Today we at the Center for Trustworthy AI publish Centru 3, the third release of our strategic reference model for optimizing AI investment, managing risk, and scaling with confidence. We publish it openly, as part of the Center’s education mission, the full specification, free to anyone. Here is why we built it.

Nations are, to a great degree, the product of the technologies available to them at their founding. So are organizations. The framers of the American Constitution, for example, considered communication so foundational that a postal service was among the few operational powers they explicitly spelled out. The telegraph, the railways, the jet age, and the internet each expanded what both the world’s nations and its firms could become over the centuries that followed. Every one of those technologies gave the institutions of their eras time to absorb it.

Artificial intelligence will not. Here is a technology that improves upon itself in a matter of months, that writes the code for its own next version, like a railway track laying more railway tracks at an ever-faster pace. No nation possesses the legislative or policy-making capacity to evolve at that speed. Neither does any organization’s operating model, governance, or architecture. This is precisely the problem our model exists to solve, for the leaders and practitioners doing the work.

The AI most people encounter daily—a clever assistant, a variably reliable bit of office productivity kit—obscures what is actually happening. A technology that reads a hundred books a minute and forgets nothing is not the AI of tomorrow; it is the AI of yesterday. In the years just ahead, it will help us cure disease, engineer new marvels, and stimulate prosperity in sectors that don’t yet exist. It will also, in the wrong hands or under negligent stewardship, do great harm. Which of these prevails depends, as US President Kennedy said of another technology racing ahead of its era, not on the technology, which “has no conscience of its own,” but on us. To be trustworthy, AI must be guided, constrained, and put to task by the human compass.

That conviction is why Centru exists, and why this release is different from anything we have published before.

Centru 3 is not another paper. It is the third release of Centru, our strategic reference model (a core model, not a large language model, designed to work with whichever LLMs an organization already trusts). This is the release in which the work completed its evolution from a framework you read into an instrument you use. Its foundation, Centru Core, pairs five pillars and twenty-five dimensions with a maturity model that measures an organization against them. For the first time, each dimension is mathematically weighted, so the model doesn’t merely describe where an organization stands, but tells it where to act. Around that Core, Centru Knowledge accumulates the patterns and practices of building AI safely and well, and Centru Skills—live agents that stand up real infrastructure under human direction—run today, in real-world organizations’ environments.

We publish the full specification of Centru Core openly because trust demands that we show our work: a model asking to guide an organization’s most consequential technology decisions should be explainable, inspectable, plainly set down, and widely shared.

So, starting today...

📄 Read the paper. Centru 3 is free to download from the Center for Trustworthy AI, where Centru is a standing project — the paper, the research behind it, and the work ahead.

⚙️ And for organizations ready to put the model to work, Centru.ai is soon opening to early access partners — learn more at https://cloudlight.house/centru.

My deep gratitude to my co-authors and contributors—Ana Welch, Chris Huntingford, and Ioana Tanase—our editor, Keegan Chambers, and to the Center’s members including AIS (Applied Information Sciences), Damco Solutions, Godel Technologies Europe, SIS Global, and Microsoft whose support makes work like this possible.

We are devoted optimists about AI’s promised miracle and wonder. But those wonders will be neither free nor without peril, and it is only by building trust brick by brick that we will prevail. This model is our contribution to that work. We hope you will take it up and, from there, make it your own.

Author note: This article introduces my new white paper, The Frontier Campus. I invite you to read the paper in its entirety, as it includes significantly more details alongside graphics, architectural diagrams, design prototypes, and other illustrations.

Eighty percent of AI pilots fail to scale or deliver real business value.

This as a call to action for C-level leaders, enterprise architects, and others charged with guiding their organization through the opening vistas of the Age of AI. It highlights a paradox we face today, that despite unprecedented advances and investment in AI, most enterprises struggle to move beyond experimental pilots to meaningful, production-scale AI solutions.

How do we break out of this “pilot purgatory” and realize AI’s transformative potential across entire organizations? I’ve grappled with this challenge in my work with big organizations around the world.

I’m excited to introduce The Frontier Campus, a white paper that presents a blueprint for globally distributed, multi-agent, multi-cloud AI architecture. The Frontier Campus offers a strategic and technical blueprint through which organizations may deploy multiple AI agents across geographies and technologies, balancing local autonomy with global scale.

Importantly, it translates vision to action, and provides a clear roadmap for implementation, a practical game plan to ensure your AI initiatives aren’t just another isolated proof-of-concept, but a journey towards becoming an AI-powered “frontier firm” built on trust.

