Do we need a European DARPA to cope with technological challenges in Europe?

The US Defense Advanced Research Projects Agency (DARPA) is often held as a model for driving technology advances. For decades, it has contributed to military and economic dominance by bridging the gap between military and civilian applications. European policymakers frequently reference DARPA in discussions, as outlined in the 2024 Draghi Report, but an EU equivalent has yet to materialise. To create such an agency, the governance and management of European innovation programmes would need drastic changes.

DARPA supports disruptive innovation

Founded in 1958, DARPA operates under the US Department of Defense (DoD) with a straightforward mission: to fund high-risk technological programmes that could lead to radical innovation. DARPA provides support throughout the innovation process, focusing on environments where new uses for technology must be invented or adapted. Although part of the DoD, DARPA funds projects that promise technological and economic superiority whether they align with current military priorities or not. DARPA has backed projects like ARPANET, the precursor to the internet, and the GPS. Today, DARPA shows interest in autonomous vehicles for urban areas and new missile technologies.

As part of its core mission, DARPA accepts high financial risks on exploration projects and makes long-term commitments to these projects. Many emblematic successes explain why DARPA is a reference agency. However, the list of failed projects is even longer. Both failures and successes feed the exploration process in emerging industrial sectors. They represent opportunities to learn together and build collective strategies in innovation ecosystems.

Five key principles of DARPA

DARPA’s success stems not just from its stability but from adhering to five organisational principles that allow it to explore deep tech in an open innovation context:

• Independence: DARPA operates independently from other military services, research & development centres and federal agencies, allowing it to explore options outside dominant research paradigms. While cooperation is possible, its decisions and directions are not influenced by other parts of the federal administration.

• Agility: The agency’s flat organisational structure minimises bureaucracy. Its independent decision-making processes and streamlined contracting allow it to pivot quickly, test new concepts and collaborate with academic or private sector partners. Agility also enables DARPA to test new exploration or experimentation methods that are often based on user-centric approaches. Potential military or civilian end-users are involved very early in innovation projects to discuss potential uses and applications. This approach has recently led DARPA to absorb the Strategic Capabilities Office (SCO), where officers from the different military services (Army, Air Force, Navy and Marines) and all military ranks test new technological solutions (from different maturity levels), fostering co-creation processes with military innovators and expanding the agency’s impact.

• Sponsorship: High-ranking executives within the DoD and other federal administrations (NASA, Department of Energy) endorse, but do not commission, DARPA’s projects. This sponsorship model increases a project’s potential impact and allows for swift adaptation if a project fails.

• Community building: DARPA creates innovation communities with a mix of diverse expertise. By bringing different perspectives together, it fosters collective strategies essential for disruptive innovation.

• Diverse leadership: Project managers come from a range of backgrounds, including civilian experts, military officers and private-sector professionals. All have demonstrated scientific and technological expertise and a solid capability to bridge dreams and foresight with reality. All have a perfect command of risk and complexity management. Managers serve three- to four-year terms focused on driving technological disruption and building new innovation ecosystems. Their diverse expertise sets DARPA apart from other federal agencies.

The challenge of a European DARPA

The Draghi Report on European competitiveness suggests that a European DARPA could help bridge technological gaps, reduce dependencies and accelerate the green transition. However, implementing this model would require a seismic shift in how European agencies operate. Creating a new agency would be ineffective without ensuring that all principles underlying the success of DARPA are implemented in Europe.

Even if Europe actively promotes deep tech and devotes significant budgets to it, European public policies and ways of working prevailing in national and European agencies are hardly consistent with the DARPA model. European agencies do not have much autonomy in their decisions about the exploration of new ventures or human resource management. They clearly demonstrate an outcome-focused orientation inconsistent with DARPA’s approach to risk.

Two main challenges

European agencies often lack the stable missions, scope and ambition seen at DARPA. The European Space Agency (ESA), the European Defence Agency (EDA) and Eurocontrol highlight the difficulties in developing cohesive, cross-border innovation ecosystems. A European DARPA would require a unified ambition among EU member states, a challenging feat given the institutional and geopolitical divides within Europe. The debates around the European Defence Fund illustrate how complex it is to reach consensus on shared objectives and funding.

