Recommender systems are AI-based algorithms that use different information to recommend products to customers. We can say that recommender systems are a subtype of machine learning because the algorithms “learn from their past,” i.e., use past data to predict the future.
Today, we’re exposed to vast amounts of information. The internet is overflowing with data on virtually any topic. Recommender systems are like filters that analyze the data and offer the users (you) only relevant information. Since what’s relevant to you may not interest someone else, these systems use unique criteria to provide the best results to everyone.
In this article, we’ll dig deep into recommender systems and discuss their types, applications, and challenges.
Types of Recommender Systems
Learning more about the types of recommender systems will help you understand their purpose.
Content-Based Filtering
With content-based filtering, it’s all about the features of a particular item. Algorithms pick up on specific characteristics to recommend a similar item to the user (you). Of course, the starting point is your previous actions and/or feedback.
Sounds too abstract, doesn’t it? Let’s explain it through a real-life example: movies. Suppose you’ve subscribed to a streaming platform and watched The Notebook (a romance/drama starring Ryan Gosling and Rachel McAdams). Algorithms will sniff around to investigate this movie’s properties:
- Genre
- Actors
- Reviews
- Title
Then, algorithms will suggest what to watch next and display movies with similar features. For example, you may find A Walk to Remember on your list (because it belongs to the same genre and is based on a book by the same author). But you may also see La La Land on the list (although it’s not the same genre and isn’t based on a book, it stars Ryan Gosling).
Some of the advantages of this type are:
- It only needs data from a specific user, not a whole group.
- It’s ideal for those who have interests that don’t fall into the mainstream category.
A potential drawback is:
- It recommends only similar items, so users can’t really expand their interests.
Collaborative Filtering
In this case, users’ preferences and past behaviors “collaborate” with one another, and algorithms use these similarities to recommend items. We have two types of collaborative filtering: user-user and item-item.
User-User Collaborative Filtering
The main idea behind this type of recommender system is that people with similar interests and past purchases are likely to make similar selections in the future. Unlike the previous type, the focus here isn’t just on only one user but a whole group.
Collaborative filtering is popular in e-commerce, with a famous example being Amazon. It analyzes the customers’ profiles and reviews and offers recommended products using that data.
The main advantages of user-user collaborative filtering are:
- It allows users to explore new interests and stay in the loop with trends.
- It doesn’t need information about the specific characteristics of an item.
The biggest disadvantage is:
- It can be overwhelmed by data volume and offer poor results.
Item-Item Collaborative Filtering
If you were ever wondering how Amazon knows you want a mint green protective case for the phone you just ordered, the answer is item-item collaborative filtering. Amazon invented this type of filtering back in 1998. With it, the e-commerce platform can make quick product suggestions and let users purchase them with ease. Here, the focus isn’t on similarities between users but between products.
Some of the advantages of item-item collaborative filtering are:
- It doesn’t require information about the user.
- It encourages users to purchase more products.
The main drawback is:
- It can suffer from a decrease in performance when there’s a vast amount of data.
Hybrid Recommender Systems
As we’ve seen, both collaborative and content-based filtering have their advantages and drawbacks. Experts designed hybrid recommender systems that grab the best of both worlds. They overcome the problems behind collaborative and content-based filtering and offer better performance.
With hybrid recommender systems, algorithms take into account different factors:
- Users’ preferences
- Users’ past purchases
- Users’ product ratings
- Similarities between items
- Current trends
A classic example of a hybrid recommender system is Netflix. Here, you’ll see the recommended content based on the TV shows and movies you’ve already watched. You can also discover content that users with similar interests enjoy and can see what’s trending at the moment.
The biggest strong points of this system are:
- It offers precise and personalized recommendations.
- It doesn’t have cold-start problems (poor performance due to lack of information).
The main drawback is:
- It’s highly complex.
Machine Learning Techniques in Recommender Systems
It’s fair to say that machine learning is like the foundation stone of recommender systems. This sub-type of artificial intelligence (AI) represents the process of computers generating knowledge from data. We understand the “machine” part, but what does “learning” implicate? “Learning” means that machines improve their performance and enhance capabilities as they learn more information and become more “experienced.”
