Neel Somani: Why Debuggability Is the Missing Foundation of Enterprise AI

Neel Somani: Why Debuggability Is the Missing Foundation of Enterprise AI

These days, the artificial intelligence conversation, particularly in the annals of enterprise leadership, has become, well, predictable: 

Companies rush to integrate AI into operations. 

Vendors promise revolutionary capabilities. 

Consultants offer frameworks for responsible deployment. 

Yet, beneath this optimistic surface, a fundamental problem continues to linger: most organizations are deploying systems they cannot actually control.

A published researcher in computer science, whose work spans privacy, artificial intelligence, and formal verification, Neel Somani has spent considerable time examining what separates AI systems that merely function from those that leaders can genuinely trust. His research reveals a critical gap between systems that can generate plausible explanations for their decisions and systems that organizations can reliably fix when they malfunction.

This distinction, says Somani, matters more than most executives realize. 

AI systems now influence lending decisions, moderate content at scale, optimize supply chains, and even screen job candidates. And when these systems fail, organizations discover they lack any systematic method to diagnose the problem, or implement a precise fix, and or even verify the solution worked. 

The typical response is to roll back entirely, accept the flawed behavior, or, in many cases, attempt repairs that may inadvertently make things worse.

When Explanations Mislead

The promise of explainable AI has become central to corporate adoption strategies. 

Modern AI platforms generate rationales for their outputs: a loan denial cites credit history, a content flag references policy violations, a hiring rejection points to resume gaps, etc. For better or worse, these explanations satisfy compliance requirements and give stakeholders confidence that decisions follow logical rules.

The trouble here is architectural. Current AI systems tend to identify patterns in training data and produce outputs through statistical inference. They then construct explanations after the fact that sound coherent. 

This after-the-fact rationalization creates a dangerous illusion of understanding. Change one input variable, and the entire explanation can collapse, revealing that the stated reasoning was never actually driving the decision.

In turn, this creates organizational blind spots that often compound over time. Many teams believe they understand why their AI systems behave in certain ways, and they build processes around these understandings. They make strategic decisions assuming the explanations are accurate. 

Unfortunately, when something breaks, the explanation provides no useful guidance for intervention, mainly because it was never mechanistically correct.

For leaders accustomed to managing complex systems, this represents a departure from established practice. In traditional software, teams can trace execution, identify the code responsible for undesired behavior, modify that specific code, and test that the modification worked. 

But in most contemporary AI systems, none of these steps is truly available. Organizations face correlation without causation, narrative without mechanism, explanation without control, and so on.

Three Pillars of Genuine Trust

Somani's framework for building trustworthy AI centers on debuggability: the technical capacity to locate failures, intervene surgically, and verify outcomes. This approach reframes trust as an engineering capability rather than a compliance checkbox.

1. Localization

The first pillar in building trust is localization. This means having a team that can identify which specific mechanisms within an AI system are responsible for particular behaviors. 

This goes beyond surface-level attribution. True localization means being able to determine whether a behavior could occur without a given mechanism being active, or whether that mechanism could be active without producing the behavior. 

Somani emphasizes developing the ability to find concrete inputs that witness cases where the behavior occurs without the mechanism, or where the mechanism activates without producing the behavior.

1. Intervention

Next comes the process of intervention. 

Teams should ask, once a problem has been localized, can we modify the responsible mechanism in a way that's both targeted and predictable? In this instance, the modification should eliminate the undesired behavior without degrading performance elsewhere in the system. 

As Somani knows, this is where AI systems typically break down. Internal representations become so entangled that changes intended to fix one issue trigger compensatory behaviors that tend to create new, often harder-to-detect problems. Without the ability to perform surgical, mechanism-level modifications, organizations are left with blunt instruments, such as ablating entire model components.

1. Certification

The third pillar is certification. For defined domains of operation, can your team make exhaustive, verifiable claims about system behavior? This doesn’t include probabilistic assurances that something is unlikely to fail, but formal guarantees that specific failure modes cannot occur within specified boundaries.

Somani notes that these are universal claims over bounded domains—not distributional or probabilistic, and not resting on sampling. This level of verification has been standard in safety-critical software for decades. Its absence in AI systems represents a fundamental gap between deployment scale and institutional control.

