You could say that data science is the driving force behind our modern world. Digital technologies are responsible for how we work, play, and socialize, and data science impacts all those areas. This field deals with how computer systems work, learn, and connect through networks. As such, the demand for data science advancements keeps growing.
Unsurprisingly, industries worldwide require more and more data science experts. Many job openings call for people with advanced degrees – a basic knowledge of data science is no longer a considerable advantage in the job market.
Today, getting a master’s degree in data science represents a surefire way to start a lucrative career. These degrees pave the way towards better-paid jobs and higher positions. Best of all, you can get an MSc data science online.
The advantage of an online master’s degree data science program is its convenience and opportunities. You can get a prestigious degree from your home, without having additional complications of moving to campus. Plus, the degree you obtain may come from a leading university, gaining you even more favorable credentials.
Let’s look at the best online data science master’s programs and what they have to offer.
Criteria for Ranking
Finding a quality program for MSc data science online requires a detailed examination according to several criteria. When creating our list, we considered the following in particular:
- University reputation and accreditation
- The content of the courses and program curriculum
- Online program accessibility and flexibility
- Available resources and student support
- Pricing and financial aid options
- Career prospects for graduates
Top Online Master Degrees in Data Science
1. Rome Business School – International Online Master in Data Science
Program Overview
The program consists of three modules. The first focuses on managerial and leadership skills based on data. The second module deals mostly with IT and data science solutions as they apply to business problem-solving.
The third module explores how your competency in data science technology reflects on data governance. The skills you’ll learn here apply to data management through specific methods and processes.
Key Features and Highlights
The International Online Master in Data Science provides the opportunity to participate in bootcamps worldwide. The locations include the U.S., Spain, Italy, and Nigeria.
Students also have the opportunity to work on real-life cases and datasets. This kind of hands-on experience will prepare you for the professional application of your data science knowledge.
Admission Requirements and Process
The admission process for this program will consist of four steps: credential evaluation, confirmation of your application, the interview, and, finally, admission.
Since the program offers introductory classes, previous knowledge of data science isn’t a strict requirement. However, experience in particular topics will count as an advantage in your evaluation.
Cost and Financial Aid Options
Participating in this program costs €6,700. The Rome Business School offers several payment options. First, students can pay in six installments after enrolling. The installments are interest-free.
Next, paying the installments before starting the course makes you eligible for a 5% discount. Finally, paying the entire sum before enrollment comes with a 10% discount.
Scholarships are also available for this program and will be determined according to the applicant’s motivation, experience, and personal profile. Eligible applicants may choose from seven scholarship types.
Career Prospects for Graduates
The program allows students to connect with industry-leading companies, learning from the best while creating meaningful connections. The Rome Business School offers a career service with soft skills, individual coaching, and other training.
2. European Leadership University – Professional Master in Data Science & Leadership
Program Overview
As a data science program with a particular focus on leadership, this program focuses on combining the two disciplines. In other words, learners become more competent as leaders through data science skills.
The program lasts for 19 months, including data skills like machine learning, Python programming, and NLP. On the leadership side, the courses teach coaching, communication, accountability, and similar skills.
Key Features and Highlights
The faculty team at the European Leadership University consists of respected academics and experts who actively practice data science. Besides the proven industry experts and their knowledge and guidance, this program has a notable distinction: it offers two certificates and a data science MSc degree.
The first certificate you’ll receive is in data science after nine months of study. The next is the certificate in leadership and action research, which will become available after month 14. Lastly, doing the final project will award you the MSc degree.
Admission Requirements and Process
The prerequisites for this program include a Computer Science or related undergraduate degree, statistics and programming knowledge, and proven experience in IT.
Candidates who meet these requirements will go through a four-stage application process. You’ll need to submit your application online, pass an analytical test, submit your diploma, and, finally, complete the entry test.
Cost and Financial Aid Options
The program costs €5,000. There are three payment options: installments, early bird, and a flexible plan. Installments are a straightforward option and may be paid during the study period. The early bird bonus refers to paying upfront, which makes you eligible for a 10% discount.
The flexible plan is particularly interesting. It includes a membership fee, paid monthly over a longer period. In addition to the tuition cost, there’s also a €250 registration fee that you don’t have to pay until you’ve been accepted.
Career Prospects for Graduates
The university and its programs have international accreditation, which means the degree you get here will be recognized worldwide. The institution also offers mentoring services and a talent accelerator program, intended to prepare learners for high-profile jobs.
3. European School of Data Science & Technology – MSc Data Science
Program Overview
The MSc Data Science program from the European School of Data Science & Technology is a comprehensive course focused on gaining a detailed knowledge of various data science aspects. Particular attention is devoted to programming, statistics, and machine learning.
The program has 12 courses across four semesters, with each course lasting for three weeks. The semesters are organized around particular subjects concerning data science: foundation, analytics and tools, visualization and application, and experiential learning, which contains the master thesis.
