The AI podcast for product teams Implementing AI into creative workflows: How to prepare yourself and protect your job
There are many reasons to debate the ethics and implications of AI. But while we do that, hundreds of the world’s biggest brands are rushing to implement the technology into creative and coding workflows. At a time when shareholders are being unforgiving and policy making is volatile, business leaders are looking to AI to gain any advantage possible.
Jan Emmanuele is one of the experts that these Fortune 500 corporations rely on to identify and build GenAI creative workflow augmentations and automations. He works for Superside —whom you might remember from our episode with Philip Maggs (Listen here)— because they’re on the leading edge of creating an LLM that interprets your briefing process, design system, brand guidelines, marketing campaigns, and data to automate high-volume creative tasks.
In this episode, we focus on how and where AI is applied within organizations and workflows. It details how organizations can prepare themselves for implementing AI and how to address the core barriers and risks of the technology.
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What was most interesting about this conversation was his prediction that the adoption of AI will explode in enterprise orgs starting in 2026 and that it could continue into the 2030s. He believes that the value of AI in enterprise has already been proven and that more use cases exist than anyone can believe. That adoption thus far has only been limited because of legal and procurement policies.
If this is true, organizations that aren’t already at least planning for this workflow-automated future will soon be at a huge competitive disadvantage. Finding 10x augmentations of creative output is routinely achieved, and more will be possible for organizations with highly-structured and easily-repeatable workflows. The gains will be largest in orgs that leverage the uniquely-LLM capability of contextualizing outputs based on data. Examples include localizing campaigns to micro-niche segments or regions of the world.
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Headwinds will reduce the number of creatives earning a living wage
As we barrel towards the increasingly inevitable reliance on LLMs, it puts creatives in the uncomfortable position of fighting for their survival and protesting for what’s ethically correct.
The music industry is the canary in the coal mine in this battle. Many artists earn the majority of their income from their back catalogues and LLMS are effectively using those albums as mulch to improve generative capabilities.
On one side, you have an entire way of life being threatened; on the other, you have artists that will quickly need to learn how to master generative capabilities to become an indispensable musician regardless of the headwinds that will reduce the amount of music earning a living wage. As platforms get better, we’ll just generate the music and images we need instead of hiring professionals.
Overcoming the uncanny valley: Not being able to determine what was generated by AI
What has made all of us feel more comfortable has been that AI still sucks at a lot of creative tasks. Blooper reels and countless articles of AI creative generative fails give us hope that the technology isn’t ready to replace anyone yet.
But we’ve learned from our latest episode and many previous ones that the technology is much more ready for primetime than we might believe. Many of the failures we see today result from the false sense of confidence the platforms offer novices. While the simplicity of these tools has exploded the amount of experimentation happening, we’re flooded with more fails than fantastic examples.
Another factor is that the simplicity of the GenAI interfaces obscures the complexity happening in the background. We believe we can generate a campaign-ready 20-second video by typing in a prompt. But the complexity comes from knowing what models, protocols, data sets, and projects to connect for the best outcomes. This is an era dominated by creative technologists who can see these possibilities and stay up-to-date with the latest capabilities.
In the hands of someone who understands how to overcome the rawness of the technology, the possibilities are limitless. And for every project we see published, there are at least another dozen working to push those capabilities further in the near future.
These developments are happening at such a pace that it’s impossible to keep up. For example, researchers have created an agentic, autonomous framework that iteratively structures and refines knowledge in situ.
The point is that whether you agree with the hype of an AI-powered future or not, businesses everywhere will implement it because the impact is increasingly undeniable.
Action items: What can we do to prepare ourselves and our work
I hate that the ethics of AI seem like an afterthought to the beating drum of business automation. It’s deeply uncomfortable that many professions and industries must adapt or face extinction.
The only way to stare into this abyss and feel hopeful is to believe that the rising tide of resentment against big tech will fuel a renaissance of altruistic misfits building the models and layers that do less harm. But that won’t calm the nerves of the musicians and artists who see an end to their way of life today.
We can mourn the tidal wave of change while also preparing for the new world order that comes next.
If you’re a creative:
* Stop undervaluing yourself and your work. Listen back to yourself explain the work you do. Recognize all the steps, decisions, and life lessons you neglect to mention. You need to document who you are to such a granular level that you spot where your genius is most pronounced and where you’re on autopilot. Then consider how to leverage AI to amplify/automate each of those.
* Tap into your most significant creative strengths. You are more than your outputs. You fell into this career for a reason and persist because of at least one exceptional creative strength. Document it and the conditions under which it enhances your work more than others. Now find AI tools that can make that happen more often and for longer periods.
* Lead the change you want to see. Don’t wait for inspiration and innovative products to land in your inbox. Go find them, test them, implement them, and prove if they can or can’t help you achieve your goals.
If you’re a business leader:
* Accept that change is coming fast. You can feel unsure about the technology, worried about the risks, and apprehensive about the costs. But you cannot wait to start imagining what the future of your business and industry might look like. Go through future casting exercises and monitor the countless startups slowly eating away at your competitive moat.
* Empower your team to succeed. Even if people tell you they aren’t worried about the coming change, they probably are. You need to lead them through this and create a shared vision of what the future version of your business and workforce can look like. Include teams in co-creation processes to determine the best ways to empower them to succeed by eliminating barriers and inefficiencies.
* Structure your data and production workflows. AI is most effective in highly repetitive situations where success can be easily evaluated. Businesses will succeed that have standardized their key workflows and have structured data that adds critical context about situations and success. Do the work now before an expensive consultant charges you millions once there’s a veritable gun to your head due to competitive concerns.
Contact me if you need help
Thank you for following the Design of AI podcast and this newsletter. This year, we’ll spend more time discussing this seemingly insurmountable challenge of implementing AI effectively.
Please comment if there are specific questions or topics you need us to discuss. And feel free to vent about topics that you’re most frustrated or concerned about so we know what our community needs.
We’re also hoping to launch some events in major markets this year to bring together early adopters and experimenters with those eager to leverage this technology effectively.
And if you need help with any consulting related work related to envisioning your AI-powered future, email me at arpy@ph1.ca
Product of the month: Raycast
Raycast is a perfect example of the disruptive potential of AI.
While everyone else is running to add bullshitty AI features to make using their products easier, others are rewriting the way we interact with digital experiences. Raycast basically looked at MacOS and said, “Let’s rebuild the entire finder and launcher experience.”
It’s ironic for me because one year ago, I worked on a project where the outcome was the real potential value of AI in a mobile phone experience would be as an assistive launcher experience that eliminates all the inefficiencies of Android.
Well, here it is!
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