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AI in Project Management – New Technical Article

How is AI changing project management? A new technical article demonstrates how artificial intelligence is currently increasing the efficiency and quality of projectsand what matters most when it comes to implementation. Artificial intelligence (AI) is fundamentally transforming project management. This is demonstrated in the new article, "Artificial Intelligence in Project Management: Vision, Use Cases, and Approaches to Implementation," by Kai Wilhelm, Philipp Larseille, Sebastian Colditz (all from Campana & Schott), and Benjamin Hettrich (Technical University of Darmstadt). The article was published in the latest issue of the trade magazine PROJEKTMANAGEMENT AKTUELL (issue 36/5).

The authors combine scientific findings with practical experience to highlight how AI currently supports project initiation, risk management, and portfolio decisions. According to the article, studies predict that up to 80% of administrative project management tasks could be automated by 2030. Notably, the greatest added value is not created by individual tools, but by a holistic approach that considers people, processes, and technology equally. AI can already significantly accelerate and improve the quality of project order creation, risk analysis, and project idea evaluation. 

According to practical experience, the greatest efficiency gains are currently being achieved in administrative tasks and documentation up to 60 minutes of working time can be saved per day. 

To successfully introduce AI into project management, the authors recommend an iterative approach: start small, gain experience, and develop solutions step by step. Empowering employees early on and actively shaping change management are crucial.

 

Curious?The full article is available exclusively to members of GPM German Project Management Society or for individual purchase from specialty magazine retailers.  Would you like to learn more about using AI in project management? Our experts at Campana & Schott would be happy to help get in touch with us! 

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Digital Workplace: How companies are clearing the Tool Jungle

Many companies struggle with an impenetrable jungle of tools when it comes to their digital workplace resulting in high costs, inefficient processes, and a lack of AI capability. This article shows how to clear the thicket and successfully navigate the path to cost efficiency and AI readiness. This January, when RTL Television's Jungle Camp (titled I'm a Celebrity...Get Me Out of Here!) returns to our screens, millions of viewers will eagerly watch the contestants battle through the thicket, search for direction, and ultimately find their way to the winner's tree house. Just as there are challenges in the Australian bush, the corporate world is confronted with its own jungle trial: navigating the digital workplace's tool jungle.

Over the years, well-intentioned individual decisions have resulted in software landscapes characterized by a multitude of applications from different providers, isolated solutions, and shadow IT. Those who lose track of the big picture pay a high price in the form of high licensing and operating costs, inefficient processes, and missed opportunities for innovation and artificial intelligence.   

Questions to ask yourself at this point:

Who is responsible for keeping track of licenses and tools?Who finds the most elegant path to cost efficiency?Who creates a solid foundation for true AI readiness? 

The good news is: Unlike on the TV show "Jungle Camp," no one has to hope for an off-screen rescue. No CIO needs to shout, "I'm a CIO; get me out of here!" With smart measures, the thicket can be cleared and the path to efficiency and innovation can be opened up. 

  The best of breed approach, which involves searching for the best solutions, is now considered outdated when it comes to selecting tools for the digital workplace. In the early days of the modern workplace, only selective solutions from different manufacturers were available, and they varied greatly in quality and functionality. Today, there are platform solutions that integrate all important functionalities.

"Best of suite" strategies are gaining popularity among organizations seeking cost-efficient and AI-ready solutions. There is good reason for this shift: a multitude of parallel tools and isolated solutions leads to a steady escalation of licensing and operating costs. Redundant functions, shadow IT, and complex integration scenarios increase administrative overhead and complicate governance.

Targeted consolidation for example, standardizing on an integrated platform opens the door to reducing license costs, support efforts, and infrastructure in the long term, usually without sacrificing functionality.  Artificial intelligence can only realize its full potential with a modern, consolidated infrastructure. Fragmented systems and data silos prevent AI applications from accessing consistent and relevant data. The necessary foundation for AI readiness can only be created by centralizing data, introducing uniform governance structures, and ensuring compliance. Navigating the tool jungle is not a quick fix; it requires a methodical approach. As in a jungle camp, it's important to overcome various challenges one step at a time and work on yourself. A structured approach ensures that measures achieve short-term and long-term effects, as well as cost efficiency and AI readiness. The following steps form a proven orientation framework:

1. Inventory: Creating transparency

The first step is a comprehensive analysis of the initial situation.Tool landscape: Which applications are in use? Where are there overlaps or shadow IT?License usage: Are existing Microsoft licenses being used to their full potential, or is there untapped potential?Infrastructure models: How is the current architecture structured: on-premises or cloud?

Added value: This assessment lays the foundation for informed decision-making and prevents wasted efforts. 

 

2. Potential Assessment: Identifying the levers

The analysis reveals concrete savings and optimization potential.Eliminating redundancies: Removing duplicate structures and parallel tools.License optimization: Avoiding over-licensing and choosing suitable models.Automation potential: Identifying processes that can be efficiently digitized. 

Added value: Companies recognize not only where costs can be reduced, but also how to lay the groundwork for AI applications. 

3. Deriving Measures: From findings to roadmap

The findings are used to create a clear action plan.

Consolidation: Consolidating the tool landscape into an integrated platform.Modernization: Migration to the cloud and introduction of governance standards.AI readiness: Centralizing data and establishing security and compliance structures.

Added value: The roadmap prioritizes quick wins and long-term steps for immediate results and long-term security. 

 

4. Implementation and Governance: Ensuring sustainability:

Implementation takes place in clearly defined phases accompanied by measures for change and adoption.Standards are introduced: Uniform processes and guidelines for use and security.Roll out automation: Implementing workflows and self-service portals.Continuous optimization: Conduct regular reviews to identify new opportunities.

Added value: Governance ensures that efficiency gains are maintained and that the organization remains AI-capable in the long term.  Modernizing and consolidating the digital workplace involves more than just technical aspects. It's also a strategic tool for achieving cost efficiency and successfully integrating artificial intelligence. Streamlining the abundance of tools establishes the foundation for innovation, security, and sustainable business success. Are you interested in maximizing the value of your existing infrastructure and preparing for AI readiness? If so, we recommend our Infravalue Workshop, where you can learn how to identify and leverage your potential.

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Agile, but ineffective?Why many agile teams are held back – and what really helps

Many organizations introduce agile ways of working, yet the expected results fail to materialize. In most cases, the root causes lie deeper within the organizational system. Over the past years, many companies have adopted agile working practices. Teams work iteratively and use regular ceremonies for planning, reviews, and retrospectives. Through the principle of inspect and adapt, they continuously review and adjust results, ways of working, and plans. The intended outcome is clear: more efficient collaboration, increased innovative capacity, and faster value creation. 

