Brands will soon be able to use one agency for all their advertising work.
Nobody wants to use a separate agency for marketing research, strategy, brand, creative, production, media, and data. It is expensive, slows things down, and is a recipe for things to go wrong.
But we have been stuck with this for decades. Not anymore… AI is changing things – so brands will be able to use one agency (or at least far fewer agencies) for everything.
This work of putting together a large-scale ad campaign has been separated between agencies over the last few decades because each part got so sophisticated and complicated that very large teams were needed for just that part.
But with AI, each piece of a single campaign can be done by far fewer people. Which makes it possible to house all the people needed for the whole process under one roof. This ends up with a better experience for brands, and creates better advertising work – as everyone in the production line works more closely together and communicates better.
We will start to see more of this in 2026 – where each agency can help their clients with much more work.
What this means for CMOs now? Change how you pick your agencies. See if you can get a lot more revenue impact for your company with your budget by working with agencies that have gotten good at using AI to do things better.
OpenAl – the company behind ChatGPT – has announced a series of models that “think more before answering”. This set of models is called OpenAl 01 models.
This is a big step forward for Al. The key difference is this: under the old paradigm, large language models (LLMs) like ChatGPT could really just think in one direction. They essentially come up with one word, and then figure out what they next word is most likely to be – and then keep doing that. A little like how it feels when I am answering a question on the spot about something that is new to me.
Deep thought
The difference with these new “01” models is that they can essentially go away and think about something. More like when I am asked a question by email about something that is new to me. I will start by running through a chain of thought – like the “next word prediction” model above. But then I take that answer and try and think about whether it is actually correct – taking that answer and starting a new chain of thought. I will also likely come up with other trains of thought from scratch to give me other possible answers. Then I will start new trains of thought to compare the possible answers to work out which one makes most sense. This is what the new 01 models do.
These new models don’t give you the first answer that comes to their heads. They come up with a bunch of possible answers. They test if those answers make sense. They compare their answers. They make improvements.
The cost of this is computing time – and so money. The same as if I need to answer a question on the spot about something new versus going away and thinking about it to give a considered answer. On the spot this might take me a minute. Going away and thinking about it might take me hours. But the benefit is that it gives us much smarter answers.
The advertising business
Now coming to how this affects advertising. OpenAl 01 and other “deep thinking” models that will inevitably be released by other companies – will be able to give us deep thought. For lovers of the Hitchhikers Guide to the Galaxy – it looks like this will be much more useful than Deep Thought there.
If we break down the processes in advertising, there are a lot of places where we have deep thinking (one or more people going away and figuring something out for a period of hours or days). This is all human at the moment. But Al will now be able to do more of this.
In the advertising process we have: market research → strategy → creative ideation → creative development → creative execution → production → media. For each of these steps, there is deep thinking to be done. Al models like ChatGPT are already being used extensively – partly as a replacement for Google search and partly to act as a colleague who can come up with a draft of something for you. The problem is that the answers are not always very good. It is still obvious when something is written by a model like ChatGPT or Claude – and what is written almost never makes complete sense. That said, it is reducing the time people need to spend on things by providing a first draft which they can then work on.
The 01-type deep thinking models will improve the quality of output we get from Al models. They will give us smarter answers.
No more humans?
The question then becomes what will happen. Will deep thinking Al models replace the need for humans in advertising? Will we no longer need creatives, client servicing teams, accounts teams, strategists, media professionals and production professionals? Will clients be able to directly tell a deep leaning Al model all about their brand and what they are trying to achieve through advertising for their business – and the Al model will be able to do all the thinking and work needed to go through all the steps of the advertising process: doing the market research, coming up with strategy, coming up with creative ideas, working out which ones will be most effective for the client, developing the creative, executing the creative, producing the final creative assets in whatever form, picking the most effective media, deploying the campaign to the right media, collecting performance data, and interpreting performance data?
By (deep) thinking through all of this I think the answer to all of these questions is no.
