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By Francesco Derchi

Purpose is a strategic tool for driving innovation, competitive advantage, and addressing AI challenges, writes Francesco Derchi.

Since the early 2000s, technology has dominated discussions among scholars and professionals about global development and economic trends, with the first wave of research regarding the internet’s impact on firms and society focusing on the enabling potential of technologies. The concept of “digital revolution,” as popularized by Nicholas Negroponte, became the new paradigm for broader considerations about the development of the firm’s macro environment, and how businesses could leverage it as an asset for creating competitive advantage. The following wave focused on the convergence of different technologies, such as manufacturing, and included the dynamics of coexistence between humans and machines. From the management side, the major challenges are related to defining effective digital transformation practices that could help to migrate organizations and exploit this new paradigm.

The current technological focus builds on these previous trends, particularly on artificial intelligence and more recently on the emergence of generative AI. The Age of AI is characterized by technology’s power to reshape business and society on a variety of levels. While AI’s pervasive impact is not new for firms, the mainstream adoption of ChatGPT for business purposes and the response to this ready adoption from big tech players like Microsoft, Google, and more recently Apple, shows how AI is reshaping and influencing companies’ strategic priorities.

From a research perspective, AI’s societal impact is inspiring new studies in the field of ethics. Luciano Floridi, now of Yale University, has identified several challenges for AI, characterizing them by global magnitudes like its environmental impact and has identified several challenges for AI security, including intellectual property, privacy, transparency, and accountability. In his work, Floridi underlines the importance of philosophy in defining problems and designing solutions – but it is equally important to consider how these challenges can be addressed at the firm level. What are the tools for managers?

Part of the answer may lie in the increasing and recent focus of management studies around “corporate purpose” and “brand purpose.” This trend represents an important attempt to deepen our understanding of “why to act” (purpose framing) and “how to act” (purpose formalizing and internalizing), while technology management studies address the “what to act” (purpose impacting) question. Furthermore, studies show that corporate purpose is critical for both digital native firms as well as traditional companies undergoing a digital transformation, serving as an important growth engine through purpose-driven innovation. It is therefore fair to ask: can purpose help in addressing any of the AI challenges previously mentioned?

Purpose concepts are not exclusively “cause-related” like CSR and environmental impact. Other types have emerged, such as “competence” (the function of the product) and “culture” (the intent that drives the business). This broadens the consideration of impact types that can help address specific challenges in the age of AI.

Purpose-driven organizations are not new. Take Tesla’s direction “to accelerate the world’s transition to sustainable energy” – it explicitly addresses environmental challenges while defining a business direction that requires constant innovation and leverages multiple converging technologies. The key is to have the purpose formalized and internalized within the company as a concrete drive for growth.

Due to its characteristics, the MTP plays a key role in digital transformation. This necessarily ambitious and long-term vision or goal – the Massive Transformative Purpose – requires firms, particularly those focused on exponential growth, to address emerging accelerating technologies with a purpose-first transformation logic. P&G’s Global Business Services division was able to improve market leadership and gain a competitive advantage over various start-ups and potential disruptors through its “Free up the employee, for free” MTP. This served as a north star for every employee, encouraging them to contribute ideas and best practices to overcome bulky processes and limitations.

My research on MTPs in AI-era firms explores their role in driving innovation to address specific challenges. Results show that the MTP impacts the organization across four dimensions, requiring commitment and synergy from management. Let’s consider these four dimensions by looking at Airbnb:

  1. Internal Impact: The MTP acts as the organization’s genetic code and guiding philosophy. It is key for leveraging employee motivation, with a strong relationship between purpose, organizational culture, and firm values. Airbnb’s culture of belonging highlights this, with its various purpose-shaping practices, starting with culture-fit interviews delivered during the recruitment process.
  2. Brand and Market Influence: The MTP contributes directly to building a strong brand and influencing the market. It allows firms to extend beyond functional and symbolic benefits to make the impact of the company on society visible. This involves addressing market demand coherently and consistently. Airbnb’s “Bélo” symbol visually represents this concept of belonging while their MTP features in campaigns like “Wall and Chain: A Story of Breaking Down Walls.”
  3. Competitive Advantage and Growth: The MTP drives innovation and can lead to superior stock market performance. In digital firms, it’s key in the creation of ecosystems that aggregate leveraged assets and third parties for value creation. The company’s “belong anywhere transformation journey” is a strategic initiative that formalized and interiorized the MTP through various touchpoints for all the different ecosystem members. As Leigh Gallagher details in her 2016 Fortune feature about the company, “When travellers leave their homes, they feel alone. They reach their Airbnb, and they feel accepted and taken care of by their host. They then feel safe to be the same kind of person they are when they’re at home.”
  4. Core Organization Identity: The MTP is considered part of the core dimension of the organization. More than a goal or business strategy, it is a strategic issue that generates a sense of direction and purpose that affects every part of the organization: internal, external, personality, and expression. This dimension also involves the role of the founder(s) and their personality in shaping the business. At Airbnb, the MTP is often used as a shortcut to explain the firm’s mission and vision. The founders’ approach is pragmatic, and instead of debating differences, time should be spent on execution. At the same time, the personalities of the three founders, Chesky, Gebbia, and Blecharcyzk, are the identity of the firm. They were the first hosts for the platform. Their credibility is key for making Airbnb a trustworthy and coherent proposal in a crowded market.

Executives and leaders of business in the current AI era should embrace three key principles. Be true: Purpose is an essential strategic tool that enables firms to identify and connect with their original selves, decoding their reason for being and embedding it into their identity. Be ambitious: The MTP allows for global impact, confronting major challenges by synthesizing business values and guiding innovation paths to address AI-related issues. Be generous: Purpose allows firms to explicitly address environmental and social issues, taking action on values-based challenges such as transparency, respect for intellectual property, and accountability. By following these principles, organizations and their leaders can maintain their direction and continue to advance in the AI era.

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Agenda Digitale: The Five Pillars of the Cloud According to NIST – A Compass for Businesses and Public Administrations
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Jun 26, 2025 7 min read

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By Lokesh Vij, Professor of Cloud Computing Infrastructure, Cloud Development, Cloud Computing Automation and Ops and Cloud Data Stacks at OPIT – Open Institute of Technology

NIST identifies five key characteristics of cloud computing: on-demand self-service, network access, resource pooling, elasticity, and metered service. These pillars explain the success of the global cloud market of 912 billion in 2025

In less than twenty years, the cloud has gone from a curiosity to an indispensable infrastructure. According to Precedence Research, the global market will reach 912 billion dollars in 2025 and will exceed 5.1 trillion in 2034. In Europe, the expected spending for 2025 will be almost 202 billion dollars. At the base of this success are five characteristics, identified by the NIST (National Institute of Standards and Technology): on-demand self-service, network access, shared resource pool, elasticity and measured service.

Understanding them means understanding why the cloud is the engine of digital transformation.

On-demand self-service: instant provisioning

The journey through the five pillars starts with the ability to put IT in the hands of users.

Without instant provisioning, the other benefits of the cloud remain potential. Users can turn resources on and off with a click or via API, without tickets or waiting. Provisioning a VM, database, or Kubernetes cluster takes seconds, not weeks, reducing time to market and encouraging continuous experimentation. A DevOps team that releases microservices multiple times a day or a fintech that tests dozens of credit-scoring models in parallel benefit from this immediacy. In OPIT labs, students create complete Kubernetes environments in two minutes, run load tests, and tear them down as soon as they’re done, paying only for the actual minutes.

Similarly, a biomedical research group can temporarily allocate hundreds of GPUs to train a deep-learning model and release them immediately afterwards, without tying up capital in hardware that will age rapidly. This flexibility allows the user to adapt resources to their needs in real time. There are no hard and fast constraints: you can activate a single machine and deactivate it when it is no longer needed, or start dozens of extra instances for a limited time and then release them. You only pay for what you actually use, without waste.

Wide network access: applications that follow the user everywhere

Once access to resources is made instantaneous, it is necessary to ensure that these resources are accessible from any location and device, maintaining a uniform user experience. The cloud lives on the network and guarantees ubiquity and independence from the device.

A web app based on HTTP/S can be used from a laptop, tablet or smartphone, without the user knowing where the containers are running. Geographic transparency allows for multi-channel strategies: you start a purchase on your phone and complete it on your desktop without interruptions. For the PA, this means providing digital identities everywhere, for the private sector, offering 24/7 customer service.