All too often, however, these effects fail to materialize. In many organizations, agile ceremonies are perceived as additional meetings rather than as enablers. The value of structured alignment and shared reviews often remains invisible to employees. The promise of greater decision-making autonomy does not automatically come true either. Many employees feel overwhelmed, and decisions take longer rather than less time. What was intended to be agile often results in unclear processes and growing disorder. After an initial phase of enthusiasm, disillusionment sets in. Why does this happen? And can agility really deliver on its promise? 

  An organization is a complex system. If agility is not approached holistically, the necessary conditions for real impact are missing. 

In most cases, the root causes lie within the organizational system itself. Agility is often introduced only at team level or applied as a kind of make-up, without being thought through end to end. Agile teams then encounter obstacles such as traditional structures, rigid budgeting processes, strong interdepartmental dependencies, control-oriented leadership systems, and tools and interfaces that are not designed for fast response times. These challenges are often compounded by mindsets among those involved that do not align with agile principles.  Teams can work in an agile way but still fail to create impact if the organizational environment is not aligned with agility. To make these interdependencies tangible, Campana & Schott works with six dimensions that are critical in determining whether agility truly delivers value or gets stuck in day-to-day operations.  Organizations that anchor agility systemically benefit not only from faster decision-making and greater innovative capacity, but also from tangible economic effects. Teams deliver value more continuously, products reach the market earlier, and resources are deployed where they create the greatest benefit. At the same time, employee satisfaction increases as people can work more focused and experience greater impact. Transparency, efficiency, and a culture of learning become second nature and strengthen long-term competitiveness. 

Agility delivers impact when the system supports it. The six dimensions highlight where organizations are being held back today and which levers are essential for agile ways of working to realize their full potential. When structure, leadership, processes, and mindset are aligned, agility becomes tangible in everyday work and creates the value it was introduced for: faster value creation, greater innovative strength, and an organization that can act with confidence in a dynamic environment. 

We support organizations in making their maturity level across the six dimensions transparent and deriving clear fields of action from it. This can take the form of a concise assessment or a workshop in which we jointly visualize and prioritize the current state. Beyond that, we provide holistic support throughout the transformation, from designing a suitable operating model to coaching and change management tailored to the specific context.  Would you like to understand how agility can become more effective in your organization and which levers really matter? 

We would be happy to talk. 

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Why agility is becoming a key success factor in the PMO

Traditional PMO models are reaching their limits. Why agility is becoming a decisive success factor for future-ready PMO. For a long time, project management was characterized by clear planning, defined milestones, and fixed processes. This approach provided organizations with stability and orientation for decades. The Project Management Office (PMO) originally emerged with the goal of standardizing projects, increasing transparency, and enabling effective governance. 

Today, however, the environment has fundamentally changed. Projects operate in a field of tension defined by high speed, growing complexity, and constant uncertainty. Linear planning has become the exception rather than the rule. As a result, traditional methods and governance models are increasingly reaching their limits. 

This shift has also transformed the PMO. What was once primarily an administrative function is evolving into a strategic hub that provides orientation, supports decision-making, and actively shapes change. At Campana & Schott, we are actively working towards the PMO of the future: a PMO that combines technology, agility, and strategic thinking and is aligned with the requirements of modern project landscapes.   

Mehr ber die weiteren Faktoren und ber das PMO 4.0 erfahren Sie hier. 

Zum Beitrag PMO 4.0: Projektmanagement neu definiert 

  In PMO, agility is not an add-on but a core prerequisite for dealing effectively with dynamics and uncertainty. Agile principles make it possible to stay focused on objectives even when conditions change. A typical example is regulatory adjustments during ongoing projects. Instead of reworking extensive plans, iterative approaches enable structured yet flexible responses. Regular reviews, transparent decision frameworks, and short feedback loops help implement necessary adjustments at an early stage. 

That agility is not a marginal topic is also confirmed by our latest multi-project management study. Organizations with high project performance consistently rely on agile or hybrid delivery models. While top performers continue to expand agile practices, low performers have recently shown a decline in agile approaches, accompanied by a return to strongly traditional methods. Agility thus proves to be a clear differentiating factor in project and portfolio management. 

AI-supported agility (for example predictive sprints or AI-based prioritization) may represent the next level of maturity. By 2026, agility will increasingly be complemented by AI-driven decision-making, with AI providing forecasts, risk insights, and resource scenarios while enabling faster iterations. Classic project management tasks will gradually be supported or even replaced by AI, shifting agility from the team level toward AI-supported enterprise agility.  In practice, however, a different picture often emerges. Agile methods are introduced while decision paths, responsibilities, and leadership culture remain largely unchanged. This disconnect becomes especially visible where agile project logic meets rigid line structures. The promised autonomy ends at hierarchical boundaries designed for stability rather than adaptability. 

Typical symptoms are easy to identify: Agile practices such as sprints and stand-ups are conducted, but key decisions are still made hierarchically Teams work iteratively, but mistakes are penalized instead of treated as learning opportunities Agile roles are defined, but lack real authority 

This form of agile theater remains ineffective and often leads to frustration. True agility means more than new rituals or tools. It requires a shift in mindset across all levels: iterative work, continuous learning, and consistent customer focus must be embedded in day-to-day operations. This is precisely where modern PMO comes into play. It creates the structural prerequisites for agility to deliver real impact.  The difference between a traditional PMO and an agile, evolved PMO is particularly evident in the way governance is designed. While traditional PMOs focus on phase models, milestones, and periodic reporting, Modern PMO operates iteratively and adaptively. Decision-making is more decentralized, roles are clearly defined, and responsibility is deliberately distributed. 

Technology also plays a central role in this transformation. Instead of isolated planning tools, integrated platforms, real-time dashboards, and data-driven decision foundations are used. The focus shifts from pure output to the actual value contribution of projects.  Agile PMO orchestrates hybrid models by shifting the focus from frameworks to value streams as the primary driver of methodology. Organizations move away from agile vs. traditional toward hybrid and fit-for-purpose approaches.  For a long time, the VUCA model served as an appropriate description of uncertain project environments. Today, even this model is reaching its limits. Technological leaps, fragile systems, and a constantly growing flood of information are creating a new quality of uncertainty. The BANI model describes this reality more accurately: 

Brittle: Systems appear stable but are internally fragile. Minor disruptions can lead to sudden breakdowns. Anxious: Continuous uncertainty creates tension, decision pressure, and a sense of loss of control. Non-linear: Developments are discontinuous. Small causes can have major effects, while major initiatives may deliver only limited impact. Incomprehensible: Complexity has reached a level where causal relationships can no longer be fully understood, even with data. 

