Redesigning Management Foundations that Integrate Growth and TrustRebuilding the Foundations of Competitiveness and Trust for the Intelligent Systems Era

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Insight | 2026-5-25

13 minute read

The sources of competitiveness are undergoing a non-linear shift. AI agents, physical AI, and quantum computing are already in motion and, over the next five years, will fundamentally reshape how enterprises create value. What is often overlooked, however, is that as value creation accelerates, the very foundations of trust and security are being rewritten at the same time.
There is no longer room to treat growth and risk as separate agendas. The real challenge is how to proactively redesign the management foundations that enable competitiveness and trust to be delivered together.

This paper reframes this structural shift through three lenses:
(1) new sources of competitiveness,
(2) the evolving nature of risk, and
(3) the redesign of management foundations that integrate growth and security.

1. Framing the Challenge: A Shift in Competitive Logic

Corporate management today is no longer at a stage of adopting isolated technologies. It is approaching a broader inflection point on which the very sources of competitiveness are being redefined. AI agents, physical AI and robotics, and quantum computing are all likely to diffuse across industries toward 2035 as what economists would describe as GPTs (general-purpose technologies).

Through sustained performance improvements and complementary innovation, they have the potential to reshape not only products and operations, but also industry structures, organizational models, and even institutional arrangements. With their ability to accelerate processes, enable greater autonomy, and deepen human-machine collaboration, these technologies are emerging as game changers in value creation and competitive advantage.

At the same time, when the sources of competitiveness change, the assumptions that underpin trust and security also change. Always-on agents, autonomous robotics, and leaps in advanced computing power create new values but they also amplify new forms of risk, including misuse, malfunction, system failures, cyberattacks, and the obsolescence of existing cryptography. The central management challenge is therefore not to separate offense from defense, but to redesign the organizational foundations that can sustain both a new model of competitiveness and a new model of trust.

The recent "Claude Mythos" incident serves as a stark illustration. It was a real-world demonstration that the immense opportunity for unprecedented leaps in productivity (offense) is now inextricably linked to a new class of threat capable of nullifying entire corporate defense models overnight (defense).

Security in the enterprise should be understood not only as protection against external attacks, but as a broader foundation of trust that also encompasses internal controls and operational risks. Building on this premise, this paper focuses on an area of growing importance in the era of general-purpose, transformative technologies: viewing security not as a set of isolated defenses, but as an integrated, end-to-end discipline. Specifically, it calls for a lifecycle approach that spans the full arc of cyber threats—from early signals and attacker preparation, through intrusion, to post-breach impact and containment.

2. New Sources of Competitiveness, New Sources of Risk

Three technologies stand out as especially important new drivers of competitiveness: AI agents, physical AI, and quantum computing. While their time horizons differ, all three are advancing along a common trajectory—toward greater speed, autonomy, generality, and closer collaboration with humans. Together, they are beginning to reshape the structure of enterprise value creation (See Figure 1).

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Conceptual diagram illustrating how three core technologies—AI agents, physical AI, and quantum computing—simultaneously drive enterprise competitiveness while reshaping assumptions around trust, security, and risk structures

Figure 1 Adoption Timeline of Three Technology Waves Reshaping the Next Competitive Landscape

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AI agents are the most likely to see broad adoption by 2030, with the potential to significantly raise the productivity of knowledge work(1). They can accelerate decision-making and execution, operate continuously beyond human limits, and create value not only in offensive domains but also in defensive functions such as security operations, audit, and anomaly detection. Physical AI is likely to spread more gradually across manufacturing, logistics, maintenance, construction, and healthcare or caregiving support, with the potential to drive a broader reconfiguration of physical labor around 2030(2).

The timeline for quantum computing requires careful interpretation. While generative AI has shifted some private investment, funding from governments, public funds, and leading corporations remains strong and is often accelerating. Competition among major countries and regions is intensifying. Quantum computing is increasingly viewed as a “strategic sovereign technology,” underpinning future industrial competitiveness and national security, and could, by around 2035, become a new source of differentiation in areas such as drug discovery, materials science, optimization, and complex calculations in finance and logistics(3).

