Adjusting to studying online is no easy task, especially if you’re used to a traditional classroom setting. But once you find your rhythm, you’ll realize that no traditional classroom can match the flexibility, convenience, and accessibility of online learning.
To help ease your success, Rosario Maccarrone, the Head of Student Services at the Open Institute of Technology, has compiled a detailed list of dos and don’ts of studying online. Stick to these guidelines, and you’ll excel at your studies as a full-time student or a working professional.
The Dos and Don’ts of Studying Online for Full-Time Students
Let’s say you’ve decided to commit to full-time online studies. Here’s what Rosario suggests for academic success.
The Dos
1. Find a Study Rhythm That Works for You
The best thing about studying online is that no one’s around to dictate your study schedule. It’s up to you to set your own learning pace and study hours. Find a steady study rhythm that aligns with your preferences, and your productivity will never falter.
2. Make a Schedule
Don’t get it twisted – a lack of a fixed class schedule doesn’t mean you should go without a schedule entirely. Keeping a schedule will help you stay organized, focused, and on track with your studies, which can be challenging when no one guides your daily routine. Your schedule should allocate time for classes (OPIT offers one live class per week for each course), revising, and, of course, taking it easy.
3. Keep a To-Do List
Just like a schedule, a to-do list can help you always stay on top of your game. Make it digital, and you can easily update it as you study or complete tasks on your computer.
4. Don’t Be Shy to Ask Questions
It’s perfectly OK to struggle with some parts of your online courses. However, it’s not OK to keep this to yourself out of shame or fear and struggle in silence. Your professors and tutors are there for you to guide you every step of the way. Remember this, and you’ll have no trouble asking questions when things get tricky.
5. Use All the Online Resources Your Program Offers
Just like your professors are there for you, so are the abundant online resources your university offers. Do a little research to get familiar with these resources and then make the most of them.
6. Collaborate With Your Classmates
You might not be able to see your peers in person, but you can—and should—communicate with them in a myriad of ways. Rosario suggests participating in chats during live sessions and taking advantage of every chance for group work. This will help you build a solid online community and nail your studies.
7. Join in on Online Campus Events
Studying is never just about learning. It’s also about meeting new people, making friends, and building a network. Online studying is no different; you’ll just do all of these incredible things online. So, make sure to keep up with your university’s virtual campus events and attend them to keep your studies (and life) more interesting.
8. Make a Study Spot
Let’s not sugarcoat it – learning from home often comes with a bunch of distractions. From your family talking in the background to the allure of your comfy bed, distractions are plenty. By making a designated study spot somewhere quiet, you’ll rid your study environment of disruptions and signal to your brain that it’s time to get down to work.
9. Take Care of Your Health
Taking care of your health is essential for excelling in all areas of life, and (online) studying is no different. So, remember to eat well, exercise, and get enough sleep to stay sharp. Exercising (or just moving continuously) is especially important given the nature of online studies.
The Don’ts
1. Procrastinate
Telling you not to procrastinate when studying is by no means revolutionary advice. However, avoiding procrastination can be extra challenging when studying online, given the lack of immediate supervision.
But putting off practice and leaving studying to the last second can only increase your anxiety levels and lead to lower grades (none of which will help you have a successful online studying experience).
2. Underestimate the Effort Online Classes Need
Some students operate under the assumption that online classes are less demanding than traditional ones. However, this couldn’t be further from the truth. Treating online classes with the same seriousness as in-person ones is the only way to succeed in your virtual academic journey.
3. Cut Yourself Off From Classmates and Professors
Online studying can feel rather isolating. That is – only if you let it. Online universities like OPIT offer numerous ways to stay in touch with your classmates and professors (e.g., Slack, Canvas discussions, and WhatsApp groups), and that’s precisely what you should do.
4. Hold Back From Asking for Help
This guide has already covered the importance of asking your professors questions. But if you feel stuck, don’t limit yourself to professors only. At OPIT, you can also ask your class coordinator or a tutor for help with anything course-related.
5. Neglect Taking Time Out for Yourself
Some students underestimate the effort necessary for online classes. At the same time, others go all out when presented with the seemingly endless possibilities of online learning. If you fall under the second category, don’t forget to take time out for yourself. This is as important as studying efficiently, as it allows you to prevent burnout.
6. Ignore Your Mental Health
Your mental health is just as important as your physical well-being. If you struggle emotionally, you should never ignore it. Your friends, family, or your university’s counseling services can help you find the best way forward. But first, you need to ask for help.
7. Miss Out on Making Connections With Peers
The beauty of online studying is the opportunity to meet new people from all over the world. OPIT alone has students from over 40 countries worldwide. Connect with them, and you’ll broaden your perspective while potentially forming meaningful connections.
Of course, you should also enjoy face-to-face interactions whenever possible.
The Dos and Don’ts of Studying Online for Working Students
Thanks to online studies, it’s never too late to go back to school. You can do so even after working for a few years, as this format allows you to pursue a new career path or enhance your skills while still earning an income.