Al is like Photoshop
Deep thinking Al will becoming increasingly important. But it will be a multiplier rather than a replacement. Advertising firms and clients that adopt Al early will have a major advantage until others catch up. But ultimately the impact on advertising will be like the introduction of creative software (like Adobe Illustrator and Photoshop) in advertising. To look at this, it helps to break down advertising to its simplest model. Brands want to convey information and emotions to humans. There are a limited number of humans out there (even less in a brand’s target market). And each of those humans only has a certain number of minutes in every day during which they are exposed to advertising (this might increase or decrease with Al but is still limited). So brands still have to compete for those limited eyeballs and that limited human attention. Because of this – media will still be limited and expensive. And because of that, brands that produce the most effective creative for their purpose will still achieve the best return on investment on their media spend. That means they will be the ones who are able to outbid their competitors for the still limited media available.
Raising the bar
Deep thinking Al models could in a few years do almost all of the steps in the advertising process. Even now, a brand could use deep thinking Al models to go through all the steps themselves. But having excellent humans working with these Al tools will produce much better work than the tools by themselves.
The analogy with creative software is helpful. When creative software became available, a brand could have taken Adobe Illustrator and produced creative assets for which they would have previously needed an advertising agency. But this did not happen for large brands – brands continued to use advertising agencies – and the advertising agency used the software. What did happen was that the bar for “good enough” advertising was raised.
A similar thing will happen with deep thinking Al models. Some tasks will become less important – for example data processing of survey results might need fewer people. Optimising media spend might need fewer people. Translating copy for local markets might need fewer people. But in each of these areas – the higher bar through competition between brands might need a greater number of people for other tasks. For data processing of market research results – there might be a lot more work on collecting the right data and working out which data will have the greatest business impact for the brand. For media spend time will be spent on adding more media types of the media mix that is optimised. And in localising advertising for international markets, competition will incentivise brands to spend more on modifying advertising more to better fit local cultures so that conversion rates are higher in that local market – to prevent being outcompeted for media once all brands can use Al models to correctly translate copy.
Why do we still need humans?
But why do we still need humans? Why can’t Al do all of this? The answer is that Al models are a different type of intelligence to humans. An analogy here is that being a Nobel-Prize winning physicist is a different kind of intelligence to being a top popular music songwriter. Deep thinking Al models are likely to quickly get exceptionally good at taking large amounts of existing information, and summarising that. And they will be good at finding the right information to find and summarise for a particular problem. For example if you ask a deep thinking model to write a hit song about heartbreak suffered by a two-headed, three-armed, hedonistic, self-centred alien – and you want this poetry to win a Pulitzer Prize for Poetry – the model will be able to gather the poetry of previous prize winners, it will be able to get poetry and other descriptions of heartbreak by hedonistic and self-centred people. It will be able to get information about aliens. It will then be able to combine this and other relevant information to write a poem.
This is somewhat similar to what humans do. But two differences are that a human will be able to come up with new ideas at the edges – which at least for now is difficult for an Al model to do well; and the human will be able to tell us of experiences they have lived in their poem – which as human consumers we seem hardwired for. For example, with the advent of photography in the 18th century, the value of human artists to precisely paint what they saw fell. But artists moved from realism to abstract, emotional and conceptual art. This would have been difficult to predict. But now this is a large employer of human time – in everything from fine art to fashion to comics to architecture to video games. For the real-human-story attraction point, a good example is chess – a lot of humans (I expect more than ever) watch other humans playing chess on platforms like Twitch – even though computers have long been able to consistently beat the best humans at chess. We like the human aspect – the story behind the player, the emotions and perhaps some other things related to them being human. This seems to have evolved strongly within us over millions of years.
So for advertising, I strongly expect that we will see more people employed in this profession over the next ten years. Across pretty much all stages of the process.
The way forward for agencies
For agencies, the formula is to keep adapting to developments as they arise. Like snowboarding down a new run – you don’t know exactly what is coming up – but you know the general direction and keep course correcting very frequently. This means rapidly bringing in new technologies, without building long-run infrastructure around any single technology – as the state-of-the-art will continue to change rapidly. It means giving your staff access to Al tools, and having your staff use these to reduce costs and more importantly raise the quality bar for clients.
