Interchangeable or Standalone Terms? Understanding the Differences Between Artificial Intelligence and Machine Learning...

Author(s): Jade Reilly & Guy Kilbey 

The Relevance of AI and ML in Today's World

Artificial Intelligence (AI) and Machine Learning (ML) have rapidly shifted from niche technical concepts to mainstream vocabulary. What was once terminology reserved for academics and engineers is now used daily by technologists, business leaders, and everyday users - often without a clear distinction between the two.

As AI becomes further embedded into products, workflows, and decision-making, understanding the difference between AI and ML isn’t just helpful - it’s essential! These terms are frequently used interchangeably, but they refer to different layers of a rapidly evolving field...

Artificial Intelligence

Columbia Engineering defines Artificial Intelligence as “the field of developing computers and robots that are capable of behaving in ways that both mimic and go beyond human capabilities.”

In simple terms, AI refers to machines performing tasks that traditionally require human intelligence: reasoning, decision-making, creativity, problem-solving, and adaptation.

The influence of AI on modern life is unmistakable. IBM reports that 35% of businesses globally already use AI, with another 42% exploring adoption. From drafting job specifications and automating workflows to generating images or analysing data at scale, AI is demonstrating extraordinary versatility across industries.

What’s particularly striking about AI today is not just what it can do - but how quickly it is reshaping expectations around productivity, creativity, and technical capability.

Machine Learning

Machine Learning is more specific. Columbia Engineering describes ML as “a pathway to artificial intelligence.” ML is a fragment of AI, focused on systems that learn from data.

Huntress expands this definition, describing ML as “a type of artificial intelligence that enables computer systems to learn from data without explicit programming… identifying patterns, making decisions, and improving accuracy through experience.”

Practically, ML relies on training models using labelled datasets where the correct outputs are already known. The model identifies patterns, generalises them, and then uses those learned relationships to make predictions or decisions.

This reliance on historical data is what distinguishes ML from broader AI. AI aims for reasoning, planning, and generative capabilities. ML provides the learned behaviours - the statistical backbone underpinning many AI systems.

The Differences

The distinction between AI and ML can be summarised clearly:

• Machine Learning is a subset of AI - focused on learning from past data.

• Artificial Intelligence is the broader field - focused on producing new outputs, behaviours, and reasoning that resemble human thinking.

As Forbes contributor Bernard Marr puts it:

ML depends on past data; AI aims to replicate human creativity and decision-making.

In other words, ML is a technique. AI is the ambition...

So Who Is Responsible for AI and ML in Our World?

As AI and ML become more deeply embedded into products and infrastructure, a range of technical roles have emerged to support their development and deployment:

• Machine Learning Engineers - Prepare data, build models, evaluate performance, and deploy systems.
• Data Scientists - Work across the entire data lifecycle - analysis, modelling, experimentation, and insight generation.
• AI Engineers - Integrate models (LLMs, vision systems, agents) into real-world products, ensuring pipelines, infrastructure, guardrails, and safety layers operate correctly.
• ML Researchers - Design novel learning algorithms, optimise architectures, and push the boundaries of model performance.
• AI Researchers - Explore deeper questions: reasoning, cognition, general intelligence, alignment, safety, and long-term capability.

Together, these roles reflect the relationship between the fields: ML provides the learning mechanisms; AI represents the system-level intelligence built on top of them.

An Insight into AI/ML From Us

Techfellow’s ML and AI Recruitment Headhunter, Guy Kilbey, offers a practical lens shaped by working closely with these technologies and the people behind them:

People use AI and ML interchangeably all the time - sometimes it’s ok but sometimes it’s not. AI is the overall goal; ML is a subset of tools and theories for how systems learn. In my world, an ML engineer is building models that learn through the maths of how they’re designed. AI engineers are usually building a product that incorporates one of these models.

This distinction aligns with the broader view across the industry: ML provides the methods; AI provides the ambition and application.

Our View of AI/ML Going Into 2026...

As a specialist tech recruitment firm, we see the real-world impact of AI and ML every day. These technologies are reshaping the talent market, influencing which skills companies prioritise, and accelerating demand for engineers who understand the difference - and can apply both effectively.

At Techfellow, we pride ourselves not only on understanding the terminology but on understanding the practical realities. We help organisations build teams that can navigate the nuances of AI and ML, ensuring the technologies driving the next wave of innovation are both powerful and responsibly deployed... AI and ML aren’t just academic concepts - they’re the foundation of the systems shaping tomorrow’s infrastructure, markets, and decision-making!

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SOURCES:

Columbia Engineering, Artificial Intelligence (AI) vs Machine Learning - https://ai.engineering.columbia.edu/ai-vs-machine-learning/

Lizzie Danielson, What is Machine Learning?, Huntress - https://www.huntress.com/cybersecurity-101/topic/machine-learning?utm_source=google&utm_medium=cpc&utm_campaign=google-bofu-emea-search-nonbrand-bmpm-eng-core-siem&hnt=7owbagafxvsq&utm_term=&campaignid=21671540783&adgroupid=170875713070&adid=712471965266&hsa_acc=7071168637&hsa_cam=21671540783&hsa_grp=170875713070&hsa_ad=712471965266&hsa_kw=&hsa_mt=b&hsa_net=adwords&hsa_src=g&hsa_tgt=dsa-3500001&hsa_ver=3&gad_source=1&gad_campaignid=21671540783&gbraid=0AAAAABmU8LQTchFx6AxpQrFLYqtYa8vS4&gclid=CjwKCAiA55rJBhByEiwAFkY1QGlyq379xwrXZkG-yV9Zs-KyHt7ZuxGcZ2wqh76_j8UsHwz7ang8YxoCwIkQAvD_BwE

IBM Data & AI Team, AI vs Machine Learning vs. Deep Learning vs. Neural Networks: What's the difference?, IBM - https://www.ibm.com/think/topics/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks

Bernard Marr, The Vital Difference Between Machine Learning and Generative AI, Forbes - https://www.forbes.com/sites/bernardmarr/2024/06/25/the-vital-difference-between-machine-learning-and-generative-ai/