Did you know that the world’s first computer programmer was a woman? That’s right, Ada Lovelace, an English mathematician and writer, is widely considered the first person to recognize the potential of a computer. She realized it could go beyond mere calculations and handle symbols and logical operations (besides numbers).
Yet, many scholars still argue that Lovelace’s contributions to the field have been vastly overstated, going as far as denying them altogether. Unfortunately, it all boils down to a belief that a woman “didn’t do, and shouldn’t do, and couldn’t do” such a thing.
Perhaps similar beliefs are the reason why women continue to be underrepresented in the field of computing today. Since Lovelace, many female tech visionaries have made significant and varied contributions to this field. And yet, the gap persists.
Is this how it will always be? Or can something be done to pave the way for a more inclusive future in computing? That’s what this article will explore.
The History of Women in Computing and Computer Science
Ada Lovelace’s work in the mid-19th century laid the foundation for modern computing, earning her the flattering title of “World’s First Computer Programmer.” But she wasn’t the only woman to make monumental contributions to computer science.
To understand the ever-growing push for equality in computing, you must first take a journey throughout history, highlighting some of these women’s most notable (and often overlooked) contributions in this field.
1952: Grace Hopper
Grace Hopper, a U.S. Navy admiral and computer scientist, invented the first computer compiler, translating English instructions into the target computer’s language. Code optimization, formula translation, and subroutines are just some computing developments inspired by Hopper’s groundbreaking work.
That’s why it shouldn’t be surprising that the world’s largest gathering of women technologists is named in her honor – the Grace Hopper Celebration.
1962: Katherine Johnson
Katherine Johnson, one of the women immortalized in the 2016 book and film “Hidden Figures,” was the one to run equations needed for John Glenn’s historic orbital flight in 1962. She would go on to work on other groundbreaking NASA missions, including the Apollo program.
1970s: Adele Goldberg
Though Adele Goldberg has made many contributions to computing, she’s best known for developing the Smalltalk programming language, which was crucial in shaping modern graphical user interfaces.
1985: Radia Perlman
The fact that Radia Perlman is often referred to as the “Mother of the Internet” probably tells you all you need to know about her importance in computing history. Perlman is renowned for inventing the Spanning Tree Protocol, a technology that greatly enhanced the reliability and efficiency of network communication.
1997: Anita Borg
In 1997, a U.S. computer scientist, Anita Borg, founded the Institute for Women in Technology. This institute had (and continues to have) two simple goals – to increase the representation of women in technical fields and enable them to create more technology.
2018: Joy Buolamwini
Joy Buolamwini, currently one of the most influential women in computer science, is primarily known for her groundbreaking graduate thesis uncovering significant racial and gender bias in AI services. She also founded the Algorithmic Justice League, a non-profit organization focusing on making tech more equitable and accountable.
The Present State of Women in Computing and Computer Science
There have undoubtedly been strides in increasing women’s representation in computing and computer sciences. Though it’s challenging to determine what came first, one of the most significant moves in this regard was giving credit where credit’s due.
For instance, the “ENIAC Six,” the six women tasked with programming the ENIAC (Electronic Numerical Integrator and Computer), weren’t initially recognized for their historic contributions. It took decades for this recognition to come, but this doesn’t make it any less monumental.
But even with these recognitions, initiatives, awareness campaigns, and annual events, the gender gap in computing persists. This gap can be seen by examining the number of women in three crucial computing and computer science stages – education, workforce, and leadership.
Today, there’s no shortage of degree programs in computer science, both traditional and online. But one look at the data about the students attending these programs, and you’ll understand the issue. Though more women hold tertiary degrees in the EU, they’re notably absent in computer science-related fields.
The situation in the computing workforce is no better. Currently, women occupy only 22% of all tech roles across European companies, and to make matters worse, this figure is on a downward trajectory.
Just when you think it can’t get any more dismal, take a look at the highest levels of professional leadership in computing and technology. One look at the C-suite (senior executives) stats reveals abysmal figures. For instance, only 9% of the U.K. C-suite leaders are women.
