Machine to Human Interaction: The “Last Mile” Problem Holding Enterprise AI Back

Thought Leaders Machine to Human Interaction: The “Last Mile” Problem Holding Enterprise AI Back Published July 29, 2026 By Marc Fernandez, CSO, Neurologyca Add Unite.AI to your preferred sources on Google Until very recently, boardroom conversations around AI centered around the models themselves. Is their output accurate? How clean was the training data? How can we reduce bias? Are hallucinations under control? Those questions still matter, of course, but output is no longer the only thing boards and shareholders are paying attention to. Instead, they’re becoming interested in something that’s much harder to measure – whether AI systems are actually working with people as effectively as they could be in day-to-day interactions.  That’s partly why IBM recently found itself facing a shareholder proposal demanding greater transparency around how it manages AI bias and governance. The proposal focused specifically on bias mitigation, but the broader takeaway was that as AI becomes more deeply embedded in decision-making processes, it’s being judged based on its operational accountability and measurable business outcomes rather than its basic level of capability. What I’m seeing in conversations with enterprises is that very few people are still debating whether the models themselves are capable. Most organizations already know these systems can generate outputs, automate workflows, and surface insights at extraordinary speed. The harder question is whether any of that is consistently improving outcomes inside the business. Are employees actually using these systems in meaningful ways? Do they trust the recommendations they’re receiving? Are decisions becoming clearer, faster, or more consistent? Or are organizations simply generating more content, more analysis, and more noise without changing how people operate? That gap between AI capability and human adoption is what I think of as the “last mile” of enterprise AI. It’s where ROI, trust, accountability, and operational consistency converge, and it’s where many deployments begin to stall.Capability isn’t the bottleneck – human context isThe IBM shareholder proposal itself centered on bias, a familiar and growing concern in the industry.  The resolution called on IBM to produce additional reporting on the methods it uses to identify and mitigate bias across its AI models, including the potential risks associated with those mitigation efforts. IBM pushed back, arguing that much of this information was already publicly available through model cards, governance documentation, transparency reporting, and submissions to Stanford’s Foundation Model Transparency Index. Most observers acknowledged IBM has done more than many vendors to operationalize responsible AI practices, while also recognizing that governance and bias remain unresolved industry-wide challenges.What’s interesting to me isn’t whether IBM’s response was right or wrong. It’s what the entire situation says about where enterprise AI conversations are heading. Boards have largely moved into a new phase of AI evaluation focused on decision quality, operational impact, employee trust, and long-term adoption inside the business.This is where a neuroscience lens leads me to a different conclusion than much of the curr