What matters, however, is that the same capabilities that create competitiveness also create new sources of risk. These risks are not simply extensions of conventional cyber threats; they differ in speed, scale, attack surface, and the forms of damage they can produce.

AI agents are not only the greatest new level of productivity; they also introduce a new class of internal control risk. Runaway permissions, automated propagation of flawed decisions, data leakage, prompt injection, and cascading failures across agents are all emerging concerns. At the same time, external threats are evolving in kind. As AI capabilities diffuse, attackers gain similar advantages, accelerating the automation, sophistication, and scale of attacks. Language-agnostic, always-on campaigns, multimodal disinformation, and supply-chain-based intrusions are exposing enterprises to broader and more persistent risks than ever before.

However, the critical point is that the very capabilities driving competitiveness are also becoming new sources of risk. These risks are not a linear extension of traditional cyber threats; they differ in speed, scale, targets, and impact.

AI agents, through privilege escalation, automated propagation of errors, data leakage, and cascading failures across agents, can act as both a powerful productivity lever and a new form of internal control risk. Moreover, AI agents often hold credentials such as API keys, tokens, and SSO access, and can interact with multiple SaaS and internal systems. If compromised, they can become entry points for privilege abuse and lateral movement. Beyond isolated misuse, interconnected agents can serve as attack pivots, enabling risks to propagate and amplify across systems and environments.

Equally important is the qualitative shift in external threats. The widespread adoption of AI is equipping attackers with similar capabilities, accelerating the automation, sophistication, and scale of attacks. Threats are no longer constrained by language or time, and include data poisoning, prompt injection, multimodal disinformation, and supply chain infiltration—exposing enterprises to broader and more persistent risk landscapes.

What this implies is a fundamental shift: risks should no longer be viewed as isolated events, but as continuous processes—from attacker preparation and initial compromise to post-breach propagation and impact.

Physical AI presents an even more acute risk. Through robot takeover, sensor spoofing, breakdowns at the IT/OT boundary, disablement of safety mechanisms, and supply chain compromise, cyber risks can translate directly into physical damage and human safety incidents. Unlike traditional cyber risks—such as data breaches or system outages—malicious manipulation in physical environments can result in real-world accidents and injuries, representing a fundamentally different order of impact.

While quantum computing holds significant promise, it also raises substantial concerns(4). The accelerating pace of development is intensifying fears of a “Q-Day,” when widely used encryption could be broken, posing a near-term threat to digital security. The transition to post-quantum cryptography (PQC) and the inventory and remediation of cryptographic assets will take considerable time. Rather than waiting for large-scale business applications to materialize, organizations must act now from a quantum security perspective, anticipating the obsolescence of existing encryption. In particular, the “harvest now, decrypt later” threat—where attackers collect encrypted data today for future decryption—has already become a growing risk, especially for organizations holding long-lived sensitive data. Despite this urgency, most organizations remain underprepared for the transition to a post-quantum world.

In short, these three technologies are not only new drivers of competitiveness; they are also reshaping the foundations of trust, security, and resilience on which future competitiveness will depend.

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3. Redesigning the Management Foundations that Balance Competitiveness and Trust

How, then, should companies respond to these shifts? The real challenge is not simply whether to adopt individual technologies or address isolated risks. It is whether management itself can be redesigned to support a new balance between competitiveness and trust—one shaped by AI agents, physical AI, and quantum computing. What is required is not a series of disconnected initiatives, but a redesign of the management foundations on which the enterprise operates. Five areas are especially important.

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First, organizational redesign.

Traditional functional structures struggle to keep pace with the widening gap between accelerating business environments and slow decision-making. With the rise of AI agents—enabling real-time execution, 24/7 autonomy, and massive scalability—hierarchical decision models are increasingly becoming bottlenecks.

