Why Risk Taking Is an Essential Part of Leveraging AI Content Creation

Lindsay Hong, CEO of SmartAssets and Chief Strategy Officer of Locaria, shares her thoughts on why a moderate tolerance of risk is needed to effectively implement AI, how new tech will preserve creativity, and why its scaling capabilities need to be approached with thought

The COO of Locaria and CEO of SmartAssets, Lindsay Hong, isn’t scared by new tech. “Anything that provides the opportunity to reduce manual, monotonous work and create space for truly creative thinking is incredibly exciting, really,” she comments. Locaria, an agency specialised in global content creation and localisation, has used machine-learning solutions as part of its linguistic offering long before AI became part of the zeitgeist. Recently, focus ramped up the creative side to match its linguistic maturity and provide clients with holistic solutions.

Lindsay is currently leading a team that’s working on a new generative AI SaaS tool, SmartAssets. The aim is to transform the future of digital advertising and delivering the right content on the right platform and to the right audience. This should make creative optimisation easy, with AI “analysing ad creative, suggesting edits based on performance data, and enhancing creative assets.”

In practice, AI will be able to comb through large datasets of tagged assets alongside their historical performance to help decision makers establish a path forward. “If we see that a particular background seems to perform better, like a woman shaving her legs in a shower at home versus at the gym, we can use AI to change the background without going back to post-production,” she explains.

Lindsay is of course aware of the weariness some hold towards the technology as its remit is far wider reaching, and increasingly so, than any AI tools before. She thinks that the attitude of “I’m not going to have a job anymore, the machines are here to take it” is misplaced but acknowledges that the nature of certain roles might change. “There are plenty of areas where you absolutely cannot use a machine to do your localisation. With the applications AI currently has, we will see more post-editing and quality assurance type of roles crop up,” Lindsay says.

The other important distinction to make is between the types of content AI can be used for. “If you’re a copywriter writing large volumes of standard non-technical content, then it doesn’t make sense to not use these tools. There is skill to master there which is writing a really good prompt, knowing what to ask for. For writing that’s more discursive, more human, more creative, there is a long way to go.”

Where does Lindsay’s confidence on this subject come from? For one, she’s clear on the audience that content is targeting: people. “Empathy is a very important part of being an effective marketer. While machines and data can do what humans can’t, we have to go back to what we mean by ‘creativity’. I think it’s about curiosity and the more curious and creative prompts that you can write for the AI, the better outcomes you’re going to get. That’s the human contribution to the success of the tool; it’s a relationship where the human part is vital.”

Technology’s implementation into our industry is inevitable since no one wants to pass on the opportunity for more content that’s easier to scale on a smaller budget. The companies that will succeed in bringing AI into the fold are “those with a degree of risk tolerance”, per Lindsay’s expertise, who also points to a “dichotomy in our industry where there’s a low tolerance of risk, whereas the tools themselves are learning tools that need mistakes and failures in order to train and develop.” Looking around at everything that’s written about AI and its uses, there are success stories and there are scare stories – but there are very few, almost none, stories of failure. Yet embracing failure and learning what it looks like is essential to properly embedding machine learning tools into business operations and activating its benefits.

One of the benefits of the new technology – the ability to produce a lot of content at scale – can also be a drawback. It’s the ‘just because you can doesn’t mean you should’ adage. “I was part of a conversation recently where one company was considering reducing the complexity and creativity of its English copy to make it easier to localise by AI, reducing the possibility of errors,” Lindsay recalls, slightly horrified. “I find that very reductive. The point is to build the tools that support creativity that really engages people, not make things that are easier for machines to work with.”

“It’s about the right content, not loads and loads of content. That’s the position that Locaria takes, both in terms of copy and visuals. We use data to understand what is the right content, rather than just producing loads of bland stuff. I think that’s the risk with machines, that it can just make you loads of stuff that you don’t need. First, narrow down what content is needed through media data, then make that. We know that effectiveness of ads hinges on creativity and we should be preserving that.”

What Lindsay wants to see more of across the board, and what Locaria already does, is engage in transparent conversations between clients and service providers about the use of AI. Education needs to take place too for clients to understand what kind of quality they can expect and the risks involved. As such, Lindsay is excited about AI’s current application and future potential, she’s also realistic about its limitations and the human involvement that’s necessary to make the product of AI’s labour resonate with audiences today.

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