The Frontier Campus is based on work at the Center for Trustworthy AI, in fulfillment of our engagement and education mission nurturing a culture of trustworthy AI, and in our development of frameworks, papers, practices, and technical tools that others may use to quickly and confidently embrace AI from a position of trust.

From pilot paralysis to frontier transformation

Successful AI requires more than technological capability. It demands the intentional strategy, a culture of digital literacy and trust, and sound architecture. Trustworthy AI is the intersection of engineering best practice, human ethics, and legal regulation.

But many organizations have poured resources into AI only to stall out because they lack the combination of trustworthy, strategic intent and the rigor to translate that intent into action. Organizations must reject isolated pilots and obviously implausible projects in favor of—as I noted in my piece A Call to Arms for Trustworthy AI—the creation of digital ecosystems that are strategic, safe, reliable, and scalable for the Age of AI.

The Frontier Campus directly addresses this need. It is born from real-world work with global enterprises (including a composite case study of a $1 trillion financial institution) and insights across sectors including financial services, healthcare, legal, energy and utilities, public sector and global non-governmental organizations.

These blueprints envision a campus of AI “agents” spread across a global organization’s technology—and, importantly, cultural—landscape. Acknowledging that different components of a large organization have different needs and constraints, the blueprints seek to empower local teams and regions to develop and deploy AI solutions tailored to their context (local languages, unique market or regulatory requirements, limited internet connectivity, etc.) without sacrificing the benefits of a common, enterprise-wide AI framework.

Crucially, the blueprint blends centralized standards with decentralized innovation. It shows how to create a core set of shared services that provide the foundations for security, data governance, AI model management, compliance, and more. Around this core, business units or regional teams can plug in their own AI agents and data sources, choosing technologies that fit their local needs. This approach ensures each team can innovate quickly with the tools of their choice while still adhering to an overall governance and integration strategy.

For example, imagine a global company where a team in Europe is building a multi-lingual customer facing agent that must comply with applicable EU law, while a team in Asia is building a research assistant agent using a different set of technologies. The Frontier Campus architecture lets both teams develop and run their solutions optimally for their environment while connecting to shared enterprise data, common security controls, and a “trustworthy AI” oversight layer. The entire organization benefits from local innovation without fragmenting into incompatible AI silos.

Three blueprints, global scale and flexibility

The Frontier Campus outlines three blueprints to serve organizations of diverse needs. They are different approaches to implementing the concept, each suited to different circumstances. Think of them as three flavors of the Frontier Campus that organizations can mix and match to suit their needs.

Blueprint A: Open Architecture

This is a vendor-agnostic model that gives quasi-autonomous groups within an organization maximum flexibility, albeit sometimes at a cost. We provide a conceptual reference architecture so that each region or business unit can build its own frontier campus using whatever technologies make sense locally. Whether mixing and matching services from multiple cloud providers or leveraging open-source tools, Blueprint A is about freedom of tech choice. It’s ideal for organizations that demand technological agnosticism and local control. Each business group essentially stands up its own campus following the common blueprint, which ensures a degree of standardization and interoperability. The trade-off is that each group requires the resources to implement and maintain its local “campus”.

Blueprint B: Microsoft Architecture

Many enterprises have invested deeply in Microsoft’s ecosystem, which offers powerful capabilities for deploying and governing AI solutions at scale. Blueprint B takes the open model and adapts it to Microsoft’s cloud and AI stack, including (but not limited to) the Microsoft Agent Framework, Microsoft Foundry, and Agent 365. In this approach, each region or business group may still maintain its own Frontier Campus, but it’s built with pre-selected Microsoft technologies (which can in many cases be scripted to deploy much more quickly than starting from scratch), or a single centralized campus may be built. The advantage is a more turn-key deployment: groups can leverage centrally built components, like pre-trained agents from a central Agent Library, and Microsoft’s tools make it easier to enforce security, compliance, and reliable performance across all your AI agents. This blueprint is well-suited for organizations standardizing on Microsoft for efficiency, while still allowing some local extension and customization of AI solutions.

