Who is “In-the-Loop” and Who is “At-the-Wheel”?
Who is In-the-Loop and Who is At-the-Wheel?
The concept of “human-in-the-loop” has become a common refrain in technology discourse, framed as a necessary condition for ethical, reliable, and accountable AI systems. As experts in enterprise AI have noted, keeping humans involved in decision-making helps balance speed with prudence, ensuring that automated insights are anchored in human judgment and accountability rather than machine autonomy alone.
Forbes contributors have stressed that AI can serve as a powerful predictor and pattern-finder, but humans are indispensable for interpreting, contextualizing, and acting on those predictions. Other industry voices go further, advocating that humans should not just be in the loop, but at the wheel, directing AI’s role rather than merely reviewing its outputs.
“I’m starting to dislike the term ‘humans in the loop.’ I think it’s ‘humans at the wheel.’ It’s not just about working with the machines. It’s about directing them.” — Kelly Jones, Chief People Officer at Cisco, quoted in Forbes on reframing human involvement from oversight to agency.
This distinction matters when AI is applied to professional work such as data extraction, data transformation, and professional document generation. Large “black box” AI systems and corporate model training often attempt to run complex tasks and surface results that are difficult to inspect, fine-tune, or verify. This frequently leads to hallucinations presented as facts, undermining trust and undermining outputs.
To be relevant, emmerging AI tools need to reframe the relationship between human and machine: the human remains in command of relevance, judgment, and final decision-making, while AI is kept "in-the-loop" as an adjunct that accelerates busywork and supports discrete, well-defined sub-tasks.
What Happens When AI is At-the-Wheel?
Many enterprises have discovered that the promise of “train your own corporate model” (putting AI at the wheel) often falls short in practice. Efforts to build and fine-tune custom models on proprietary data can be time-consuming, technically complex, and fraught with errors, especially when workflows are brittle and context is lost. According to a major industry study:
“Only about 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact… Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.” — MIT State of AI in Business 2025 report on enterprise AI pilots.
This high failure rate underscores the challenges enterprises face when they treat model-training as a panacea. Instead of attempting to build monolithic, self-learning systems that must ingest and generalize from all of an organization’s data, AI tasks should be broken into smaller, well-scoped executions where the system’s role is clearly defined and bounded. In practice, this means segmenting complex work into discrete steps, designing templates and prompts with precise intent, and verifying extracted data in a human-controlled “staging area” before it influences any downstream content. Doing so reduces the likelihood of large errors and insidious hallucinations, ensures each automated contribution is deliberate and transparent, and preserves human agency at every point where meaning, consequence, and professional responsibility are at stake.
All that Effort - And Then the Model Collapses
In addition to the well‑documented challenges with enterprise model-training AI pilots, there is also a not-so-subtle risk that models can deteriorate when they are trained on data that includes their own previous outputs. This phenomenon, sometimes described in the research literature as model collapse, occurs when errors and artifacts from AI‑generated data are fed back into training, causing the model to lose fidelity over time.
Rather than relying on a coporate AI model that must be continually retrained and where mistakes can become amplified and hallucinations entrenched, a more reliable strategy is to break work into small, precisely defined tasks with tight human oversight.
Tag Keeps the Human-at-the-Wheel
In Tag’s AI‑in‑the‑Loop design, each step, from data extraction to transformation to final assembly, is controlled by humans, with AI executing only well‑scoped operations where and when it makes sense.
In this “white box” approach, the human user can build a library of prompts that precisely define the level of latitude AI is allowed for each task. For instance, a prompt can instruct the AI to “extract verbatim” (minimal latitude), “summarize” (moderate latitude), or “interpret” (maximum latitude), with all outputs reviewable and editable in a staging area before influencing downstream results. This approach minimizes the risk of errors propagating, preserves human judgment, and ensures AI contributes effectively without compromising workflow integrity.
In our next post, we will explore how the Tag system is architected for data integrity and security, including how customer data flows, how we avoid training on or storing client data, and how compliance with standards such as HIPAA and PIPEDA is maintained.
