution, with a majority vote used to decide the final answer.

Regardless of the test-time compute technique used, reasoning models are particularly effective for tasks with definitive answers — like solving math equations or logic puzzles — while their benefits can diminish for more subjective questions. While they can mimic reasoning patterns, they don't truly comprehend abstract concepts like irony or personal values.

Three business benefits of AI reasoning

Now we’re clear on what AI reasoning is and how it works “under the hood,” let’s look at three ways in which it could deliver practical benefits for businesses.

1. More effective AI agent use.

Enterprises are just starting to unlock productivity by deploying AI agents, which interact with tools to run autonomous, multi-step workflows. The agents are able to share information and strategies, supporting dynamic, flexible workflows. Reasoning has the potential to upgrade agents’ abilities, opening up higher-level tasks with more complex challenges. A model’s reasoning capabilities could give an agent an initial plan and then guide it to execute it in a way that responds flexibly to unexpected variables and complexity. Currently, an agent might struggle to recover from sub-par or incorrect search results and to try a new set of search terms, for example. A reasoning-powered agent would be more able to recover from such problems and find its way to a solution.

2. Logical and mathematical reasoning across data.

Reasoning models should be especially valuable in industries that require a lot of data-crunching, such as healthcare and banking. One of the problems enterprises have is how to use their data — often stored across departments in different formats — to make good, accurate decisions. LLMs with stronger reasoning skills could use the data to make more advanced decisions or recommendations based on company policies and regulations. Consider a bank that uses AI to help it detect fraudulent transactions. Banks have long used machine-learning tools to help them detect fraud, but these systems struggle to spot deeper, more complex patterns and lack insights into the reasons behind anomalies, leading to high false positive rates. A reasoning model’s multi-step process would enable it to surface deeper behavioral patterns and to flag why a transaction is suspect, enabling compliance teams to act faster and with fewer erroneous results. The traceability of its reasoning would also support a bank’s audit readiness.

3. Stronger transparency and trust.

Reasoning offers something increasingly valuable in today’s business AI landscape: insight into how a model thinks. While the final answers may not always perfectly align with the intermediate steps, the ability to inspect a model’s reasoning steps can build user trust within organizations — especially when dealing with complex or high-stakes queries. With reasoning models, tracing can also reveal where a model’s logic falters, helping teams debug behavior, improve prompt engineering, and guide human-in-the-loop systems. For organizations deploying AI in areas like customer service, financial advising, or in healthcare settings, this transparency can support safer adoption.

Balancing reasoning opportunities with costs

Reasoning may be an exciting new frontier for enterprise AI, but it also comes with some drawbacks and limitations that leaders will need to consider.

For example, while reasoning models can handle more complex tasks, that comes at the expense of additional compute power. The intensive reasoning process during inference means these models require many times more tokens for each completion than a “traditional” LLM. Reasoning also takes more time, increasing latency. It can take minutes to deliver the answers — an unacceptable delay for many business use cases.

"Reasoning models have upsides and downsides. The upside is deeper logic, but they end up emitting more tokens — you get these big chains-of-thought; that’s costlier and slower. If your use case is a quick answer or simple tasks, you don’t always want that overhead. Sometimes you want minimal tokens and a faster response. It’s all about finding that sweet spot." – Nick Jakobi, Director of Product, Cohere

This makes it clear that businesses shouldn’t be thinking of applying reasoning to everything they do. Many functions, such as virtual AI assistants and document summarization, only require traditional models to work perfectly well. Using reasoning models for these uses would be like using a rocket ship for a trip to the supermarket.

Business leaders will therefore need to be strategic and selective about deploying reasoning’s advanced capabilities for functions that deliver a clear return on investment, and where latency isn’t such a concern.

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Edward Kim