The way forward for brands
For brand, it means working with agencies who are most effectively adopting Al. The agencies who you can see are raising the standard of output they give you per dollar spent – by using Al to automate some of what was previously done by humans – and instead using that time to materially increase the quality of your creative work – as measured by its ultimate impact on your customers and the translation of that into your long term business goals. Other brands will inevitably catch up in quality – but you might get a two or three year head start – which could dramatically increase the long run competitive positioning and so value of your brand.
Humans are social creatures – we do what others do.
The pattern is evolutionary – we have a rule to do what everyone else is doing for most things – both as it seems safer (like Duracell batteries); and it acts as a social signal (with memes like “he/she’s a 10 but he/she has an Android..”).
This works for products from iPhones to Louis Vuitton bags to Fortnite.
Advertising has been an important part of this process. If you see a lot of high quality advertising (in all forms from billboards to video ads) for a product – that has been a signal that that will be the product that will become the standard. Because of that, people buy that product – and then it becomes the standard.
The future – a world with AI
As AI improves, people will still need to make choices – picking which brand of batteries to buy, or which brand of luxury watch to buy is still going to be based on human choice.AI makes it easier for all brands to create high quality advertisements – look at Google’s AI film-making tool Flow. And we are still early – this will get a lot better over the next couple of years.
This removes the signalling effect of high quality advertisements – for helping work out which product will “win” and become the standard – as every brand can create high quality advertisements (for example advertising films) and more money won’t create higher quality.
So brands will find new ways of signalling that they will become the standard. This is likely to be in many forms – including higher competition for creative.
Ads that are more truly creative will stand out. Producing amazing creative is expensive – largely because there aren’t that many people in the world who are experienced at truly original creative ideation. So producing something truly original like Dove’s Real Beauty campaign or Cadbury’s Gorilla will stand out and act as a signal that people will converge on that brand becoming the “standard” – in a world where instead producing a beautiful iteration of a standard soap or chocolate ad won’t act as an effective signal anymore.
Creative in this context will be defined as things that AI can’t come up with. AI (I expect even in ten years) won’t be capable of coming up with completely new campaigns like Real Beauty and Gorillas were – as the right training data doesn’t exist for them. The training data that would be needed is a lot of data on how humans emotionally react to a large number of things – which is really complicated and complex – and there is no very complete training data for. As humans we can do a relatively good job with this because we can introspect and empathise with other people. The training data that AI needs to learn this might become available once highly advanced computer-brain interfaces are developed – but we are still in the infancy of that area so I am guessing this is at least 10 or 20 years away.
“Storytelling” has become a buzzword – and a bit of a confused word in advertising.
By Storytelling here, I narrowly mean telling stories – where there are people who want to do something and face some obstacle to achieving it.
Stories work with humans. I don’t think anyone fully understands it – but we don’t need to. Stories are an excellent way to pass along information. We are good at keeping our attention when someone is telling us a story, at understanding the story, at remembering the story, and at recalling things from the story later.
AI is going to make the world much more complicated. Over the next few years we will find ourselves living in a world of many more products, and of many more complex products. The many more products will mean that there are a lot more things out there trying to get our limited attention. And the more complex products will mean it will take longer for us to “get” each new product- trying to get your head around things like companies trying to sell us AI agents, or understanding if new preventative health checks for serious diseases are worth doing, or working out things like whether you should sell your house and buy in a new area that will become a lot more valuable because of driverless cars is really difficult. It’s even more complicated at work – things like trying to decide whether to buy systems that are sold as being able to take over some human work, or deciding whether to now build in-house software for more of your business because software development costs have fallen so much, or whether to buy new machines for your facilities while not knowing whether new technology will mean that those machines will be out-of-date in a year or two – these things are incredibly difficult.
When things are too difficult to understand, most people generally do nothing.
For companies (and for humanity) this is not ideal. For companies, if they can’t sell their products, they will grow slowly or fail. For humanity, it means that we waste a lot of time – as there are things out there that could improve the world but we are not adopting because we can’t understand them.
This is where storytelling comes in. It lets companies get their message across. It gets the message across in a way that people hear (without getting bored and switching off halfway through the message), in a way that people understand (rather than the message going in one ear and out the other), in a way that people remember (rather than some fact that is completely forgotten), and in a way that people remember (for example when they are in a situation the next time, they automatically remember this story about the product that could help them in these situations).
