How to reframe product packaging with AI for growth hacking

Artificial intelligence is knocking on the door of product packaging design.

Changes for the better. Creating packaging customers will love at the speed of light, right down to pitching product ideas that raise VC money.

Product packaging influences 72% of Americans in purchasing decisions alongside a sustainable packaging market expanding at a CAGR of 8.5% during 2023-2031.

The AI approach accelerates design, prototyping, and production.

So, if tech giant Amazon uses AI with chatbots, product recommendations, Alexa-based voice shopping, and warehouse/delivery optimization—why wouldn't you?

I'll show you how to leverage AI on the go for your product development cycle.

Check where your project has room for AI assistance

No, AI isn't the answer to every single business department. First off, the goal is to assess what feasible tasks and processes add up to the actual product value.

It's all about finding gaps where AI integration makes sense. But gaps of what kind?

According to Statista, the most important factors of packaging include:

Hygiene/Safety. From maintaining hygiene standards with AI monitoring to designing smart packaging that alerts when the product is compromised.
Shelf life. AI's vast datasets of product packaging components and specs translate into optimal material combination ideas for increasing shelf life and reducing waste.
Ease of use. Besides streamlining the packaging UX so it's intuitive to open, handle, and store—consider making it disability-inclusive.
Durability. Simulate AI-scenarios to test and predict packaging durability. This helps engineer packages that withstand stress while preserving product integrity.
Information on label. Natural Language Processing (NLP) models have the capability of writing easy-to-understand product information. And instructions.
Appearance. Through AI image generators you can brainstorm product visual concepts, and even come up with interesting variations to present.
Environmental impact. Green values for the win. Access AI-driven guidance on sustainable materials and manufacturing processes to reduce carbon footprint.

Whether it's in-house designers' work or tasks you outsource, AI becomes a productivity-enhancing magic wand. For ideation, mockups, pitch decks, and so on.

Optimize viable aspects of the product development cycle

What I mean by viable is not reinventing already-functioning wheels.

Once you've determined the to-fill gaps, there's no time to lose. Here's when you open the AI design tool on a new tab – DALLE-2 or Midjourney – and lights, camera, prompt.

For the record, I pointed out two mainstream AI image tools for the sake of brevity.

Breaking down the product development cycle, you'll come across these stages: ideation → validation → prototyping → marketing → development → launch → improvement.

With AI, you're capable of streamlining at least 4 out of 7 phases. Ideation, prototyping, marketing, and improvement. That impacts ~60% of the overall workflow.

Let's imagine this hypothetical example.

An old rotary dial phone company that once led the market wants to launch a new product line. The plan is to work on a remake of the iconic rotary phone. This time, following circular design principles and using sustainable materials. Style-wise, it's looking for a mix of modern sleek, and nostalgic look-and-feel, matching the 2020s decade.

I tried that exact prompt in three different AI image generators and the results were impressive. Depending on the startup branding service, you're saving from $5K-$50K. Still, hiring a freelancer or agency is a good idea to get the job done. All of a sudden, you got clarity on the product direction that you wouldn't have been able to do so fast otherwise.

Steal ideas from the crème de la crème in your industry

When it comes to visualizing execution, learning from the best – in most cases – is the way to go. These aren't a one-size-fits-all but a reference for product roadmapping inspo.

Anticipate potential challenges—and solutions too

As with every status quo-shaking technology in sight, changes arrive. Sooner or later. It happened with the Internet Revolution in the 90s. And AI-driven design is no exception.

Obstacles (and possible solutions worth-planning ahead):

Designs lacking emotional resonance with the audience → Implement a human-in-the-loop approach, where designers develop further AI concepts.

Infringement of other brands' copyrights → Run checks to ensure your design is AI-inspired and creator-friendly instead of copycatting material.

Protecting your brand uniqueness/differentiator → Input brand-specific elements. Like logos and color palettes, as constraints in the AI design process.

Not-so-straightforward AI's ROI → Use minimalistic A/B testing to compare AI designs against traditional ones. And then see what performs best.

Disrupting existing design workflows because of AI integration → Adopt a multi-phased approach, gradually introducing AI to different stages of design.
Skepticism and apprehension towards all things AI-generated → Educate on how AI and humans work hand-in-hand so there's more space for creative thinking.

Cultural nuances overlook (stereotypical biases) → Incorporate cultural awareness in your AI-assisted frameworks and collab with local experts to review designs.

The best of both worlds is my personal favorite recipe—artificial and human intelligence. Just go easy and start with baby steps, validate fast what's working, then amplify.