Blueprint C: Offline Architecture

Connectivity is unreliable or in certain environments. Blueprint C addresses this by enabling AI at the edge, even fully offline. In this model, the organization provides users with self-contained AI packages that run offline on local devices using a small language model (SLM). Think of deploying small-scale language models and agents onto a mobile device so they can function without internet access. Microsoft Foundry and the Microsoft Agent Framework are key enabling components both of this architecture and in promoting a “build once, deploy anywhere” approach when mixing and matching blueprints, though Blueprint C can be implemented in non-Microsoft centric organizations, as well. This approach is critical in scenarios like rural deployments, disaster response, low-bandwidth regions, or high-security settings where cloud connectivity is limited or prohibited. Blueprint C ensures that no part of the organization is left behind, and even the most physically challenging environments can benefit from AI-driven tools. The trade-off here is that without cloud connectivity, these local agents may operate in silos with limited central oversight or data sharing. Still, for many cases, offline AI capabilities are a game-changer, enabling innovation anywhere.

These three blueprints are not mutually exclusive, and more complex organizations might implement a mix. For instance, a company could primarily use the Microsoft Architecture for most regions to streamline operations, but also provide an Offline Architecture solution for environments with poor connectivity. The Frontier Campus is designed to be flexible; it’s a toolkit of approaches that you tailor to each environment’s needs. Indeed, most organizations will choose one primary model, but having options ensures that AI can be deployed even in outlier scenarios without reinventing the wheel. The unifying factor across all three blueprints is a common set of principles: modular multi-agent design, robust data management, integration across cloud platforms, built-in security and compliance, and the ability for local teams to customize solutions responsibly.

From vision to action: A roadmap for implementation

A core thesis of The Frontier Campus is that architecture is only as good as your ability to execute it. Grand plans won’t help if there’s no clear path to move from idea to production. So, the paper lays out a concrete, actionable roadmap that made sense for the case subject organization, guiding its transformation in horizons spread over six to eighteen months. This roadmap is organized into major priorities and sequenced steps, helping ensure that early AI successes lead to broader, sustainable adoption.

We identified five strategic priorities for rolling out the Frontier Campus in the case organization. Others should think clearly about how to adapt this to their own specific vision. Think of these as the “big buckets” of work on which we must focus to turn vision and architecture into reality.

1. Build the Foundation. Establish the core platform and governance foundation for your AI ecosystem. This includes the cloud infrastructure, security controls, data storage and pipelines, and an initial Agent Library, the “app store” of AI agents that your organization can develop and reuse. Start small but with scalability in mind (honoring both the “Principle of Restraint” and the “Principle of Evolution” – get an initial version running and improve from there).

2. Establish Trust and Overcome Barriers. Bake trustworthiness into the architecture from day one. Address key barriers like multilingual needs, cultural differences, data privacy regulations, and ethical AI considerations up front. This might involve implementing robust AI governance and risk management tools, ensuring regulatory compliance based on the jurisdictions in which you operate (e.g., the EU AI Act) and AI risk management standards (for instance, NIST’s AI Risk Management Framework). Overcoming these barriers early creates a strong foundation of trust, which is critical for any AI to be embraced by users, security teams, and regulators—including industry regulations, not solely the AI-specific regulations that have emerged in some jurisdictions—themes I’ve underscored in previous articles as well.

3. Improve Customer Outcomes. AI must drive real business or mission outcomes. Identify high-impact “big win” workloads where AI agents can significantly improve customer experience, decision-making, or operational efficiency. Then deliver on those opportunities with focused AI solutions. By targeting specific problems (for example, a customer-facing AI assistant to help users navigate services, or an AI tool to streamline an internal process), you demonstrate value quickly and tangibly. Early wins will build momentum, boost adoption, and justify further investment.

4. Support Colleagues. Your colleagues determine the success of AI (or any technological) transformation. A Frontier Campus isn’t just about technology; it’s about people. This priority focuses on enabling and educating your workforce. Develop training programs and digital literacy initiatives so employees know how to use AI tools confidently and responsibly. Create “ AI champion” teams or a Center for Enablement to share best practices. The Frontier Campus model calls for empowering those colleagues (whether in a corporate office or in the field) to collaborate with AI, trust its outputs, and even build their own solutions with proper guidance. By investing in your people’s skills and addressing their concerns about AI, you ensure the culture is ready to fully leverage new capabilities. In short, supporting your colleagues is supporting your customers, because skilled, AI-empowered colleagues will deliver better service.