The four machine learning techniques recommender systems love are:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep learning
Supervised Learning
In this case, algorithms feed off past data to predict the future. To do that, algorithms need to know what they’re looking for in the data and what the target is. The data in which we know the target label are named labeled datasets, and they teach algorithms how to classify data or make predictions.
Supervised learning has found its place in recommender systems because it helps understand patterns and offers valuable recommendations to users. It analyzes the users’ past behavior to predict their future. Plus, supervised learning can handle large amounts of data.
The most obvious drawback of supervised learning is that it requires human involvement, and training machines to make predictions is no walk in the park. There’s also the issue of result accuracy. Whether or not the results will be accurate largely depends on the input and target values.
Unsupervised Learning
With unsupervised learning, there’s no need to “train” machines on what to look for in datasets. Instead, the machines analyze the information to discover hidden patterns or similar features. In other words, you can sit back and relax while the algorithms do their magic. There’s no need to worry about inputs and target values, and that is one of the best things about unsupervised learning.
How does this machine learning technique fit into recommender systems? The main application is exploration. With unsupervised learning, you can discover trends and patterns you didn’t even know existed. It can discover surprising similarities and differences between users and their online behavior. Simply put, unsupervised learning can perfect your recommendation strategies and make them more precise and personal.
Reinforcement Learning
Reinforcement learning is another technique used in recommender systems. It functions like a reward-punishment system, where the machine has a goal that it needs to achieve through a series of steps. The machine will try a strategy, receive back, change the strategy as necessary, and try again until it reaches the goal and gets a reward.
The most basic example of reinforcement learning in recommender systems is movie recommendations. In this case, the “reward” would be the user giving a five-star rating to the recommended movie.
Deep Learning
Deep learning is one of the most advanced (and most fascinating) subcategories of AI. The main idea behind deep learning is building neural networks that mimic and function similarly to human brains. Machines that feature this technology can learn new information and draw their own conclusions without any human assistance.
Thanks to this, deep learning offers fine-tuned suggestions to users, enhances their satisfaction, and ultimately leads to higher profits for companies that use it.
Challenges and Future Trends in Recommender Systems
Although we may not realize it, recommender systems are the driving force of online purchases and content streaming. Without them, we wouldn’t be able to discover amazing TV shows, movies, songs, and products that make our lives better, simpler, and more enjoyable.
Without a doubt, the internet would look very different if it wasn’t for recommender systems. But as you may have noticed, what you see as recommended isn’t always what you want, need, or like. In fact, the recommendations can be so wrong that you may be shocked how the internet could misinterpret you like that. Recommender systems aren’t perfect (at least not yet), and they face different challenges that affect their performance:
- Data sparsity and scalability – If users don’t leave a trace online (don’t review items), the machines don’t have enough data to analyze and make recommendations. Likewise, the datasets change and grow constantly, which can also represent an issue.
- Cold start problem – When new users become a part of a system, they may not receive relevant recommendations because algorithms don’t “know” their preferences, past purchases, or ratings. The same goes for new items introduced to a system.
- Privacy and security concerns – Privacy and security are always at the spotlight of recommender systems. The situation is a paradox. The more a system knows about you, the better recommendations you’ll get. At the same time, you may not be willing to let a system learn your personal information if you want to maintain your privacy. But then, you won’t enjoy great recommendations.
- Incorporating contextual information – Besides “typical” information, other data can help make more precise and relevant recommendations. The problem is how to incorporate them.
- Explainability and trust – Can a recommender system explain why it made a certain recommendation, and can you trust it?
Discover New Worlds with Recommender Systems
Recommender systems are growing smarter by the day, thanks to machine learning and technological advancements. The recommendations were introduced to allow us to save time and find exactly what we’re looking for in a jiff. At the same time, they let us experiment and try something different.
While recommender systems have come a long way, there’s still more than enough room for further development.
-
From the same author
Read the articleRecommender systems are AI-based algorithms that use different information to recommend products to customers. We can say that recommender systems are a subtype of machine learning because the algorithms “learn from their past,” i.e., use past data to predict the future.