Most AI implementations today lack all three capabilities. Organizations regularly operate on faith that their systems will continue behaving acceptably, with no systematic recourse when that faith proves misplaced.

The Compliance Trap

On the plus side, regulatory frameworks are evolving to address AI risks. The European Union's AI Act establishes requirements for high-risk applications. The National Institute of Standards and Technology has published guidelines for trustworthy AI. Industry consortia are developing best practices for ethical deployment. 

Yet, compliance with these frameworks, while necessary, isn't sufficient for genuine control.

Organizations can document their AI governance processes, implement human oversight, and pass regulatory audits, says Somani, while still lacking the technical capability to fix their systems when they fail. This is because compliance becomes performative when it's disconnected from engineering reality. An organization can satisfy external requirements while remaining fundamentally unable to diagnose why a system failed, implement a targeted fix, and verify that the intervention preserved desired behaviors.

The distinction between compliance and capability matters, mainly because the risks manifest differently. Regulatory violations are episodic and often survivable through fines, remediation, and public relations. But loss of institutional control is structural and compounding. Each deployment of an uncontrollable system tends to increase organizational dependence on black-box decision-making. Each failure that can't be systematically diagnosed and repaired erodes confidence in the technology and in leadership's ability to manage it effectively.

Traditional enterprise risk management relied on factors like transparency in decision-making, auditability of system behavior, and clear accountability when things went wrong. Unfortunately, AI systems built on opaque architectures undermine all three simultaneously. Decisions emerge from learned patterns that even their creators struggle to interpret. Auditing becomes sampling and inference rather than comprehensive verification. Accountability diffuses when no one can definitively explain why a system produced a particular outcome.

Engineering for Control

The gap between current practice and genuine control isn't impossible to overcome. But closing it requires different priorities than those driving most AI deployments in today’s enterprise leadership environment. 

No one can prove that a web browser will never crash. But software engineers can prove that specific routines are memory-safe, that sandboxing prevents certain classes of process escape, that critical invariants are preserved across refactors, and that a given patch eliminates a vulnerability without introducing regressions. As Somani knows, these are compositional guarantees over bounded domains, not global assurances about all possible behaviors.

Applied to AI, this approach means shifting from optimization for performance to engineering for control. For a lending model operating on a specific customer segment, it should be possible to prove that certain protected attributes cannot influence decisions, that interventions to remove bias don't degrade accuracy on legitimate factors, and that the system cannot bypass oversight mechanisms designed to catch edge cases. 

Meaningful debuggability consists of guarantees such as: this subcircuit cannot activate a forbidden feature on domain D, or this intervention removes a failure mode while preserving all other behaviors in scope, or this pathway is structurally incapable of bypassing a guard unless a specific internal condition is met.

The technical foundations for this exist in adjacent fields. Formal verification has been used in aerospace, cryptography, and medical devices for decades. Somani's research points to several developments that demonstrate feasibility, including sparse circuit extraction, which shows that models contain relatively isolated, algorithmic subcircuits that remain stable under targeted intervention. 

Neural verification work establishes that exhaustive reasoning is possible once models are reduced to verification-friendly components on bounded domains. Alternative attention mechanisms suggest that standard attention—the dominant barrier to verification in transformers—is an architectural choice rather than a necessity.

Somani’s project on symbolic circuit distillation provides an early example of automated extraction, where neural mechanisms can be proven formally equivalent to symbolic programs on bounded domains. This work demonstrates that the vision of debuggable AI is not speculative, but grounded in existing technical capabilities that need scaling and integration.

Strategic Implications

For executives navigating AI strategy, Somani's framework presents several concrete decisions about capability building, vendor selection, and deployment standards.

First, it requires assembling teams with different skill profiles than those currently dominant in enterprise AI. Most data science organizations optimize for model accuracy and inference speed. Building debuggable systems requires expertise in formal methods, verification frameworks, and the mathematical foundations of provable system properties. 

These skills are not widely distributed, and acquiring them represents a significant investment that won't show immediate returns in performance metrics.

Second, it demands establishing internal criteria for when debuggability is mandatory. 