To work out the right story to tell, brands need to get to the heart of their product – to be able to see past all the complexity and completely understand what their product does for the consumer. This is much more difficult than it sounds. They then need to understand their customers and create the right story that will resonate with them – and will resonate with them in the right way where they will be able to get the value of the product and will take action to buy and use the product.
Apples’ 1984 ad is an excellent example of how to do this. Nike is famously good at this. Most of the consumer work I have done has been story-telling based. This came very naturally when TV ads were the main medium. And it always seems a little like magic how stories can take something complex and boring to most of us – like the biochemical mechanics of what a pharmaceutical product does or the relative risk-reward profile of a financial product – and make it attention grabbing, fun and often funny. This type of storytelling has reduced a little as primary media for many brands has changed from 30 second TV ads to digital/social.
As competition and complexity increases thanks to AI, we will find ourselves in a new golden age of storytelling.
The upshot for companies and creatives. It’s time to think more about storytelling for your brands. This is important for all brands – but it’s especially important if you need to communicate something complicated.
A Big Idea is bet. You do your strategy work to understand your customers. And then you try to come up with a Big Idea that will resonate with those customers. GOATs are Just Do It, Think Small, Got Milk, Think Different, A Diamond is Forever, and The Best a Man Can Get.
The playbook has been – You keep trying a new Big Idea (a new bet) at most every year until you strike on something that resonates with customers and pumps your sales. You then stick with that Big Idea until your customers or competitors change in some way that stops your Big Idea from working. Some Big Ideas work for a few years like Old Spice’s “The Man Your Man Could Smell Like”. Some end up working for more than fifty years like “Have a Break, Have a KitKat”.
Brands could only try one Big Idea a year (for brands that hadn’t yet hit their jackpot Big Idea) because it was too expensive to try more than one.
To get a Big Idea to the point it can go public for a large global consumer brand, might cost $30m+. The money would go into strategy, Big Idea development, creative development, creative expansion across markets, and production.
But AI changes that math. It might reduce this cost from $30m+ in some cases to $10m+. This is mainly because AI can reduce production costs.
This makes it possible to try two or three Big Ideas a year. And that can double or triple the speed of hitting your Big Idea jackpot.
At the beginning of the internet, there were just websites.
Then Google, Facebook, LinkedIn, Instagram, Twitter and other platforms came along. And advertising moved there.
Advertising moved to their large platforms – with their excellent targeting and tools – allowing brands to efficiently advertise at scale.
The platforms are still the bulk of digital advertising – but off-platform advertising (like in the 1990s) is coming back.
Off-platform advertising includes newsletters, podcasts and direct advertising on websites. It also includes content that goes on platforms – but where you pay the creator directly – like sponsorship of YouTube videos. Anything where you are not paying the platform.
Podcasts – there are now over half a billion podcast listeners globally, and half the US population over the age of 12 listens to at least one podcast a month (eMarketer). Revenue is expected to go from $30bn last year to $130bn by 2030 (Horizon Grand View Research).
Newsletters – smaller but growing. $11bn last year, projected at 18bn for 2027 (statistica).
Influencers – gone from $2bn in 2016 to $24bn in 2024 (statistica). Estimated to reach $48bn by 2027 (dash).
I’m not sure why the platforms are not more aggressively getting involved. It might partly be because of new regulations. It might just be that competition is higher and it’s now less profitable for them that it used to be. And it might be that they will get more involved in the future.
The opportunity for brands is finding new ways to reach their audience. The ROI on this advertising can be excellent in many cases. The difficulty is that it can take more work to find the right places to advertise, and then to negotiate terms.
For smaller brands, off-platform advertising offers a massive opportunity. For the largest brands, they can be a valuable addition to the brand’s advertising mix.
Luis is a conceptual artist and an incredible advertising creative. Luis is now based in Barcelona.
He talks to us about the future of advertising.
Haniah: What are your thoughts on how new technologies will change advertising?
