Contributor guidelines

1. Purpose and readership

Transformation Expert Insights examines how organisations change: the decisions, behaviours, systems, technologies and working conditions that influence whether transformation delivers meaningful results.

We welcome contributions that connect research with practice, challenge familiar assumptions and help readers understand difficult transformation questions.

Our readers include executives, board members, transformation leaders, functional leaders, programme directors and experienced practitioners. They may be evaluating an opportunity, preparing for change, addressing delivery problems or assessing whether an investment has improved organisational performance.

Articles should offer a clear argument, credible evidence and useful implications for those readers. They should also demonstrate the author’s particular expertise and judgement.

2. Editorial scope and topic areas

We consider the full range of business and enterprise transformation, including changes within individual functions and across organisational boundaries.

These areas overlap. Strong contributions often explain the interaction between them—for example, how an AI initiative changes workforce responsibilities, financial controls and decision authority.

Sector-specific contributions are welcome when they make the operating context clear and identify which lessons may transfer elsewhere.

The list is indicative. We consider additional subjects where the connection to organisational transformation is clear.

3. What makes a suitable contribution?

A strong article:

• Addresses a specific question, decision or difficulty.
• Develops an identifiable argument or perspective.
• Explains relevant organisational conditions and consequences.
• Adds original interpretation, professional experience or research synthesis.
• Uses evidence appropriate to its claims.
• Recognises uncertainty, trade-offs and contextual limits.
• Leaves the reader better equipped to recognise an issue and judge an appropriate next step.

We welcome thoughtful disagreement and examination of established methods. Explain the reasoning and evidence behind a challenge.

We generally do not accept generic introductory lists, lightly rewritten summaries, promotional brochures, unsupported predictions or articles that merely attach “transformation” to an unrelated topic.

4. Article formats and length

Word ranges exclude the abstract, references and author biography.

These are editorial guides rather than targets to fill. Propose a longer treatment when the subject warrants it.

5. Recommended article structure

Use the following structure as a starting point. Headings should fit the argument; every article does not need identical sections.

Title

Identify the substantive subject and angle. Prefer precise language over slogans, exaggerated promises or clickbait.

For example: Executive accountability in AI-enabled operating models is more informative than The AI revolution every leader must embrace.

Abstract: approximately 80–120 words

Summarise the question, central argument and practical significance. Make the abstract useful in its own right.

Opening and context

Introduce the issue and explain who encounters it, why it matters and the circumstances in which it arises. Establish the argument early.

Analysis

Develop the reasoning through connected sections. Explain relevant mechanisms, decisions, dependencies and competing interpretations.

Evidence and examples

Integrate research and professional examples where they support the argument. Make the distinction between evidence and interpretation visible.

Implications for practice

Explain what leaders or practitioners should pay attention to, what assumptions deserve examination and where experienced judgement is needed.

Conclusion

Bring the argument to a clear close. Identify its central implication without simply repeating the introduction.

References and disclosures

Include the sources cited, relevant interests, permissions and any required AI-assistance disclosure.

Author biography

Provide a short account of the experience relevant to this contribution.

6. Writing standards

Write for intelligent, busy readers who may be experienced in transformation without specialising in your discipline.

Use clear, professional language and developed reasoning. Explain specialist terms and define abbreviations on first use.

Our house style uses Australian English, including “organisation”, “behaviour” and “programme”. Preserve official names and source titles in their original spelling.

Please:

• Use connected paragraphs and descriptive headings.
• Prefer concrete observations to broad assertions.
• Explain how a problem arises and why it matters.
• Use examples that clarify rather than decorate the argument.
• Use tables or diagrams when they improve understanding.
• Keep lists purposeful and avoid excessive fragmentation.
• Make recommendations proportionate to the available evidence.

Avoid:

• Generic consultancy language, motivational filler and fashionable terminology without explanation.
• Repetitive rhetorical contrasts and dramatic one-sentence paragraphs.
• Claims that a method “always works”, “eliminates risk” or “guarantees success”.
• Unsupported labels such as “world-leading”, “revolutionary” or “best practice”.
• Treating disagreement, caution or operational constraints as evidence that employees are simply “resistant”.

An academic tone should come from precision and intellectual discipline. The writing should remain accessible and recognisably human.

7. Demonstrating expertise while protecting proprietary methods

Articles should help readers understand the nature of a challenge, recognise important conditions and appreciate the judgement involved in responding.

Authors are not expected to disclose trade secrets or provide their complete delivery methodology.

Suitable material includes:

• Explanations of why a problem occurs.
• Distinctions that improve diagnosis or decision-making.
• Questions leaders should examine.
• Carefully selected observations and examples.
• Trade-offs, dependencies and common interpretation errors.
• High-level accounts of an approach and its boundaries.

Proprietary diagnostics, scoring systems, intervention sequences, detailed playbooks, commercial templates and implementation instructions may remain part of the author’s paid practice.

Protecting those methods should not leave the article empty. Readers should gain a substantive understanding, even where applying that understanding requires specialist assistance.

Demonstrate complexity honestly. Do not manufacture anxiety, make straightforward matters sound inaccessible or withhold the explanation needed to support your argument.

8. Evidence and referencing

Support externally verifiable claims with sources that readers can locate and assess.

Prefer original research, official statistics, standards, regulator publications, original technical documentation and attributable primary records. Use reputable secondary analysis where it adds value or makes an otherwise inaccessible source understandable.