Not every AI application carries the same risk profile. A recommendation engine for internal knowledge management differs fundamentally from a model that influences credit decisions or medical diagnoses. Organizations need clear thresholds that trigger requirements for localization, targeted intervention, and formal verification based on the potential impact of system failures.

Third, it changes the procurement conversation with vendors. The default evaluation criteria for AI platforms focus on accuracy benchmarks, processing speed, integration capabilities, and total cost of ownership. 

Adding debuggability as a requirement means asking fundamentally different questions: 

• Can we isolate the mechanisms responsible for specific behaviors? 
• Does the architecture support surgical interventions that don't create cascading failures? 
• Can we formally verify properties about system behavior on defined input domains? 
• Can the vendor demonstrate that their system supports the three pillars of localization, intervention, and certification?

Vendors who can credibly demonstrate these capabilities will have substantial competitive advantages as debuggability becomes standard practice. Most organizations aren't prioritizing these questions yet, primarily because they haven't experienced enough pain from uncontrollable systems. 

As failures accumulate and their costs become more visible, the procurement landscape will shift rapidly, and companies with verification-ready systems will be better positioned than those who must retrofit control into architectures never designed for it.

Reframing Risk

The prevailing narrative around AI risk emphasizes dramatic scenarios: adversarial attacks that compromise system integrity, data poisoning that introduces hidden biases, malicious actors weaponizing AI capabilities, existential concerns about artificial general intelligence, and so on. These scenarios capture attention in strategy documents and board presentations.

Somani's research suggests these narratives distract from more immediate and pervasive threats.

 The primary risk most organizations face isn't external weaponization of their AI systems, says Somani. It's that AI will fail during normal operations, and leadership will discover they lack any systematic method to diagnose why it failed, fix the specific problem, and verify the fix worked. This isn't a hypothetical future scenario—it's a pattern occurring repeatedly across industries as AI deployments scale.

This reframing moves AI risk from the domain of security and ethics into operational management. The question isn't whether your organization will face AI failures. It's whether you'll have the capability to respond effectively when failures occur, without resorting to wholesale rollbacks that sacrifice the benefits the system was supposed to provide.

Every deployment of an uncontrollable AI system represents accumulated technical debt. The debt remains invisible while the system performs acceptably. It becomes catastrophically visible when the system fails in ways that expose the organization's inability to diagnose, repair, and verify. The longer this debt accumulates, the more expensive and disruptive it becomes to address.

The Path Forward

We're approaching a divide in how organizations manage AI deployment. 

One path continues current practice: optimize for capabilities, move quickly, accept lack of clarity as the price of performance, and manage failures reactively through rollbacks and manual overrides. This path maximizes short-term deployment speed at the cost of long-term institutional control.

The alternative path, according to Somani's research, treats debuggability as a core requirement from the start. It accepts that some AI architectures, despite strong performance, cannot be deployed in consequential applications because they don't support systematic diagnosis and repair. It invests in verification infrastructure before problems emerge rather than after. It evaluates vendors based on their ability to support organizational control, not just deliver impressive benchmarks.

Somani’s work on formal methods and mechanistic interpretability suggests that the endgame for AI control is not better monitoring or more oversight, but building systems that can be debugged with the same rigor that safety-critical software demands today. This vision does not require that frontier AI models be fully verifiable end-to-end. 

What matters in these times is constructing a family of verified, compositional abstractions that faithfully reproduce model behavior on bounded domains and support predictable intervention.

For leadership teams making AI strategy decisions today, the question isn't whether artificial intelligence will transform operations. That transformation is already underway across industries. 

The question is whether your organization will maintain meaningful control over the systems driving that transformation, or whether you'll find yourself increasingly dependent on black boxes you can explain but cannot truly govern.

Organizations that build the technical infrastructure for localization, intervention, and certification early will be able to deploy AI in high-stakes applications with confidence, respond systematically when problems emerge, and scale their use of AI without sacrificing institutional control. 

Those who prioritize velocity over control will eventually face a choice between limiting AI to low-stakes applications or accepting a dependency on systems they cannot fundamentally manage. That distinction will determine which organizations successfully navigate the AI transition and which discover too late that speed without control isn't progress—it's accumulated risk waiting to materialize.