With your BSc in Computer Science achieved, you have a ton of technical knowledge in coding, systems architecture, and the general “whys” and “hows” of computing under your belt. Now, you face a dilemma, as you’re entering a field that over 150,000 people study for per year, meaning competition is rife.
That huge level of competition makes finding a new career difficult, as UK-based computer science graduates discovered in the mid-2010s when the saturation of the market led to an 11% unemployment rate. To counter that saturation, you may find the siren’s call of the business world tempts you toward continuing your studies to obtain an MBA.
So, the question is – can I do MBA after Computer Science?
This article offers the answers.
Understanding the MBA Degree
MBAs exist to equip students with the knowledge (both technical and practical) to succeed in the business world. For computer science graduates, that may mean giving them the networking and soft skills they need to turn their technical knowledge into career goldmines, or it could mean helping them to start their own companies in the computing field.
Most MBAs feature six core subjects:
- Finance – Focused on the numbers behind a business, this subject is all about learning how to balance profits, losses, and the general costs of running a business.
- Accounting – Building on the finance subject, accounting pulls students into the weeds when it comes to taxes, operating expenses, and running a healthy company.
- Leadership – Soft skills are just as important as hard skills to a business student, with leadership subjects focusing on how to inspire employees and foster teamwork.
- Economic Statistics – The subject that most closely relates to a computer science degree, economic statistics is all about processing, collecting, and interpreting technical data.
- Accountability/Ethics – With so many fields having strict compliance criteria (coupled with the ethical conundrums that arise in any business), this subject helps students navigate potential legal and ethical minefields.
- Marketing – Having a great product or service doesn’t always lead to business success. Marketing covers what you do to get what you have to offer into the public eye.
Beyond the six core subjects, many MBAs offer students an opportunity to specialize via additional courses in the areas that interest them most. For instance, you could take courses in entrepreneurship to bolster your leadership skills and ethical knowledge, or focus on accounting if you’re more interested in the behind-the-scenes workings of the business world.
As for career opportunities, you have a ton of paths you can follow (with your computer science degree offering more specialized career routes). Those with an MBA alone have options in the finance, executive management, and consulting fields, with more specialized roles in IT management available to those with computer science backgrounds.
Eligibility for MBA After BSc Computer Science
MBAs are attractive to prospective post-graduate students because they have fairly loose requirements, at least when compared to more specialized further studies. Most MBA courses require the following before they’ll accept a student:
- A Bachelor’s degree in any subject, as long as that degree comes from a recognized educational institution
- English language proficiency
- This is often tested using either the TOEFL or IELTS tests
- A pair of recommendation letters, which can come from employers or past teachers
- Your statement of purpose defining why you want to study for an MBA
- A resume
- A Graduate Management Admissions Test (GMAT) score
- You’ll receive a score between 200 and 800, with the aim being to exceed the average of 574.51
Interestingly, some universities offer MBAs in Computer Science, which are the ideal transitional courses for those who are wary of making the jump from a more technical field into something business-focused. Course requirements are similar to those for a standard MBA, though some universities also like to see that you have a couple of years of work experience before you apply.
Benefits of Pursuing an MBA After BSc Computer Science
So, the answer to “Can I do MBA after BSc Computer Science,” is a resounding “yes,” but we still haven’t confronted why that’s a good choice. Here are five reasons:
- Diversify your skill set – While your skill set after completing a computer science degree is extremely technical, you may not have many of the soft skills needed to operate in a business environment. Beyond teaching leadership, management, and teamwork, a good MBA program also helps you get to grips with the numbers behind a business.
- Expand career opportunities – There is no shortage of potential roles for computer science graduates, though the previously mentioned study data shows there are many thousands of people studying the same subject. With an MBA to complement your knowledge of computers, you open the door to career opportunities in management fields that would otherwise not be open to you.
- Enhance leadership and management skills – Computer science can often feel like a solitary pursuit, as you spend more time behind a keyboard than you do interacting with others. MBAs are great for those who need a helping hand with their communication skills. Plus, they’re ideal for teaching the organizational aspects of running (or managing) a business.
