Maintaining Creative Control Whilst Embracing AI with Hannah Ginno

Hannah Ginno is a graphic designer who specialises in B2B marketing content, crafting impactful visuals that spark conversation and drive engagement.

Whilst working at Nelson Bostock she has refined her skills in the development of social media assets, document design, presentation design and event collateral for product launches and campaigns.

When not found at her desk she’ll be attending gigs of all genres or on court with her local netball team.

LBB> What’s the most impactful way that AI is helping you in your current role?

Hannah> Whilst simple, the most noticeable impact of AI in my day-to-day role is in expanding images and adding or removing objects. What was once a time-consuming task requiring skill and effort has now become the click of a ‘generate’ button, thanks to Adobe.

I’m often repurposing a hero image for various assets and AI allows me to quickly expand the frame or add elements. The real game-changer is how the AI-generated content becomes its own editable layer, which can be turned on or off and refined without affecting the original image (not all tools provide this).

It’s a simple feature, but it’s made a huge difference in saving time whilst maintaining creative control.

LBB> We hear a lot about AI driving efficiencies and saving time. But are there any ways that you see the technology making qualitative improvements to your work, too?

Hannah> I’m fortunate to work with a tight-knit, supportive team that’s always exploring new ways to maximise efficiency. Using AI is an aspect of this. However, we’re mindful that just because something can be automated doesn’t mean it should be.

Great design often requires slowness, iteration, and human empathy – things AI can’t replicate. AI is a part of the toolbox, not the entire workshop.

For example, there are circumstances where AI is the perfect aid to making qualitative improvements, such as for resizing, background removal, or masking. Refinement here means designers can focus on more creative aspects like storytelling, aesthetics, and user experience.

But it’s also a great tool for larger tasks like enhancing brainstorming and ideation. Recently, I assisted our PR team with their Cannes Young Lions Award entries. With only 24 hours to complete the pitch, and design a minor feature, I used ChatGPT and Adobe Firefly early in the process to explore potential directions. By the time I sat down to mock up the final design, I was already a few steps ahead creatively.

LBB> What are the biggest challenges in collaborating with AI as a creative professional, and how have you overcome them?

Hannah> I’ll be honest, I was originally in the ‘AI is going to take my job’ camp. If it could do everything, or at least most parts of the job, instantly and get you 85% of the way there immediately, why bother paying an entire team of creatives for the other 15%.

One of the greatest challenges has been removing this mindset and engaging with it in the first place, learning about the growing capabilities with an open AI mindset. (That’s a pun ChatGPT couldn’t offer me).

As a designer, some of my main hurdles have been technical, often related to error messages due to rate requests on the system or my prompts not being understood and requiring refinement.

I’m confident that over time, these platforms will improve, becoming better at handling more conversational tones and managing higher traffic (like OpenAI’s recent GPT 4.0 release).

LBB> How do you balance the use of AI with your own creative instincts and intuition?

Hannah> I always start a brief by drawing on tried-and-true creative processes. If I hit a blocker or need different interpretations of an idea, that’s when I lean into AI. It’s an antidote to writer’s block, a defogging of designer’s haze.

Before I bring AI into use within a larger project, I clarify the concept or emotional tone I want to convey. That way, I’m not just reacting to AI outputs – I’m directing them with purpose.

And honestly, if something feels off, I’ll go back to manual tools or check in with a teammate. With years of real-world experience, stakeholder context, and deep brand knowledge, the human input is invaluable.

LBB> And how do you ensure that the work produced with AI maintains a sense of authenticity or human touch?

Hannah> AI is getting better all the time. Take OpenAI’s recent GPT 4.0 release for example, now capable of text rendering and 20-something instruction handling per prompt! It’s impressive, but like any creative tool, its real value comes from how it’s used by humans. Though trained on our materials, AI can’t deliver the full package of a creative team, it can only assist. And assist based on what already exists out there to pull from.

In my own work for a global tech brand, we follow strict brand guidelines around the use of AI. That includes clear labelling of generated content and setting limits on where and how it’s used. These quality checks help preserve authenticity and ensure that anything produced still aligns with the brand’s values and tone. As the tech evolves, some of these rules might loosen but good creative practice will always include a human review process.

LBB> Do you think there are any misconceptions or misunderstandings in the way we currently talk about AI in the industry?

Hannah> One common misconception is that AI can fully replace creative workflows right now. People who are aware of AI, but don’t use it hands-on, often overestimate what it can literally do.

Take this scenario: a client wants to use AI to generate a three minute educational video about their product. On paper, it sounds like a great way to embrace innovation, cut costs, and speed up production. But in practice, relying solely on AI for every stage of the process is still unrealistic.