5. Catalyze Regional Implementations. If your organization operates in multiple regions or business units, it’s crucial to jump-start the adoption everywhere. Use a “pilot region” to lead the way, and collaborate closely with that region (or business group) to stand up their Frontier Campus first. This pilot will produce reusable assets: playbooks, technical templates, and a knowledge base that other regions can leverage to accelerate their own deployments. Essentially, turn your first success into a repeatable model. By seeding the Frontier Campus in one region and capturing the lessons learned, you create a ripple effect that will shorten time-to-value when scaling out to additional regions across the globe.

With these priorities in place, The Frontier Campus then maps out a sequenced horizon-based roadmap. The roadmap breaks down specific activities across three horizons. For example, Horizon One (the first four to five months in the case of the case study) might focus on establishing the core platform and delivering a few key AI agents to demonstrate value. Horizon Two then builds on that foundation, adding more advanced capabilities like the Offline Architecture for connectivity-challenged environments and expanding language support to make the AI agents effective in local languages and contexts. Horizon Three looks further out at scaling enterprise-wide, refining governance, and continuously adding to the agent library as new use cases emerge.

A blueprint for the Age of AI

The Frontier Campus is both a practical guide and a call to action. It builds upon a core message I’ve shared before: that to thrive in the Age of AI, organizations need a clear strategy, a scalable ecosystem-oriented architecture, and a commitment to trustworthy AI. By presenting concrete reference architectures and an actionable roadmap, our aim is to empower organizations to break out of the AI pilot trap and start scaling innovations that deliver real value whether as a global financial institution, a government agency, a healthcare network, an NGO on the frontlines of humanitarian work, or any of the many other organizations that will see something of themselves in the paper.

This blueprint is not theoretical. It’s built on hard-won lessons from the front lines of enterprise AI projects. It acknowledges that different teams face different realities, and it equips leaders with multiple pathways to move forward. Crucially, it embeds Trustworthy AI principles into the architecture from the ground up, because scaling AI without trust is not simply unsustainable, it is likely catastrophic. The pursuit of trustworthy, scalable AI must be shouldered by every leader and every organization that wishes to survive and thrive in the coming age.

I invite you to read The Frontier Campus white paper and begin adapting these blueprints for your own organization. The paper dives deeper into the technical reference models, previews design prototypes, and elaborates on the roadmap with timelines for implementation. I hope that it will spark ideas for how your organization can fast-track its AI journey to truly become a “frontier firm” in this new era.

The Boy in the Bubble was the first track of Paul Simon’s Graceland, in my view, the greatest musical album released in my lifetime. The year was 1986.

Lately I find myself quietly singing Bubble’s refrain.

These are the days of miracle and wonder. This is the long distance call.

You can listen below.

The genius of Simon’s lyrics is in the juxtaposition of tragedy and progress. Nearly 40 years later, the line “these are the days of miracle and wonder” comes to mind when contemplating the nature of this moment in humanity’s technological progress. Yet consider the song’s final verse, which you really do need to hear: lasers in the jungle, staccato signals of constant information, a loose affiliation of millionaires and billionaires. Simon wrote it in 1986.

Last month I represented The Center for Trustworthy AI at the absolutely brilliant AI for Good Global Summit—the United Nations conference on artificial intelligence—hosted by the International Telecommunication Union in Switzerland. There I joined with C-level leaders, heads of state and government, and renowned engineering experts in AI and related fields to consider AI’s future and its implications for humankind.

Most readers of this newsletter will know that there is no AI without data, and that to be trustworthy, AI requires trustworthy data. An obvious manifestation of the Data Divide is the separation between the data “have” and “have not” organizations. Put another way, organizations possessing relevant, clean data sets stored in modern, AI-addressable data technologies enjoy a massive advantage in what AI can do for them, and in how quickly they can bring it to bear in service of the challenges they face. This is fast becoming a critical asset for organizations. The next couple of years will see a stark separation in fortunes for these have and have not organizations.

Yet, for all the underinvestment that many organizations have given (or denied) their data, a typical firm by and large possesses far healthier data sets—or can get to a healthy state given some investment—versus practical data concerning the physical world. Werner Vogels, Amazon’s CTO, points out that “most maps are not mapped for humanity, but rather for economic progress”. This is obvious when one considers the difference in the quality and completeness of geospatial data in (say) Boston or València when compared to rural West Virginia or the Spanish countryside, to say nothing of the state of geospatial data in the world’s least developed, most remote places.

