The HR Model That Got You Here Will Not Get You to 2035 — and Why AI Is Your Co-Pilot, Not Your Successor
From the desk of CEO Jerome D. Dummars, with Christina Pucana of Kismet HR: why outdated HR operating models fail the next decade, and why the data says AI augments far more than it automates.
There is a hard truth executive teams must confront: the HR model that sustained your organization for the past decade will not sustain it for the next one.
This is not about trends. It is about structural change.
Many organizations digitized outdated HR processes instead of redesigning them. They automated inefficiency rather than eliminating it. The result? Advanced tools operating inside outdated models.
If any of the following sound familiar, transformation is no longer optional: performance reviews happen annually while skills evolve every 12–24 months; recruiting, workforce planning, and learning operate in silos; hiring begins only after attrition occurs; compliance dominates strategy instead of enabling innovation.
The solution is not adding AI to job descriptions or purchasing another platform. Transformation means repositioning HR as enterprise architecture. It requires moving from support function to strategic partner, from vacancy filling to capability engineering, and from reporting to predictive intelligence.
By 2035, HR will function as an embedded operating system across the enterprise — powered by data, predictive analytics, and integrated workforce design. The only decision ahead is this: will transformation happen deliberately, or under pressure?
At DCL, we guide organizations through this evolution using our T.I.M.E. framework — Technology, Integration, Management, and Expertise. We do not modernize HR for appearance. We modernize it for performance. The road to 2035 has already begun.
The "Replacement" Myth: Why AI Is Your New Co-Pilot, Not Your Successor — Christina Pucana, Team Partner, Kismet HR
The 9% reality: why AI augments more than it automates. The headlines love a good ghost story, and right now, the ghost is AI coming for your paycheck. But as we look toward the future, the data tells a much more nuanced — and optimistic — story. If your leadership team makes decisions based on fear of displacement rather than the strategy of augmentation, you are already falling behind.
The statistics vs. the scares. Current research from the IMF and OECD indicates that while up to 60% of jobs in advanced economies are "exposed" to AI, only about 9% of those roles are at high risk of total automation. The determining factor isn't the job title — it's the task bundle.
The three factors of replacement. Whether a role is replaced or augmented generally comes down to three structural factors. Codifiable vs. tacit knowledge: AI excels at codifiable tasks — writing a standard report, basic coding, or data entry. It struggles with tacit knowledge: the intuition, empathy, and lived experience that an expert brings to a complex problem. Repetitive vs. variable environments: if a job happens in a perfectly predictable digital box, it's at risk; if it requires navigating human emotions, physical unpredictability, or ethical gray areas, it is primed for augmentation. The entry-level gap: we are seeing a hollowing out of junior-level tasks. This is the real challenge for HR — how do we train the next generation of experts if the grunt work they used to learn on is now handled by an AI agent?
The HR impact: architecting the human-in-the-loop. HR's value is shifting from hiring a person for a box to human-AI integration. That means redesigning workflows — identifying which 30% of a manager's job should be offloaded to AI so they can spend 100% of their human time on coaching and strategy. It means upskilling for governance — teaching employees not just how to use AI, but how to audit it, guide it, and verify its output. And it means managing the pipeline — creating new ways for junior talent to gain tacit experience now that the entry-level tasks have changed.
The road ahead. The goal isn't to build a dark warehouse of AI agents. The goal is to build an organization where humans are super-powered by technology. Ensure your people have the high-level skills that AI simply cannot replicate. The future isn't AI vs. human; it's the human + AI team vs. the organization that is still stuck in the past. Deconstruct existing roles into specific tasks to identify where AI should automate routine data-processing, then aggressively reinvest the saved time into structured training for uniquely human power skills like ethical judgment, complex empathy, and strategic ambiguity.