Today, we’re exposed to vast amounts of information. The internet is overflowing with data on virtually any topic. Recommender systems are like filters that analyze the data and offer the users (you) only relevant information. Since what’s relevant to you may not interest someone else, these systems use unique criteria to provide the best results to everyone.
In this article, we’ll dig deep into recommender systems and discuss their types, applications, and challenges.
Types of Recommender Systems
Learning more about the types of recommender systems will help you understand their purpose.
Content-Based Filtering
With content-based filtering, it’s all about the features of a particular item. Algorithms pick up on specific characteristics to recommend a similar item to the user (you). Of course, the starting point is your previous actions and/or feedback.
Sounds too abstract, doesn’t it? Let’s explain it through a real-life example: movies. Suppose you’ve subscribed to a streaming platform and watched The Notebook (a romance/drama starring Ryan Gosling and Rachel McAdams). Algorithms will sniff around to investigate this movie’s properties:
- Genre
- Actors
- Reviews
- Title
Then, algorithms will suggest what to watch next and display movies with similar features. For example, you may find A Walk to Remember on your list (because it belongs to the same genre and is based on a book by the same author). But you may also see La La Land on the list (although it’s not the same genre and isn’t based on a book, it stars Ryan Gosling).
Some of the advantages of this type are:
- It only needs data from a specific user, not a whole group.
- It’s ideal for those who have interests that don’t fall into the mainstream category.
A potential drawback is:
- It recommends only similar items, so users can’t really expand their interests.
Collaborative Filtering
In this case, users’ preferences and past behaviors “collaborate” with one another, and algorithms use these similarities to recommend items. We have two types of collaborative filtering: user-user and item-item.
User-User Collaborative Filtering
The main idea behind this type of recommender system is that people with similar interests and past purchases are likely to make similar selections in the future. Unlike the previous type, the focus here isn’t just on only one user but a whole group.
Collaborative filtering is popular in e-commerce, with a famous example being Amazon. It analyzes the customers’ profiles and reviews and offers recommended products using that data.
The main advantages of user-user collaborative filtering are:
- It allows users to explore new interests and stay in the loop with trends.
- It doesn’t need information about the specific characteristics of an item.
The biggest disadvantage is:
- It can be overwhelmed by data volume and offer poor results.
Item-Item Collaborative Filtering
If you were ever wondering how Amazon knows you want a mint green protective case for the phone you just ordered, the answer is item-item collaborative filtering. Amazon invented this type of filtering back in 1998. With it, the e-commerce platform can make quick product suggestions and let users purchase them with ease. Here, the focus isn’t on similarities between users but between products.
Some of the advantages of item-item collaborative filtering are:
- It doesn’t require information about the user.
- It encourages users to purchase more products.
The main drawback is:
- It can suffer from a decrease in performance when there’s a vast amount of data.
Hybrid Recommender Systems
As we’ve seen, both collaborative and content-based filtering have their advantages and drawbacks. Experts designed hybrid recommender systems that grab the best of both worlds. They overcome the problems behind collaborative and content-based filtering and offer better performance.
With hybrid recommender systems, algorithms take into account different factors:
- Users’ preferences
- Users’ past purchases
- Users’ product ratings
- Similarities between items
- Current trends
A classic example of a hybrid recommender system is Netflix. Here, you’ll see the recommended content based on the TV shows and movies you’ve already watched. You can also discover content that users with similar interests enjoy and can see what’s trending at the moment.
The biggest strong points of this system are:
- It offers precise and personalized recommendations.
- It doesn’t have cold-start problems (poor performance due to lack of information).
The main drawback is:
- It’s highly complex.
Machine Learning Techniques in Recommender Systems
It’s fair to say that machine learning is like the foundation stone of recommender systems. This sub-type of artificial intelligence (AI) represents the process of computers generating knowledge from data. We understand the “machine” part, but what does “learning” implicate? “Learning” means that machines improve their performance and enhance capabilities as they learn more information and become more “experienced.”
