Have you ever played chess or checkers against a computer? If you have, news flash – you’ve watched artificial intelligence at work. But what if the computer could get better at the game on its own just by playing more and analyzing its mistakes? That’s the power of machine learning, a type of AI that lets computers learn and improve from experience.
In fact, machine learning is becoming increasingly important in our daily lives. According to a report by Statista, revenues from the global market for AI software are expected to reach 126 billion by 2025, up from just 10.1 billion in 2018. From personalized recommendations on Netflix to self-driving cars, machine learning is powering some of the most innovative and exciting technologies of our time.
But how does it all work? In this article, we’ll dive into the concepts of machine learning and explore how it’s changing the way we interact with technology.
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) that focuses on building algorithms that can learn from data and then make predictions or decisions and recognize patterns. Essentially, it’s all about creating computer programs that can adapt and improve on their own without being explicitly programmed for every possible scenario.
It’s like teaching a computer to see the world through a different lens. From the data, the machine identifies patterns and relationships within it. Based on these patterns, the algorithm can make predictions or decisions about new data it hasn’t seen before.
Because of these qualities, machine learning has plenty of practical applications. We can train computers to make decisions, recognize speech, and even generate art. We can use it in fraud detection in financial transactions or to improve healthcare outcomes through personalized medicine.
Machine learning also plays a large role in fields like computer vision, natural language processing, and robotics, as they require the ability to recognize patterns and make predictions to complete various tasks.
Concepts of Machine Learning
Machine learning might seem magical, but the concepts of machine learning are complex, with many layers of algorithms and techniques working together to get to an end goal.
From supervised and unsupervised learning to deep neural networks and reinforcement learning, there are many base concepts to understand before diving into the world of machine learning. Get ready to explore some machine learning basics!
Supervised Learning
Supervised learning involves training the algorithm to recognize patterns or make predictions using labeled data.
- Classification: Classification is quite straightforward, evident by its name. Its goal is to predict which category or class new data belongs to based on existing data.
- Logistic Regression: Logistic regression aims to predict a binary outcome (i.e., yes or no) based on one or more input variables.
- Support Vector Machines: Support Vector Machines (SVMs) find the best way to separate data points into different categories or classes based on their features or attributes.
- Decision Trees: Decision trees make decisions by dividing data into smaller and smaller subsets from a number of binary decisions. You can think of it like a game of 20 questions where you’re narrowing things down.
- Naive Bayes: Naive Bayes uses Bayes’ theorem to predict how likely it is to end up with a certain result when different input variables are present or absent.
Regression
Regression is a type of machine learning that helps us predict numerical values, like prices or temperatures, based on other data that we have. It looks for patterns in the data to create a mathematical model that can estimate the value we are looking for.
- Linear Regression: Linear regression helps us predict numerical values by fitting a straight line to the data.
- Polynomial Regression: Polynomial regression is similar to linear regression, but instead of fitting a straight line to the data, it fits a curved line (a polynomial) to capture more complex relationships between the variables. Linear regression might be used to predict someone’s salary based on their years of experience, while polynomial regression could be used to predict how fast a car will go based on its engine size.
- Support Vector Regression: Support vector regression finds the best fitting line to the data while minimizing errors and avoiding overfitting (becoming too attuned to the existing data).
- Decision Tree Regression: Decision tree regression uses a tree-like template to make predictions out of a series of decision rules, where each branch represents a decision, and each leaf node represents a prediction.
Unsupervised Learning
Unsupervised learning is where the computer algorithm is given a bunch of data with no labels and has to find patterns or groupings on its own, allowing for discovering hidden insights and relationships.
- Clustering: Clustering groups similar data points together based on their features.
- K-Means: K-Means is a popular clustering algorithm that separates the data into a predetermined number of clusters by finding the average of each group.
- Hierarchical Clustering: Hierarchical clustering is another way of grouping that creates a hierarchy of clusters by either merging smaller clusters into larger ones (agglomerative) or dividing larger clusters into smaller ones (divisive).
- Expectation Maximization: Expectation maximization is quite self-explanatory. It’s a way to find patterns in data that aren’t clearly grouped together by guessing what might be there and refining the guesses over time.
