If there is one constant in the world of compliance, it is the reality of change. However, in 2025, change takes on a new vector: artificial intelligence, not just as a tool, but as a force reshaping how organizations think, decide, and act. In their article “What Is a Designed Intelligence Environment?” authors Michael Schrage and David Kiron examined how enterprises must rethink their intelligence and compliance strategies to survive and thrive in the new world of AI-rich operations. I found their insights for compliance professionals both practical and transformative. Today, I begin a short two-part blog post series on Designed Intelligence. Today, in Part 1, we consider what is meant by Designed Intelligence. Tomorrow, we take a deeper dive into what it means for compliance.
From Managing Compliance to Orchestrating Intelligence
Traditional compliance frameworks have always focused on managing risk, enforcing controls, and responding to regulatory shifts. But what happens when decision-making itself is no longer exclusively human? In a designed intelligence environment, humans and machines learn, reason, adapt, and improve together. This is not simply the automation of existing workflows; it’s the emergence of a new kind of enterprise, where “epistemic engineering”—the design of how knowledge is generated, shared, and executed—becomes the bedrock of effective compliance.
The first insight for compliance professionals is that we can no longer assume governance is solely about drawing lines around human behavior. Our job is to architect environments in which both human and machine intelligences operate responsibly and transparently, ensuring that knowledge, decisions, and accountability flow where they are needed most.
Computational Irreducibility: The End of Predictive Planning
Stephen Wolfram’s principle of computational irreducibility may sound academic, but its implications are anything but theoretical for compliance leaders. In a nutshell, this principle holds that in highly complex systems, such as those created when humans and AI interact, the future cannot be predicted without running the system in real-time. In other words, the classic compliance cycle of “predict, plan, execute, and measure” is mathematically impossible in many AI-rich contexts.
For compliance professionals, this means shifting from static policy planning to dynamic, real-time oversight. Consider an example from pharmaceutical R&D. A global company faced paralysis in prioritizing compounds for its oncology pipeline. Instead of relying on fixed rankings or endless meetings, leadership created a computational observatory: multiple agentic models simultaneously analyzed each compound from different perspectives (biological plausibility, market readiness, synthetic feasibility)—cross-model consensus and visualization, rather than managerial heuristics, guided decisions, surfacing previously hidden breakthroughs.
Compliance Lesson: Build for Observability, Not Just Control
In today’s world, compliance cannot rely solely on auditing after the fact. The future lies in building observability into the core of decision environments: real-time monitoring, feedback loops, and experimental frameworks that enable compliance to identify emergent risks as they arise, not just when it’s too late. This is the heart of “runtime intelligence.”
Semantic Formalization: Making Compliance Computable
Most compliance programs are based on documentation, training, and knowledge management. But semantic formalization, another key concept, goes much further. It requires organizations to define core business concepts (like “customer value,” “operational risk,” or “conflict of interest”) so precisely that both humans and AI agents can “compute” with them. This is not a matter of semantics for its own sake; it is about ensuring that rules, policies, and standards are unambiguously actionable by both people and machines.
For example, a multinational retailer’s use of large language models (LLMs) for customer support faced breakdowns because definitions of customer experience (CX) varied by region and role. By creating a semantic kernel, which is an enterprise ontology that maps complaints, resolution pathways, sentiment clusters, and CX metrics, the company trained its models (and its people) to reason with consistent, computable definitions. This enabled root-cause analysis and adaptive, system-wide learning that wasn’t possible in the old script-driven model.
Compliance Lesson: Define, Don’t Just Describe
Compliance teams must become architects of semantic infrastructure. That means working cross-functionally to formally define compliance concepts, risks, and obligations so that every AI, dashboard, and human team member speaks the same language, in the same way, everywhere. This is how you build “reasoning standardization” and reduce the friction, ambiguity, and risk that come with AI-driven scale.
Rulial Space: Translating Between Multiple Realities
Perhaps the most disruptive insight for compliance comes from the concept of rule-based space: the recognition that different “intelligences”—whether human teams, AI systems, or even other departments—operate under distinct rule sets, generating unique realities. Finance assesses risk through Monte Carlo simulations, operations analyze it through failure mode analysis, and AI identifies it through statistical correlations. Traditional efforts to force alignment through training or incentives may be fundamentally flawed. What is needed is translation, not assimilation.
In aerospace manufacturing, for example, friction between design engineers and LLMs led to productivity-killing standoffs. Instead of forcing one side to conform to the other, leadership installed an honest mediator: an explicit layer for mapping, negotiating, and reconciling the assumptions, rules, and heuristics of both human and AI systems. This moved the organization from “compliance by enforcement” to “compliance by comprehension,” a far more powerful and sustainable model for managing both risk and innovation.
Compliance Lesson: Become a Translator, Not Just an Enforcer
The future of compliance is not just about enforcing standards but about building systems and processes that can explicitly map and translate between different rule sets: human, machine, and hybrid. This requires cognitive compilers: protocols and infrastructure for negotiating meaning, resolving conflicts, and arbitrating outputs across diverse intelligences. The result is intelligent orchestration of more innovative, safer, and more adaptive enterprises.
Why Smarter Tools Aren’t Enough: Compliance by Design, Not Just Technology
It’s tempting to think that more innovative tools or more sophisticated AI models will solve all compliance challenges. But as the article warns, deploying intelligence as automation—without rethinking the architecture of decision environments—will leave most enterprises stuck with mediocre results. Intelligence, whether human or machine, must be designed into the very infrastructure of the organization: how decisions are made, how meaning is generated, and how value and risk are understood.
For compliance professionals, this means a dramatic expansion of your remit. You must help design the runtime environment for intelligence where learning, adaptation, and ethical execution are embedded, not bolted on. This requires technical fluency, cross-disciplinary collaboration, and a willingness to challenge the old boundaries of policy, training, and audit.
Conclusion: The Compliance Opportunity in Designed Intelligence
The transition to designed intelligence environments represents both a challenge and a once-in-a-generation opportunity for compliance leaders. Those who lean in, who help architect real-time observability, semantic formalization, and rule-based mediation, will become essential strategic partners in their organizations’ transformation. Those who don’t risk being left behind by systems they can neither see, steer, nor secure.
The era of “predict and control” is coming to an end. The age of “orchestrate and observe” is here. As compliance professionals, our calling is clear: to lead the design, governance, and stewardship of intelligence environments that are fit for the complexity and promise of AI. Only then can we ensure that innovation and integrity go hand in hand in the enterprises of tomorrow.
Join us tomorrow for Part 2, where we delve deeper into the compliance considerations.
Prof. Marc I. Steinberg is a leading expert and prodigious scholar in the field of US securities and corporate law. He is the Rupert and Lillian Radford Chair in Law and Professor of Law at SMU’s Dedman School of Law. He has served as a professor, fellow, or lectured at several other prominent universities, including HKU, the University of Cambridge, Oxford University, King’s College-University of London, Moscow State University, University of Sydney, UCLA, and the University of Pennsylvania.
Tram Anh Nguyen is the chairwoman of the Global Women in AI (GWAI) group and co-founder of the London-headquartered Centre for Finance, Technology and Entrepreneurship (CFTE). GWAI is best thought of as a global community empowering women to shape the future of artificial intelligence. Its mission is to equip women across industries with the skills, networks, and visibility they need to thrive in an AI-driven world.
