How superfluid enterprises reshape organizations for competitive edge

Horizon 1: Foundation building

Sara arrives at work to find her AI assistant has prepared a briefing on overnight activities. Her company has deployed smart contracts for routine supplier payments, but she still needs to approve any contracts above US$50,000. Three AI agents have flagged potential supply chain risks based on weather patterns and geopolitical news, including tariff negotiations; Sara reviews their recommendations and decides which backup suppliers to activate. Her morning includes three video calls: one with the AI governance committee to review agent performance metrics, another with plant managers to discuss human-AI collaboration training results and a third with the tax function to discuss the tax implications of using backup suppliers.

Foundation-building organizations develop AI-native capabilities while maintaining familiar business structures. Their focus is on building infrastructure, conducting smart contract pilots, and improving human-AI collaboration skills. Sara's day is 40% shorter than it used to be, with AI handling routine coordination, but she remains deeply involved in strategic decisions and exception handling.

Success hinges on meeting specific metrics: 70% employee adoption of AI tools, a 30% reduction in process cycle times, and over 150% ROI on pilot programs. Organizations deploying comprehensive AI automation exemplify this systematic approach, with 78% of global organizations now using AI in at least one business function and 71% regularly using generative AI. The journey shows careful scaling from proof-of-concept to enterprise-wide transformation through structured governance, pipeline management, and resilient architecture design.⁵

Financial services organizations dominate this landscape due to their data-driven operations and complex risk management needs. Santander's One Pay FX uses blockchain to ensure transaction accuracy and security, offering customers faster and cheaper international payments while positioning the bank as a leader in blockchain adoption across European markets.⁶ Decentralized finance platforms now manage over US$214 billion in assets, demonstrating significant market appetite for automated financial services.⁷
 

Horizon 2: Autonomous coordination

Sara's Monday morning routine has changed significantly. She reviews an overnight dashboard showing her AI agents have independently resolved 15 operational issues, adjusted production schedules to accommodate a supplier delay, and negotiated better rates with three vendors through automated smart contract bidding. This enables Sara to focus her time on strategic partnerships, ethical review of AI decisions, and working with ecosystem partners across industries. When a complex quality problem arises that falls outside AI’s scope, the system automatically escalates it to Sara with full context and suggested solutions from multiple AI agents.

The second horizon marks the shift from "humans in the loop" to "humans on the loop," where organizations adopt quantum-enhanced AI systems for complex optimization while implementing hybrid governance models that combine human oversight with algorithmic decision-making. Sara's role has changed from operational manager to strategic orchestrator, with AI agents managing 80% of routine decisions while humans focus on exceptions, creativity and ethical oversight.

Goals include 80% autonomous decision-making for routine tasks, 50% faster responses to market changes, and over 200% ROI from ecosystem coordination. Healthcare transformation focuses on this horizon through integrated care ecosystems that coordinate patient outcomes across traditionally fragmented providers. Blockchain-based health records allow patients to control how their information is shared while ensuring privacy and security, with healthcare representing a target of 15% blockchain market penetration by 2030.⁸

Manufacturing firms, like Sara's company, approach this frontier through adaptive production networks that dynamically coordinate capacity, expertise and market access across traditional organizational boundaries. Distributed manufacturing platforms allow for rapid product development and market entry without requiring large investments in production infrastructure, cutting time-to-market by 50-70% and reducing capital requirements by 60%.⁹
 

Horizon 3: Full superfluidity

Sara's role has become almost unrecognizable from her 2025 position. She now focuses on strategic vision, creative problem-solving, and ensuring the company's autonomous operations align with human values and social impact goals. The company's AI systems manage full end-to-end operations — from customer acquisition to product delivery — while keeping transparency through digital twins that Sara can inspect anytime. Her Monday mornings involve reviewing the AI systems' strategic recommendations for entering new markets, and assessing ethical implications of autonomous decisions including seamless analysis of global and local tax laws and regulations, as well as collaborating with other human leaders across the ecosystem on challenges that need uniquely human judgment, empathy and creative thinking.