Vogels devoted his Summit keynote to proposing that if we think of maps as data models (which we should), a world of constant flux requires dynamic maps and thus new approaches to dealing with geospatial data. Good maps require data at multiple levels that change on different timescales, including the physical Earth Layer (the Himalayas are not going anywhere), the Infrastructure Layer including roads and buildings that experience major change perhaps once each year on average, the Seasonal Layer experiencing often-predictable seasonality such as flood patterns, and the Real-Time Layer including both human driven and natural phenomena such as weather.

My colleagues and I have grappled with these challenges in (say) applying AI to the analysis of geospatial data to identify patterns in the movement of people driven by climate change. Which is to say, the concept is prescient in my own, recent work.

In other words, though I can tell you where my daughter’s backpack is located even from 3,500 miles away, our difficulty applying AI in service of humans—often in the hour of their greatest need—is an often overlooked yet significant challenge of the Data Divide, leading to real-world consequences in scenarios inherently linked to physical place such as search, rescue, and humanitarian response.

These are the days of miracle and wonder.

Now, consider language.

English accounts for approximately 50% of the internet’s content, with Spanish representing only approximately 6% as the second most prevalent language. Because large language models are largely trained on generally available internet data, English is highly overrepresented in training and grounding, which in turn improves the efficacy of English-based AI scenarios while also producing far more English language synthetic data (i.e., data produced by AI itself). Further, though generative AI has not been widely available long enough for us to yet possess long-term data on the subject, I am increasingly concerned that the dominance of English in AI models may catalyze the decline of other less-spoken languages, accelerating a trend that had been a byproduct of globalization for some years. Finally, the prevalence of English in training and grounding data creates an inherent bias of AI towards ideas and perspectives commonly expressed in English, the intellectual tradition of Britain and her former lands—including the United States—as well as the intellectual tradition of societies that are non-native yet highly fluent in English such as Sweden or the Netherlands.

This natural bias towards perspectives common in the English language is likely to haunt engineers and society alike for years to come, to say nothing of the cultural loss were AI to crowd out the unique ideas inherent to other tongues.

These are the days of miracle and wonder.

In another line of thinking, Yoshua Bengio, computer scientist and professor at Université de Montréal (said to be the world’s most cited scientist among any discipline), argues for restraint in the development of agentic technology. Bengio calls for what he terms “Scientist AI” as an alternative to purely agentic AI, which he suggests should be designed to prioritize safety, transparency, and human alignment in the development of AI systems.

Specifically, he envisions Scientist AI as a system that aids humans in scientific discovery rather than acting autonomously, generates explanations and theories based on data and observations, and avoids taking direct action planning and executing tasks independently. Instead of being driven by goals or rewards (which can lead to misalignment or manipulation), Scientist AI is structured to model the world and answer questions, support human reasoning and decision-making, and evaluate risks of actions proposed by other AI systems.

Bengio and his collaborators argue that agentic AI—systems that autonomously pursue goals—poses serious risks including self-preservation behaviors already seen in frontier AI models that have resorted to blackmail as well as industrial espionage to avoid being shut down, reward tampering and goal misalignment that could lead to unintended, potentially catastrophic outcomes, and malicious use by bad actors that becomes more feasible with autonomous capabilities.

These are the days of miracle and wonder.

I am increasingly contemplating the paradox of societal versus personal productivity.

I have long believed that one of the most significant medium-term benefits of AI is its potential to lift societies out of the productivity doldrums that have afflicted many nations. Meanwhile, data in recent years points to the emergence of a two-tier economy wherein the most productive organizations far outpace the least productive, as is the case in (for example) the United Kingdom where from 2010 to 2019 the most productive firms realized an 11% average worker productivity gain whilst the least productive firms saw no rise at all. AI seems likely to turbocharge this phenomenon, which would be an economic boon to the aging and (in some cases) shrinking societies that can responsibly take advantage of it.

Yet, as both a writer and a technologist whose work requires a great deal of creativity, my intuition has been that grappling with challenging ideas and problems is a significant, immutable part of humans’ creative capacity. I write my own material, for writing is at least as much about the quiet exploration and revision of ideas as it is about the end product. I produce my own slides, for I see slides as the storyboard upon which I bring many ideas to life. From time to time I even build my own apps, for in so doing I work out architectural problems that seemed insurmountable when first putting hands to keyboard. Outsourcing that creative process to AI has intuitively felt to me like an efficiency improvement in the short run at the expense of our creative, cognitive abilities in the long run.

Supposed degradation of our cognitive faculties was the realm of my idle speculation and intuition until I saw MIT’s June 2025 study, Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing. Oh my...