For the PMO, this means that reporting evolves from static status reports to adaptive dashboards. Decision processes become more flexible, escalation paths clearer, and compliance gates more risk-based. Capacity planning becomes dynamic, priorities are reviewed regularly, and change management focuses on continuous communication and fast feedback loops. 

Modern PMO addresses this reality by establishing principles rather than merely defining processes. Outcomes take precedence over strict process adherence, transparency over control, and learning over perfection. This preserves the organizations ability to act even as predictability decreases.  In many organizations, there is still a gap between ambition and lived reality. Agility is conceptually introduced but not structurally supported. Future-ready PMO closes this gap by creating clear responsibilities, fostering trust, and making change manageable. 

In practice, this means that agility is not only anchored at the team level but also embedded at the departmental and portfolio levels. Key roles such as Product Owners and Agile Leads are empowered, and the conditions are created for agile behaviors to emerge. Agility is therefore not merely a methodology but is firmly embedded in culture, collaboration, and organizational design. 

An example from our practice illustrates this approach: A global IT service provider with more than 10,000 employees initiated a comprehensive agile transformation of its IT organization as part of a group-wide reorganization. Campana & Schott supported a central sub-project that involved migrating a global communication solution from an on-premise environment to the cloud while fundamentally modernizing the user interface, all during ongoing operations. The key challenge was to ensure innovation and stability at the same time. In addition to technological aspects, the focus was on new roles, adapted structures, and the further development of leadership culture. Campana & Schott supported the transformation by building agile teams, providing targeted role coaching for Product Owners, Scrum Masters, and Chapter Leads, and establishing efficient collaboration models. As a result, the organization was able to sustainably strengthen both its innovative capacity and resilience.  Successful agile PMOs are characterized by the following factors in particular: 

Clearly defined roles and responsibilities A culture of trust and transparency Iterative ways of working and continuous learning Active stakeholder engagement Use of modern technologies and data Adaptive governance and flexible resource management Anchoring agile principles across portfolio, departmental, and team levels  In a world that is no longer linear but discontinuous, adaptability becomes the decisive success factor. PMO 4.0 provides the framework to effectively embed agility while still offering orientation. Agility is neither an end in itself nor a short-lived trend. It only delivers impact when structure, mindset, and capability work together. 

Three aspects are key. First, a holistic system is required in which culture, processes, capabilities, and portfolio interact. Second, agile project work without this system remains ineffective, which explains why some organizations revert to traditional methods. Third, when agility is truly lived, it creates tangible value and clearly differentiates organizations from their competitors. 

Organizations that recognize these interdependencies early lay the foundation for long-term sustainability. The PMO thus evolves from an administrative governance function into a strategic hub for modern project landscapes: a PMO for the future.  Agility and a modern PMO only unfold their full impact when structure, mindset, and execution align. We would be happy to discuss how PMO 4.0 can be meaningfully designed and effectively embedded in your organization. 

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Agility at the foundation of modern PMOBeware of agile theaterFrom traditional to future-ready PMOThe new reality: from VUCA to BANIHow agility is effectively embedded in the PMOSuccess factors for agility in PMOConclusion: agility as a strategic success factor

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PMO Trends: Project management redefined.

Revolutionize your project management - with agility, AI and sustainability, your PMO will become a strategic driver of innovation. Imagine being able to complete projects faster, increase stakeholder satisfaction, and reduce costs-thanks to modern methods such as artificial intelligence (AI), agile approaches, and sustainable principles. What sounds like a dream of the future is within reach for many organizations today. But the reality is often different: Many project management offices (PMOs) still operate with complex structures and rigid processes that hinder agile working and no longer meet the needs of the modern workplace.  In recent years, PMOs have established themselves as an indispensable pillar of the enterprise. They optimize processes, set standards, and create transparency - critical success factors for modern organizations.  

The commonly recognized benefits of a successful PMO are  

A well-run PMO ensures efficiency by creating clear structures, processes and standards.It provides transparency by tracking progress, risks and resources.Finally, a PMO acts as a central interface between project teams, managers and other stakeholders, significantly improving communication and collaboration.

Despite these benefits, many PMOs are reaching their limits. Changing market conditions, digital transformation, and a growing focus on sustainability require a fundamental rethink of how companies are organized, as well as how we think about projects and project management. A PMO is no longer just an assistant for projects or an annoying watchdog for standards compliance and template usage. In an ever-changing business world (keyword: VUCA), it is crucial that companies continuously evolve their project management strategies. The PMO plays a central role in this.  

At CS, we think about project management holistically; we do project management with our customers and on the system, rather than from the top and with specifications. With modern PMO, we are rethinking project management. We see a future-proof PM(O) characterized by innovation, agility and sustainability.  

Traditional PMOs are often too rigid and focused on administration, standardization and control. As a result, they lack the flexibility to respond quickly to change. Technological innovations such as artificial intelligence and data-driven decision models often go unused, even though they offer tremendous potential to manage projects more efficiently and accurately. In addition, many PMOs play a purely administrative role and are not actively involved in implementing strategic goals.

Another problem is a lack of focus on people. Aspects such as change management, transparent communication and modern work cultures are neglected, which often leads to resistance and frustration in teams. As a result, the PMO is not perceived as a value-adding partner, but rather as a rigid administrative apparatus that sometimes hinders innovation and change rather than actively driving it.  

To remain fit for the future, companies need to fundamentally rethink the role of their PMOs.  A modern PMO is not an end in itself, but a response to the growing needs of modern organizations. Our strategy for PMOs represents the next level of the Project Management Office. It integrates scaled agile approaches, AI and advanced project management tools to manage projects more efficiently and effectively. These new methods and technologies enable organizations to respond more quickly to market changes and reduce project costs.  Die Erstellung dieser Agents kann ber den Agent Builder erfolgen und zustzlich ber das Microsoft Copilot Studio weiter optimiert werden. Fr anspruchsvollere Anpassungen bietet Visual Studio Code die Mglichkeit, mageschneiderte Plugins zu entwickeln. Dies gewhrleistet den Zugriff auf aktuelle Informationen aus internen Lsungen wie der CS PPM Power Suite und frdert so die intelligente Nutzung unternehmensspezifischer Daten. All diese Funktionen stehen in der Microsoft 365 Copilot App und im BizChat zur Verfgung und tragen erheblich zum Erfolg von Projekten bei. 

