Rewriting Sensemaking: Leadership's Most Human Capability in the AI Playbook

“Sensemaking is more than an act of analysis; it’s an act of creativity.”
—Deborah Ancona & Peter Senge

Why Sensemaking Needs a Rewrite

Sensemaking has long been considered one of the most essential capabilities of leaders and teams. Karl Weick described it as the human process of interpreting complex, shifting environments by “making the strange familiar, and the familiar strange.”

In today’s AI-enabled organizations, sensemaking is changing shape. The challenge is no longer about whether leaders alone can “make sense” of volatile environments. It’s about how leaders orchestrate human + machine sensemaking systems by curating, questioning, and integrating insights generated by algorithms, digital twins, and always-on data flows.

From Human-Only to Meta-Sensemaking

AI doesn’t remove the need for sensemaking. It multiplies it. Leaders now face a new kind of work: meta-sensemaking. Instead of interpreting the environment directly, they seek to understand how the AI interprets the environment. They must ask:

• What assumptions sit inside the model? This refers to the implicit biases encoded in training data, model architecture, or human-labeling decisions. The antidote entails the adoption of a what could be called a cartographic mindset that starts by asking: “What did we choose to represent, and what did we omit?” Explainable AI (XAI) techniques like model auditing, data provenance tracking, and causal inference tools can help surface hidden assumptions.
• Which signals are amplified, and which are muted? Investigation of AI signals is facilitated by tools like feature attribution and model sensitivity analysis. This also includes uncovering feedback loops, where AI systems amplify prior patterns that sometimes distort reality. This mirrors the idea of relating in leadership by determining whose voices are being heard and whose are being overlooked? Importantly, it also maps to sensemaking by recognizing how attention is shaped, distorted, or focused by automated filters. In safety-critical domains like finance, health, and justice, it's becoming mandatory.
• How does algorithmic output align with—or distort—human experience? AI must complement, not replace, human judgment. However, aligning with subjective human experience is still a significant problem, especially across diverse populations. Tools like counterfactual explanations, user-in-the-loop testing, and fairness metrics aim to measure alignment. Just as a leader must relate, envision, and invent, AI must also sensemake by making its reasoning legible while aligning with human values and context, supporting shared goals and understanding (visioning), as well as enabling practical, trustworthy outcomes (inventing).
  
  

Meta-Sensemaking Toolkit

The risk isn’t that AI takes over sensemaking. It’s that leaders will outsource meaning to the machine without applying human judgment.

From Narrative to Simulation

Historically, sensemaking has been about plausible narratives. Leaders weave scattered signals into stories people can understand and act upon.

In the digital twin era, this expands into prospective sensemaking. Leaders don’t just tell stories, they simulate them. With DTOs, leaders can model “what if” futures, test them under stress, and rehearse decisions before making them.

The crucial point is that narratives and human context must come first. Narratives reveal lived experience, tacit knowledge, and anomalies that don’t show up in data exhaust. AI and DTOs can then extend those narratives by clustering, simulating, and scaling them into multiple futures. Without this grounding, leaders risk treating algorithmic outputs as objective “truth” rather than as one perspective.

While stories are still how people make meaning, simulations now make those stories testable and adaptable.

From Personal to System Credibility

Credibility has always multiplied the impact of leadership action. In the AI era, credibility extends beyond the leader, as employees, customers, and regulators now ask: Can we trust the systems you deploy?

Leaders must become the stewards of system credibility by becoming transparent about data lineage and model bias, accountable for ethical use and privacy, and ready to explain failure modes before they happen.

A leader can be personally credible yet lose legitimacy if the systems they sponsor are opaque or unfair. In the AI playbook, credibility must apply equally to leaders and the intelligent systems they rely on.

From Inward Teams to Hybrid X-Teams

Sensemaking has also been a collective endeavor. Boundary-spanning, externally oriented teams have always been stronger at it. In the AI era, teams must go further by becoming Hybrid X-Teams, where human and machine agents collaborate, AI agents scan and cluster weak signals, humans probe contradictions and frame meaning, and digital twins test and coordinate futures. In this manner, leadership shifts from “assembling the right people” to assembling the right people + the right machines in a dynamic configuration.

The Six Shifts in Sensemaking

The table below outlines six key differences between traditional and AI-era sensemaking, highlighting how leadership, credibility, narrative, and data work are evolving to meet the demands of the digital twin and artificial intelligence era.

A New Sensemaking Loop

In the AI playbook, sensemaking looks less like a one-time event and more like a continuous loop:

This loop turns sensemaking into an organizational system capability, not just a personal trait of leaders.

The Aggressive View

Traditional notions of sensemaking are incomplete. It is no longer just a human capability. It is an AI-augmented leadership system, and leaders who ignore AI in sensemaking risk irrelevance. Also, those who over-rely on AI risk hollow credibility, but those who integrate narrative-first inputs with machine-driven simulation will set the pace for the intelligent enterprise.

Closing Thought

Sensemaking has always been the “first move” of leadership. In the AI playbook, it is also the most human move. Because while machines can process signals and simulate futures, only humans can ask: Which future is worth pursuing, and why?

That is the new work of sensemaking. And it is the essential discipline of leaders building the organizations of tomorrow.

Further Reading

• Karl Weick, Sensemaking in Organizations (1995).
• Deborah Ancona & Peter Senge, In Praise of the Incomplete Leader (Harvard Business Review, 2007).
• Gary Klein, Sources of Power: How People Make Decisions (1999) and the Plausibility Transition Model (2020s).
• MIT Center for Collective Intelligence, “AI-Assisted Sensemaking” research.
• Dave Snowden & the Cynefin framework on narrative and distributed ethnography.

Articles in This Series

This is the second article in a DTO series. Read additional articles in this series: 

• Article 1 : The Missing Link Between AI Strategy & Human-Centric Transformation
• Article 3: From Authority to Orchestration: Decision-Making in the DTO Era