A confluence of technologies

Superfluid enterprises utilize three core technologies working in synchronized harmony:
 

1. Agentic AI for coordination

In a superfluid enterprise, advanced AI agents do more than automate individual tasks, they coordinate complex, multi-step processes across organizations. These agents understand context, make decisions within set parameters, and adapt to changing conditions without human intervention.

Real-world implementations demonstrate this potential. CrewAI's enterprise adoption platform, now adopted by 60% of Fortune 500 companies, allows AI agents to work together on complex projects, with some implementations replacing entire departments' coordination tasks. Teams report spending 60% more time on creative problem-solving and 40% less time on status updates and administrative coordination.¹¹ JPMorgan's autonomous payment systems coordinate multiple banking functions that previously required extensive human coordination.¹²
 

2. Smart contracts for governance

Blockchain-based smart contracts replace traditional contracts that require human interpretation and enforcement. They execute automatically when predefined conditions are met, enabling business processes to run at algorithmic speed with perfect consistency and transparency. Enterprise adoption is growing rapidly, with large organizations (10,000+ employees) representing 60% of smart contract usage and 90% of global organizations having started adopting blockchain technology.¹³

Power Ledger's blockchain platform showcases this potential in the energy sector. The system enables peer-to-peer energy trading through smart contracts that automatically match buyers and sellers, process payments, and optimize renewable energy distribution. Platform participants are saving 15-25% on energy costs while boosting renewable energy usage by 40% — outcomes that would be impossible to achieve through traditional utility coordination systems.¹⁴
 

3. Digital twins for visibility

Digital twins — dynamic, virtual replicas of a company’s physical objects, processes or systems — offer unmatched transparency and control for autonomous operations. When AI agents run the vast majority of a finance function, digital twins can generate real-time, auditable models of those processes, maintaining human oversight even in fully autonomous operations. Advanced implementations of digital twins reach 90-95% predictive accuracy compared to 60-70% for traditional monitoring systems.¹⁵

Organizations that can unlock enterprise data securely and compliantly while enabling autonomous coordination will achieve superfluidity first. The winners won't just be those with the best AI—they'll be those who can orchestrate AI, smart contracts, and human creativity into seamless networks that adapt faster than traditional hierarchies can respond.
 

The human quotient: building hybrid human-AI partnerships

The most effective superfluid transformations focus not on replacing humans but on forming partnerships that enhance human abilities in ways that pure automation cannot reach. Research consistently shows that hybrid human-AI approaches perform better than either humans or AI working alone.

Still, there is much that business leaders can and should be doing now to build the foundation and enable the move to superfluidity. Here are five near-term actions leaders can take in Horizon 1, while also building a foundation to prepare for the move to Horizons 2 and 3.
 

1. Lay the technology foundation

Evaluate your current technology setup to prepare for AI readiness and ecosystem compatibility. Invest in technology that eliminates data silos, connects your current systems and creates a stable platform for AI tools to operate reliably. This foundation enables AI systems to access the necessary information and work together effectively, even in complex or unpredictable business situations.

Superfluid transformation is cumulative. Organizations that begin building capabilities now will develop advantages that become increasingly difficult for competitors to replicate. Those who can unlock enterprise data securely and compliantly will achieve superfluidity first. Early investments in data quality, integration capabilities, and AI-native architectures compound over time, creating organizational capabilities that provide sustained competitive advantage.

Organizations waiting for "perfect" AI solutions will find themselves increasingly isolated from ecosystem partners who have embraced current capabilities with continuous improvement approaches. Perfection is less important than progression. Companies that start now with imperfect but improving systems will develop the expertise, partnerships, and infrastructure that late entrants cannot quickly acquire.
 