The traditional PMO is no longer up to the challenges of the modern workplace. Organizations need a PMO that is agile, strategic and technology-enabled - a PMO ready for the future. It combines innovation, change and sustainability, transforming the PMO from an administrator to a driver of business success.

The journey to the modern PMO 4.0 begins now. With PMO 4.0, organizations will be well-equipped to successfully manage their projects and ensure long-term success.

Stay tuned - the future of project management awaits!  

Table of Contents

PMO Status QuoWhy the classic PMO is no longer enoughTrends shaping the future-ready PMOConclusion and outlook

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From PMO to TMO: The Evolution of Project Management

Traditional PMOs are increasingly reaching their limits: Modern transformation requires more than control and reporting. Why companies now need to take the step toward a TMO and what is crucial for this. In a business world characterized by technological leaps and a high pace of change, the traditional role of the Project Management Office (PMO) is no longer sufficient. A PMO that primarily focuses on control and oversight can only partially meet the growing demands of modern organizations. What is needed is an entity that not only supports projects but actively contributes to the companys strategic direction.

A strategic PMO ensures that initiatives clearly align with corporate goals, resources are used efficiently, and decision-making processes remain transparent. It leverages data for prioritization and governance and fosters a work culture that enables change. This creates the foundation for the next step: the Transformation Management Office (TMO). A TMO goes beyond the traditional PMO function and takes on a central role in shaping and implementing complex transformation programs. It consolidates strategic priorities, coordinates projects within the context of the companys long-term vision, and establishes governance structures that make transformation predictable and measurable.

Experience shows that an effective PMO reduces project management efforts and significantly increases transparency. Industry benchmarks suggest that PMOs can reduce project management workload by 15 to 30% through standardization, governance, and improved resource allocation. This figure is often cited in PMO ROI discussions but varies depending on maturity level and industry. Building on this foundation, a TMO can translate strategic initiatives into impactful results more quickly. With direct connection to executive leadership, clear prioritization, and a holistic view of the portfolio, it becomes the central driver of successful transformations. A TMO focuses on the companys long-term strategy and the implementation of transformation goals. It evaluates projects based on how strongly they contribute to strategic alignment and value creation and directs resources specifically to drive the companys transformation forward.

Portfolio management, on the other hand, looks at the entirety of all projects and programs within the organization. It ensures a balanced approach between risk, cost, and benefit, optimizes the project landscape, and makes sure the right projects are included in the portfolioregardless of whether they are strategically transformative or operationally oriented.While portfolio management concentrates on the breadth and efficiency of the overall project landscape, the TMO focuses specifically on strategic priorities and the successful execution of transformations. Architektur und Technologie bersetzen die Konsolidierungsstrategie in belastbare Entscheidungen. Hier entsteht das Zielbild, an dem sich alle weiteren Schritte ausrichten. Besonders wichtig sind dabei folgende Punkte:  In short:

The PMO is tactical: It ensures consistent project execution.The TMO is strategic: It orchestrates change and ensures that transformations are not only implemented but also sustainably embedded.Practical example: A corporation is undergoing a digital transformation. The TMO manages the entire initiative, ensures strategic alignment, and measures progressboth in cultural change and digital maturity. The PMO supports individual projects within the transformation by applying methodologies, planning resources, and providing structured reporting. Hier kommen KI-Agenten ins Spiel: Sie agieren wie ein erfahrener Kollege, der das Unternehmen nie verlsst, Zugriff auf relevante interne Daten hat und daraus kontextbezogene Empfehlungen ableiten kann. Selbst die Erfassung neuer Informationen kann durch den Agenten untersttzt werden etwa durch das strukturierte Sammeln von Risikoeinschtzungen im Team. Ganz im Sinne des Mottos: Jedes Projektmitglied ist ein Risikomanager.     

In der Praxis gengt heute bereits ein Projektsteckbrief, um den KI-Agenten zu aktivieren. Dieser analysiert vergleichbare Projekte, identifiziert bekannte Risiken, extrapoliert neue ber Bereichsgrenzen hinweg und liefert konkrete Hinweise zu Eintrittswahrscheinlichkeiten, Kostenfolgen und bewhrten Gegenmanahmen. So wird Risikomanagement nicht nur effizienter, sondern auch integrativer Bestandteil des Projektstarts.  The TMO creates the framework in which change initiatives can be consistently managed and ensures that the following principles are embedded in every endeavor:

Strategy Development and ImplementationA TMO has direct access to executive leadership, enabling it to influence strategic decisions and align projects with overarching goals. It responds quickly to changes and develops transformation strategies that prepare the company for future challenges. Through close collaboration with leadership, the TMO ensures that transformation initiatives are successfully implemented and contribute to achieving corporate objectives.Value Creation Focus and Resource EfficiencyA TMO focuses on continuous value creation by prioritizing promising projects and using resources efficiently. It evaluates projects to identify those with the greatest potential for the company and ensures that resources are allocated to strategically important initiatives. Through regular reviews, the TMO optimizes resource allocation and ensures that projects make a sustainable contribution to the corporate strategy. The TMO is more than just an administrative entity. It is a true business enabler that aligns projects with corporate objectives, optimizes resources, and increases efficiency. In a world shaped by rapid change and technological advancements, the TMO must be flexible, data-driven, and people-oriented. Companies that invest in the right capabilities now secure a decisive long-term competitive advantage.

Do you want to unlock the full potential of a TMO for your company? 

Together, we develop tailored solutions that increase efficiency and secure long-term competitive advantages.

Table of Contents

The Transformation Management Office the strategic alignment of the PMODistinction between strategic TMO and portfolio managementHow does a TMO differ from traditional PMO?The key components of a TMO at a glancePrinciples of a TMOPrerequisites for an effective TMOFirst steps on the path to a TMOConclusion

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Between AI, Portals, and Complexity: Why Orientation Is Becoming a Core Task of Internal Communications

A conversation with Martin Weinhardt about the transformation of internal communications, the role of AI and portal ecosystems, and how organizations can successfully master the last mile to their employees. Internal communications is at a turning point. The world of work has become more complex, digital tools and AI increasingly shape everyday work, and hybrid models are now firmly established. At the same time, employees need for orientation is growing. Traditional information formats are reaching their limits. 

In this conversation, Martin Weinhardt, Head of Employee Experience at Campana & Schott, discusses how internal communications is currently evolving and why orientation plays a central role in this transformation.  CS: Martin, when you look back at internal communications over recent years, especially in 2025, what has fundamentally changed and what has become particularly visible? 