Luis: I think any tool can enhance or limit you. So it depends on how you use a tool. If a tool is being used because of laziness or in looking for a quick answer, then I don’t see it as something that can enhance the potential of creativity. But if you use it just as an extra brain then that is a good use.
You still need someone to choose and pick what’s the right thing and to filter the results of AI. In the end you are like a curator of machines.
A client could possibly type into an AI to give them the best layout for something but do they have the knowledge to curate it, to know whats the best option/outcome for them. You need a specialist for it, just like you need a good cook who uses a knife in order to chop something in the best way possible. Or like you have a pan but do not know how to fry an egg, then the pan is not much use to you. You have the tools but you need to know how to use them.
You need to have the right mindset as well. As creatives we can use tools and explore the limits of things. We don’t get satisfied from the first answer. We always push push push – keep digging. The machine is there but you need the creative to go further and offer more. The people building the tools are also creative.
I use AI a lot to visualize concepts. Before when you would need to do a storyboard, you needed an artist to help you draw but nowadays you can just get a first attempt at storyboarding quite quick, even when crafting a look and feel for a concept. So in this sense, it’s very useful to use AI to start generating concepts or the storyboards. It saves you time trying to visualise a concept. So it is a lot more efficient.
Before when you had an experimental thought, you needed to brief a producer or 10 people to work out to see if something is even possible but now with AI you can do all that yourself. You have less limits and no one to say no this is not possible. AI saves you costs by allowing you to visualise and experiment until you are confident – then you can take a more finished idea to a client in a much shorter time frame. Like a maqueta (mock), you create a mock version of the project before doing the project itself. They are more organic, experimental and less polished.
Haniah: It is getting harder and harder to get consumers’ attention. How would you advise brands to do this?
Luis: I think that’s the biggest challenge we have right now. There’s so much noise. That’s the number one problem surfacing for brands and for creatives as well.
I could say I don’t think there is a formula. Of course, you need to deserve that attention. That’s the most important thing. I know some brands are like, let’s do this trend because this trend is popular on social media, so now everybody will engage with us because we are jumping on the trend. But maybe you are too late to that trend. And maybe it doesn’t feel natural, so this way at the end there is no recipe.
I think you just need to do something that has value in terms of entertainment, information or emotion. It really needs to have a value at some point. You need to come from a place that is natural and real. If it’s too false, I think people notice when you are trying to be something you are not. As a brand you should be cool and be loud if that’s who you are as a brand.
So, the most important thing is for a brand to be as authentic as you can. A good example of this is the brand Liquid Death.They are bizarre from the start. They are not pretending to be crazy, that’s who they truly are. So when they do something bizarre, you believe it.
Haniah: How is technology changing advertising in Spain?
Luis: Definitely we are not a leading market when it comes to technology like the US or some other countries. But I think yes, we’re embracing technology. Nobody is really scared of technology in that sense. but perhaps we’re a bit slower in going full on. Everybody knows this is important and a revolution, but we’re not fully embracing it yet or even providing the necessary training.
If we take a step back and look at the culture, it is a bit laid back. We are letting everyone else do cartwheels and by the time it’s fully figured out, we will embrace it fully. There is a theory that says that cold countries are where people innovate more, because you spend more time indoors and you need to be stuck in between four walls and keep going until you produce something innovative. We are more social and if you’re socializing, perhaps there’s not much time for innovation. Perhaps innovation needs a little bit more blood, sweat, tears and darkness to make that happen versus a beautiful sunny day.
Haniah: What would be your advice to young creatives who are stepping into the creative industry?
Luis: My advice would be that whatever you do, be hungry. Want to do something that will even amaze and surprise yourself, and will excite others. You should feel passionate about what you do and this is what I think a young creative person must have. If you are not passionate, then you are not in the right industry. If you are self-driven everything will come naturally, you will embrace technology, you will research, you will try things, you will just go beyond the expected or beyond what the first answer.
Haniah: What would be your advice to brands who want to be future ready?
Luis: Everybody is scared of failing. Fear of failing is what will make you not embrace the future. You need to take risks and surprise yourself. Most brands are in ‘the’ safe space. Things won’t move forward and they will miss opportunities. I know brands follow a structure and don’t want to try new things. Small brands take risks and try new things. Maybe the big brands need to learn from the smaller brands. They are flexible, agile and not afraid of risks. Most big brands will embrace change in little portions.If you want impact and future readiness you have to take risks.
