HUMAN-CENTERED AI  Use AI to strengthen how your teams work

AI can undeniably accelerate your work. However, teams still need to decide where it adds value, where human judgment matters most, and how the work itself should change.

Those decisions shape whether AI improves the work or simply increases the volume of output.

LUMA's Human-Centered AI approach gives teams a practical way to make those choices. It brings AI into the way people investigate problems, develop ideas, collaborate, and decide while keeping human judgment, creativity, and accountability at the center.

Mural is where the work happens. LUMA is how the work happens. Together, they help organizations build the capabilities and ways of working needed to use AI effectively.

What is Human-Centered AI?

Human-Centered AI is the discipline of strengthening human capabilities by integrating AI into the way people work.

LUMA grounds that discipline in three principles:

• Keep people in the lead. Human judgment, creativity, collaboration, and accountability remain at the center.
• Use AI with intention. Teams decide where AI creates value, where human judgment carries more weight, and when AI should stay out of the workflow.
• Build human capability, not dependency. AI should strengthen how teams think, decide, create, and work together over time.

AI can generate output, surface patterns, and suggest possibilities. People provide the context required to evaluate those contributions and decide what happens next.

As access to AI becomes widespread, organizations gain more from the quality of their decisions about AI than from access alone. Teams need the judgment to apply it selectively, evaluate what it produces, and remain accountable for the outcome.

Apply Human-Centered AI across the LUMA System

LUMA applies a Human-Centered AI lens to the skills teams use to understand situations, develop possibilities, and execute together.

Look

Start with evidence, context, and diverse perspectives.

AI can broaden exploration and surface additional questions. Teams ground the work in real people and real conditions, giving everyone a stronger basis for evaluating what AI contributes.

Understand

Frame the problem before moving toward a solution.

AI can challenge assumptions or help identify what may be missing. People interpret the evidence, determine what matters, and decide which problem deserves attention.

Make

Turn ideas into something you can test and improve.

AI can help teams explore alternatives and create starting points. People evaluate those possibilities, refine them, and decide what is worth developing further.

Align

Build shared understanding and make decisions together.

AI can help organize information and clarify tradeoffs. People discuss different perspectives, resolve disagreements, and own the decision.

The LUMA System gives teams a structure for deciding where AI can contribute and where human judgment needs to lead.

See Human-Centered AI in a team workflow

Consider a team deciding which ideas or initiatives deserve attention first.

A LUMA Importance/Difficulty Matrix gives the group a shared way to compare options based on their relative importance and the difficulty of implementing them. The method makes priorities, assumptions, and tradeoffs visible so the team can discuss them together.

An Importance/Difficulty Matrix is a simple 2×2 grid that helps teams compare options and make priorities visible. Importance runs from low to high on the x-axis, while Difficulty runs from low to high on the y-axis. Teams plot each idea, initiative, or task on the matrix based on those two factors, creating a shared visual of what matters most and what may be hardest to accomplish.

AI can support this method at several points.

Before: prepare the inputs

AI can help teams organize relevant information before the activity begins.

That preparation can reduce time spent sorting through material and give participants a clearer starting point. The team reviews those inputs, adds missing context, and determines whether the information represents the situation accurately enough to use.

The work begins with a prepared set of inputs rather than a blank starting point, while people remain responsible for what belongs in the discussion.

During: support the team's thinking

As participants work through the matrix, AI can help organize input or surface information relevant to the discussion.

The team determines what is important and what is difficult.

Those judgments depend on factors such as organizational priorities, customer needs, technical constraints, risk, experience, and the perspectives represented in the room. Participants compare those factors, challenge assumptions, and adjust their thinking as the conversation develops.

AI supports the decision process. The team makes the decision.

After: synthesize what the team learned

After the activity, AI can help synthesize findings and identify possible next steps.

The team reviews that synthesis against the discussion, corrects anything that misses the intent of the work, and decides what should happen next.

Across the workflow, AI can reduce preparation, organization, and synthesis work. People provide the reasoning, context, and human judgment that give the method its value.

LUMA Workplace supports continued application with on-demand resources and AI guidance for all 36 LUMA methods, giving teams support as they apply the methods to real situations.

How LUMA helps organizations build Human-Centered AI capability

Applying AI well in one workflow is useful. Organizations also need a way to expand that capability across teams and sustain it as the technology changes.

LUMA approaches that work through three connected stages: Assess, Equip, and Scale.

Assess

Understand your organization’s current AI readiness, where AI creates the greatest opportunity, and where teams need support.

A clear baseline helps leaders identify priorities, surface risks, and focus attention where changes to skills or workflows can create the most value.

Equip

Build the skills, judgment, and shared ways of working people need to use AI with intention.

Teams learn to identify the right problems, decide where AI belongs in the workflow, and recognize where human judgment matters most. Connecting that learning to real work turns AI capability into something people can practice and improve.

Scale

Reinforce effective practices through internal champions, continued application, and ongoing learning.

As AI changes, teams need ways to revisit how they work, share what they learn, and improve their practices across the organization. Internal ownership makes that progress easier to sustain.

Put Human-Centered AI into everyday practice

Human-Centered AI becomes more and more useful as teams use it to augment organization-wide capabilities, applying it repeatedly to real work.

LUMA Workplace gives teams continued access to the LUMA System, with on-demand resources and AI guidance for all 36 methods. Teams can use those resources when they need to understand a situation, explore possibilities, prioritize options, or align around a decision.

Over time, that practice builds stronger human judgment about where AI belongs in the work, how to evaluate what it contributes, and when people need to take the lead.

The result is a more deliberate approach to human-AI collaboration, grounded in the needs of the work rather than the availability of the technology.

From AI access to lasting capability

Access to AI does not automatically change how an organization works.

Teams need clear priorities for where AI can contribute, the discernment to evaluate what it produces, and shared practices that connect the technology to real decisions and workflows. They also need opportunities to keep learning as AI capabilities and business needs change.

LUMA helps organizations build that foundation by providing a proven framework to develop practical skills, reinforce new behaviors, and create internal ownership for how AI is applied across the organization.

The goal is a way of working that can improve over time: people remain responsible for the decisions, teams become more confident about where AI belongs, and the organization builds capability that lasts beyond a single tool, pilot, or training program.

Explore Human-Centered AI with LUMA and Mural

AI transformation depends on how people apply the technology in their day-to-day work.

LUMA helps organizations assess where they are today, equip teams with the methods and judgment to use AI effectively, and scale those practices through continued application and internal ownership.

Mural gives teams a shared place to put that work into practice, turning individual contributions into shared understanding, alignment, and action.

Together, LUMA and Mural help organizations build more confident, intentional, and human-centered ways of working with AI.

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