This will come as no surprise – we are well past the pilot phase of enterprise AI. Business and technology leaders are adopting AI at a rapid pace. As with all things, however, there is bad with the good. AI deployed properly brings massive productivity gains but it also presents us with a new threat vector. Every week, another tool emerges that lets non‑technical employees build something that looks and acts like software, such as a language model to automate a routine workflow. Creating these kinds of capabilities used to require a product team and a developer’s mindset. Now they merely require curiosity, need, and a few minutes.
Accepting that this new AI era is here to stay as its business benefits are many, we must also accept a new challenge: how to keep your AI-powered workflows trusted and secure. To build trust in your AI program, it must be engineered into the systems and processes surrounding it. In the new intelligent enterprise, people are no longer the production line for content or code; they must be the layer of judgment and accountability that keeps AI-powered speed reliable and reasonable to trust. The purpose of people working in an intelligent enterprise is not to slow the machine but to enforce a decision perimeter that aligns AI, makes the output something we can trust, and prevents catastrophic events. Let’s look at how leaders can do just that.
Accountability: From Feeling to Framework
AI Accountability, in the context of agentic AI, is not a vague feeling but rather a measurable function of the modern enterprise. It means tying a clear consequence for every model output to an invested decision-maker with an undeniable chain of evidence. In traditional business where this was required before AI, it would have been a signature for a junior’s work, but in many emerging AI systems, this chain often evaporates between the model’s suggestion and the system’s action.
Our central task as leaders is to design roles within the intelligent enterprise to restore clarity, ensuring a human owner is accountable for every significant AI output that is part of the delivery chain. This practice is what is meant by having “humans in the loop.” These are not auditors tasked with re-doing the machine’s work, but credentialed decision-makers who can realistically judge a specific type of AI output and are mandated to approve or reject it before the output is used for critical actions. And that judgment must be supported by new tools and processes to ensure it is both defensible and fast. Any accountability that demands a line-by-line reconstruction of the model’s process by people in order to verify it will burn out your best people and stall your progress.
The Software Engineer as Accountability Anchor
The tools we’ve seen emerge this year in the software development landscape offer a clear illustration of how this accountability layer can work in practice. New AI development tools have emerged from two camps, each requiring a different approach to risk and roles:
1. Developer Augmentation Tools: Tools like Cursor started by embedding AI assistance into a programmer’s existing working environment. AI proposes edits and runs commands with the engineer always in control. This approach, backed with proper software development practices, is AI in a system that demands accountability by design. The human operator owns every commit, they approve diffs and they gate AI suggested terminal commands, adding trust back in and signing off on the AI’s outputs as it is working at the level of code. Code reviewers see evidence rather than just an AI narrative, which allows for a huge velocity boost without collapsing the control that allows a human to sign off on each pull request. In effect, the system forces the developer to understand, judge, and take accountability for the code as it is written – but without forcing the developer to do the work of generating the code themselves. This type of development practice is fast enough to matter and reviewable enough to scale.
2. Full Generation Tools: Platforms like Lovable generate complete, deployable codebases from plain-English prompts. While astonishingly productive for low-risk tasks like prototypes or internal dashboards, fully generating an entire codebase with AI shifts the burden of accountability for ensuring the code is correct, secure, and scalable off of the developers and entirely onto the model. Because AI generated the output entirely without judgement or review by a qualified engineer who can accept accountability, Lovable-generated apps must be strategically partitioned until accountability and trust in the code can be validated. Such applications can only be trusted when an enterprise applies accountability scaffolding like tests, engineering reviews, approvals, and support policies. The cost of adding that judgement, review, and accountability after the fact is often more expensive than the development augmentation method. Full stack AI code generation is best used for prototyping and research, and then thrown away.
The lesson is practical: usefulness and speed happen when we ensure that accountability establishes trust for the AI output. AI cannot be accountable, therefore we do that when we make sure a human, capable of judging the AI output, has the right evidence to make that decision and is empowered to realign when things go wrong.
The Anti-Patterns: When Trust Collapses
Ignoring this redesign of the human purpose leads to dangerous anti-patterns that hollow out the value we gain from adopting AI and erodes morale:
- The Copy-Pasta Chef: This is the employee who is reduced to copying instructions into an AI and relaying the model’s draft back for review to someone else. This usually happens whenever an employee was, before AI, responsible for generating content but was not accountable for signing off on the work. The actual work climbs the organizational chart, burdening senior staff with unverified AI outputs. Verification can easily become re-work, creating a fragile equilibrium where a few exhausted experts carry a hidden load of validation work, while the organization mistakes rapid generation, without verification, for progress. It’s not progress, because verification is now the value – what the AI model outputs is now the easy part of everything we ship; what’s hard is making such output trustworthy.
- People as the Plan: This occurs when leaders recognize the validation gap but expect an existing workforce to simply absorb the new burden without having what is required: new tools, role redesign, and support. For instance, a SOC analyst asked to “just verify the AI investigation” can find themselves redoing full investigations to verify the AI conclusion because the AI’s drafts were not backed by a clear chain of verified evidence. The risk is that people become professional second-guessers, feeling like friction rather than contributors to your value chain. This anti-pattern is a cultural failure; the system automates the surface of the old job instead of re-architecting the role and the tools needed for team members to accomplish the new work of a modern intelligent enterprise: verification, judgment, and re-alignment.
Scaling Human Judgment
Speed in the world of AI requires leaders to staff, manage, and measure knowledge work differently. When we replace human execution of tasks with AI, it’s imperative that we build systems that isolate the thinking work of human judgment and empower it with new authority and accountability. Every element of alignment and accountability must be pulled out of our old workflows and rebuilt into roles designed for collaboration with intelligent systems, not competition against them.
When you create a clearly accountable and empowered owner for each AI decision, meaning someone who can review the evidence bundle and either approve, adjust, or escalate, you replace hope and blind trust on an inherently untrustworthy model with a defensible process that proves why you can trust what you produced collaboratively with AI. This is the promise of the accountability layer: it turns “move fast” from a slogan into a sustainable habit, ensuring that the right humans, with the right gates, make it possible to accelerate without bracing for impact.
About the Author
Alexander Feick is the Vice President of Labs at global cybersecurity leader eSentire. He leads a multidisciplinary team currently exploring how AI and automation can transform business operations without sacrificing transparency or control. Over a security career spanning architecture, threat response, and strategic innovation, he has guided organizations through the practical realities of keeping their enterprises secure through transformational shifts and technological disruptions. Feick is also the author of On Trust and AI: A blueprint for confidence in the intelligent enterprise, which acts as a field guide for executives navigating the promise and peril of AI transformation.