If we think of brands like a person, if you try new things that doesn’t mean you are a different person. It just means you are trying new things. Why don’t brands try this? Go far, then pull back a bit. If you play it safe and don’t innovate you will stay in the same place while the new brands will be more relevant. Find the balance between evolving but keep bits of your personality. Just don’t get stuck because nobody likes that old smell.
A little about Luis and his creative approach
Haniah: How did you get into advertising?
Luis: I got into advertising in unexpected ways because I came from a fine arts and experimental background. From there I got interested in the world of design. I met Till Hohmann who was very interested to have a design specialist in his team. He felt it was one of the profiles that was lacking in advertising agencies. Because designers have a thinking process that is different from Art Directors. Art directors are thinking about big ideas but the detail for the craft comes from designers.
Advertising was a new world for me because I went from crafting things to also communicating things. Design and communication clicked for me and I got trapped into this world of advertising. I couldn’t escape from it.
Haniah: How do you balance your focus between ideas and design?
Luis: Depends on the media. I mean, if you’re doing a campaign that is focussed on content or an above-the-line (ATL) campaign, perhaps the design part is a bit diluted. I would say with other media like out of home (OOH) or branding definitely the design skills can play a big part in the impact and quality of the final creative output. If you have a solid base in design it strengthens everything else. For example if you are working on a film brief, your design base allows you to put some order in the chaos. To come up with a rational way of expressing the ideas.
My predictions for advertising in 2025 – I have two big strategic predictions for this year. These are the economy and AI. And I have a few smaller tactical predictions.
The big things
The economy is going to be the biggest thing that affects advertising this year. The new US government is likely to make changes that increase how much people and companies spend over the next year. This is going to increase advertising spend. This will increase media costs, increase competition for consumer attention, and will increase the value of highly creative work. Many brands and agencies will only notice this later in the year. Brands and agencies who get ahead of this will have an advantage – by being able to lock in costs before they increase, and by being ready with creative campaigns ahead of increased competition.
The second biggest is AI. AI got a strong hold of our imaginations over the last two years. The hype has faded slightly, but the work of integrating AI into industry continues. In advertising, the main effect of AI is that it reduces costs. Across the board. It reduces the cost of market research, strategy, ideation, creative development, expansion, adaptation, production, personalization and analytics. But because the set-up of the advertising game is a lot of brands chasing the same consumers, this does not mean that total advertising spend reduces. It means that advertisers compete to produce better advertising – so they can outbid their competitors for more expensive media slots. The effect will be like the move from advertising before computers to advertising after computers. Costs of each stage of the process fell – but total advertising spend increased by a lot.
Tactical predictions
These are smaller changes – which are opportunities for brands to get ahead of the competition.
Off-platform advertising – we are seeing smaller creators take direct ownership of their audiences through mediums like podcasts and newsletters. These formats don’t use traditional social media platforms. Social media platforms manage the advertising for creators who use them. For these off-platform formats, creators need to work directly with brands. The audiences of these creators are often valuable as they reach highly specialist groups – at a time when targeting on social media platforms is getting more difficult. As the number of these off-platform grows, and as managing smaller audiences becomes easier thanks to the help of AI for identifying and tracking performance, expect to see advertising on off-platform podcasts and newsletters grow this year.
Branded gaming advertising – AI is very quickly reducing the costs of making phone video games. Expect more brands to start making their own video games – where their branding is part of the video game. This is at a time when mobile gaming is getting more popular by the number of people playing and the number of hours they play. It covers many demographics – across ages, income and gender – depending on the type of game. Depending on the target audience, combining branded games with live-streaming campaigns on platforms like Twitch and YouTube, and communities on platforms like Discord can quickly magnify the ROI of these campaigns.