Adopting DARPA’s five organisational principles would represent a cultural revolution for European agencies in relation to EU bureaucratic norms and the budgetary controls of individual member states. Implementing these changes would also disrupt the existing power balance between countries. The DARPA model is inconsistent with the European “fair returns” model that refers to proportionality rules between funding, research operations and then industrial repartition during the production phase between member states in each project. The DARPA model would only focus on existing competencies, excellence, risk-taking approaches and entrepreneurial mindsets.

Establishing a European DARPA would require a fundamental rethinking of public policy management in Europe. Its success would depend on whether European stakeholders are willing to adopt DARPA’s core principles, including its independence, agility and willingness to accept failure. Creating an agency is one thing; ensuring it adheres to the structures that make DARPA effective is another. The question remains: Is Europe ready for this transformation?

The European Academy of Management (EURAM) is a learned society founded in 2001. With over 2,000 members from 60 countries in Europe and beyond, EURAM aims at advancing the academic discipline of management in Europe.

David W. Versailles has received funding from the French Ministry of Defence to develop this research.

Valérie Mérindol has received funding from the French Ministry of the Armed Forces to develop this research.

Do we need a European DARPA to cope with technological challenges in Europe?

The US Defense Advanced Research Projects Agency (DARPA) is often held as a model for driving technology advances. For decades, it has contributed to military and economic dominance by bridging the gap between military and civilian applications. European policymakers frequently reference DARPA in discussions, as outlined in the 2024 Draghi Report, but an EU equivalent has yet to materialise. To create such an agency, the governance and management of European innovation programmes would need drastic changes.

DARPA supports disruptive innovation

Founded in 1958, DARPA operates under the US Department of Defense (DoD) with a straightforward mission: to fund high-risk technological programmes that could lead to radical innovation. DARPA provides support throughout the innovation process, focusing on environments where new uses for technology must be invented or adapted. Although part of the DoD, DARPA funds projects that promise technological and economic superiority whether they align with current military priorities or not. DARPA has backed projects like ARPANET, the precursor to the internet, and the GPS. Today, DARPA shows interest in autonomous vehicles for urban areas and new missile technologies.

As part of its core mission, DARPA accepts high financial risks on exploration projects and makes long-term commitments to these projects. Many emblematic successes explain why DARPA is a reference agency. However, the list of failed projects is even longer. Both failures and successes feed the exploration process in emerging industrial sectors. They represent opportunities to learn together and build collective strategies in innovation ecosystems.

Five key principles of DARPA

DARPA’s success stems not just from its stability but from adhering to five organisational principles that allow it to explore deep tech in an open innovation context:

• Independence: DARPA operates independently from other military services, research & development centres and federal agencies, allowing it to explore options outside dominant research paradigms. While cooperation is possible, its decisions and directions are not influenced by other parts of the federal administration.

• Agility: The agency’s flat organisational structure minimises bureaucracy. Its independent decision-making processes and streamlined contracting allow it to pivot quickly, test new concepts and collaborate with academic or private sector partners. Agility also enables DARPA to test new exploration or experimentation methods that are often based on user-centric approaches. Potential military or civilian end-users are involved very early in innovation projects to discuss potential uses and applications. This approach has recently led DARPA to absorb the Strategic Capabilities Office (SCO), where officers from the different military services (Army, Air Force, Navy and Marines) and all military ranks test new technological solutions (from different maturity levels), fostering co-creation processes with military innovators and expanding the agency’s impact.

• Sponsorship: High-ranking executives within the DoD and other federal administrations (NASA, Department of Energy) endorse, but do not commission, DARPA’s projects. This sponsorship model increases a project’s potential impact and allows for swift adaptation if a project fails.

• Community building: DARPA creates innovation communities with a mix of diverse expertise. By bringing different perspectives together, it fosters collective strategies essential for disruptive innovation.

• Diverse leadership: Project managers come from a range of backgrounds, including civilian experts, military officers and private-sector professionals. All have demonstrated scientific and technological expertise and a solid capability to bridge dreams and foresight with reality. All have a perfect command of risk and complexity management. Managers serve three- to four-year terms focused on driving technological disruption and building new innovation ecosystems. Their diverse expertise sets DARPA apart from other federal agencies.