- Association Rule Learning: Association Rule Learning looks to find interesting connections between things in large sets of data, like discovering that people who buy plant pots often also buy juice.
- Apriori: Apriori is an algorithm for association rule learning that finds frequent itemsets (groups of items that appear together often) and makes rules that describe the relationships between them.
- Eclat: Eclat is similar to apriori, but it works by first finding which things appear together most often and then finding frequent itemsets out of those. It’s a method that works better for larger datasets.
Reinforcement Learning
Reinforcement learning is like teaching a computer to play a game by letting it try different actions and rewarding it when it does something good so it learns how to maximize its score over time.
- Q-Learning: Q-Learning helps computers learn how to take actions in an environment by assigning values to each possible action and using those values to make decisions.
- SARSA: SARSA is similar to Q-Learning but takes into account the current state of the environment, making it more useful in situations where actions have immediate consequences.
- DDPG (Deep Deterministic Policy Gradient): DDPG is a more advanced type of reinforcement learning that uses neural networks to learn policies for continuous control tasks, like robotic movement, by mapping what it sees to its next action.
Deep Learning Algorithms
Deep Learning is a powerful type of machine learning that’s inspired by how the human brain works, using artificial neural networks to learn and make decisions from vast amounts of data.
It’s more complex than other types of machine learning because it involves many layers of connections that can learn to recognize complex patterns and relationships in data.
- Neural Networks: Neural networks mimic the structure and function of the human brain, allowing them to learn from and make predictions about complex data.
- Convolutional Neural Networks: Convolutional neural networks are particularly good at image recognition, using specialized layers to detect features like edges, textures, and shapes.
- Recurrent Neural Networks: Recurrent neural networks are known to be good at processing sequential data, like language or music, by keeping track of previous inputs and using that information to make better predictions.
- Generative Adversarial Networks: Generative adversarial networks can generate new, original data by pitting two networks against each other. One tries to create fake data, and the other tries to spot the fakes until the generator network gets really good at making convincing fakes.
Conclusion
As we’ve learned, machine learning is a powerful tool that can help computers learn from data and make predictions, recognize patterns, and even create new things.
With basic concepts like supervised and unsupervised learning, regression and clustering, and advanced techniques like deep learning and neural networks, the possibilities for what we can achieve with machine learning are endless.
So whether you’re new to the subject or deeper down the iceberg, there’s always something new to learn in the exciting field of machine learning!
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From the same author
Read the articleHave you ever played chess or checkers against a computer? If you have, news flash – you’ve watched artificial intelligence at work. But what if the computer could get better at the game on its own just by playing more and analyzing its mistakes? That’s the power of machine learning, a type of AI that lets computers learn and improve from experience.
In fact, machine learning is becoming increasingly important in our daily lives. According to a report by Statista, revenues from the global market for AI software are expected to reach 126 billion by 2025, up from just 10.1 billion in 2018. From personalized recommendations on Netflix to self-driving cars, machine learning is powering some of the most innovative and exciting technologies of our time.
But how does it all work? In this article, we’ll dive into the concepts of machine learning and explore how it’s changing the way we interact with technology.
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) that focuses on building algorithms that can learn from data and then make predictions or decisions and recognize patterns. Essentially, it’s all about creating computer programs that can adapt and improve on their own without being explicitly programmed for every possible scenario.
It’s like teaching a computer to see the world through a different lens. From the data, the machine identifies patterns and relationships within it. Based on these patterns, the algorithm can make predictions or decisions about new data it hasn’t seen before.
Because of these qualities, machine learning has plenty of practical applications. We can train computers to make decisions, recognize speech, and even generate art. We can use it in fraud detection in financial transactions or to improve healthcare outcomes through personalized medicine.
Machine learning also plays a large role in fields like computer vision, natural language processing, and robotics, as they require the ability to recognize patterns and make predictions to complete various tasks.
Concepts of Machine Learning
Machine learning might seem magical, but the concepts of machine learning are complex, with many layers of algorithms and techniques working together to get to an end goal.
From supervised and unsupervised learning to deep neural networks and reinforcement learning, there are many base concepts to understand before diving into the world of machine learning. Get ready to explore some machine learning basics!