Martin Weinhardt: For a long time, internal communications primarily functioned as a broadcast channel. Information was provided, news was published, employees were informed. That understanding has been outdated for quite some time. The working environment has changed significantly over the past years. Hybrid work models, a growing number of digital tools, and new technologies such as AI have made the work environment considerably more complex. For employees, this means more channels and systems, but not necessarily more orientation. 

As a result, the role of internal communications has shifted. Today, the focus is less on producing ever more content and more on providing orientation. Internal communications is increasingly taking on a strategic role. It is responsible not only for content, but also for structures and for contextualizing information within employees day-to-day work. 

This shift is particularly evident in the evolution of the intranet. Many organizations are moving away from purely information-driven or social approaches toward so-called portal ecosystems, where information, applications, and processes are brought together. The goal is greater transparency, consistency, and clarity in everyday work. Put simply, employees today need orientation above all not more content.  CS: One of the drivers of this development is AI. What role did it play in internal communications in 2025? 

Martin Weinhardt: In 2025, AI became a tangible part of internal communications. On the one hand, it supports employees in their daily work. AI is used to find information more quickly, to structure and prepare content, and to enable faster action. It helps increase individual productivity regardless of role or function. 

On the other hand, AI has established itself as an important tool for communicators and business functions alike. It supports the preparation of topics, the derivation of content for different target groups and channels, and the development of headlines and content variants. While AI does not replace communicative responsibility, it significantly increases both speed and quality. 

In addition, a new interaction channel emerged in 2025. Employees increasingly interact directly with AI chatbots or AI agents, for example in HR or IT contexts, or through specialized AI assistants for communication. 

As a result, AI has evolved from a pure tool into a digital assistant in everyday work. Not as a future vision, but as concrete support that is already being actively used today.  In der Praxis zeigt sich jedoch hufig ein anderes Bild. Agile Methoden werden eingefhrt, whrend Entscheidungswege, Verantwortlichkeiten und Fhrungskultur weitgehend unverndert bleiben. Besonders deutlich wird der Bruch dort, wo agile Projektlogiken auf starre Linienstrukturen treffen. Der versprochene Handlungsspielraum endet an hierarchischen Grenzen, die fr Stabilitt ausgelegt sind, nicht fr Dynamik.  

Die Symptome sind oft deutlich erkennbar:  

Agile Methoden wie Sprints und Stand-ups finden statt, aber wichtige Entscheidungen werden weiterhin hierarchisch getroffen  Teams arbeiten iterativ, aber Fehler werden sanktioniert statt als Lerngelegenheit verstanden  Agile Rollen sind definiert, aber ohne echte Befugnisse ausgestattet  

Dieses agile Theater" bleibt wirkungslos und fhrt nicht selten zu Frustration. Echte Agilitt bedeutet mehr als neue Rituale oder Tools. Sie erfordert ein Umdenken auf allen Ebenen: Iteratives Vorgehen, kontinuierliches Lernen und konsequente Kundenzentrierung mssen im Alltag verankert sein. Genau hier setzt das PMO 4.0 an: Es schafft die strukturellen Voraussetzungen, damit Agilitt wirksam werden kann.  CS: When you look at todays digital work environment, what role do intranets and portals play for internal communications and employees daily work? 

Martin Weinhardt: The intranet is currently undergoing a significant transformation. It is evolving from a pure information hub into an active work environment where information, communication, and processes converge. These modern intranet and portal approaches are closely integrated with workplace platforms. They serve as central workspaces where content, applications, and processes are bundled. 

The key difference compared to earlier approaches is that portals no longer merely link to processes, but integrate them directly. By connecting HR, IT, or ERP systems, many everyday tasks can be completed directly within the portal interface, such as time tracking, vacation requests, or access to payroll information. This reduces system switching, saves time, and creates clarity. 

It is important to note that portals do not replace core business systems. Employees who work extensively in ERP or specialized systems will continue to use them. The portal ecosystem complements the digital workplace by simplifying entry points, providing orientation, and bundling processes. 

For internal communications, this represents a consistent evolution. It operates not only in content, but also where employees actually work and take action. This is a key lever for reducing complexity and enabling a better employee experience in everyday digital work.  CS: Wo siehst du aktuell die grten Herausforderungen bei der Gestaltung digitaler Arbeitsumgebungen fr Mitarbeitende? 

Martin Weinhardt: Eine der grten Herausforderungen liegt in der Vielfalt der Arbeitssituationen. Mitarbeitende arbeiten unter sehr unterschiedlichen Bedingungen, etwa am Desktop, mobil, im Auendienst, in Produktionsumgebungen oder im Retail. Diese Vielfalt lsst sich nicht mit einer einzigen Standardoberflche abbilden. 

Viele digitale Arbeitsumgebungen sind historisch auf klassische Office-Arbeitspltze ausgerichtet. Informationen, Prozesse und Anwendungen sind zwar vorhanden, erreichen aber nicht alle Zielgruppen gleichermaen. Genau hier zeigt sich, was hufig als die letzte Meile bezeichnet wird. Entscheidend ist nicht, ob Systeme, Portale oder KI existieren, sondern ob sie die Mitarbeitenden im Arbeitsalltag tatschlich erreichen und untersttzen. Wenn Zugnge nicht zur jeweiligen Arbeitssituation passen, entsteht Orientierungslosigkeit.  

Am Ende zeigt sich an dieser letzten Meile, ob digitale Arbeitsumgebungen Orientierung schaffen oder zustzliche Komplexitt erzeugen. Sie ist damit ein zentraler Hebel fr eine funktionierende Employee Experience.  CS: If we consider AI, portals, and integrated processes together, how do you see central work interfaces for employees evolving? 

Martin Weinhardt: We currently see many developments converging. Portals bundle information and processes, AI supports searching, contextualizing, and acting. At the same time, the complexity of digital work environments continues to increase. This raises the question of where these interactions will be consolidated in the future. 

We observe that more and more organizations are moving toward a shared work interface a central entry point through which employees organize their daily tasks and receive support. This is not about introducing a new tool, but about creating an overarching user interface that provides orientation and brings different functions together. 

In this context, we refer to what we call an Employee UI. This is a place where information, communication, processes, and AI support converge. What this Employee UI will look like in detail is still open. What is clear, however, is that employees need a central place where they can organize their work, contextualize information, and receive support and thereby reduce the need to switch between multiple interfaces. 

It is crucial that this work interface functions for all employees, regardless of where they work. The Employee UI thus becomes an important building block for creating orientation and reaching different target groups.   CS: Looking ahead to the coming years, what are the key challenges facing internal communications? 