The four machine learning techniques recommender systems love are:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep learning
Supervised Learning
In this case, algorithms feed off past data to predict the future. To do that, algorithms need to know what they’re looking for in the data and what the target is. The data in which we know the target label are named labeled datasets, and they teach algorithms how to classify data or make predictions.
Supervised learning has found its place in recommender systems because it helps understand patterns and offers valuable recommendations to users. It analyzes the users’ past behavior to predict their future. Plus, supervised learning can handle large amounts of data.
The most obvious drawback of supervised learning is that it requires human involvement, and training machines to make predictions is no walk in the park. There’s also the issue of result accuracy. Whether or not the results will be accurate largely depends on the input and target values.
Unsupervised Learning
With unsupervised learning, there’s no need to “train” machines on what to look for in datasets. Instead, the machines analyze the information to discover hidden patterns or similar features. In other words, you can sit back and relax while the algorithms do their magic. There’s no need to worry about inputs and target values, and that is one of the best things about unsupervised learning.
How does this machine learning technique fit into recommender systems? The main application is exploration. With unsupervised learning, you can discover trends and patterns you didn’t even know existed. It can discover surprising similarities and differences between users and their online behavior. Simply put, unsupervised learning can perfect your recommendation strategies and make them more precise and personal.
Reinforcement Learning
Reinforcement learning is another technique used in recommender systems. It functions like a reward-punishment system, where the machine has a goal that it needs to achieve through a series of steps. The machine will try a strategy, receive back, change the strategy as necessary, and try again until it reaches the goal and gets a reward.
The most basic example of reinforcement learning in recommender systems is movie recommendations. In this case, the “reward” would be the user giving a five-star rating to the recommended movie.
Deep Learning
Deep learning is one of the most advanced (and most fascinating) subcategories of AI. The main idea behind deep learning is building neural networks that mimic and function similarly to human brains. Machines that feature this technology can learn new information and draw their own conclusions without any human assistance.
Thanks to this, deep learning offers fine-tuned suggestions to users, enhances their satisfaction, and ultimately leads to higher profits for companies that use it.
Challenges and Future Trends in Recommender Systems
Although we may not realize it, recommender systems are the driving force of online purchases and content streaming. Without them, we wouldn’t be able to discover amazing TV shows, movies, songs, and products that make our lives better, simpler, and more enjoyable.
Without a doubt, the internet would look very different if it wasn’t for recommender systems. But as you may have noticed, what you see as recommended isn’t always what you want, need, or like. In fact, the recommendations can be so wrong that you may be shocked how the internet could misinterpret you like that. Recommender systems aren’t perfect (at least not yet), and they face different challenges that affect their performance:
- Data sparsity and scalability – If users don’t leave a trace online (don’t review items), the machines don’t have enough data to analyze and make recommendations. Likewise, the datasets change and grow constantly, which can also represent an issue.
- Cold start problem – When new users become a part of a system, they may not receive relevant recommendations because algorithms don’t “know” their preferences, past purchases, or ratings. The same goes for new items introduced to a system.
- Privacy and security concerns – Privacy and security are always at the spotlight of recommender systems. The situation is a paradox. The more a system knows about you, the better recommendations you’ll get. At the same time, you may not be willing to let a system learn your personal information if you want to maintain your privacy. But then, you won’t enjoy great recommendations.
- Incorporating contextual information – Besides “typical” information, other data can help make more precise and relevant recommendations. The problem is how to incorporate them.
- Explainability and trust – Can a recommender system explain why it made a certain recommendation, and can you trust it?
Discover New Worlds with Recommender Systems
Recommender systems are growing smarter by the day, thanks to machine learning and technological advancements. The recommendations were introduced to allow us to save time and find exactly what we’re looking for in a jiff. At the same time, they let us experiment and try something different.
While recommender systems have come a long way, there’s still more than enough room for further development.
Read the articleTens of thousands of businesses go under every year. There are various culprits, but one of the most common causes is the inability of companies to streamline their customer experience. Many technologies have emerged to save the day, one of which is natural language processing (NLP).
But what is natural language processing? In simple terms, it’s the capacity of computers and other machines to understand and synthesize human language.