2. Establish governance that evolves with superfluid adoption

Three-quarters (75%) of technology leaders cite governance as their main concern when deploying autonomous systems, yet only 18% of organizations have established comprehensive AI governance councils.¹⁹ This governance gap creates serious risks related to accountability, compliance and ethical decision-making as systems become more autonomous. Meanwhile, organizations with comprehensive AI governance frameworks report significantly higher transformation success rates, with 78% of organizations using AI in at least one business function and those with structured approaches showing stronger outcomes compared to those pursuing rapid, uncontrolled deployments.²⁰

Develop hybrid governance models — frameworks that blend human strategic oversight with AI-powered operational decisions. In these systems, humans define objectives, set ethical boundaries and monitor outcomes, while AI systems handle routine choices within established parameters. Begin with pilot programs in non-critical business areas, set clear success metrics and develop frameworks for scaling successful methods across the organization. As these systems prove reliable and build stakeholder confidence, organizations can gradually expand the scope of autonomous decision-making while maintaining human accountability for strategic direction.

This requires rethinking traditional approval processes, risk management frameworks and quality assurance systems. Superfluid governance emphasizes setting parameters and monitoring outcomes rather than controlling processes. Leaders must become comfortable with systems that make operational decisions independently while maintaining clear accountability for strategic direction and ethical boundaries. The fundamental shift is from "approving every decision" to "ensuring decisions align with our values and objectives."

3. Build key workforce skills and initiate cultural change

Fifty-eight percent of CIOs report that cloud computing skills are in the shortest supply, while 40% of organizations cite skills gaps as major barriers to transformation.²¹ The move to advanced operations requires new skills that combine technical knowledge with business strategy and ethical reasoning — skills that are currently scarce in the market.

Systematically invest in developing key skills in your workforce, such as context engineering, strategic thinking and story-driven innovation skills. Measure success by business impact: track how AI-enabled employees contribute to revenue growth, customer acquisition and competitive advantage rather than traditional productivity metrics like hours worked or tasks completed. Focus training programs on human-AI collaboration instead of traditional skill replacement or purely technical training.

The transformation to superfluid enterprise requires cultural change as much as developing skills. Employees will thrive by learning to work with AI agents as collaborative partners rather than just tools, becoming comfortable delegating routine decisions and focusing on higher-value creative and strategic work.

The main challenge in this cultural shift isn't technology, it's management’s willingness to give up operational control while keeping strategic oversight. Traditional management depends on human oversight for key decisions, but autonomous operations require trust in systems to make operational choices within set boundaries.
 

4. Rethink your ecosystem strategy

Systematically map your value network and identify opportunities for smart contract automation and cross-organizational coordination. Start with simple bilateral agreements and grow toward complex multi-stakeholder orchestration as capabilities develop. The aim is to create network effects that make your organization more valuable to ecosystem partners.

Successful ecosystem strategies prioritize removing friction for all participants instead of maintaining proprietary advantages. Lowering rather than raising switching costs may seem counterintuitive, but it can be very successful in gaining market adoption — as demonstrated by open source software models — and build stronger competitive positions through network effects and collaborative value creation.
 

5. Transform Organizational Structure and Talent Management

The shift to superfluidity demands fundamental changes in organizational design. Traditional hierarchical structures with rigid chains of command will be replaced by flatter networks of executives collaborating with interconnected AI agents. This change affects talent management in three key areas:

• Leadership Development: Prepare executives to coordinate AI agent networks instead of overseeing traditional reporting structures. Leaders need to become comfortable with decentralized decision-making while still maintaining strategic oversight.
• Role Redesign: Rethink roles around human-AI collaboration instead of only human tasks. Focus on creating roles that use distinct human skills — creativity, ethics, strategic thinking — while letting AI agents handle coordination and execution.
• Performance Management: Move from individual productivity metrics to measures of ecosystem impact. Assess employees on their ability to enhance AI capabilities, foster cross-functional innovation and contribute to network effects instead of just traditional task completion metrics.
   

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