Martin Weinhardt: In many organizations, several developments are currently converging. The digital work environment has become significantly more complex, while employees expectations for orientation, clarity, and a shared entry point into their working day continue to rise. 

Internal communications therefore faces the core task of providing orientation in an environment shaped by hybrid work models, diverse target groups, and a growing number of digital tools. The focus is less on individual channels or tools and more on orchestrating the overall user experience. 

At the same time, the shift toward dialogue-oriented communication continues. Employees no longer want to be merely informed, but actively involved. Personalization is becoming increasingly important, supported by AI. Hybrid and mobile formats are well established, while topics such as purpose, values, and emotional connection are gaining relevance. 

Internal communications is thus evolving further into a strategic driver of culture, change, and engagement within organizations. Its success will increasingly be measured by whether it provides orientation, reduces complexity, and helps employees navigate their digital work environment with confidence.  This conversation shows: Today, internal communications must above all provide orientation in a working world that has become more complex and continues to evolve.  Do you have questions about internal communications, employee experience, or portal solutions? 

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AI Agents in the Enterprise: A Central Data Platform as a Prerequisite for AI Success

AI agents require a stable foundation: the centralization of data sources as well as governance across the data estate are key success factors for AI transformation in the enterprise. AI agents have the potential to transform both organizational structures and operational processes. They enable companies to increase quality and speed to a level that cannot be achieved with traditional ways of working. 

According to an IBM study, 93 percent of surveyed German executives believe that agent-based AI will deliver measurable returns within the next two years. This expectation highlights the strategic potential companies see in agent-based AI solutions. At the same time, practical experience shows that many AI agents remain isolated pilot projects or are limited to individual use cases. The reason is rarely the AI technology itself, but rather the question of which data basis these agents operate on and how they are integrated into existing processes. 

In principle, two layers can be distinguished: the action or process layer (Action Layer) and the data context layer (Data Layer). This article focuses on the latter.  AI agents place different demands on the use of data than traditional analytics or reporting solutions. While reporting systems are typically based on clearly defined questions, data models, and KPIs, AI agents operate in a task- and context-driven way. 

Although they also access structured data sources, they must decide depending on the situation which information is relevant and how it should be connected and interpreted. 

To do so, AI agents need not only accurate data, but also a consistent, up-to-date, and domain-specific data context. The more heterogeneous the data landscape and the more information is distributed across different systems, the more important it becomes to establish a shared data foundation that agents can access in a controlled and traceable manner. 

This makes one thing clear: AI agents are not an isolated AI topic. Whether they can be deployed effectively depends on the quality of the underlying data foundation and on how data is organized, maintained, and used within the enterprise.  Modern data platforms create the essential prerequisites for ensuring that AI agents not only work in individual scenarios, but can be sustainably integrated into processes and scaled beyond single business units. 

A central data platform consolidates data from multiple sources and establishes core security capabilities that support the definition and enforcement of fine-grained access rights. Responsibilities and governance rules should be defined from the outset and closely embedded into the platform. For AI agents, this means they can access curated data, operate within defined rules, and evolve in a controlled way. 

Modern data platforms based on data lake approaches can also serve as the link between structured and unstructured data. Especially with unstructured data, an additional transformation step is often required before it can be effectively used.  In practice, another central challenge quickly becomes apparent: AI agents rarely work exclusively with data from a single system environment. Business-critical information is often stored in third-party systems such as ERP, ITSM, or specialized business applications. Selective interfaces or individual integrations reach their limits quickly. They are costly to maintain, difficult to scale, and increase the complexity of governance and security. 

A central data platform therefore plays an intermediary role: it brings together data from different source systems, harmonizes it, and provides it in a unified context. 

On this basis, AI agents can act across systems in a scalable, traceable, and secure way and become usable enterprise-wide.  Modern data platforms such as Microsoft Fabric address exactly this need. They bring data and analytics workloads together in a shared environment and create a unified data foundation through central data storage such as OneLake. At the same time, they integrate governance, security, and identity mechanisms that are indispensable for the productive deployment of AI agents.   Zwischen Anspruch und gelebter Realitt klafft in vielen Organisationen noch eine Lcke. Agilitt ist konzeptionell eingefhrt, wird aber strukturell nicht untersttzt. Das PMO 4.0 schliesst diese Lcke, indem es klare Verantwortlichkeiten schafft, Vertrauen frdert und Vernderung handhabbar macht. 

In der Praxis bedeutet das: Agilitt wird nicht nur auf Team-Ebene verankert, sondern auch auf Bereichs- und Portfolioebene etabliert. Zentrale Rollen wie Product Owner und Agile Leads werden befhigt, und es werden Voraussetzungen geschaffen, damit sich agile Verhaltensweisen entfalten knnen. Dabei ist Agilitt nicht nur eine Methodik, sondern fest in Kultur, Zusammenarbeit und Organisation verankert. 

Ein Beispiel aus unserer Praxis: Ein globaler IT-Dienstleister mit ber 10.000 Mitarbeitenden hat im Rahmen einer konzernweiten Reorganisation eine umfassende agile Transformation seiner IT-Organisation initiiert. Campana & Schott begleitete dabei ein zentrales Teilprojekt, das die Migration einer globalen Kommunikationslsung von einer On-Premise-Umgebung in die Cloud sowie die grundlegende Erneuerung der Benutzeroberflche umfasste, und das bei laufendem Betrieb. Die zentrale Herausforderung bestand darin, Innovation und Stabilitt gleichzeitig sicherzustellen. Neben technologischen Fragestellungen standen insbesondere neue Rollen, angepasste Strukturen und die Weiterentwicklung der Fhrungskultur im Fokus. Campana & Schott untersttzte den Transformationsprozess durch den Aufbau agiler Teams, gezieltes Rollencoaching fr Product Owner, Scrum Master und Chapter Leads sowie durch die Etablierung effizienter Zusammenarbeitsmodelle. Auf diese Weise konnte die Organisation ihre Innovationskraft und Resilienz nachhaltig strken.  The decisive factor is not an individual feature, but the interaction of all components. When data integration, analytics, governance, and AI capabilities converge on one platform, a consistent context emerges. AI agents can access this data without requiring new interfaces, special solutions, or separate security concepts for every scenario.  With the deployment of AI agents, not only technical architectures change, but also organizational questions. 

Who is accountable for the outcomes produced by an agent? Which decisions may it prepare or automate? And how can traceability of results be ensured? 

These questions cannot be answered through technology alone. They require clear responsibilities, aligned processes, and a shared understanding of how data and AI should be applied in the enterprise. Governance thus evolves from an abstract rule set into an operational steering instrument. 