Going forward, the focus must shift from functional silos to a “work-chart” model built around workflows and units of value creation. Flatter, more fluid structures are required, where humans and autonomous systems collaborate to deliver end-to-end outcomes. At the same time, this shift goes beyond organizational structure. AI agents embed decision-making within processes, requiring a fundamental redefinition of accountability and performance metrics—from functional ownership to outcome-based KPIs. In effect, this calls for a redesign of business governance itself.

Moreover, as agents become interconnected, decisions and errors can propagate and amplify across entire workflows. At the same time, attackers increasingly target internal structures—such as credentials, access scope, and lateral movement pathways—to gain entry and expand their reach, making AI agents potential pivot points for escalation.

Risk, therefore, should not be treated as isolated incidents, but as something that propagates through business processes. Organizations must move beyond mere concealment and instead design for compromise—embedding principles such as least privilege, segmentation, and containment to limit propagation and enhance resilience.

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Two professionals discussing data analytics and artificial intelligence (AI) on laptops and a large monitor displaying 'AI' text and network graphics in a modern office."

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Second, the redesign of work and operating models.

AI agents are entering knowledge work, while physical AI is transforming frontline activities, shifting enterprises toward execution models that integrate not only humans, but also digital and physical AI workers (see Figure 2). Implementation will begin in areas such as design, procurement, quality, maintenance, and back-office functions, ultimately requiring a reconfiguration of end-to-end workflows that connect IT and OT, as well as cyber and physical domains.

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Conceptual diagram illustrating an integrated operating model in which humans, AI agents, and physical AI collaborate across IT and OT domains to redesign end-to-end enterprise workflows

Figure 2 Digital Workers, Physical AI Workers, and the Direction of Future Integration

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At the same time, this integration introduces new risks. As the IT/OT boundary blurs, cyber risks can propagate into the physical domain, leading to robot malfunctions or the disabling of safety mechanisms, with direct implications for human safety. From an attacker’s perspective, realistic entry points include bypassing safety shutdowns, weak or unclear human access controls and accountability, and infiltration through maintenance vendors. These risks underscore the need for safety-by-design in operating models where physical AI and humans work together.

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Third, the redesign of talent and the workforce.

AI workers are not temporary experiments, but new strategic assets—offering scalability, 24/7 autonomous execution, and advanced knowledge-processing capabilities. Combined with human judgment, direction-setting, empathy, and creativity, they redefine how value is created.

Accordingly, the workforce must be reconfigured around an optimal mix of humans and AI. Human roles shift toward judgment, oversight, and exception handling, while AI assumes task execution, forming a complementary model. Under current legal and ethical frameworks, AI workers cannot be treated as accountable entities, requiring a distinct approach to performance management. Organizations should therefore combine shared outcome-based KPIs with role-specific evaluation metrics for humans and AI.

At the same time, effective governance is essential. Risks such as runaway permissions, automated propagation of errors, data leakage, and prompt injection must be addressed through embedded guardrails and oversight mechanisms.

Ultimately, all employees, including senior executives, must evolve into managers of AI workers, capable of orchestrating their capabilities and translating them into outcomes.

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Fourth, the redesign of trust and security.

Traditional approaches—focused on post-incident response and known threats—are no longer sufficient. As generative AI and agentic AI accelerate the speed and sophistication of cyberattacks, defensive measures viewed solely from the enterprise perspective will increasingly fall short.

Going forward, organizations must integrate three layers of security: reactive responses to contain impact after incidents, proactive defenses to reduce exposure through vulnerability and configuration management, and preemptive approaches that detect attacker signals and intent early—blocking, deceiving, or neutralizing threats before execution. In other words, security must evolve into a dynamic model that spans the entire attack lifecycle, from preparation to intrusion to post-compromise response.

While reactive and proactive measures are already widely adopted, preemptive security—anticipating and acting from the attacker’s perspective—is emerging as a critical frontier. This includes approaches such as Deny, Deceive, and Disrupt, supported by technologies like obfuscation, deception, moving target defense, predictive threat intelligence, and exposure management (See Table 1)(5).