The Reasons Behind the Current State of Women in Computing
By now, you probably agree that something needs to change to address the gender disparity in computing. And it needs to change drastically. But to propose effective solutions, you must first examine the root of the problem.
Though it’s challenging to pinpoint a single explanation for the underrepresentation of women in computing, let’s break down factors that might’ve contributed to the current situation.
The Lack of Women Peers and Mentors
Paradoxically, women might be less willing to enter the computing field due to the lack of visible representation and mentorship. Essentially, this creates a never-ending cycle of underrepresentation, thus only deepening the gender gap.
Societal Stereotypes and Biases
Deep-rooted stereotypes about gender roles can, unfortunately, dissuade women from pursuing computer science. The same goes for stereotyping what average computer scientists look like and how they act (the “nerd” stereotype often reinforced by media).
Fortunately, initiatives promoting diversity and inclusion in computer science are breaking down these stereotypes gradually yet efficiently. The more women join this field, the more preconceived (and misguided) notions are shattered, demonstrating that excellence in computing knows no gender.
Hostile or Unwelcoming Work Environments
It’s well-documented that highly collaborative fields were less welcoming to gender minorities throughout history, and computer science was no different. Though the situation is much better today, some women might still fear working within a predominantly male team due to these lingering concerns from the past.
Educational Disparities
Numerous studies have shown that precollege girls are less likely to be exposed to various aspects of computing, from learning about hardware and software to dissecting a computer. So, it’s no wonder they might be less inclined to pursue a career in computing after lacking exposure to its foundational aspects.
A Worse Work-Life Balance
Many big tech companies are notorious for long working hours. The same goes for computer science as a field. The result? Some women might perceive this field as too demanding and impossible to reconcile with raising a family, leading them not to consider it.
How to Change the Curve
Though the past might’ve seemed bleak for women in computing, the present (and future) hold promise for positive change. Of course, no fundamental changes can happen without collective commitment and decisive action. So, what can be done to change the curve once and for all and promote greater gender diversity in computing?
Striving to Remove the Barriers
So, you believe women should experience all the opportunities that come with a career in computing. But this can only be done by actively addressing and eliminating the barriers impeding their progress in the field.
This means launching campaigns to dismantle the deep-rooted stereotypes, introducing policies to create supportive working (and learning) environments, and regularly recognizing and celebrating women’s achievements in computing.
Making the Field Exciting for Women
Educational institutions and companies also must pull their weight in making the computing field more appealing to women despite the existing challenges. This might involve hands-on and collaborative learning, showcasing diverse role models in the field (e.g., at the annual Grace Hopper Celebration of Women in Computing), and establishing mentorship programs.
Relying on Mutual Support
As long as women have a strong enough support system, they can conquer anything, including the often daunting field of computer science. Here are some organizations that can provide just that: (See if you can spot some familiar individuals in their names!)
Other than that, women now have access to a whole host of resources and opportunities they can use to advance their knowledge and excel in the field. These include the following:
- Coding bootcamps
- Career fairs for women in tech
- STEM scholarships
Gaining Access to Education
Allowing equal access to education to women might be the most crucial element in changing the curve. After all, proper education serves as a direct gateway to opportunities and empowerment in computer science (and beyond).
With the popularization of online studying, many of the obstacles (both actual and perceived) that traditionally hindered women’s involvement in computing have disappeared. Now, women can learn about (and engage in) computer science from the comfort of their own homes, going at their own pace.
That’s precisely a part of the reason Alona, a Latvian student at the Open Institute of Technology, chose to pursue online education in computer science. Even with two children and a job (and a Bachelor’s degree in linguistics), she can find time to study and potentially earn her degree in as little as two years. Talk about an outstanding work-life balance!
When pursuing a degree in computer science at the OPIT, there are no hostilities, inadequacies, or barriers, only boundless opportunities.
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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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