- Potential for higher salary and career growth – According to Indeed, the average salary in the computer science field is $103,719. Figures from Seattle University suggest those with MBAs can far exceed that average, with the figures it quotes from the industry journal Poets and Quants suggesting an average MBA salary of $140,924.
Challenges and Considerations
As loose as the academic requirements for being accepted to an MBA may be (at least compared to other subjects), there are still challenges to confront as a computer science graduate or student.
- The time and financial investments – Forbes reports the average cost of an MBA in the United States to be $61,800. When added to the cost of your BSc in Computer Science, it’s possible you’ll face near-six-figure debt upon graduating. Couple that monetary investment with the time taken to get your MBA (it’s a full-time course) and you may have to put more into your studies than you think.
- Balancing your technical and managerial skills – Computer science focuses on the technical side, which is only one part of an MBA. While the skills you have will come to the fore when you study accounting or economic statistics, the people-focused aspects of an MBA may be a challenge.
- Adjusting to a new academic environment – You’re switching focus from the computer screen to a more classroom-led learning environment. Some may find this a challenge, particularly if they appreciate the less social aspects of computer science.
MBA Over Science – The Thomas Henson Story
After completing his Bachelor’s degree in computer information systems, Thomas Henson faced a choice – start a Master’s degree in science or study for his MBA. Having worked as a software engineer for six months following his graduation, he wanted to act fast to get his Masters’s done and dusted, opening up new career opportunities in the process.
Eventually, he chose an MBA and now works as a senior software engineer specializing in the Hortonworks Data Platform. On his personal blog, he shares why he chose an MBA over a Master’s degree in computer science, with his insights possibly helping others make their own choice:
- Listen to the people around you (especially teachers and mentors) and ask them why they’ve chosen their career and study paths.
- Compare programs (both comparing MBAs against one another and comparing MBAs to other post-graduate degrees) to see which courses serve your future ambitions best.
- Follow your passion (James loved accounting) as the most important thing is not necessarily the post-graduate course you take. The most important thing is that you finish.
Choosing the Right MBA Program
Finding the right MBA program means taking several factors into consideration, with the following four being the most important:
- Reputation and accreditation – The reputation of the institution you choose, as well as the accreditation it holds, plays a huge role in your decision. Think of your MBA as a recommendation. That recommendation doesn’t mean much if it comes from a random person in the street (i.e., an institution nobody knows), but it carries a lot of weight if it comes from somebody respected.
- Curriculum and specialization – As Thomas Henson points out, what drives you most is what will lead you to the right MBA. In his case, he loved accounting enough to make an MBA a possibility, and likely pursued specializations in that area. Ask yourself what you specifically aim to achieve with your MBA and look for courses that move you closer to that goal.
- Networking opportunities – As anybody in the business world will tell you, who you know is often as important as what you know. Look for a course that features respected lecturers and professors, as they have connections that you can exploit, and take advantage of any opportunities to go to networking events or join professional associations.
- Financial aid and scholarships – Your access to financial aid depends on your current financial position, meaning it isn’t always available. Scholarships may be more accessible, with major institutions like Harvard and Columbia Business School offering pathways into their courses for those who meet their scholarship requirements.
Speaking of Harvard and Columbia, it’s also a good idea to research some of the top business schools, especially given that the reputation of your school is as important as the degree you earn. Major players, at least in the United States, include:
- Harvard Business School
- Columbia Business School
- Wharton School of Business
- Yale School of Management
- Stanford Graduate School of Business
Become a Business-Minded Computer Buff
With the technical skills you earned from your BSc in Computer Science, you’ll be happy to find that the answer to “Can I do MBA after BSc Computer Science?” is “Yes.” Furthermore, it’s recommended as an MBA can equip you with soft skills, such as communication and leadership, that you may not receive from your computing studies. Ultimately, the combination of tech-centric and business skills opens the door to new career paths, with the average earnings of an MBA student outclassing those of computer science graduates.
Your choice comes down to your passion and the career you wish to pursue. If management doesn’t appeal to you, an MBA is likely a waste of time (and over $60,000), whereas those who want to apply their tech skills to the business world will get a lot more out of an MBA.
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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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