The challenge of a European DARPA

The Draghi Report on European competitiveness suggests that a European DARPA could help bridge technological gaps, reduce dependencies and accelerate the green transition. However, implementing this model would require a seismic shift in how European agencies operate. Creating a new agency would be ineffective without ensuring that all principles underlying the success of DARPA are implemented in Europe.

Even if Europe actively promotes deep tech and devotes significant budgets to it, European public policies and ways of working prevailing in national and European agencies are hardly consistent with the DARPA model. European agencies do not have much autonomy in their decisions about the exploration of new ventures or human resource management. They clearly demonstrate an outcome-focused orientation inconsistent with DARPA’s approach to risk.

Two main challenges

European agencies often lack the stable missions, scope and ambition seen at DARPA. The European Space Agency (ESA), the European Defence Agency (EDA) and Eurocontrol highlight the difficulties in developing cohesive, cross-border innovation ecosystems. A European DARPA would require a unified ambition among EU member states, a challenging feat given the institutional and geopolitical divides within Europe. The debates around the European Defence Fund illustrate how complex it is to reach consensus on shared objectives and funding.

Adopting DARPA’s five organisational principles would represent a cultural revolution for European agencies in relation to EU bureaucratic norms and the budgetary controls of individual member states. Implementing these changes would also disrupt the existing power balance between countries. The DARPA model is inconsistent with the European “fair returns” model that refers to proportionality rules between funding, research operations and then industrial repartition during the production phase between member states in each project. The DARPA model would only focus on existing competencies, excellence, risk-taking approaches and entrepreneurial mindsets.

Establishing a European DARPA would require a fundamental rethinking of public policy management in Europe. Its success would depend on whether European stakeholders are willing to adopt DARPA’s core principles, including its independence, agility and willingness to accept failure. Creating an agency is one thing; ensuring it adheres to the structures that make DARPA effective is another. The question remains: Is Europe ready for this transformation?

The European Academy of Management (EURAM) is a learned society founded in 2001. With over 2,000 members from 60 countries in Europe and beyond, EURAM aims at advancing the academic discipline of management in Europe.

David W. Versailles has received funding from the French Ministry of Defence to develop this research.

Valérie Mérindol has received funding from the French Ministry of the Armed Forces to develop this research.

Generative AI can boost innovation – but only when humans are in control

Generative artificial intelligence (AI) tools like ChatGPT or Dall-E are changing how creative work is done, particularly in industries that rely on innovation.

However, AI use in the innovation process requires careful considerations. Our research shows that the key to success is understanding and leveraging the distinct but complementary roles that both humans and AI play.

Innovation is vital for any business that wants to succeed today. In fact, 83 per cent of companies see innovation as a top priority, yet only three per cent are ready to turn this priority into action. This shows how much companies need to improve their approach to innovation.

Innovation is about solving complex problems that result in real improvement. It’s not just about coming up with good ideas — it also involves knowledge work, which is the process of using information to create something valuable.

Generative AI can help businesses get ready to innovate by making knowledge work easier, but its full potential in this area is still not fully understood.

Design sprints

Our team, which includes academic researchers with expertise in emerging digital technologies and a practitioner experienced in leading human-centred innovation projects, conducted a detailed study of how generative AI was used in design sprints at three organizations. (The study is available as a pre-print and has been submitted to a journal for peer review).

A design sprint is a fast, structured process for solving important problems that helps teams test if a product, service or strategy will work. Sprints are useful because they reduce the risks and costs of traditional product development

During a design sprint, a small team of five to seven employees from different areas works together intensely for a few days to solve a problem. Their work is co-ordinated by a facilitator, who organizes activities, guides the team, keeps track of progress, makes sure the goals are clear and that time is used efficiently.

The first stage of a design sprint focuses on understanding and defining the problem, while the second stage is about creating and testing a solution. Both stages require teams to use two key types of thinking:

1. Divergent thinking, which means coming up with many different ideas and possibilities.

2. Convergent thinking, which means narrowing those ideas down to identify priorities or solutions.

Our study examined how the facilitator used generative AI tools like ChatGPT, DALL-E 3 or Uizard to help the team engage effectively in both divergence and convergence.

