The Next Market Will Be Built on Logic

Written By Abubaker Obeidalla

Is there another way to think about data?

        DATA
         │
         ▼
     KNOWLEDGE
         │
         ▼
       LOGIC
         │
         ▼
     EXECUTION
         │
         ▼
       OUTCOMES

A Small Story

A few years ago, a clinician faced a familiar situation.

An elderly patient arrived with a complex medical history: multiple medications, several diagnoses, conflicting test results. The clinical guidelines existed. The electronic record contained thousands of data points. AI-assisted tools were available.

Yet the key question remained unresolved:

What should be done next?

The knowledge existed.
The technology existed.
The data certainly existed.

What was missing was something simpler and deeper:

a clear logical pathway connecting knowledge to action.

This experience is not unique to medicine. The same pattern appears across industries. Organisations accumulate vast stores of knowledge but struggle to convert them into consistent decisions.

That gap — between information and action — is where the next major market is beginning to emerge.

The Historical Pattern of Markets

Each major technology era has been defined by a foundational layer.

EraDominant LayerExample Companies1990sInfrastructureCisco, Intel2000sSoftware PlatformsMicrosoft, Oracle2010sData PlatformsSnowflake, Databricks2020sArtificial IntelligenceOpenAI, Anthropic

Today, organisations possess unprecedented technological capability.

Yet a fundamental problem persists.

Systems can process information at enormous scale, but they still struggle to reason transparently about what should happen next.

The Missing Layer

Most systems currently follow this pattern:

        Data → Software → Automation

But something essential is missing.

A more stable structure looks like this:

                 Data → Knowledge → Logic → Execution → Outcomes

The logic layer determines:

  • when a decision should occur

  • which knowledge is relevant

  • what conditions must be satisfied

  • what action should follow

Without this layer, systems rely on either manual interpretation or opaque automation.

Why This Matters Now

Three forces are converging.

1. Information Overload

The volume of data available to organisations continues to grow exponentially.

Research by the International Data Corporation (IDC) estimates that global data creation reached over 120 zettabytes annually by the mid-2020s.

More information does not automatically produce better decisions.

2. Automation Everywhere

Automation has moved from manufacturing into knowledge work.

Workflow systems, AI tools, and decision-support platforms now influence financial systems, healthcare, logistics, and public policy.

However, many of these systems operate as black boxes.

This raises questions of transparency, accountability, and trust.

3. Trust and Governance

Governments and regulators increasingly demand that automated decisions be explainable.

The European Union’s AI Act, for example, requires transparency around high-risk automated systems.
Similarly, research in explainable AI highlights the need for systems that can justify decisions rather than simply produce outputs.

This places pressure on organisations to make their decision logic explicit.

The Emerging Logic Market

When a structural need becomes widespread, a new market usually forms around it.

Historically, markets formed around:

  • networking infrastructure

  • operating systems

  • cloud platforms

  • data warehouses

The next emerging category appears to be decision infrastructure.

This includes tools that allow organisations to:

  • define decision pathways

  • test reasoning structures

  • audit automated processes

  • govern AI behaviour

In other words, systems that treat logic itself as infrastructure.

Early Signals

Several developments already hint at this shift.

Examples include:

  • policy engines in cloud computing (such as Open Policy Agent)

  • workflow orchestration systems (e.g., Temporal)

  • explainable AI research

  • rule-based automation frameworks

  • decision intelligence platforms

These systems represent early attempts to formalise reasoning inside operational environments.

The Deeper Insight

Knowledge alone has never been sufficient.

In 1945, engineer Vannevar Bush wrote an essay titled “As We May Think” describing the growing challenge of organising scientific knowledge. Even then, the problem was clear: humanity could produce information faster than it could meaningfully use it.

Nearly eighty years later, the challenge remains.

We have built systems that can generate knowledge rapidly.

What we have not yet fully built are systems that can structure and execute reasoning at scale.

Where the Opportunity Lies

The organisations that define the logic layer will influence how knowledge becomes action.

They will control:

  • how decisions are structured

  • how automation is governed

  • how AI is safely deployed

In effect, they will control the bridge between information and outcome.

One Line Insight

The next major technology market will not simply be about generating intelligence.

It will be about building the systems that decide how intelligence is applied.

References

  • Bush V. As We May Think. The Atlantic, 1945.

  • European Union. Artificial Intelligence Act. 2024 regulatory framework.

  • IDC. Global DataSphere Forecast.

  • Doshi-Velez F, Kim B. Towards a Rigorous Science of Interpretable Machine Learning. arXiv.

  • Temporal Technologies. Workflow orchestration systems.

  • Open Policy Agent (OPA) project documentation.

Abubaker Obeidalla
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The Decision Economy