Key Features and Highlights
The curriculum for this program was created according to the latest requirements and trends in the industry. Expert teachers offer one-on-one mentoring throughout each course. The program is structured to provide relevant knowledge that you can apply immediately.
Admission Requirements and Process
To enlist in the European School of Data Science & Technology (ESDST) MSc Data Science online program, you’ll need a Bachelor’s degree. However, the degree doesn’t have to be in data science or a related discipline. Lacking a degree, you can also apply for the program if you have more than three years of relevant work experience.
Since the course is in English, you’ll need proof of proficiency with sufficient scores on IELTS, PTE, TOEFL, or another accepted test. Upon the review of your application, you’ll have an interview after which you’ll be notified of whether you’re accepted or not.
Cost and Financial Aid Options
This MSc program costs €490 per month plus the registration fee of €1,500. You can pay the program fee in monthly installments or cover the entire cost (program and registration fee) at once for a 5% discount. Additional options are to pay the entire program fee for a €1,000 reduction, or pay €4,000 initially and cover the rest in 12 monthly installments of €1,500.
It’s worth mentioning that the ESDST offers several scholarships to students who meet specific standards. The scholarships may cover from 25% to 50% of the program fee.
Career Prospects for Graduates
Besides the theoretical knowledge, this program offers plenty of practice in data science, exposing students to all facets of this discipline. The experience from the projects you do during the courses will represent an advantage in the job market. In addition, you’ll be paired with a mentor from a specific industry who can provide further career assistance.
4. University of Glasgow – Data Analytics MSc
Program Overview
The Data Analytics MSc from the University of Glasgow is a three-year program. It consists of 11 core courses and two electives. The first two years follow a pace of two courses per trimester, while the final year consists of the final project and an MSc dissertation.
The program is part-time and covers all crucial facets of data science, including analytics, machine learning, programming, and predictive modeling.
Key Features and Highlights
Renowned international experts and academics teach the courses. The part-time structure allows learners to maintain a job while studying with full freedom in setting their pace. This data analytics MSc also has a fast-track option, letting you obtain the degree in two instead of three years.
Admission Requirements and Process
To enter this program, you’ll need a Bachelor’s degree equivalent to the UK upper second class, which means a GPA of 4.0-4.5. The degree doesn’t need to be from data science or statistics, but it should include mathematics on a higher level.
Alternatively, you may substitute the degree with relevant experience in data analytics or a related field. The application for the program is done entirely online.
Cost and Financial Aid Options
The total cost of the program is £15,000 or about €17,200. Additional fees may apply during the program, but only in specific cases like applying for a dissertation reassessment. Students can apply for a UK scholarship or a country-specific loan if you’re from the UK.
Career Prospects for Graduates
This MSc program creates opportunities in particular data science fields like finance, medical research, statistics, and pharmaceutics. University of Glasgow graduates reportedly boast an enviable track record in terms of employment.
5. University of Europe for Applied Sciences – Data Science MSc
Program Overview
Built with flexibility in mind, this online master degree data science program offers two, three, or four-semester options. These award 60, 90, and 120 ECTS points, respectively. All variants include courses on data analytics, engineering, and science, while the three and four-semester programs also have data visualization and marketing analytics.
Key Features and Highlights
In addition to detailed knowledge of data science, the program teaches other crucial skills, particularly in the first semester. During that time, you’ll obtain advanced English skills, learn the foundations of programming and the Office suite, and get familiar with scientific writing.
Admission Requirements and Process
You’ll need to have completed a Bachelor’s program (not necessarily in data science) to apply to this MSc. A basic grasp of programming will also be required, although a preparatory course is available if you lack any programming experience.
The admission process will consist of an entry exam and an online interview.
Cost and Financial Aid Options
Tuition fees for EU students are expressed in monthly values: €820 per month for any curriculum. Non-EU students will pay a €10,938 yearly fee. A 15% or larger discount is applicable for early applicants. Additionally, scholarship may be available on a case-by-case basis.
Career Prospects for Graduates
The University of Europe for Applied Sciences collaborates with European, multinational, and global partners. This collaboration opens up career paths for students, including actual projects and internships with leading companies like Daimler and BASF.
Tips for Choosing the Right Online MSc Data Science Program
Choosing an MSc data science online program will require careful consideration. Here’s what you should take into account to ensure you’ve enrolled in the right program:
- Whether the program aligns with your career goals
- Flexibility and time requirement
- Faculty quality and the curriculum
- The reputation and accreditation of the university
- Fees and available financial aid
Enroll in the Best Online Data Science Master’s Program
Once you gain an MSc in data science, your job opportunities will increase. The demand for new experts in the market is always high, with those holding relevant degrees having the upper edge. If you’re certain that a career in data science is right for you, don’t hesitate to complete an MSc in one of the leading institutions in the field.
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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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