While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI’s role in learning.

Thus, the paradox between AI’s sorely needed productivity gains across societies and organizations versus the long-term cognitive risk of individual humans over relying on it. Thought of another way, we need AI, but you ought to be judicious in how you come to rely upon it.

These are the days of miracle and wonder.

I am a devoted optimist in AI’s promised miracle and wonder, not just the general productivity of organizations and societies, but in the realms of science, medicine, and even—when judiciously used as a tool of humans rather than as imitation thereof—art.

Whether AI will become a force for good or ill depends on humans. And many around the globe are working tirelessly to harness and channel this technology, to solve these challenges for the good of all in organizations like United Nations Human Rights and AI for Good, Microsoft Elevate as well as the good people I know building responsible, trustworthy AI into technical products themselves, and of course The Center for Trustworthy AI, which I am proud to lead.

These wonders will be neither free nor without peril, though if wisely purchased and grounded in trust, they may be nothing short of miraculous.

These are the days of miracle and wonder.

People and organizations around the world are asking, “Can we trust artificial intelligence?”.

I know this because I’ve spent the last two and a half years circling the globe helping organizations strategize, architect, and action their way towards the promised benefits on the AI horizon. And that is what I hear in country after country, organization after organization, leader after leader and colleague after colleague in every group I stand before helping them see this through:

Can we trust artificial intelligence?

Sure, can we trust AI to be responsible, to generate accurate, trustworthy responses to our prompts?

But look more deeply and you will find that truly Trustworthy AI is about so much more.

Do we trust that our investments in AI are wise, that we are not throwing effort and money after nonsense?

Do we trust that we are moving rigorously, purposefully, and efficiently towards AI’s promised land?

Do we trust that we are selecting AI capabilities—tools, models, technologies—that are not just superior to alternatives in the aggregate but also appropriate to our organization and our purpose?

Do we trust that we have put in place the apps, data, security, governance, compliance required to achieve our AI aspirations?

Do we trust our ability to scale the investments we’re making, both technologically and culturally, within our organizations?

In today’s world of all-too-often declining trust in institutions, governments, and humanity writ large, can we trust AI to be applied and used for the good of our colleagues, our neighbors, and of all humankind?

How do we realize AI’s transformative potential in the realms of science, medicine, engineering, and productivity across the economy while not sacrificing our institutions, our culture, our communities, our human rights to its use?

We must prevent AI’s maladies graduating from the realm of inaccuracy to significant financial calamity, and again from financial calamity to the facilitation of—for example—cyber crime or sex trafficking.

On the 12th of September 1962 at Rice University in Texas, in one of the most astonishingly brilliant speeches ever recorded, America’s President Kennedy rallied his fellow citizens to what he called the “high costs and hardships, as well as high reward” of their nation’s exploration of space. He proclaimed:

“For space science, like nuclear science and all technology, has no conscience of its own. Whether it will become a force for good or ill depends on man… I do not say that we should or will go unprotected against the hostile misuse of space any more than we go unprotected against the hostile use of land or sea, but I do say that space can be explored and mastered without feeding the fires of war, without repeating the mistakes that man has made in extending his writ around this globe of ours.”

It is a brilliant speech, and I believe required viewing for anyone in need of a moral compass in today’s beleaguered world, to say nothing for those of us working in and around artificial intelligence. Enjoy.

You see, to be trustworthy, AI must be guided, constrained, and put to task by the human compass. In more practical terms, Trustworthy AI is about creating the structures, about building digital ecosystems that are strategic, responsible, safe, reliable, and scalable for the Age of AI.

But how?

Most organizations don’t have a clue where to start, but are eager to safely harness AI. Trustworthy AI enables organizations to make wise, strategic investments. These organizations scale as they differentiate themselves in the market and deliver higher quality to customers (or their citizens). Responsible use of AI reduces risk amidst confusing laws and regulations that are different everywhere. The workforce and workplace culture is more productive when empowered with the most reliable AI tools.

Whether in seizing opportunity or in avoiding calamity, the achievement of Trustworthy AI is an effort that must be shouldered by every user, every leader, and every organization that wishes to survive and thrive in the coming age.

Last year I led a team who wrote and published the AI Strategy Framework (author’s note that the AI Strategy Framework was folded into the “Centru” model in 2026, so this link now points there), which set forth five pillars and twenty-five dimensions of what I view to be a comprehensive, holistic approach to Trustworthy AI. For we have come to understand that AI cannot be truly trustworthy if not adopted and used in the context of these dimensions.