For commercial research or vendor material, identify its origin and consider its methodology and interests.

Use author–date citations in the text, with a reference list at the end. Link the citation or reference to the original source wherever possible.

For a journal article, provide:

Author surname and initials. (Year). Article title. Journal title, volume(issue), pages or article number. DOI or stable URL.

For a report or webpage, provide:

Author or organisation. (Year or full publication date). Title. Publisher, where different from the author. URL.

For changing technical material, include the version or update date and the date accessed where useful.

References should support the particular claim being made. Do not cite a source you have not checked, or present a secondary account as though you consulted the original.

For statistics, state the population, period and context needed to interpret the figure. Explain material limitations. Widely repeated transformation failure rates require the same scrutiny as any other claim.

Clearly distinguish:

• Established research findings.
• Practitioner observations.
• Personal interpretation.
• Hypotheses or emerging propositions.
• Predictions and scenarios.

There is no minimum reference count. The evidence should be sufficient for the argument.

9. Case studies, confidentiality and attribution

Case analyses should explain:

1. The organisational context and problem.
2. The author’s precise role and responsibilities.
3. What was done and the reasoning behind it.
4. The observed outcomes and measurement period.
5. Attribution limits, uncertainties and lessons.

Distinguish an individual’s previous experience from results delivered by their current firm. An organisation’s total investment is not automatically an investment personally managed or a benefit personally delivered.

Use only material you are authorised to publish. Obtain relevant permissions for identifiable client information, quotations, images, data and third-party material.

Anonymisation must be meaningful; removing a name may still leave an organisation or individual identifiable.

Label hypothetical and composite examples explicitly. Do not present them as individual real cases or use them to substantiate claimed results.

10. Standards for AI, digital and agentic topics

Technical claims should identify the product, model, version, deployment setting and date where those details affect interpretation.

Distinguish between:

• A vendor claim and independently examined evidence.
• A demonstration, pilot and production deployment.
• A proposed capability and an observed outcome.
• Technical capability and organisational readiness.
• Task automation and broader changes to work or accountability.

For agentic systems, explain what “agent” means in the article: the actions permitted, degree of autonomy, oversight arrangements and operational boundaries.

Consider relevant implications for data, permissions, security, evaluation, accountability, exceptions, workforce responsibilities and ongoing operating costs.

Avoid universal productivity claims based on a narrow task or a single experiment. Explain what was measured and what remains unknown.

11. Search visibility and AI-assisted discovery

Articles should address questions that relevant readers genuinely ask, using the language they use to describe their work.

Before drafting, identify:

• One primary reader question.
• The intended reader and their situation.
• The article’s central contribution.
• Four to six descriptive keywords or phrases.
• Related questions that genuinely belong within its scope.

Use the central subject naturally in the title, abstract, introduction and relevant headings. Explain important concepts clearly enough that individual passages remain understandable when quoted.

Do not repeat keywords mechanically, create unnecessary FAQs or broaden an article merely to capture unrelated searches.

Search relevance should follow from useful, original writing and clear evidence. Publication does not guarantee rankings, citations or recommendations by an AI system.

These principles follow Google’s emphasis on helpful, reliable content written for people.

12. Use of AI in preparing submissions

Authors remain responsible for every claim, reference, quotation and example submitted.

AI tools may assist with activities such as organising notes, exploring wording or editing. They must not substitute for the author’s expertise, source checking or editorial judgement.

Disclose substantive AI assistance, including drafting, analysis, translation or image generation. Identify the tool and explain its role. Routine spelling and grammar checks do not require a separate statement.

Do not submit fabricated references, invented professional experiences, generated quotations or unverified technical claims. Do not place confidential or restricted material into tools without appropriate authorisation.

AI tools are not credited as authors. This approach is consistent with COPE’s position on authorship and AI tools.

13. Author profiles and commercial interests

Provide a biography of approximately 80–120 words covering:

• Your current professional role.
• Relevant experience and specialist expertise.
• Your location and markets served, where useful.
• Verified qualifications or publications relevant to the article.
• One professional website and an optional LinkedIn profile.

Declare relationships that could influence how readers interpret the article, including employment, consulting work, product ownership, sponsorship or a commercial interest in a method discussed.

A relevant professional link may appear with the author profile. Promotional claims, repeated sales messages and disguised product endorsements are unsuitable for the article body.

A brief invitation to discuss the subject may be considered where it follows naturally from the contribution.

14. Submission and editorial process

Please begin with a short proposal to editor@transformationexpertinsights.org. Include:

• A working title and article format.
• The intended reader and primary question.
• A 150–250-word outline of the argument.
• The experience or evidence you will draw on.
• Your proposed delivery date and brief biography.
• Any prior publication, commercial interests or permission requirements.

Once the proposal is agreed, submit an editable Word document or shared document to editor@transformationexpertinsights.org. Supply figures separately with captions, source details and accessible descriptions.

Original work is preferred. Disclose earlier versions, related publications and intended republication before acceptance.

The Founder & Editor will assess relevance, originality, evidence, clarity and editorial fit. Revisions may be requested. Substantive changes to the author’s argument will be discussed before publication.

Editorial review should not be described as academic peer review unless a separate formal process has been established and identified.

Acceptance, timing, remuneration, publication permission and any exclusivity or reuse arrangements will be agreed separately. Notify the editor promptly if you discover a material error after publication.