The conversation around workplace AI is shifting. While 2025 focused on basic AI literacy such as teaching employees to use ChatGPT for emails or automate repetitive tasks, 2026 demands something fundamentally different: building truly AI-native teams that completely redesign how work gets done.
The distinction matters as McKinsey reports, 88% of organizations are now regularly using AI in at least one business function, up from 78% the previous year. More critically, the research reveals that half of all respondents report their organizations are using AI in three or more functions, indicating that AI is no longer optional but becoming deeply embedded across enterprise operations. While proficient users leverage AI to optimize existing work and deliver incremental improvements, AI natives use first-principles thinking to reimagine and orchestrate solutions with AI at their core to deliver transformational impact.
Consider a practical example: Instead of asking ChatGPT to create a job description, an AI-native approach connects an AI notetaker to a Zoom role intake meeting between the hiring manager and people team. At the meeting’s conclusion, an AI agent drafts all required job posting materials based on the transcript. The agent prompts the hiring manager for review, and a simple response or emoji triggers the agent to post the role externally. This approach rethinks the entire process end-to-end, not just one step within it.
Start with a Mindset Shift
There isn’t one way to execute a transformation as every company is unique with its own circumstances and context. One thing that is universal is it requires a mindset shift. HR leaders need to acknowledge that the goal of AI literacy is efficiency and the goal of AI-native capability is reinvention. It’s moving from “everyone should know how to safely use AI” to “we need to change our operating model because AI is always on.”
Then, begin with a pilot process using an AI-native approach that can be iteratively tested and improved to provide a low-stakes yet impactful starting place for any function.
As technology and tools improve, these processes will need to evolve continuously. Understanding the exact goal and reverse-engineering the definition of success helps teams establish guardrails around what will inevitably be an ongoing, ever-evolving process.
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Build the Core Capabilities
Next, it’s time to build the three core capabilities that define AI-native teams. First, teams need to get a thorough understanding of workflow design. They need to learn how core workflows are built, identify handoffs, and determine which tasks are better handled by human versus artificial intelligence.
Second, they need to master decision design, quality, and speed. Teams need to understand the types and frequency of decisions made in their functional area along with dependencies. When decisions are highly manual, slow, and have predictable dependencies, prompt-chaining agents can dramatically improve the process. Coupled with conditional logic, multi-step instructions, and quality control patterns, decision-making velocity and quality can be AI-augmented.
Third, understanding the difference between data literacy and iterative improvement. That includes understanding predictive versus reactive feedback signals. AI natives must be familiar with leading versus lagging indicators and how to leverage predictive insights and strategic modeling to configure AI for bringing foresight to decisions.
Break the Knowledge Bottleneck
Emerging tools can help with these learnings. Loom now prompts users to create operating protocols from recorded videos. Script allows teams to document processes while in the flow of work itself through screen-sharing, auto-generating repeatable processes.
This also serves to eliminate traditional team bottlenecks between the tenured employee who is the only person knowing how to complete a specific process and the new employee who must train for months before becoming productive. As long as there is one person within the function who is AI-native and can reimagine processes and workflows with enough functional expertise to knowledge-share, teams can ensure someone starts producing instantly.
The Biggest Misconception Holding Teams Back
The most common misconception is that HR must build a formal professional development program or class to “teach” people how to use AI. This is not an initiative HR can solely own. Similar to any transformation, it may be championed by People Teams, but it must be owned by the entire leadership team to drive widespread adoption.
This means cultivating a culture of experimentation, encouraging everyone across the organization to test different solutions and approaches, and having a data-driven approach to problem-solving so teams can evaluate what’s working, what isn’t, and where AI can meaningfully improve speed, quality, or consistency. It means engaging all leaders as stewards of AI transformation, ensuring that every department leverages AI.
Adoption doesn’t come from training alone. It comes from leaders modeling new behaviors, redesigning workflows, and reinforcing the expectation that AI is a core part of how we operate in 2026 and beyond. When AI becomes embedded into the operating rhythm of the company, not as a class people complete, but as a capability everyone practices every day, every team will become an AI native team.
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