Broad access moves security from the physical perimeter to the digital identity and introduces zero-trust architecture, where every request is authenticated and authorized regardless of the user’s location.

All you need is a network connection to use the resources: from the office, from home or on the move, from computers and mobile devices. Access is independent of the platform used and occurs via standard web protocols and interfaces, ensuring interoperability.

Shared Resource Pools: The Economy of Scale of Multi-Tenancy

Ubiquitous access would be prohibitive without a sustainable economic model. This is where infrastructure sharing comes in.

The cloud provider’s infrastructure aggregates and shares computational resources among multiple users according to a multi-tenant model. The economies of scale of hyperscale data centers reduce costs and emissions, putting cutting-edge technologies within the reach of startups and SMBs.

Pooling centralizes patching, security, and capacity planning, freeing IT teams from repetitive tasks and reducing the company’s carbon footprint. Providers reinvest energy savings in next-generation hardware and immersion cooling research programs, amplifying the collective benefit.

Rapid Elasticity: Scaling at the Speed ​​of Business

Sharing resources is only effective if their allocation follows business demand in real time. With elasticity, the infrastructure expands or reduces resources in minutes following the load. The system behaves like a rubber band: if more power or more instances are needed to deal with a traffic spike, it automatically scales in real time; when demand drops, the additional resources are deactivated just as quickly.

This flexibility seems to offer unlimited resources. In practice, a company no longer has to buy excess servers to cover peaks in demand (which would remain unused during periods of low activity), but can obtain additional capacity from the cloud only when needed. The economic advantage is considerable: large initial investments are avoided and only the capacity actually used during peak periods is paid for.

In the OPIT cloud automation lab, students simulate a streaming platform that creates new Kubernetes pods as viewers increase and deletes them when the audience drops: a concrete example of balancing user experience and cost control. The effect is twofold: the user does not suffer slowdowns and the company avoids tying up capital in underutilized servers.

Metered Service: Transparency and Cost Governance

The dynamic scale generated by elasticity requires precise visibility into consumption and expenses : without measurement there is no governance. Metering makes every second of CPU, every gigabyte and every API call visible. Every consumption parameter is tracked and made available in transparent reports.

This data enables pay-per-use pricing , i.e. charges proportional to actual usage. For the customer, this translates into variable costs: you only pay for the resources actually consumed. Transparency helps you plan your budget: thanks to real-time data, it is easier to optimize expenses, for example by turning off unused resources. This eliminates unnecessary fixed costs, encouraging efficient use of resources.

The systemic value of the five pillars

When the five pillars work together, the effect is multiplier . Self-service and elasticity enable rapid response to workload changes, increasing or decreasing resources in real time, and fuel continuous experimentation; ubiquitous access and pooling provide global scalability; measurement ensures economic and environmental sustainability.

It is no surprise that the Italian market will grow from $12.4 billion in 2025 to $31.7 billion in 2030 with a CAGR of 20.6%. Manufacturers and retailers are migrating mission-critical loads to cloud-native platforms , gaining real-time data insights and reducing time to value .

From the laboratory to the business strategy

From theory to practice: the NIST pillars become a compass for the digital transformation of companies and Public Administration. In the classroom, we start with concrete exercises – such as the stress test of a video platform – to demonstrate the real impact of the five pillars on performance, costs and environmental KPIs.

The same approach can guide CIOs and innovators: if processes, governance and culture embody self-service, ubiquity, pooling, elasticity and measurement, the organization is ready to capture the full value of the cloud. Otherwise, it is necessary to recalibrate the strategy by investing in training, pilot projects and partnerships with providers. The NIST pillars thus confirm themselves not only as a classification model, but as the toolbox with which to build data-driven and sustainable enterprises.

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ChatGPT Action Figures & Responsible Artificial Intelligence
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Jun 23, 2025 6 min read

You’ve probably seen two of the most recent popular social media trends. The first is creating and posting your personalized action figure version of yourself, complete with personalized accessories, from a yoga mat to your favorite musical instrument. There is also the Studio Ghibli trend, which creates an image of you in the style of a character from one of the animation studio’s popular films.

