Let’s look into the future and imagine it’s 2035. The Futurist in me believes the media and marketing landscape will have settled into a new equilibrium after nearly a decade of AI-driven transformation. Looking back, the industry will have branched into two distinct archetypes: the thriving ‘Craft Integrators’ with robust margins and enviable talent retention, and the struggling ‘AI Dependents’ caught in a commoditised spiral of shrinking margins and talent shortages.
The difference would not be determined by which organisations adopted AI, they all did. The divergence occurred based on how organisations managed technology integration with their most valuable asset: human intellectual property and craft skills.
Three critical decision points between 2025-2030 will have determined the path organisations will have taken, creating ripple effects that have defined 2035 winners and losers.
So, let’s look at three hypothetical decision points of the next decade:
Decision Point 1: The entry-level talent decision (2025-2027)
As Robotic Process Automation (RPA¹) and Generative AI² increasingly handled routine tasks, organisations faced a strategic challenge, how to evolve entry-level positions?
The Craft Integrators recognised entry-level positions served a purpose beyond menial output, they were essential training grounds where young professionals developed foundational skills, best practice habits and craft expertise. They transformed them into "AI + Human" traineeships where juniors learned traditional skills while also developing AI expertise in collaboration.
But the AI Dependents took a more expedient approach, reducing the number of junior positions. While delivering financial benefits, by 2026, these organisations encountered an unexpected challenge: their talent pipeline had critical gaps. The professionals who should have been developing into managers simply weren't there, creating a generation gap in their organisational structure.
And so, we move onto decision point 2 -the knowledge transfer decision (2026-2028)
As AI sophistication increased, organisations faced another critical choice: how to capture and transfer knowledge, wisdom and skill in their experienced professionals.
The Craft Integrators implemented deliberate knowledge transfer through integrated cadetship and mentorship programs where seniors and juniors collaborated on AI-led projects. They documented not just what was done, but importantly why it was done, transferring valuable intellectual property (IP) knowledge.
The AI Dependents assumed their AI systems were automatically capturing and retaining craft IP. They failed to recognise that the most valuable IP wasn't the final output but, in the creation, and decision-making process. By 2028, as experienced professionals departed, these organisations discovered they had created a knowledge vacuum their AI systems couldn't fill.
Decision Point 3: The AI Governance Decision (2028-2029)
By 2028, AI tools had become so seamlessly integrated that organisations faced a profound question: where should boundaries be drawn around AI usage to prevent skill dilution?
The Craft Integrators established clear ‘AI governance frameworks’ that designated certain activities as ‘human-first, AI-assisted’. They even implemented regular ‘AI-free days’ where teams worked with traditional methods to maintain a skills-first culture.
The AI Dependents embraced unlimited automation. The efficiency gains were impressive, but by the start of 2028, their human teams had lost the ability to think critically. When faced with novel problems outside the AI's training parameters, these teams struggled to develop innovative solutions.
And so, we see the industry move into, The Craft Renaissance of 2030.
By 2030, the competitive advantage of human expertise had become undeniable. Organisations that maintained craft capabilities consistently outperformed AI-dependent competitors in new business pitches, strategic innovation, employee health and happiness and client retention.
This sparked the ‘Craft Renaissance’, period dominated by heavy investment in rebuilding lost capabilities. The AI Dependents scrambled to recreate cadetship and mentor programs, facing a significant challenge: who would do the teaching when an entire generation of craft practitioners had left the industry?
Some organisations paid premium salaries to lure craft experts out of retirement back to the industry. Others merged with Craft Integrators to access their talent pipelines. The most desperate launched expensive ‘craft academies’ to rebuild capabilities from scratch, all at a far higher cost than would have been required to maintain these capabilities in the first place.
So, what lessons from this hypothetical future world can we learn? For leaders in 2025 looking toward this future, the message is clear: integrating AI isn't a choice between human craft and technological efficiency. The organisations that will thrive in 2035 are already making decisions that preserve human craft alongside AI adoption.
The path forward requires:
- Re-designing institution education, readying students for evolved entry level roles.
- Creating AI-augmented entry level cadetships.
- Creating explicit programs for transferring intergenerational IP.
- Establishing AI usage governance to prevent long-term craft skill dilution.
- Investing in long-term continuous craft development at all levels.
The technology convergence we're experiencing isn't about replacing human capability; it's about enhancing it. The organisations that understand this fundamental truth are already building their competitive advantage for 2035, one craft skill at a time. Are you?
Geoff Clarke, Chief Operating Officer, IPG Mediabrands, Australia
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¹RPA, or Robotic Process Automation, is a technology that uses software robots (or bots) to automate repetitive, rule-based tasks in business processes. These bots mimic human actions, interacting with applications and systems to perform tasks like data entry, form filling, and report generation, freeing up human employees for more complex work.
²Generative AI refers to artificial intelligence systems that can create new content, such as text, images, audio, and video, rather than just analysing or classifying existing data.