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Table comparing three cybersecurity approaches—reactive, proactive, and preemptive—by outlining their purpose, role in the security lifecycle, and representative techniques

Table 1 Preemptive Security (3D Model – Simplified View)

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As physical AI becomes more pervasive, this shift becomes even more critical. Security must extend beyond cyber domains to include OT environments, safety mechanisms, and on-site accountability—integrating cyber and physical risk into a unified design.

Fifth, organizations must redesign quantum security and guardrails

Rather than waiting for the full maturity of quantum computing, companies should proactively prepare for the obsolescence of current cryptography by advancing PQC migration, crypto-asset inventory, and the reassessment of long-lived sensitive data. In particular, the “harvest now, decrypt later” threat underscores that quantum is not only a future competitiveness factor, but also a present-day trust risk.

At the same time, quantum risk should not be treated in isolation as a future issue. It must be integrated with today’s security priorities. Strengthening core controls—such as credential management (IDs and API keys), least privilege and segmentation, and the prevention of lateral movement—not only address current threats but also lays the foundation for resilience in the quantum era.

Building on these perspectives, as intelligent systems become more autonomous, the importance of guardrails—such as explainability, human oversight, and auditability—will continue to increase. Accordingly, governance and control models must also be redesigned to keep pace with this shift. AI, OT, and quantum should not be treated as separate themes, but as core elements of a broader management transformation with shared trajectories.

Against this backdrop, we now turn to the key perspectives that executives need to adopt.

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4. Management Implications: Three Strategic Lenses for Executives

The faster technology evolves, the more difficult management becomes. What leaders need today is not simply the ability to evaluate individual technologies, but the ability to understand how competitiveness and trust must be built together.
The following three perspectives translate this design into executive decision-making.

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1) A lens that does not separate offense from defense

AI agents, physical AI, and quantum computing are not only technologies of growth and efficiency; they also reshape the assumptions that underpin trust, security, and resilience. For that reason, growth strategy and risk management can no longer be pursued as separate agendas. In the era of intelligent systems, competitiveness and trust must be designed together from the outset.

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2) A lens that redesigns the organization and people

As intelligent systems become more deeply embedded in business operations, the role of humans does not simply shrink change. Judgment, supervision, exception handling, and redeployment become more important, not less. The central management question is therefore not merely how to deploy AI, but how to redesign workflows, roles, and accountability structures so that humans and AI can work as complementary partners.

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3) Anticipating the gap between technology and preparedness

As quantum security illustrates, in some domains waiting for full-scale adoption is already too late. Trust and security must be understood not as responses to isolated incidents, but as a continuous spectrum of attacks and threats. This requires an end-to-end design across the entire attack lifecycle—from pre-emptive measures that detect early signals and intent, to proactive defenses that prevent intrusion and limit impact, and reactive capabilities for containment and recovery after compromise.
Moreover, as AI and quantum technologies are increasingly exploited by attackers, security must be designed to counter them using the same technologies.

These perspectives are integrated along the timeline of attack and defense in the framework shown in Figure 3.

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Management framework illustrating how competitiveness and trust are implemented together by integrating offense and defense, redesigning organizations and people, and anticipating the gap between technology and preparedness across the security lifecycle of before, during, and after attacks

Figure 3 A Mirror Timeline of Adversary and Enterprise Across Three Security Phases

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The next source of competitive advantage will not be the technologies themselves, but how quickly and proactively organizations can redesign the management foundations that support them. In other words, it is not enough to adapt to change; only those organizations that are designed for change—and can autonomously drive continuous adaptation—will sustain a lasting advantage.

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Dr. Jianmin Jin (Ph.D in International Economic Law)
Chief Digital Economist Fujitsu Ltd.
Senior Director
Marketing Promotion Office

2020 Fujitsu Ltd., Chief Digital Economist. 1998 Fujitsu Research Institute, Senior Fellow.
Dr. Jin's research mainly focuses on global economic, digital innovation/digital transformation, and Dr. Jin has published books such as ”Towards the Creation of a Japan’s Silicon Valley”(2020), etc.

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