To be sure, you cannot achieve maturity in each dimension before widely using AI. The framework is a guide, not a pre-requisite or a brake on progress. It is only through using AI that an organization can become mature in its trustworthy application.

For many, this will be about reducing risk as much as it is about seizing opportunity. Europe, the United Kingdom, many U.S. states, and nations the world over find themselves in various stages of crafting laws to protect their citizens and their economies from AI’s misuse, both hostile and benign.

And even where rules for AI are not legislated directly, for example nationally in the United States under the current administration, organizations must contend with AI’s second-order regulations. Though a government may decline to regulate AI directly, the organizations within its jurisdiction must still contend with laws in their sector separate and apart from AI. In other words, one is not absolved of their violation of privacy regulations, doctor-patient or lawyer-client privilege, or financial crime (to name a few) just because “the AI did it”, even when the AI itself was not regulated.

Those who withhold their best effort from the pursuit of Trustworthy AI do so at their own, grave peril. Given the degree to which almost nobody yet realizes how powerful these technologies are today—to say nothing of what they will soon achieve—it is a neglect so severe as to warrant the firing of leadership today, and the likely end of the organization itself in a few short years.

Trustworthy AI is not the sole domain of those organizations that prize competitive advantage, risk mitigation, or civic responsibility. It is, rather, the universal domain of those organizations that wish to survive.

Nearly everyone whose notion of AI is a clever chatbot or a variably reliable bit of office productivity tech fundamentally misunderstands what’s happening. The AI of their daily use prevents most of us from seeing the forest for the trees.

For AI’s absolutely transformative power is in its ability to pore through, assess, and act on data at a mind numbing scale that was previously far, far out of humankind’s grasp. This is not the AI of tomorrow; it is the AI of yesterday.

In many cases, this AI will be a giant leap for mankind, in our ability to cure disease, engineer new marvels, spot risks to the safety of populations before they materialize, and stimulate as yet unimagined prosperity in sectors that don’t yet exist.

In many other cases, this AI will be devastating in its ability to displace communities and individuals left behind, empower lone individuals to synthesize deadly pathogens, and to enable repressive regimes and geopolitical enemies to spy on one another—and on their own people—at a previously unimagined scale.

In any case, it is only in building trust brick by brick that we will prevail.

But a framework alone is insufficient to the task before us. So, in the coming weeks I will be so proud to share the launch of initiatives, tools, and global forums that will enable the world’s leading organizations to adopt and scale Trustworthy AI.

We begin here. Until then, I will leave you with a final line of Kennedy’s warning and cause for hope those six decades ago.

“There is no strife, no prejudice, no national conflict in outer space as yet. Its hazards are hostile to us all. Its conquest deserves the best of all mankind, and its opportunity for peaceful cooperation may never come again.”

This is not the column I intended to write this week. Nor is this a column about politics. Pundits and historians will litigate the politics of this moment for generations to come. They don’t need The Cloud Strategist spoiling their good time.

But as the Trump administration aggressively moves to shrink the size of the federal workforce, discontinue staggering sums of foreign aid, and with efforts to freeze federal funding of domestic US programs in doubt, well, I come bearing an urgent message for leaders across the humanitarian and public sectors, many of whom I have come to know personally over my (gasp) 24-year career in technology.

You see, in the public and humanitarian domain—as in business and commerce—it is the fabric of data, security, apps, and, increasingly, artificial intelligence that makes possible much of what society takes for granted. An ecosystem of interwoven technologies, often invisible to the end recipient of any service, orchestrate the vast, historically unthinkable systems through which students are educated, police are assigned to duty, the hungry are fed, and the most vulnerable migrants are transported from conflict, housed, and treated for their injuries.

I often use the word “fabric” to describe this fact of modern life, for it is indeed an intricate tapestry of data and computing infrastructure through which modernity is stitched together.

The last two weeks have set that tapestry ablaze both in the form of the experts who weave it being enticed or forced from their jobs and in the sudden evaporation of funds upon which these organizations rely to maintain, repair, and strengthen its fibers.

Leaders of these organizations now face a moment of reckoning and—in the chaos of that moment—an opportunity to craft the modern, technologically nimble institutions resilient enough to continue delivering the crucial services upon which their publics so desperately depend. They must use technology to drive out cost, with the core thesis being that to emerge stronger, they must become more modern organizations that use technology to reduce cost and improve services, rather than viewing technology as an ever-expanding cost center.