Of course, you can also choose to work and study at the same time immediately after graduating from high school.
Whatever the case, here’s what Rosario suggests doing (and not doing).
The Dos
1. Balance Your Work and Study Time
When you have both work and studies, you must allocate your time wisely. Creating a realistic schedule that you can keep up with daily is step one.
2. Talk to Your Boss About Supporting Your Study Goals
Most bosses are highly supportive of their employees’ professional development. After all, your personal growth can only positively reflect on your company. So, don’t be afraid to talk to your boss about your studies.
With some luck, you’ll be able to schedule shifts around your classes, take days off for studying (or taking exams), and even study at work during downtime.
3. Strive for Flexibility at Work
If there’s one thing the COVID-19 pandemic has taught us, it’s that most jobs can be done from home (and done successfully). So, if possible, switch to working from home, as this will save you a lot of time. Just think of it this way – no more getting ready and commuting to and from work.
4. Connect With Colleagues in Similar Situations
Balancing work and studies can sometimes feel overwhelming. Connect with colleagues who are also going through the same thing (or did in the past), and you’ll get all the encouragement you need to keep pushing forward.
5. Grab Chances to Build Skills for Work and School
Let’s face it – most people study to be able to find a decent job. If you’re already working in your desired field – perfect! Seize every chance to apply your practical skills in your studies. Even if you aren’t currently working in your desired field, there are always skills you can learn to benefit your studies and current (and future) career.
6. Set Realistic Study Goals
Doing (or trying to do) too much can bring you no good. So, set realistic study goals that align with your work life. For instance, studying for three hours a day will be a Herculean task when working overtime but a breeze on your day off.
7. Talk to Your Professors
Just like you should let your boss know you’re studying, you should also inform your professors that you’re working. This way, they might be able to make some accommodations for you if your work is squeezing your study time.
8. Keep Your Study Area Separate
Let’s say you work from home. If so, make sure you have separate “work” and “study” areas. This will help you maintain a clear boundary between your professional and academic responsibilities.
9. Find Ways to Manage Stress
Juggling work and studying can be rather stressful. Finding a way (or ways) to manage this stress is critical to successfully keeping up with both responsibilities. Meditating, exercising, taking a walk, spending more time with friends, or taking up a hobby are just some of the methods that might work for you.
Taking regular breaks is also an excellent way to manage stress as well as remain focused on your work or studies.
The Don’ts
1. Let School Stress Affect Your Work Performance
If you’re already working in your dream field, remember that studying is only there to enhance your skills and knowledge. In other words, don’t let academic stress affect your work performance, as this can only jeopardize your future in your dream career. It’s all about prioritizing correctly.
2. Sacrifice Sleep or Good Health Habits
Juggling work and studies is no excuse to fall into bad health habits, including sacrificing sleep. Not only will this affect you physically and mentally, but it will also affect your performance in both fields. Talk about a lose-lose scenario!
3. Be Afraid to Ask for Help
Your support network is all you need to navigate any challenge effortlessly. And with both working and studies, you know you’ll have no shortage of those. So, don’t be afraid to ask for help from the people around you, whether to simply listen to you vent or help with a specific task.
4. Let Your Studies Take Over Your Personal Time
For most people, off time is a chance to rest and recharge. However, for students, a part of this time must be dedicated to studying. But the key word here is “part.” Don’t let your studies consume all of your personal time, as this is a one-way ticket to burnout.
5. Miss Out on Networking in Your Field
If there’s one piece of advice every university (online or offline) will give you, it is network, network, network. If you’re already working in your field, you’re in a unique position to do this even before completing your studies. So, take every chance to meet new people and open up new doors for your career.
6. Neglect Seeking Mutual Work-Study Benefits
Your job and studies can likely benefit each other in more ways than one. By neglecting to explore these avenues, you might miss out on valuable opportunities.
For instance, let’s say you’re getting your Bachelor’s degree in Digital Business from OPIT and working in social media management. Modules like “Digital Marketing” and “Digital Platforms and Ecosystems” could directly contribute to your role (and a promotion). At the same time, your practical experience will help you breeze through most of the course material.
This is just one of the ways your studies can open new doors at your work.
7. Overcommit Socially
Your time is precious. As much as it’s important not to use it solely for work and studying, it’s also crucial to not overcommit socially. Talk to your friends and family (especially those with no similar experiences) and set realistic expectations about your availability. Prioritize quality over quantity in your social interactions, and you’ll never feel like you’re missing out.
The Biggest Do: Study at OPIT
As you’ll learn from this list, everyone at OPIT knows what our students need to succeed on and off campus. And everyone at OPIT, from Rosario to your professors, is more than eager to support you in this journey. Apply to study at OPIT and experience what quality studies, academic assistance, and career guidance can do for you firsthand.
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From the same author
Read the articleSource:
- 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.
Read the articleSource:
- 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.
-
Go to all authors
Related posts
Source:
- 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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