It may already seem like it would be important in the business world and trust us – it is. Enterprises rely on this sophisticated technology to facilitate different language-related tasks. Plus, it enables machines to read and listen to language as well as interact with it in many other ways.
The applications of NLP are practically endless. It can translate and summarize texts, retrieve information in a heartbeat, and help set up virtual assistants, among other things.
Looking to learn more about these applications? You’ve come to the right place. Besides use cases, this introduction to natural language processing will cover the history, components, techniques, and challenges of NLP.
History of Natural Language Processing
Before getting to the nuts and bolts of NLP basics, this introduction to NLP will first examine how the technology has grown over the years.
Early Developments in NLP
Some people revolutionized our lives in many ways. For example, Alan Turing is credited with several groundbreaking advancements in mathematics. But did you also know he paved the way for modern computer science, and by extension, natural language processing?
In the 1950s, Turing wanted to learn if humans could talk to machines via teleprompter without noticing a major difference. If they could, he concluded the machine would be capable of thinking and speaking.
Turin’s proposal has since been used to gauge this ability of computers and is known as the Turing Test.
Evolution of NLP Techniques and Algorithms
Since Alan Turing set the stage for natural language processing, many masterminds and organizations have built upon his research:
- 1958 – John McCarthy launched his Locator/Identifier Separation Protocol.
- 1964 – Joseph Wizenbaum came up with a natural language processing model called ELIZA.
- 1980s – IBM developed an array of NLP-based statistical solutions.
- 1990s – Recurrent neural networks took center stage.
The Role of Artificial Intelligence and Machine Learning in NLP
Discussing NLP without mentioning artificial intelligence and machine learning is like leaving a glass half empty. So, what’s the role of these technologies in NLP? It’s pivotal, to say the least.
AI and machine learning are the cornerstone of most NLP applications. They’re the engine of the NLP features that produce text, allowing NLP apps to turn raw data into usable information.
Key Components of Natural Language Processing
The phrase building blocks get thrown around a lot in the computer science realm. It’s key to understanding different parts of this sphere, including natural language processing. So, without further ado, let’s rifle through the building blocks of NLP.
Syntax Analysis
An NLP tool without syntax analysis would be lost in translation. It’s a paramount stage since this is where the program extracts meaning from the provided information. In simple terms, the system learns what makes sense and what doesn’t. For instance, it rejects contradictory pieces of data close together, such as “cold Sun.”
Semantic Analysis
Understanding someone who jumbles up words is difficult or impossible altogether. NLP tools recognize this problem, which is why they undergo in-depth semantic analysis. The network hits the books, learning proper grammatical structures and word orders. It also determines how to connect individual words and phrases.
Pragmatic Analysis
A machine that relies only on syntax and semantic analysis would be too machine-like, which goes against Turing’s principles. Salvation comes in the form of pragmatic analysis. The NLP software uses knowledge outside the source (e.g., textbook or paper) to determine what the speaker actually wants to say.
Discourse Analysis
When talking to someone, there’s a point to your conversation. An NLP system is just like that, but it needs to go through extensive training to achieve the same level of discourse. That’s where discourse analysis comes in. It instructs the machine to use a coherent group of sentences that have a similar or the same theme.
Speech Recognition and Generation
Once all the above elements are perfected, it’s blast-off time. The NLP has everything it needs to recognize and generate speech. This is where the real magic happens – the system interacts with the user and starts using the same language. If each stage has been performed correctly, there should be no significant differences between real speech and NLP-based applications.
Natural Language Processing Techniques
Different analyses are common for most (if not all) NLP solutions. They all point in one direction, which is recognizing and generating speech. But just like Google Maps, the system can choose different routes. In this case, the routes are known as NLP techniques.
Rule-Based Approaches
Rule-based approaches might be the easiest NLP technique to understand. You feed your rules into the system, and the NLP tool synthesizes language based on them. If input data isn’t associated with any rule, it doesn’t recognize the information – simple as that.
Statistical Methods
If you go one level up on the complexity scale, you’ll see statistical NLP methods. They’re based on advanced calculations, which enable an NLP platform to predict data based on previous information.
