From graph to impact: How Adoption Architecture turns graph data into decisions

In my work with major enterprises, I’ve seen graph technology provide the contextual awareness people need for real-time decision-making in use cases from aviation to insurance.

In a table-based world, users need to query multiple systems and reports to manually analyze factors. A graph connects the data so we can easily see how different processes interact and affect outcomes.

But here’s the key: Graph only becomes valuable in the hands of a decision-maker. Unless you create a simple, intuitive adoption layer (a chatbot, a workflow tool, a visualization), your graph is technically impressive but practically unused.

That’s why we developed Adoption Architecture: a framework that flips the usual approach on its head. Instead of starting with the data model, we start with the decision-maker. It’s built on four layers:

1. Decisions – What real-world questions are users trying to answer?
2. User Experience – How will they interact with the graph day-to-day?
3. Foundations – What infrastructure makes it reliable and secure?
4. Graph Model – Only then do we design the nodes and relationships.

Last week, I shared this framework and my learnings with audiences at AWS re:Invent in Las Vegas. Here’s what you need to know.

Layer 1: Decisions

Defining the purpose of your graph

Users aren’t asking for a graph, they are asking for answers from their data. Before beginning to model, you need to know what their questions are.

At a major U.S. airline, maintenance technicians troubleshooting faults on planes were asking: Has this issue happened before? What solved it last time? Is this going to delay the next departure?

Likewise at a financial services firm, leaders wanted to understand how individuals, teams, and offices were operating for workforce planning. The decisions at hand were about keeping regional offices performing in line with business goals, spotting where needs more investment and enablement to do so.

Only when you know what questions will be asked of the graph can you begin to assess how to deliver the answers.

Layer 2: User experience (UX)

The value layer

Adoption is about ease of integration into everyday life; users want to feel empowered, not disrupted. You need a deep understanding of their existing workflows to design a UX layer that is compatible, so you’ll need to work closely – sometimes physically closely – to achieve this. The time and commitment required to do this is often underestimated.

At the airline, our users were technicians tasked with fixing faults as quickly as possible. We visited hangars across the East Coast, observing day and night shifts, to see how they searched for information - and what slowed them down. These site visits proved the need for a chatbot interface, delivered on an app in their pocket, which they could query on-the-go.

Meanwhile, the analysts at our insurance client were already comfortable using dashboards to interrogate data. They needed the ability to get hands-on with our graph. Neo4j Bloom provided the visualization layer to search the graph for gaps and trends, creating no-code dashboards that teams could integrate into their reporting and decision-making.

Layer 3: Foundations

The capability layer

The foundations exist to support the user experience, not the other way around; focus on what the downstream UX layer actually requires.

The Foundations layer includes:

• Neo4j Aura for managed hosting*: fast deployment, low infra overhead, enterprise-ready
• Data pipelines: Databricks jobs, AWS Glue, Google Dataflow, orchestration tools
• APIs and endpoints: connecting the graph to chatbots, dashboards, and internal systems
• Authentication and role-based access: ensuring people see what they’re supposed to see
• Governance and lineage: give transparency for compliance and audit
  

This layer is what keeps the graph secure, up-to-date, and available. It is about more than technical capability; it is how you will deliver accurate, context-aware recommendations that build user trust and drive adoption.

*A note on hosting: Self-hosting introduces DevOps overhead, capacity planning, security reviews, and long approval cycles — all before users even touch the graph. Starting with managed services like Aura accelerates adoption because it lets teams focus on proving value first.

Layer 4: Graph Model

Designed last, defined by context

Why the model comes last: once the decisions are clear, the user experience is defined, and the foundations are in place, the graph model becomes far easier to design – and far more aligned to value.

In the airline, for example, early conversations explored a plethora of data points across operations which made the model too complex and, frankly, irrelevant. Once we defined the technicians’ workflow, the model’s data requirements simplified quickly: the equipment involved, the issue being reported, what had been tried before, what actions were taken. The final model was more intuitive, far easier to maintain, and quicker to get into production in order to start driving value.

Concluding thoughts

Graph isn’t a data project – it’s a decision project.

When you design your graph around the decisions your users need to make, you create something far more powerful than a dataset. When you design a graph that answers business-critical questions, with the infrastructure and interface that people use and trust, you create clarity.

From there, everything else becomes easier: analytics, automation, and the oh-so-sought-after AI integrations. To get there, you must first prove the value of your graph through adoption – and the ROI of the decisions being made. With the Adoption Architecture framework, this outcome is defined from the start.

To learn more about Kubrick’s approach to delivering value from graph technology with Neo4j, get in touch: speaktous@kubrickgroup.com