AI and humans working together

In divergent thinking activities, we found two main benefits of using generative AI. First, it encouraged teams to explore more possibilities by providing baseline ideas as a starting point. Second, it helped to rephrase and synthesize unclear ideas from team members, ultimately leading to better communication within the teams.

One participant told us:

“Sometimes we had a lot of ideas, and the AI summarized them into a concise text. This allowed us to wrap our head around it. It gave us a base, there were many fragmented ideas that everyone had contributed, and now we had a text we all agreed on. This way, we started from the same base which served as a springboard to move forward.”

The real value of generative AI was thus not in contributing brilliant new ideas itself, but in the valuable synergies that emerged from the process. Team members used their contextual knowledge and stayed in charge of the process while the AI helped to better convey their ideas, expand exploration and address possible blind spots.

Making better informed decisions

We noticed different dynamics in convergence activities where teams had to make decisions after demanding sessions of idea generation. By that point, team members were usually mentally exhausted. Generative AI was especially helpful for doing the heavy lifting during this part.

The AI helped manage the information-intensive tasks necessary for team alignment like reformulating, summarizing, organizing, comparing and ranking options. This reduced the mental strain on team members, allowing them to focus on important tasks like evaluating ideas. In this process, the team was responsible for:

1. Checking AI’s outputs to make sure the content was accurate and useful. For example, ChatGPT and Uizard helped create draft scenarios and prototype drafts to validate their concept, but the team still had to refine them to meet project goals.
2. Adding their own insights and contextual nuances to guide final decisions, considering factors like feasibility, ethics and long-term strategic impact.

One participant said:

“Sometimes, the AI would focus onto details that were insignificant to us…Sometimes we needed less general synthesis and more personalized input.”

Overall, this form of human-AI collaboration in convergent activities helped the team make better informed and more confident decisions about which problem to focus on and which solution to pursue. This made them feel in control of the sprint’s final outcomes.

One participant said:

“For pivotal phases like making decisions or voting on something important like a success factor, if we relied solely on AI to determine what is important, there would be rejection. We are better positioned to know. We are the employees who will execute the final solution.”

Challenges and opportunities

Consistent with research on cognitive automation and intelligent automation, we found that generative AI was of great help in handling cognitively demanding tasks like reformulating poorly articulated ideas, summarizing information and recognizing patterns in team members’ contributions.

A key challenge with using Generative AI in innovation is ensuring it complements, rather than replaces, human involvement. While AI can act as a useful companion, there’s a risk it could reduce team engagement or ownership of the project if overused.

The design sprint facilitator told us:

“Feasibility needs to be balanced with desirability. You could technically automate most of the process but that would kill the need for pleasure, interaction, and humans’ doubts won’t be addressed; plus humans need to own the problem — all these are essential elements in a human-centred innovation process.”

Consequently, regularly assessing AI’s impact within this process is crucial in order to maintain a healthy balance. Automation should enhance creativity and decision-making without undermining the human insights that are central to innovation.

As AI continues to develop, its role in innovation will grow. Companies that integrate AI into their workflows will be better equipped to handle the fast-paced demands of modern innovation. But it’s important to understand both the strengths and limits of AI and humans to ensure this collaboration is effective.

This article was co-authored by Cédric Martineau, CEO and innovation management consultant at Carverinno Consulting.

The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

Generative AI can boost innovation – but only when humans are in control

Generative artificial intelligence (AI) tools like ChatGPT or Dall-E are changing how creative work is done, particularly in industries that rely on innovation.

However, AI use in the innovation process requires careful considerations. Our research shows that the key to success is understanding and leveraging the distinct but complementary roles that both humans and AI play.

Innovation is vital for any business that wants to succeed today. In fact, 83 per cent of companies see innovation as a top priority, yet only three per cent are ready to turn this priority into action. This shows how much companies need to improve their approach to innovation.

Innovation is about solving complex problems that result in real improvement. It’s not just about coming up with good ideas — it also involves knowledge work, which is the process of using information to create something valuable.

Generative AI can help businesses get ready to innovate by making knowledge work easier, but its full potential in this area is still not fully understood.

Design sprints

Our team, which includes academic researchers with expertise in emerging digital technologies and a practitioner experienced in leading human-centred innovation projects, conducted a detailed study of how generative AI was used in design sprints at three organizations. (The study is available as a pre-print and has been submitted to a journal for peer review).