Companies that want to successfully deploy AI agents should therefore think about platform, organization, and data culture together.  Without a centrally orchestrated data platform, agents risk remaining isolated and difficult to scale. When data is consistent, contextualized, and securely available, AI agents can unlock their full potential. Data platforms therefore provide the foundation on which agents can be meaningfully built. 

Companies that recognize this connection early do not only create the basis for deploying AI agents. They strengthen their overall data and AI strategy and lay the groundwork for the sustainable evolution of data-driven use cases. 

Against this backdrop, Campana & Schott supports enterprises in shaping this path in a structured way from developing a robust data and AI strategy to building central data platforms and anchoring adoption within the organization. 

The goal is to treat AI applications not as isolated experiments, but to integrate them sustainably into processes and ways of working.  Are you planning to deploy AI agents or strategically evolve your data platform?

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Fast AI, Slow IT? Why Companies Urgently Need a Shared Target Vision

In many organizations, business units are already driving AI initiatives forward, while IT is still working on the foundational prerequisites. How can this tension be resolved and a secure, shared path be established? Artificial intelligence has arrived in companies. A recent McKinsey study shows that nearly all organizations are already using AI. At the same time, most are still in the experimentation or pilot phase. Almost two thirds have not yet started scaling AI across the enterprise. 

This is exactly where a central tension emerges. While business units increasingly want to leverage AI to drive efficiency gains, reduce costs, and unlock new business opportunities, IT is facing a significantly more complex reality. Promises of plug-and-play solutions, automation, and rapid productivity improvements increase the pressure to implement use cases quickly, even when essential prerequisites are still missing. 

How is IT supposed to introduce AI securely when business units are already implementing their own solutions? Requirements emerge in a decentralized way, budgets are used independently, and initial tools or applications are introduced without overarching alignment. Without shared strategic guidelines and a clear target vision, this leads to shadow solutions, isolated applications, and new data silos. Operational effort increases, costs rise, and IT can no longer ensure security or scalability in a holistic way. 

At the same time, it is undisputed within organizations that AI can only deliver sustainable impact if infrastructure, data foundations, governance, and the organization itself are prepared accordingly. This is where the gap between expectations and technical feasibility becomes particularly evident and IT is the first to feel it.  From our experience supporting AI and transformation initiatives, a similar pattern emerges again and again. When business and IT operate at different speeds and key prerequisites are not clarified early on, even well-intentioned AI efforts lose impact. Instead of scaling, isolated solutions emerge, complexity increases, and frustration grows on both sides. 

In many organizations, the root cause runs deeper. IT often works with limited resources, complex approval processes, and in some cases missing technical foundations to reliably meet AI requirements. At the same time, existing operating models, roles, and responsibilities are being questioned as AI is introduced. Established structures are usually not designed to enable innovation quickly or to continuously evolve. This creates a clear need to review IT operating models in a targeted way and further develop them so that they can enable innovation and the productive use of AI in the first place. 

While business units are already bringing in concrete use cases, IT faces the task of rethinking architecture, data flows, application and data security, and the integration of AI into existing systems and processes all at once. AI thus becomes a litmus test for IT maturity and for its ability to evolve its own way of working and strengthen its role as a strategic partner to the business.  To close the gap between business expectations and technical feasibility, companies first need transparency about their actual level of AI maturity. From our experience, one thing becomes clear again and again: Only when IT and business share a common, realistic view of the status quo can discussions about priorities, investments, and operating models be conducted constructively. 

The AI Readiness Check provides a structured entry point. It makes visible where a company truly stands today not only technologically, but also organizationally. The assessment shows which structures, processes, and roles support or slow down AI initiatives, where decision paths are unclear, where governance is missing, and which capabilities still need to be built within the organization. On this basis, next steps can be derived in a targeted way. The result is a prioritized roadmap that fits the organizations actual maturity level and outlines realistic development paths. 

This roadmap also forms the foundation for a shared vision and a sustainable AI strategy. IT and business develop it together, with a clear focus on functional objectives, organizational feasibility, and technical conditions. The strategy must not be created in isolation. It needs to be understood, supported, and integrated into the existing organization by the people within the company.  The productive use of AI requires an evolution of existing IT operating models and ways of working together. Traditional role distributions, linear handoffs, and clearly separated responsibilities reach their limits when AI applications need to be continuously developed and operated. What is needed are organizational models in which business units, AI experts, and IT jointly take responsibility for use cases and collaborate closely in interdisciplinary teams. 

At the center is the question of how responsibility, governance, and ongoing operations are concretely organized with clear ownership, short decision-making paths, and tight alignment between business requirements and technical implementation. IT plays a shaping role by considering architecture principles, security requirements, and stable operations early on and integrating them into the collaboration. A modern operating model creates transparency around responsibilities, facilitates collaboration across organizational boundaries, and ensures that innovation does not fail due to organizational friction. 

On this organizational foundation, the technical prerequisites can then be built effectively. This includes a resilient data architecture, clear data flows, and defined interfaces, as well as consistent identity and access management concepts. Questions of scalability, performance, model integration, versioning, and the secure operation of AI applications must also be addressed early. Without an appropriate platform architecture, aligned governance, and clearly defined operational processes, AI often remains limited to isolated pilot projects. Only an integrated technical foundation enables stable, secure, and scalable productive deployment.  Once companies have developed a shared vision, clear technical foundations, and an aligned operating model, the role of IT in execution can be defined more deliberately. In practice, three models have become established. They differ primarily in how strongly IT co-owns the development of use cases, how much governance is defined centrally, and how closely implementation happens in joint teams. The chosen model should be defined consciously and firmly anchored within the organization.  For AI to deliver reliable impact within an organization, IT and business need a shared starting point. Once the actual maturity level of data, infrastructure, processes, and organizational setup is understood, use cases can be prioritized meaningfully and implemented securely. Transparency about the status quo is therefore the critical first step to ensure that AI is not approached in isolation, but anchored in a structured way across the enterprise. 

The AI Readiness Check creates clarity on technical and organizational prerequisites, highlights concrete areas for action, and provides the foundation for a roadmap aligned with the organizations maturity level.  Organizations that first want to assess the maturity, levers, and potential of their IT can additionally use the IT Quick Check to evaluate whether their IT organization, operating models, and technological foundations are ready to support AI in a scalable way over the long term.  Campana & Schott supports organizations in shaping this journey consistently. Based on the AI Readiness Check, we derive a robust roadmap and outline which evolution of the IT organization is necessary to embed AI sustainably. In execution, we support the organizational and technical transformation of IT from new operating and collaboration models to architecture and data flows, all the way to the development and prioritization of viable AI use cases.  Would you like to learn more or do you have a concrete project in mind? 

