Strategic Considerations for AI Video Tool Adoption – Lessons from the Dawn of Desktop Publishing

Observations

The business landscape is witnessing the rapid proliferation of AI video creation tools. These platforms offer the compelling prospect of generating video content quickly, at low cost, and without the need to invest in cameras and studios. In turn, this enables staff outside traditional video creative departments to produce video materials.

This mirrors the excitement surrounding the advent of desktop publishing (DTP) in the mid-1980s, a period that offers valuable lessons for navigating the current technological shift.

Learning From the Past

The DTP revolution was catalysed by the convergence of the Apple Macintosh personal computer, the Apple LaserWriter printer equipped with Adobe’s PostScript language, and intuitive page layout software like Aldus PageMaker. Later, competitors like QuarkXPress and Ventura Publisher further expanded the market. This combination democratised the creation of professional-looking documents – brochures, newsletters, reports – which previously required specialised typesetting skills, expensive equipment, and significant lead times, limiting production to large firms or dedicated print houses. DTP promised in-house control, cost savings, and faster publishing¹.

However, the initial reality of DTP adoption was fraught with challenges. Early systems suffered from technical limitations. More significantly, the ease of access to DTP tools did not translate directly into high-quality output. A widespread issue arose from untrained users producing poorly organised, unprofessional layouts, often dubbed the ransom note effect.

This highlights a critical point: providing access to a powerful design tool does not automatically confer the expertise needed to use it effectively. Achieving professional results with DTP required learning the principles of typography, page layout, graphic design, and even pre-press production – skills traditionally held by trained professionals. The democratisation of the tools, therefore, paradoxically led to an initial dip in the average quality of published materials, as the number of unskilled users vastly outnumbered those with the requisite expertise.

Achieving a positive return on investment required more than just purchasing software; it demanded strategic implementation, skill development, and a focus on quality.

Back to the Future with AI

These historical parallels serve as a crucial advisory for adopting AI video tools. The allure of easy video creation risks repeating the pitfalls of early DTP. Distributing powerful AI video tools without robust training, strategic oversight, and quality control mechanisms could lead to inefficient use of staff time, inconsistent branding, and outputs that fail to meet business objectives.

Creating effective video content necessitates more than software proficiency; it requires understanding narrative structure, visual storytelling, brand identity, ethical considerations, and editing techniques – skills analogous to the design expertise vital for successful DTP.

There is a tangible risk of employees spending valuable time generating mediocre videos that detract from core duties and deliver little real business value, confusing output volume with impact. The history of DTP also suggests that such technologies ultimately augment rather than eliminate skilled roles.

While DTP transformed workflows and required new skill sets, it increased the demand for proficient designers who could leverage the tools effectively, thereby professionalising the field.

Similarly, AI video tools are best viewed as powerful assistants for skilled communicators, designers, and marketing professionals, enhancing their capabilities rather than replacing their strategic and creative input, particularly for critical communications. A strategic, measured approach is therefore essential, balancing technological potential with indispensable human expertise and oversight.

Next Steps

To navigate the adoption of AI video tools effectively, management should consider the following actions:

• Initiate Pilot Programmes: Implement small-scale trials with specific teams and defined use cases to assess tool suitability, workflow impacts, training needs, and output quality before rolling out to a broader audience.
• Develop Clear Guidelines: Establish comprehensive policies that cover brand consistency, ethical usage (particularly regarding digital likenesses), acceptable quality standards, data privacy, and appropriate scenarios for AI-generated videos.
• Invest in Targeted Training: Provide training beyond basic tool operation, focusing on video production fundamentals, storytelling, brand alignment, and ethical considerations for designated users. Identify and address skill gaps.
• Define Roles & Responsibilities: Assign responsibilities for tool usage, differentiating between specialist augmentation and potential non-specialist use under strict guidelines and supervision. Consider creating a home base in the organisation for the function of video creation (not necessarily a dedicated role), much like for website creation, as a service.
• Focus on Integration & Value: Prioritise integrating AI tools strategically within existing communication and creative workflows. Measure success by tangible business outcomes (i. e. increased click-through rates, higher sales results, etc.) and quality improvements (sentiment and comment analysis), not merely video output volume.
• Appoint Oversight: Designate a specific team or individual to oversee AI tool implementation, monitor adherence to guidelines, evaluate performance, manage risks, and adapt strategy as the technology evolves.

Table: AI Video Tool Comparison

While the AI video generation landscape is rapidly evolving, here’s a comparison of some prominent tools. Please note that pricing and specific features are subject to change frequently.

Important Considerations:

• Accessibility: some tools, like OpenAI’s Sora, have limited public access or waitlists. Veo 2 is becoming more accessible through Google’s platforms.
• Focus: some tools specialise in specific areas, such as AI avatars (HeyGen, Synthesia, Deepbrain AI) or cinematic realism (Luma Dream Machine).
• Intended Use: consider whether you need short clips for social media, longer marketing videos, training materials with avatars, or highly stylised artistic content.
• Ease of Use: some platforms are more user-friendly for beginners, while others offer more advanced controls for experienced creators.
• Free Plans/Trials: many platforms offer free plans with limitations or trial periods, which is a great way to test them out.
• Integration.
• Content interoperability.

Footnotes

1. ‘Design Software History: Aldus PageMaker: Revolutionizing Design with Desktop Publishing in the 1980s’, NovEdge, 2025. ‘Desktop Publishing 1985–1991: The Apple Computer Story’, Museums Victoria Collections, 2020. ‘The Evolution of Digital Marketing: 30 Years in the Past & Future’, Digital Marketing, 2024.