Supervised Learning
Supervised learning involves training the algorithm to recognize patterns or make predictions using labeled data.
- Classification: Classification is quite straightforward, evident by its name. Its goal is to predict which category or class new data belongs to based on existing data.
- Logistic Regression: Logistic regression aims to predict a binary outcome (i.e., yes or no) based on one or more input variables.
- Support Vector Machines: Support Vector Machines (SVMs) find the best way to separate data points into different categories or classes based on their features or attributes.
- Decision Trees: Decision trees make decisions by dividing data into smaller and smaller subsets from a number of binary decisions. You can think of it like a game of 20 questions where you’re narrowing things down.
- Naive Bayes: Naive Bayes uses Bayes’ theorem to predict how likely it is to end up with a certain result when different input variables are present or absent.
Regression
Regression is a type of machine learning that helps us predict numerical values, like prices or temperatures, based on other data that we have. It looks for patterns in the data to create a mathematical model that can estimate the value we are looking for.
- Linear Regression: Linear regression helps us predict numerical values by fitting a straight line to the data.
- Polynomial Regression: Polynomial regression is similar to linear regression, but instead of fitting a straight line to the data, it fits a curved line (a polynomial) to capture more complex relationships between the variables. Linear regression might be used to predict someone’s salary based on their years of experience, while polynomial regression could be used to predict how fast a car will go based on its engine size.
- Support Vector Regression: Support vector regression finds the best fitting line to the data while minimizing errors and avoiding overfitting (becoming too attuned to the existing data).
- Decision Tree Regression: Decision tree regression uses a tree-like template to make predictions out of a series of decision rules, where each branch represents a decision, and each leaf node represents a prediction.
Unsupervised Learning
Unsupervised learning is where the computer algorithm is given a bunch of data with no labels and has to find patterns or groupings on its own, allowing for discovering hidden insights and relationships.
- Clustering: Clustering groups similar data points together based on their features.
- K-Means: K-Means is a popular clustering algorithm that separates the data into a predetermined number of clusters by finding the average of each group.
- Hierarchical Clustering: Hierarchical clustering is another way of grouping that creates a hierarchy of clusters by either merging smaller clusters into larger ones (agglomerative) or dividing larger clusters into smaller ones (divisive).
- Expectation Maximization: Expectation maximization is quite self-explanatory. It’s a way to find patterns in data that aren’t clearly grouped together by guessing what might be there and refining the guesses over time.
- Association Rule Learning: Association Rule Learning looks to find interesting connections between things in large sets of data, like discovering that people who buy plant pots often also buy juice.
- Apriori: Apriori is an algorithm for association rule learning that finds frequent itemsets (groups of items that appear together often) and makes rules that describe the relationships between them.
- Eclat: Eclat is similar to apriori, but it works by first finding which things appear together most often and then finding frequent itemsets out of those. It’s a method that works better for larger datasets.
Reinforcement Learning
Reinforcement learning is like teaching a computer to play a game by letting it try different actions and rewarding it when it does something good so it learns how to maximize its score over time.
- Q-Learning: Q-Learning helps computers learn how to take actions in an environment by assigning values to each possible action and using those values to make decisions.
- SARSA: SARSA is similar to Q-Learning but takes into account the current state of the environment, making it more useful in situations where actions have immediate consequences.
- DDPG (Deep Deterministic Policy Gradient): DDPG is a more advanced type of reinforcement learning that uses neural networks to learn policies for continuous control tasks, like robotic movement, by mapping what it sees to its next action.
Deep Learning Algorithms
Deep Learning is a powerful type of machine learning that’s inspired by how the human brain works, using artificial neural networks to learn and make decisions from vast amounts of data.
It’s more complex than other types of machine learning because it involves many layers of connections that can learn to recognize complex patterns and relationships in data.
- Neural Networks: Neural networks mimic the structure and function of the human brain, allowing them to learn from and make predictions about complex data.
- Convolutional Neural Networks: Convolutional neural networks are particularly good at image recognition, using specialized layers to detect features like edges, textures, and shapes.
- Recurrent Neural Networks: Recurrent neural networks are known to be good at processing sequential data, like language or music, by keeping track of previous inputs and using that information to make better predictions.