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How sustainability is redefining the standards of project governance

Sustainability is shifting the benchmarks of project governance. For PMOs, this raises a central question: How does the approach to project management need to change when ecological, social, and regulatory requirements matter just as much as time, budget, and quality? Sustainability is no longer an optional guiding principle. It has become a binding dimension of corporate governance. Regulatory demands, capital market expectations, and societal pressure increasingly require organizations not only to define environmental, social, and economic objectives, but to integrate them effectively into decision-making and control processes. 

This shift is particularly visible in project management. Projects are the key instrument through which strategies are executed, transformations realized, and investments translated into impact. Decisions about resource usage, supply chains, technologies, or timelines have immediate consequences for an organizations sustainability performance. 

Current data underlines this development: In a survey of 10,000 project and portfolio managers, 73 percent stated that sustainability is of high or very high relevance to project delivery. Forty-three percent identified sustainability-related challenges as a decisive influencing factor on their projects. 

As a result, the Project Management Office (PMO) is moving further into focus. As an established authority for governance, transparency, and prioritization, the PMO has always shaped how projects are evaluated and managed. The PMOs role itself is not fundamentally changing, but the reference framework for project steering is.  Project management has traditionally been guided by the so-called iron triangle of time, budget, and quality. This logic has provided reliability for decades, yet it reaches its limits when projects must account not only for efficiency and output quality, but also for long-term effects. 

Sustainability expands the definition of project success. Projects generate ecological, social, and economic impacts that extend far beyond completion. ESG risks, rising energy and resource costs, and reputational considerations can no longer be treated separatelythey directly affect classical control dimensions. 

Conceptually, this perspective shift is reflected in the Triple Bottom Line model, which treats economic, environmental, and social objectives as equally relevant. For project management, this does not replace established principles, but extends them: Success is no longer measured solely by efficient delivery, but by the sustainable value creation that projects enableor prevent.  Sustainability cannot be anchored through isolated projects or individual initiatives alone. Without consistent evaluation standards, organizations face inconsistent priorities, limited comparability, and insufficient transparency around trade-offs and impact. 

Many organizations today have ambitious sustainability goals as well as established PMO structures. What is often missing, however, is a systematic connection between these two levels. Projects continue to be prioritized primarily according to classical efficiency criteria, while sustainability is reported or documented in parallel without tangible influence on steering decisions. 

The key point is this: Sustainability is not decided at a single moment. It unfolds across the entire project lifecycle through consistent organizational embedding. The decisive factor is whether sustainability truly reaches the PMOs governance mechanisms not only being reported, but actively used as a criterion for prioritization and decision-making.  This is where the structural relevance of the PMO becomes clear. As the central governance and steering function, it connects strategic objectives with operational decision-making and ensures consistency across projects and programs. 

Sustainability does not fundamentally change the PMOs role, but it does change the standards by which projects are evaluated and prioritized. A future-ready PMO is less a new organizational model and more a critical question: 

Do todays steering mechanisms still meet the requirements sustainability places on prioritization, evaluation, and decision-making? 

A sustainability-oriented PMO systematically expands its governance logic across four levels:  1. Portfolio and strategy level: Sustainability becomes part of project objectives, business cases, and prioritization decisions. Projects are no longer assessed solely by short-term financial returns, but also by their contribution to long-term value creation and risk reduction.  Praxisimpuls: In einem Kundenprojekt fhrte Campana & Schott einen Projektportfolio-Prozess ein. Durch das PMO wurde die Strategie operationalisiert und ein klar definiertes Priorisierungsvorgehen entwickelt, das den Beitrag der Projekte auf die Nachhaltigkeitsziele des Unternehmens bercksichtigt. Dieses Vorgehen schafft Transparenz, strkt die strategische Ausrichtung und frdert eine nachhaltige Projektumsetzung.  2. Governance and process level: Project management standards and methods are enhanced with sustainability aspects, such as resource-efficient planning, sustainable procurement, or social impact considerations. The goal is not additional bureaucracy, but a consistent basis for decision-making. 3. Performance and steering level: In addition to classical KPIs, sustainability indicators are established to make ecological and social effects visible. These extended benchmarks enable more differentiated portfolio steering and greater transparency around trade-offs and risks.  4. Cultural level: The PMO helps embed sustainability as an integral part of professional project work. It promotes shared understanding, strengthens competencies, and supports project teams in making responsible decisions.  Established frameworks such as the P5 Standard for Sustainability in Project Management provide orientation for integrating sustainability into project management. 

Its role must be clearly understood: P5 is not a project control tool, but a conceptual framework. It considers sustainability across five dimensions: People, Planet, Prosperity, Process, and Product. It supports project leaders in systematically analyzing ecological, social, and economic impacts and integrating them into project governance. 

Aligned with the United Nations Sustainable Development Goals (UN SDGs), the standard offers not only theoretical principles but also practical instruments for improving sustainability performancethereby contributing to ESG reporting and strategic corporate development. 

However, such a framework only becomes effective when embedded into PMO governance structures and steering mechanisms. Sustainability does not emerge through the application of a model, but through consistent decisions based on clearly defined criteria.  Integrating sustainability into project governance is not a normative add-on. It increases transparency around risks, strengthens resilience against regulatory and market shifts, and supports strategically sound prioritization of initiatives. 

At the same time, sustainability acts as an innovation driver. It opens new solution spaces and strengthens trust among investors, customers, and employees.  A PMO that takes sustainability seriously as a governance dimension contributes directly to the long-term stability and competitiveness of organizations.  Sustainability requirements are changing the benchmarks of PMO action. Time, budget, and quality remain essential control dimensionsbut they are no longer sufficient on their own to assess projects strategically. 

The PMO of the future represents an expanded governance logic. For organizations, this leads to a concrete question: 

To what extent is your PMO today able not only to document sustainability, but to use it as a criterion for effective steering? 

The answer determines whether sustainability becomes part of operational realityor remains confined to target visions, reports, and isolated initiatives.  Would you like to embed sustainability systematically into your project governance? Learn more about our services: 

Project and Transformation Management | Campana & Schott

Sustainability Services | Campana & Schott

If you have questions or would like to discuss specific initiatives, 

feel free to reach out.  Table of Contents

The limits of traditional success metrics in project managementSustainability as a governance and scaling challengeModern PMO: extending an established governance logicStandards as Guidance, not a Substitute for Governance: The P5 FrameworkThe P5 Standard. Sustainability in Project Management.Conclusion: the key test for future-ready PMO

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