LLM Chatbot advertising – AI large language models (LLMs) are expensive to run and the market is competitive across firms like OpenAI (ChatGPT), Anthropic (Claude) and Google (Gemini). Expect some AI firms to start using paid advertising to help fund the costs of running customer queries to keep their products free for their customers. This may be very similar to Google’s advertising on search – where you get sponsored links in addition to the search results that Google’s algorithm found for you. So if you ask an LLM chatbot’s advice on how to pick the right new electric car to buy for yourself, you might get ads by car brands together with the answer. This could be a very powerful form of media for brands – as consumers will have high purchase intent for many questions.
Video games are excellent media for advertisers. People will play their favourite games thousands of times – over years. People love their favourite games.
Video game production is now rapidly becoming cheaper – game engines like Unity, Unreal Engine, Godot and Cryengine have dramatically reduced development costs. And Al-powered tools like procedural content generation, Al-driven game asset creation, and Al-driven playtesting are now starting to rapidly reduce costs by another level.
This reduction in cost unlocks creating entire video games to advertise a single brand. This has been done in the past – for example Burger King released Sneak King, Pepsi sponsored Pepsi Invaders and Horlicks made NutriQuest. But these games were, in the past, expensive to develop and maintain. That is now changing.
It is similar to how Red Bull often sponsors entire extreme sports events like Red Bull Cliff Diving and Red Bull Rampage – making them free for spectators. Similarly firms like Nike and JPMorgan sponsor marathons and races – making them free for participants.
The Current State of Play
Advertising in video games is becoming increasingly sophisticated. In mobile games, we have interstitial and banner ads – these get in front of the user but do usually disrupt gameplay – especially formats like unskippable video.
We are getting more in-game advertising – where ads are placed in the virtual world of the game. For example, games like NBA 2K and Forza Horizon have billboards with ads from real world brands.
Sponsored content is also growing – where gamers can get free or paid branded content in their game. For example, Fortnite offers gamers branded skins from brands like Nike. Fortnite held an in-game concert which was sponsored. In Animal Crossing: New Horizons, you can get in- game clothing from brands like Marc Jacobs and Valentino. and In Gran Turismo players can drive cars from brands including Ferrari, Porsche and Audi. Influencers and Streamers are also increasingly collaborating with brands to promote their products. For example, LG collaborated with popular streamers to promote their products.
Branded Games
Branded video games can be highly non-intrusive for users. They don’t need pop-ups or unskippable videos. Because the brand is completely integrated into the game – the brand name is usually part of the game title – the user is reminded of the brand and messaging about the brand without needing to be interrupted.
For example, if a consumer electronics brand wants to develop their brand as being a company that is at the cutting edge of technology – the content of the game might be around cutting edge technology. This with the brand name in the game title, and the brand’s visual identity like their colour palette in the game, associates the cutting edge content with the brand in consumers’ minds.
If the brand can create a game that consumers love – the consumer will play the game repeatedly over a very long time. My favourite games are some of my favourite ways to spend time – and I will play some many times a week for years. That is an opportunity to create real love with the brand who created the game for me and gave it to me for free.
The Technology That Is Changing The Maths
Game engines like Unity, Unreal Engine, Godot, Cryengine and GameMaker Studio have dramatically reduced development costs by providing developers with a framework to build from. Developers no longer need to create everything from scratch. The game engine provides them with everything from graphics rendering, to physics simulation, to the handling of user input, to audio, to animation.
Graphics and visual fidelity that has until now been limited to the largest video game creators is now generally available through advanced rendering techniques like Unreal Engine 5’s Nanite (for detailed environments) and Lumen (for real-time lighting) – allowing independent developers to make very realistic graphics. The amazing Hades is a good example of a highly polished game created by an indie developer. Al-supported procedural generation is allowing game content like environments, levels and characters to be algorithmically generated – massively reducing the time needed to develop each of these manually. Tools like NVIDIA GauGAN generate landscapes from simple sketches. Al- tools like Artbreeder and RunwayML are helping designers create characters, textures and animations from simple inputs.
First-Mover Advantage For Brands
There is an opportunity for brands who can move fast, to develop and release games early – getting attention in a market with little competition.
The Process
Core to making a game that can be successful is the strategy process working out exactly what the brand’s target audience would enjoy playing, what games are successful in that category, and how to create marketing around the game. The work then moves on to developing the game design and creative – and then developing the game.