- Generative Adversarial Networks: Generative adversarial networks can generate new, original data by pitting two networks against each other. One tries to create fake data, and the other tries to spot the fakes until the generator network gets really good at making convincing fakes.
Conclusion
As we’ve learned, machine learning is a powerful tool that can help computers learn from data and make predictions, recognize patterns, and even create new things.
With basic concepts like supervised and unsupervised learning, regression and clustering, and advanced techniques like deep learning and neural networks, the possibilities for what we can achieve with machine learning are endless.
So whether you’re new to the subject or deeper down the iceberg, there’s always something new to learn in the exciting field of machine learning!
Read the articleAs artificial intelligence and machine learning are becoming present in almost every aspect of life, it’s essential to understand how they work and their common applications. Although machine learning has been around for a while, many still portray it as an enemy. Machine learning can be your friend, but only if you learn to “tame” it.
Regression stands out as one of the most popular machine-learning techniques. It serves as a bridge that connects the past to the present and future. It does so by picking up on different “events” from the past and breaking them apart to analyze them. Based on this analysis, regression can make conclusions about the future and help many plan the next move.
The weather forecast is a basic example. With the regression technique, it’s possible to travel back in time to view average temperatures, humidity, and other variables relevant to the results. Then, you “return” to present and tailor predictions about the weather in the future.
There are different types of regression, and each has unique applications, advantages, and drawbacks. This article will analyze these types.
Linear Regression
Linear regression in machine learning is one of the most common techniques. This simple algorithm got its name because of what it does. It digs deep into the relationship between independent and dependent variables. Based on the findings, linear regression makes predictions about the future.
There are two distinguishable types of linear regression:
- Simple linear regression – There’s only one input variable.
- Multiple linear regression – There are several input variables.
Linear regression has proven useful in various spheres. Its most popular applications are:
- Predicting salaries
- Analyzing trends
- Forecasting traffic ETAs
- Predicting real estate prices
Polynomial Regression
At its core, polynomial regression functions just like linear regression, with one crucial difference – the former works with non-linear datasets.
When there’s a non-linear relationship between variables, you can’t do much with linear regression. In such cases, you send polynomial regression to the rescue. You do this by adding polynomial features to linear regression. Then, you analyze these features using a linear model to get relevant results.
Here’s a real-life example in action. Polynomial regression can analyze the spread rate of infectious diseases, including COVID-19.
Ridge Regression
Ridge regression is a type of linear regression. What’s the difference between the two? You use ridge regression when there’s high colinearity between independent variables. In such cases, you have to add bias to ensure precise long-term results.
This type of regression is also called L2 regularization because it makes the model less complex. As such, ridge regression is suitable for solving problems with more parameters than samples. Due to its characteristics, this regression has an honorary spot in medicine. It’s used to analyze patients’ clinical measures and the presence of specific antigens. Based on the results, the regression establishes trends.
LASSO Regression
No, LASSO regression doesn’t have anything to do with cowboys and catching cattle (although that would be interesting). LASSO is actually an acronym for Least Absolute Shrinkage and Selection Operator.
Like ridge regression, this one also belongs to regularization techniques. What does it regulate? It reduces a model’s complexity by eliminating parameters that aren’t relevant, thus concentrating the selection and guaranteeing better results.
Many choose ridge regression when analyzing a model with numerous true coefficients. When there are only a few of them, use LASSO. Therefore, their applications are similar; the real difference lies in the number of available coefficients.
Elastic Net Regression
Ridge regression is good for analyzing problems involving more parameters than samples. However, it’s not perfect; this regression type doesn’t promise to eliminate irrelevant coefficients from the equation, thus affecting the results’ reliability.
On the other hand, LASSO regression eliminates irrelevant parameters, but it sometimes focuses on far too few samples for high-dimensional data.
As you can see, both regressions are flawed in a way. Elastic net regression is the combination of the best characteristics of these regression techniques. The first phase is finding ridge coefficients, while the second phase involves a LASSO-like shrinkage of these coefficients to get the best results.
Support Vector Regression
Support vector machine (SVM) belongs to supervised learning algorithms and has two important uses:
- Regression
- Classification problems
Let’s try to draw a mental picture of how SVM works. Suppose you have two classes of items (let’s call them red circles and green triangles). Red circles are on the left, while green triangles are on the right. You can separate these two classes by drawing a line between them.
