Science, Technology & Innovation Authority Director EA FAQs — Why Research Grants, Innovation Programs, and IP Systems ≠ Science & Innovation Enterprise Architecture?

Most Science, Technology & Innovation (STI) Authorities still treat Enterprise Architecture as a portfolio of research grants, startup programs, technology parks, and IP management systems. As a result, EA initiatives fail to convert research into usable capability, align funding with national priorities, connect academia with industry outcomes, reduce duplication across programs, or build sustained innovation pipelines.

STI EA ≠ Research IT.

This Director EA FAQ explains where traditional EA breaks down and how a true enterprise anatomy reveals the structure that funding mechanisms, platforms, and policies alone cannot see, align, or repair.

It explains the logic of shadow innovation anatomies, execution drift across agencies and institutions, and the One Science & Innovation One Anatomy™ imperative.

Q1. Why do research grants, innovation programs, and IP systems ≠ Science & Innovation Enterprise Architecture?

Myth

STI EA = grant portals + innovation programs + IP registries + incubators.

Reality

Science & Innovation is not a funding or program function. It is a national knowledge-to-capability conversion enterprise.

STI Authorities operate through 15 core functions (D1–D15) such as National STI Strategy & Prioritisation, Research Agenda & Mission Definition, Funding & Program Design, Research Institution Governance, Talent & Researcher Lifecycle Management, Infrastructure & Lab Ecosystems, Industry & Commercialisation Interface, Startup & Innovation Enablement, IP & Knowledge Transfer, International Collaboration, Ethics & Standards Oversight, Impact Measurement & Evaluation, and Oversight & Accountability — each with its own P1–P6 execution cycle.

STI IT is only one enabling layer.

EA (Grant & IP Systems) ≠ Enterprise Anatomy.

A dashboard cannot show how mission intent, funding logic, research execution, commercial pathways, and national outcomes align across the innovation lifecycle.

Q2. Why do so many STI IT initiatives fail to represent the enterprise?

Because STI IT automates isolated P5 tasks, while the real operating architecture of science and innovation lives in P1–P4.

Every innovation lifecycle — idea to research to application — operates on a full P1–P6

structure.

P1 (Strategy) defines national missions, priority domains, and capability goals.

P2 (Process) defines agenda setting, funding, research, transfer, and scaling.

P3 (System Logic) defines selection criteria, milestone rules, IP pathways, and funding

continuity.

P4 (Component Spec) defines programs, institutions, labs, IP assets, and datasets.

This is the architecture of science and innovation governance.

Most IT initiatives focus on:

1. application and review workflows

2. grant administration

3. IP filing and tracking

4. reporting and dashboards

These operate largely in P5.

The underlying structure (P1–P4) remains fragmented across agencies, disciplines, and institutions.

This creates the core mismatch:

• IT systems automate funding and tracking

• Innovation operates on knowledge accumulation, transition, and adoption logic that was never unified

Because P1–P4 was never architected:

1. research fragments across themes

2. duplication proliferates

3. promising work stalls post-grant

4. IP remains unused

5. national missions underperform

STI IT does not fail because systems are weak. It fails because it is built on an incomplete representation of the science and innovation enterprise.

Q3. What drives the high project count in science and innovation authorities?

Because innovation is uncertain, exploratory, and path-dependent.

1. A new mission reorients funding.

2. A breakthrough shifts research priorities.

3. A market signal changes commercial focus.

4. A geopolitical constraint alters collaboration.

Each change touches multiple execution layers simultaneously.

High project count reflects innovation system complexity, not inefficiency.

Q4. What is unique about the Science & Innovation functional anatomy?

Science & Innovation uniquely combines long-horizon research with short-horizon policy and funding cycles.

Key drift-prone functions include:

1. Agenda Setting — priorities detached from capability needs

2. Funding Design — milestones misaligned with discovery cycles

3. Research Governance — autonomy without integration

4. Commercialisation — transfer gaps between lab and market

5. Impact Measurement — outputs counted, outcomes unclear

These functions generate strong P1–P6 drift, creating shadow innovation practices across institutions.

Q5. What does P1–P6 look like in the science and innovation context?

This explains how mission intent (P1) degrades by execution time (P6).

P1 Strategy: missions, priorities, capability goals

P2 Process: agenda, funding, research, transfer

P3 Logic: selection, milestone, IP rules

P4 Components: programs, labs, IP assets

P5 Implementation: grant and IP systems

P6 Operations: research, transfer, adoption

Innovation drift occurs when these layers no longer form a single knowledge-to-capability logic chain.

Q6. We already fund research and track outputs. Why redo this?

Myth

More funding and reporting produce innovation.

Reality

Funding enables activity.Enterprise Anatomy enables conversion.

Like the human body, innovation depends on tightly coupled systems — agenda, talent, funding, infrastructure, transfer, and adoption — none optional, none independent.

A Science & Innovation Enterprise Anatomy = 13 Functions × P1–P6.

Traditional documentation never shows:

1. where ideas stall

2. why duplication persists

3. how research fails to scale

4. where IP remains unused

5. why missions underdeliver

You get grants. Not capability.

One Science & Innovation One Anatomy™ collapses complexity into one integrated innovation execution model.

Q7. How do we evolve from EA (STI IT) → EA (Functions) → One Science & Innovation One Anatomy™?

Most authorities stop at EA = grant and IP platforms.

The required evolution is:

Step 1: Elevate EA (STI IT)

Create the P1–P4 model of STI IT itself —mission intent, funding and review processes, embedded selection and IP logic, and system components.

Step 2: Create EA (Functions)

Map all science and innovation functions end-to-end across P1–P6 — agenda, funding, research, transfer, and adoption.

Step 3: Create One Science & Innovation One Anatomy™

Unify all functional models into one integrated science and innovation enterprise anatomy governing discovery, translation, and impact.

This is where fragmentation stops — and national innovation outcomes emerge.

Q8. What can One Science & Innovation One Anatomy™ do that traditional EA cannot?

Traditional EA documents systems.

It cannot see that each agency and institution operates its own shadow innovation logic.

Typical fragmentation includes:

• disconnected research agendas

• duplicated funding

• stalled commercialisation

• unused IP

• diffused accountability

Traditional EA records this fragmentation. One Science & Innovation One Anatomy™ replaces it.

It establishes:

1. one mission intent

2. one funding-to-impact logic

3. one research-to-market pathway

4. one accountability chain

How It Impacts Core Science & Innovation Use Cases

Using One Science & Innovation One Anatomy™, authorities can stabilise:

1. mission alignment

2. research continuity

3. commercialisation outcomes

4. industry integration

5. global competitiveness

6. long-term capability building

With One Science & Innovation One Anatomy™, national innovation governance becomes coherent, sustained, and impact-driven — because it runs on one integrated knowledge-to-capability logic stack.