Short answer

Prioritize designing AI systems that augment human collective intelligence and decision-making processes through collaborative sensemaking, rather than solely focusing on automated efficiency.

Field
Innovation & Design
Source
EPJ Data Science (2026)
Method
Scoping Review and Theoretical Framework Development
Evidence
Moderate effect

AI systems can be designed not just to optimize tasks, but to actively support human groups in interpreting complex information, coordinating actions, and evolving shared values. This innovation & design research insight is drawn from a 2026 study published in EPJ Data Science. Using Scoping review and theoretical framework development, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Prioritize designing AI systems that augment human collective intelligence and decision-making processes through collaborative sensemaking, rather than solely focusing on automated efficiency.

Study
Innovation & DesignNew This WeekModerate effect

AI as a Collaborative Sensemaker: Shifting from Optimization to Collective Meaning-Making

AI systems can be designed not just to optimize tasks, but to actively support human groups in interpreting complex information, coordinating actions, and evolving shared values.

EPJ Data Science · 2026

01

Key Findings

  • 01Current HCAI often focuses on idealized interactions, neglecting real-world pressures and social dynamics.
  • 02AI systems can actively participate in social processes of interpretation, coordination, and value evolution.
  • 03Three key research areas for Sensemaking AI are: sensemaking-aware automation, collective agency for network control, and value-aware sensemaking.
02

Application

Design takeaway

Prioritize designing AI systems that augment human collective intelligence and decision-making processes through collaborative sensemaking, rather than solely focusing on automated efficiency.

How to apply

When designing AI for teams or collaborative environments, consider features that allow users to collectively interpret data, challenge AI outputs, and negotiate shared understanding and goals.

Project actions

  • 01Consider how your design could help users interpret information collaboratively.
  • 02Think about how users can collectively control or influence the AI's actions.
  • 03Explore how your design might support users in discussing and aligning on values.
03

Method & Evidence

AimHow can AI be designed to actively support collective meaning-making and democratic deliberation within human-AI networks, moving beyond purely optimization-driven objectives?
MethodScoping Review and Theoretical Framework Development
ProcedureThe research involved a scoping review of Human-Centred AI (HCAI) literature, drawing upon theories of sensemaking and decision-making to propose a new research and design agenda for 'Sensemaking AI'.
ContextHuman-AI Networks, Complex Systems, Sociotechnical Systems

Variables

IVDesign of AI features supporting sensemaking (e.g., interpretability, collaborative tools, agency controls).
DVQuality of group sensemaking, effectiveness of collective decision-making, perceived value alignment.
CVComplexity of the problem domain, group composition, existing knowledge base.
04

Strengths & Limitations

Strengths

  • +Addresses a critical gap in current AI design by focusing on collective intelligence and meaning-making.
  • +Provides a forward-looking research agenda for more human-centric AI development.

Limitations

It can be challenging to measure 'collective sensemaking' or 'value alignment' objectively in a design project.

Reliability & validity

Reliability could be assessed by consistent user ratings of sensemaking support across different groups. Validity would be addressed by ensuring the measures accurately capture the constructs of collective sensemaking and decision-making.

Think critically

How can we ensure that AI designed for collective sensemaking doesn't inadvertently lead to groupthink or the suppression of minority viewpoints?

05

Design Principles

"Design AI to be a partner in collective sensemaking, supporting interpretation, coordination, and value evolution within human-AI networks."

This perspective challenges the prevailing focus on AI for pure efficiency. By designing AI to facilitate collective sensemaking, we can foster more adaptive, resilient, and democratically aligned sociotechnical systems, especially in complex and evolving problem domains.

06

What This Means for Your Design

Instead of just making AI do things faster, we should design AI to help people understand things better together and make decisions as a group.

How to use in your project

  • 1.Use this research to justify a design approach that emphasizes collaborative interpretation and decision-making over pure automation.
  • 2.Cite this paper when discussing the limitations of purely optimization-focused AI and the need for human-AI collaboration in sensemaking.
07

Add to My Project

08

Quick Cite

Paragraph starter

This design project adopts a 'Sensemaking AI' approach, moving beyond traditional optimization paradigms. Recognizing that complex systems benefit from collective interpretation and adaptation, the design aims to support human-AI networks in collaboratively making meaning, coordinating responses, and evolving values, rather than solely focusing on algorithmic efficiency. This is achieved through features that facilitate shared understanding and collective agency.

09

Source

EPJ Data Science

Sensemaking AI: Introducing a research and design agenda for human–AI networks

journal · 2026

View source

Questions About This Research

What does the research say about ai as a collaborative sensemaker: shifting from optimization to collective meaning-making?
Prioritize designing AI systems that augment human collective intelligence and decision-making processes through collaborative sensemaking, rather than solely focusing on automated efficiency. Evidence: EPJ Data Science (2026).
Why does "AI as a Collaborative Sensemaker: Shifting from Optimization to Collective Meaning-Making" matter for design?
This perspective challenges the prevailing focus on AI for pure efficiency. By designing AI to facilitate collective sensemaking, we can foster more adaptive, resilient, and democratically aligned sociotechnical systems, especially in complex and evolving problem domains.
How can designers apply this research?
Prioritize designing AI systems that augment human collective intelligence and decision-making processes through collaborative sensemaking, rather than solely focusing on automated efficiency.
What were the main findings?
Current HCAI often focuses on idealized interactions, neglecting real-world pressures and social dynamics.. AI systems can actively participate in social processes of interpretation, coordination, and value evolution.. Three key research areas for Sensemaking AI are: sensemaking-aware automation, collective agency for network control, and value-aware sensemaking.
What research method was used?
Scoping Review and Theoretical Framework Development.
How strong is the evidence?
Evidence strength is rated Moderate effect, based on a 2026 journal from EPJ Data Science.
What should I do differently in my next project?
When designing AI for teams or collaborative environments, consider features that allow users to collectively interpret data, challenge AI outputs, and negotiate shared understanding and goals.
What are the limitations?
The proposed agenda is theoretical and requires empirical validation; real-world implementation challenges in complex sociotechnical systems are significant.