Things get a bit more complicated if you have red circles in the middle and green triangles wrapped around them. In that case, you can’t draw a line to separate the classes. But you can add new dimensions to the mix and create a circle (rectangle, square, or a different shape encompassing just the red circles).
This is what SVM does. It creates a hyperplane and analyzes classes depending on where they belong.
There are a few parameters you need to understand to grasp the reach of SVM fully:
- Kernel – When you can’t find a hyperplane in a dimension, you move to a higher dimension, which is often challenging to navigate. A kernel is like a navigator that helps you find the hyperplane without plummeting computational costs.
- Hyperplane – This is what separates two classes in SVM.
- Decision boundary – Think of this as a line that helps you “decide” the placement of positive and negative examples.
Support vector regression takes a similar approach. It also creates a hyperplane to analyze classes but doesn’t classify them depending on where they belong. Instead, it tries to find a hyperplane that contains a maximum number of data points. At the same time, support vector regression tries to lower the risk of prediction errors.
SVM has various applications. It can be used in finance, bioinformatics, engineering, HR, healthcare, image processing, and other branches.
Decision Tree Regression
This type of supervised learning algorithm can solve both regression and classification issues and work with categorical and numerical datasets.
As its name indicates, decision tree regression deconstructs problems by creating a tree-like structure. In this tree, every node is a test for an attribute, every branch is the result of a test, and every leaf is the final result (decision).
The starting point of (the root) of every tree regression is the parent node. This node splits into two child nodes (data subsets), which are then further divided, thus becoming “parents” to their “children,” and so on.
You can compare a decision tree to a regular tree. If you take care of it and prune the unnecessary branches (those with irrelevant features), you’ll grow a healthy tree (a tree with concise and relevant results).
Due to its versatility and digestibility, decision tree regression can be used in various fields, from finance and healthcare to marketing and education. It offers a unique approach to decision-making by breaking down complex datasets into easy-to-grasp categories.
Random Forest Regression
Random forest regression is essentially decision tree regression but on a much bigger scale. In this case, you have multiple decision trees, each predicting a certain output. Random forest regression analyzes the outputs of every decision tree to come up with the final result.
Keep in mind that the decision trees used in random forest regression are completely independent; there’s no interaction between them until their outputs are analyzed.
Random forest regression is an ensemble learning technique, meaning it combines the results (predictions) of several machine learning algorithms to create one final prediction.
Like decision tree regression, this one can be used in numerous industries.
The Importance of Regression in Machine Learning Is Immeasurable
Regression in machine learning is like a high-tech detective. It travels back in time, identifies valuable clues, and analyzes them thoroughly. Then, it uses the results to predict outcomes with high accuracy and precision. As such, regression found its way to all niches.
You can use it in sales to analyze the customers’ behavior and anticipate their future interests. You can also apply it in finance, whether to discover trends in prices or analyze the stock market. Regression is also used in education, the tech industry, weather forecasting, and many other spheres.
Every regression technique can be valuable, but only if you know how to use it to your advantage. Think of your scenario (variables you want to analyze) and find the best actor (regression technique) who can breathe new life into it.
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- Agenda Digitale, published on November 25th, 2025
In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.
This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.
Blockchain, AI, and IoT: The Technological Triad of Regeneration
Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.
Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.
Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .
Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.
These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.
New business models: from product-as-a-service to incentive tokens
Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.
At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.
To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.
Measuring Impact: Integrated Metrics for Net-Positiveness
One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.
In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.
Case studies: RePlanet Energy, RIFO, and Ogyre
Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.
These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.
From Net Zero to Net Positive: The Role of Massive Transformative Purpose
The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.
This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.
Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.
The Regenerative Future: Technology, Skills, and Shared Value
Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.
The combination of blockchain, AI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.
Source:
- Raconteur, published on November 06th, 2025
Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?
Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.
“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”
But while the importance of this framework is clear, how should enterprises establish and embed it?
“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.
Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.
“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”
An open approach
The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.
“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”
A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.
“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”
A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”
Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.
“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”
Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.
In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.
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