Both of these are possible thanks to OpenAI’s GPT-4o-powered image generator. But what are you risking when you upload a picture to generate this kind of content? More than you might imagine, according to Tom Vazdar, chair of cybersecurity at the Open Institute of Technology (OPIT), in a recent interview with Wired. Let’s take a closer look at the risks and how this issue ties into the issue of responsible artificial intelligence.

Uploading Your Image

To get a personalized image of yourself back from ChatGPT, you need to upload an actual photo, or potentially multiple images, and tell ChatGPT what you want. But in addition to using your image to generate content for you, OpenAI could also be using your willingly submitted image to help train its AI model. Vazdar, who is also CEO and AI & Cybersecurity Strategist at Riskoria and a board member for the Croatian AI Association, says that this kind of content is “a gold mine for training generative models,” but you have limited power over how that image is integrated into their training strategy.

Plus, you are uploading much more than just an image of yourself. Vazdar reminds us that we are handing over “an entire bundle of metadata.” This includes the EXIF data attached to the image, such as exactly when and where the photo was taken. And your photo may have more content in it than you imagine, with the background – including people, landmarks, and objects – also able to be tied to that time and place.

In addition to this, OpenAI also collects data about the device that you are using to engage with the platform, and, according to Vazdar, “There’s also behavioral data, such as what you typed, what kind of image you asked for, how you interacted with the interface and the frequency of those actions.”

After all that, OpenAI knows a lot about you, and soon, so could their AI model, because it is studying you.

How OpenAI Uses Your Data

OpenAI claims that they did not orchestrate these social media trends simply to get training data for their AI, and that’s almost certainly true. But they also aren’t denying that access to that freely uploaded data is a bonus. As Vazdar points out, “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

OpenAI isn’t the only company using your data to train its AI. Meta recently updated its privacy policy to allow the company to use your personal information on Meta-related services, such as Facebook, Instagram, and WhatsApp, to train its AI. While it is possible to opt-out, Meta isn’t advertising that fact or making it easy, which means that most users are sharing their data by default.

You can also control what happens with your data when using ChatGPT. Again, while not well publicized, you can use ChatGPT’s self-service tools to access, export, and delete your personal information, and opt out of having your content used to improve OpenAI’s model. Nevertheless, even if you choose these options, it is still worth it to strip data like location and time from images before uploading them and to consider the privacy of any images, including people and objects in the background, before sharing.

Are Data Protection Laws Keeping Up?

OpenAI and Meta need to provide these kinds of opt-outs due to data protection laws, such as GDPR in the EU and the UK. GDPR gives you the right to access or delete your data, and the use of biometric data requires your explicit consent. However, your photo only becomes biometric data when it is processed using a specific technical measure that allows for the unique identification of an individual.

But just because ChatGPT is not using this technology, doesn’t mean that ChatGPT can’t learn a lot about you from your images.

AI and Ethics Concerns

But you might wonder, “Isn’t it a good thing that AI is being trained using a diverse range of photos?” After all, there have been widespread reports in the past of AI struggling to recognize black faces because they have been trained mostly on white faces. Similarly, there have been reports of bias within AI due to the information it receives. Doesn’t sharing from a wide range of users help combat that? Yes, but there is so much more that could be done with that data without your knowledge or consent.

One of the biggest risks is that the data can be manipulated for marketing purposes, not just to get you to buy products, but also potentially to manipulate behavior. Take, for instance, the Cambridge Analytica scandal, which saw AI used to manipulate voters and the proliferation of deepfakes sharing false news.

Vazdar believes that AI should be used to promote human freedom and autonomy, not threaten it. It should be something that benefits humanity in the broadest possible sense, and not just those with the power to develop and profit from AI.

Responsible Artificial Intelligence

OPIT’s Master’s in Responsible AI combines technical expertise with a focus on the ethical implications of AI, diving into questions such as this one. Focusing on real-world applications, the course considers sustainable AI, environmental impact, ethical considerations, and social responsibility.

Completed over three or four 13-week terms, it starts with a foundation in technical artificial intelligence and then moves on to advanced AI applications. Students finish with a Capstone project, which sees them apply what they have learned to real-world problems.

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