Those that seize the opportunity may survive. Those who let it slip away will fail.

Get apps, data, and AI components into users’ hands very quickly

The age of the multi-year technology project was already in its twilight. Now these humanitarian and public sector agencies must nearly entirely divest themselves of that weight.

Through platform technologies like Microsoft’s Fabric and Power Platform, these organizations now have the ability to turn solutions around to their users in a matter of weeks. These solutions may not be as beautiful or contain every imaginable bell and whistle, but the priority needs be knocking down challenges to the mission and creating value very quickly. Many humanitarian and public sector organizations won’t be able to pay technology consultancies and software implementers to build much of this, so their focus ought to be on enabling the workforces they can retain to do more with less, and to augment knowledge gaps with “guided engineering”, essentially providing colleagues who remain with the technical and architectural guidance they need to build capabilities themselves.

The best architects and expert engineers will be called on to cover orders of magnitude more ground with less support, a chasm that can only be closed by enabling colleagues with the expertise in the technologies that make rapid turnaround possible.

Focus the most complex efforts on the highest strategic priorities

The mind of every executive leader I know is racing as they realign their strategic priorities for this new era. Much of the technology to enable these changing priorities should follow the same rapid-time-to-value pattern I’ve outlined above, but some of it will be too ambitious, too untested, too far-reaching to quite achieve that kind of rapid turnaround. These are efforts on which organizations need to place their complexity chips. This year (or next) is probably not the year for business-as-usual modernization of your ERP. This is go-big-or-go-home year. To the extent that an organization has the budget to invest in complex technologies, its leaders must be ruthless in their investment in truly game changing technologies that will alter their long-term trajectory. They need to focus their big development bets on their strategic priorities rather than frittering limited resources away on things that can be achieved at lighter weight and lower cost.

Maximize the investments that you’ve already made

Organizations that are some combination of lucky and forward-thinking may already have technologies in place that will help them weather the storm. The aforementioned Fabric and Power Platform are excellent examples, for they are notoriously good at squeezing more from less. Some organizations may have also, smartly, begun to transition away from point solutions towards a more ecosystem-oriented architecture upon which they can build without reinventing the proverbial wheel. Others may have made investments that are very domain-specific to their particular mission and purpose.

They now need to load as much functionality and realize as much value as possible from what they already have today. They must therefore prioritize meeting the above two goals (rapid value and strategic priorities) by leveraging investments already made. A big part of this will be channeling the efforts of technology projects that do get funded into using technology products that already exist, rather than inventing the wheel every time as I have seen again and again in the many organizations that seem to have (and pay for) twelve different departmental-specific solutions to the same problem. The latter is what we call “technical debt”, which almost nobody can any longer afford.

Eliminate duplicative investments to reduce costs

On which note...

As time and budget permit, these organizations need to shrink their cost basis and eliminate the technical debt that drives those costs ever skyward. This will come in, over time, phasing out the one-off point solutions that consume budget to duplicate functionality. I can think of hundreds of examples across dozens of organizations just as I write this sentence, so I’ll leave the reader to ponder your own.

Get paused initiatives back on track when budget permits

...but don’t do it the old-fashioned way.

I have found that estimates to build critical technologies always come in much higher than they should because (a) every team and field office demand bells, whistles, and customizations to suit their particular way of doing things, and (b) these technologies must integrate with those that came before it, or have the data from its predecessor systems migrated in.

But, as a CIO I worked with many years ago once told me, “Data migration and integration is always the long pole in the tent”.

As budget allows paused initiatives to get back on track, these initiatives must be scrutinized in accordance with the principles I’ve outlined above:

• Is there a “rapid time to value” approach that would suffice, here?

• Does this initiative really offer an outsized impact to our strategic priorities?

• Are we fully maximizing investments we’ve already made?

• Are we reducing or spawning new duplicative technology and technical debt?

Some of these paused initiatives will get a second life in another time, because many are critical to the fabric of the modern world. But most will have their shot at redemption on a leaner budget than what the bygone era might have allowed. Here we need to take a bit of a “rapid value” approach, guiding colleagues to build as much of the functionality as possible, admitting that we can’t customize for every whim, and then separately costing “the long pole in the tent” such that tradeoffs can be made as we decide what’s truly important, and what is not.

Above all, this moment must serve as a clean break from the old ways, not as an interregnum after which we’ll return to business as usual. There can be no going back.