Neural Networks and Deep Learning
You might be thinking: “Neural networks? That sounds like something out of a medical textbook.” Although that’s not quite correct, you’re on the right track. Neural networks are NLP techniques that feature interconnected nodes, imitating neural connections in your brain.
Deep learning is a sub-type of these networks. Basically, any neural network with at least three layers is considered a deep learning environment.
Transfer Learning and Pre-Trained Language Models
The internet is like a massive department store – you can find almost anything that comes to mind here. The list includes pre-trained language models. These models are trained on enormous quantities of data, eliminating the need for you to train them using your own information.
Transfer learning draws on this concept. By tweaking pre-trained models to accommodate a particular project, you perform a transfer learning maneuver.
Applications of Natural Language Processing
With so many cutting-edge processes underpinning NLP, it’s no surprise it has practically endless applications. Here are some of the most common natural language processing examples:
- Search engines and information retrieval – An NLP-based search engine understands your search intent to retrieve accurate information fast.
- Sentiment analysis and social media monitoring – NLP systems can even determine your emotional motivation and uncover the sentiment behind social media content.
- Machine translation and language understanding – NLP software is the go-to solution for fast translations and understanding complex languages to improve communication.
- Chatbots and virtual assistants – A state-of-the-art NLP environment is behind most chatbots and virtual assistants, which allows organizations to enhance customer support and other key segments.
- Text summarization and generation – A robust NLP infrastructure not only understands texts but also summarizes and generates texts of its own based on your input.
Challenges and Limitations of Natural Language Processing
Natural language processing in AI and machine learning is mighty but not almighty. There are setbacks to this technology, but given the speedy development of AI, they can be considered a mere speed bump for the time being:
- Ambiguity and complexity of human language – Human language keeps evolving, resulting in ambiguous structures NLP often struggles to grasp.
- Cultural and contextual nuances – With approximately 4,000 distinct cultures on the globe, it’s hard for an NLP system to understand the nuances of each.
- Data privacy and ethical concerns – As every NLP platform requires vast data, the methods for sourcing this data tend to trigger ethical concerns.
- Computational resources and computing power – The more polished an NLP tool becomes, the greater the computing power must be, which can be hard to achieve.
The Future of Natural Language Processing
The final part of our take on natural language processing in artificial intelligence asks a crucial question: What does the future hold for NLP?
- Advancements in artificial intelligence and machine learning – Will AI and machine learning advancements help NLP understand more complex and nuanced languages faster?
- Integration of NLP with other technologies – How well will NLP integrate with other technologies to facilitate personal and corporate use?
- Personalized and adaptive language models – Can you expect developers to come up with personalized and adaptive language models to accommodate those with speech disorders better?
- Ethical considerations and guidelines for NLP development – How will the spearheads of NLP development address ethical problems if the technology requires more and more data to execute?
The Potential of Natural Language Processing Is Unrivaled
It’s hard to find a technology that’s more important for today’s businesses and society as a whole than natural language processing. It streamlines communication, enabling people from all over the world to connect with each other.
The impact of NLP will amplify if the developers of this technology can address the above risks. By honing the software with other platforms while minimizing privacy issues, they can dispel any concerns associated with it.
If you want to learn more about NLP, don’t stop here. Use these natural language processing notes as a stepping stone for in-depth research. Also, consider an NLP course to gain a deep understanding of this topic.
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- 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.
The Open Institute of Technology (OPIT) is the perfect place for those looking to master the core skills and gain the fundamental knowledge they need to enter the exciting and dynamic environment of the tech industry. While OPIT’s various degrees and courses unlock the doors to numerous careers, students may not know exactly which line of work they wish to enter, or how, exactly, to take the next steps.
That’s why, as well as providing exceptional online education in fields like Responsible AI, Computer Science, and Digital Business, OPIT also offers an array of career-related services, like the Peer Career Mentoring Program. Designed to provide the expert advice and support students need, this program helps students and alumni gain inspiration and insight to map out their future careers.