A design sprint is a fast, structured process for solving important problems that helps teams test if a product, service or strategy will work. Sprints are useful because they reduce the risks and costs of traditional product development

During a design sprint, a small team of five to seven employees from different areas works together intensely for a few days to solve a problem. Their work is co-ordinated by a facilitator, who organizes activities, guides the team, keeps track of progress, makes sure the goals are clear and that time is used efficiently.

The first stage of a design sprint focuses on understanding and defining the problem, while the second stage is about creating and testing a solution. Both stages require teams to use two key types of thinking:

1. Divergent thinking, which means coming up with many different ideas and possibilities.

2. Convergent thinking, which means narrowing those ideas down to identify priorities or solutions.

Our study examined how the facilitator used generative AI tools like ChatGPT, DALL-E 3 or Uizard to help the team engage effectively in both divergence and convergence.

AI and humans working together

In divergent thinking activities, we found two main benefits of using generative AI. First, it encouraged teams to explore more possibilities by providing baseline ideas as a starting point. Second, it helped to rephrase and synthesize unclear ideas from team members, ultimately leading to better communication within the teams.

One participant told us:

“Sometimes we had a lot of ideas, and the AI summarized them into a concise text. This allowed us to wrap our head around it. It gave us a base, there were many fragmented ideas that everyone had contributed, and now we had a text we all agreed on. This way, we started from the same base which served as a springboard to move forward.”

The real value of generative AI was thus not in contributing brilliant new ideas itself, but in the valuable synergies that emerged from the process. Team members used their contextual knowledge and stayed in charge of the process while the AI helped to better convey their ideas, expand exploration and address possible blind spots.

Making better informed decisions

We noticed different dynamics in convergence activities where teams had to make decisions after demanding sessions of idea generation. By that point, team members were usually mentally exhausted. Generative AI was especially helpful for doing the heavy lifting during this part.

The AI helped manage the information-intensive tasks necessary for team alignment like reformulating, summarizing, organizing, comparing and ranking options. This reduced the mental strain on team members, allowing them to focus on important tasks like evaluating ideas. In this process, the team was responsible for:

1. Checking AI’s outputs to make sure the content was accurate and useful. For example, ChatGPT and Uizard helped create draft scenarios and prototype drafts to validate their concept, but the team still had to refine them to meet project goals.
2. Adding their own insights and contextual nuances to guide final decisions, considering factors like feasibility, ethics and long-term strategic impact.

One participant said:

“Sometimes, the AI would focus onto details that were insignificant to us…Sometimes we needed less general synthesis and more personalized input.”

Overall, this form of human-AI collaboration in convergent activities helped the team make better informed and more confident decisions about which problem to focus on and which solution to pursue. This made them feel in control of the sprint’s final outcomes.

One participant said:

“For pivotal phases like making decisions or voting on something important like a success factor, if we relied solely on AI to determine what is important, there would be rejection. We are better positioned to know. We are the employees who will execute the final solution.”

Challenges and opportunities

Consistent with research on cognitive automation and intelligent automation, we found that generative AI was of great help in handling cognitively demanding tasks like reformulating poorly articulated ideas, summarizing information and recognizing patterns in team members’ contributions.

A key challenge with using Generative AI in innovation is ensuring it complements, rather than replaces, human involvement. While AI can act as a useful companion, there’s a risk it could reduce team engagement or ownership of the project if overused.

The design sprint facilitator told us:

“Feasibility needs to be balanced with desirability. You could technically automate most of the process but that would kill the need for pleasure, interaction, and humans’ doubts won’t be addressed; plus humans need to own the problem — all these are essential elements in a human-centred innovation process.”

Consequently, regularly assessing AI’s impact within this process is crucial in order to maintain a healthy balance. Automation should enhance creativity and decision-making without undermining the human insights that are central to innovation.

As AI continues to develop, its role in innovation will grow. Companies that integrate AI into their workflows will be better equipped to handle the fast-paced demands of modern innovation. But it’s important to understand both the strengths and limits of AI and humans to ensure this collaboration is effective.

This article was co-authored by Cédric Martineau, CEO and innovation management consultant at Carverinno Consulting.

The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.