AI can be incredibly helpful in certain phases: storyboarding, scriptwriting, even stitching together short clips or adding transitions. But when it comes to generating a polished, end-to-end video, the tech just isn’t there yet.

Current tools can typically produce clips in four to five second bursts, which makes longer-form content labour-intensive to assemble and is often disjointed. Compare that to hiring a videographer for a day’s shoot; you walk away with high-quality, brand-aligned footage that’s versatile and reusable. You get real faces, real voices, and authentic interactions that AI simply can’t replicate.

I have no doubt that video production will be one of the next key features in the AI race but for now, it’s not quite there.

LBB> What ethical considerations come to mind when using AI to generate or assist with creative content?

Hannah> Well, there are two lenses to look at here isn’t there? An obvious concern from a personal perspective: authorship and authentication. AI is trained on publicly available content but not always with consent.

A recent example is Meta’s Llama 3, which has faced accusations of using materials from shadow libraries like LibGen rather than pursuing licensed (but more expensive) data sources. Regardless of how the lawsuits unfold, the core issue remains: once that data is part of a model, it can’t be unwritten.

And with this comes a question of ownership: who actually owns the output?

While much of the conversation focuses on the above concerns, the second lens is AI’s environmental impact which is often overlooked by day-to-day users. AI data centres mostly rely on non-renewable power sources and need significant levels of water which is adding to the already stressed climate. Though the speed of AI development is exciting, we must balance technological advancement with environmental responsibility – especially if AI continues to grow at the rate we have seen.

At Nelson Bostock, it’s embedded in our company culture that, though the effects aren’t visible in our day-to-day office life, we must remain aware of our impact and committed to achieving our environmental goals. This could be a real turning point of AI centres who, at a much larger scale, can use the same principles of carbon offsetting, circular practices and energy optimisation as they continue to grow.

LBB> Have you seen attitudes towards AI change in recent times? If so, how?

Hannah> As AI evolves, so does public perception. Outside of the industry, people are starting to have fun with it. It’s been hard to avoid the image generation boom of ‘pocket action figure’ packs on LinkedIn and the endless reams of Studio Ghibli-fied images. More so, common use of ChatGPT for meal plans, holiday itineraries or gym workouts.

This casual use builds familiarity and confidence, which will naturally carry over into more serious applications.

LBB> Broadly speaking, does the industry’s current conversation around AI leave you feeling generally positive, or generally concerned, about creativity’s future?

Hannah> I’d say I’m neutral but hopeful.

I’m generally positive about AI and appreciate the features that are already enhancing my day-to-day work. I’m less worried about its impact on professional designers though and more concerned about how it's being used in general, often without transparency and attribution – have a quick listen to ‘The Rest is Entertainment’ podcast from March 31st to get you thinking.

LBB> Do you think AI has the potential to create entirely new forms of art or media that weren’t possible before? If so, how?

Hannah> Yes and no. AI has the power to spark new ideas for designers to build on but in terms of new forms of art and media, it’s a bit more complex.

There is certainly an art to prompting, and with each one comes a never-before generated visual. However, AI runs off data and has been trained on existing materials. So, while each AI-generated image or piece of content might be unique, it’s ultimately a remix of what's come before – like a music sample that still carries the DNA of the original track (to a certain degree).

LBB> Thinking about your own role/discipline, what kind of impact do you think AI will have in the medium-term future? To what extent will it change the way people in your role work?

Hannah> We're already seeing AI automation have a noticeable impact within our team. With the pace of model development accelerating so rapidly, it’s difficult to predict even a year ahead but I do believe AI’s role in the creative process will become even more integrated. That said, no matter how advanced the tools get, human creativity, intuition, and expertise remain irreplaceable.

Features like GPT 4.0’s ability to generate images with embedded copy are a game-changer. Visuals are no longer just decorative, they promise to function as complete, ready-to-use marketing assets.

This has huge potential not only for small businesses or individuals starting from scratch, but also in my own day-to-day work, particularly for pitch decks, early concept development or quick-turnaround social content.

Whilst I don’t believe AI will fully upend the role of a graphic designer, it’s already enhancing many parts of the creative process. Some manual skills may be automated, but having those foundational skills is still essential as they’re what shape a designer’s eye and expertise in the first place. More so, many brands remain cautious about how much they can rely on AI due to ongoing questions around governance, IP, and copyright.

Even in my own experience, technical issues like ‘too much traffic on the site’ show that the technology isn’t quite bulletproof yet. Until legal and functional reliability catches up, it may be difficult for some teams to adopt these tools at scale.