Introducing the OPIT Peer Career Mentoring Program
As the name implies, OPIT’s Peer Career Mentoring Program is about connecting students and alumni with experienced peers to provide insights, guidance, and mentorship and support their next steps on both a personal and professional level.
It provides a highly supportive and empowering space in which current and former learners can receive career-related advice and guidance, harnessing the rich and varied experiences of the OPIT community to accelerate growth and development.
Meet the Mentors
Plenty of experienced, expert mentors have already signed up to play their part in the Peer Career Mentoring Program at OPIT. They include managers, analysts, researchers, and more, all ready and eager to share the benefits of their experience and their unique perspectives on the tech industry, careers in tech, and the educational experience at OPIT.
Examples include:
- Marco Lorenzi: Having graduated from the MSc in Applied Data Science and AI program at OPIT, Marco has since progressed to a role as a Prompt Engineer at RWS Group and is passionate about supporting younger learners as they take their first steps into the workforce or seek career evolution.
- Antonio Amendolagine: Antonio graduated from the OPIT MSc in Applied Data Science and AI and currently works as a Product Marketing and CRM Manager with MER MEC SpA, focusing on international B2B businesses. Like other mentors in the program, he enjoys helping students feel more confident about achieving their future aims.
- Asya Mantovani: Asya took the MSc in Responsible AI program at OPIT before taking the next steps in her career as a Software Engineer with Accenture, one of the largest IT companies in the world, and a trusted partner of the institute. With a firm belief in knowledge-sharing and mutual support, she’s eager to help students progress and succeed.
The Value of the Peer Mentoring Program
The OPIT Peer Career Mentoring Program is an invaluable source of support, inspiration, motivation, and guidance for the many students and graduates of OPIT who feel the need for a helping hand or guiding light to help them find the way or make the right decisions moving forward. It’s a program built around the sharing of wisdom, skills, and insights, designed to empower all who take part.
Every student is different. Some have very clear, fixed, and firm objectives in mind for their futures. Others may have a slightly more vague outline of where they want to go and what they want to do. Others live more in the moment, focusing purely on the here and now, but not thinking too far ahead. All of these different types of people may need guidance and support from time to time, and peer mentoring provides that.
This program is also just one of many ways in which OPIT bridges the gaps between learners around the world, creating a whole community of students and educators, linked together by their shared passions for technology and development. So, even though you may study remotely at OPIT, you never need to feel alone or isolated from your peers.
Additional Career Services Offered by OPIT
The Peer Career Mentoring Program is just one part of the larger array of career services that students enjoy at the Open Institute of Technology.
- Career Coaching and Support: Students can schedule one-to-one sessions with the institute’s experts to receive insightful feedback, flexibly customized to their exact needs and situation. They can request resume audits, hone their interview skills, and develop action plans for the future, all with the help of experienced, expert coaches.
- Resource Hub: Maybe you need help differentiating between various career paths, or seeing where your degree might take you. Or you need a bit of assistance in handling the challenges of the job-hunting process. Either way, the OPIT Resource Hub contains the in-depth guides you need to get ahead and gain practical skills to confidently move forward.
- Career Events: Regularly, OPIT hosts online career event sessions with industry experts and leaders as guest speakers about the topics that most interest today’s tech students and graduates. You can join workshops to sharpen your skills and become a better prospect in the job market, or just listen to the lessons and insights of the pros.
- Internship Opportunities: There are few better ways to begin your professional journey than an internship at a top-tier company. OPIT unlocks the doors to numerous internship roles with trusted institute partners, as well as additional professional and project opportunities where you can get hands-on work experience at a high level.
In addition to the above, OPIT also teams up with an array of leading organizations around the world, including some of the biggest names, including AWS, Accenture, and Hype. Through this network of trust, OPIT facilitates students’ steps into the world of work.
Start Your Study Journey Today
As well as the Peer Career Mentoring Program, OPIT provides numerous other exciting advantages for those who enroll, including progressive assessments, round-the-clock support, affordable rates, and a team of international professors from top universities with real-world experience in technology. In short, it’s the perfect place to push forward and get the knowledge you need to succeed.
So, if you’re eager to become a tech leader of tomorrow, learn more about OPIT today.
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