Agata Mlynarczyk, Head of Marketing at Cashflows, explores the barriers to implementing generative AI.
When we come to look back on 2023, there will be one topic dominating the history books: generative artificial intelligence (AI).
Just one year ago it felt that few people were taking AI seriously; that it was confined to a topic among tech employees in buzzy start-ups. The release of ChatGPT, GitHub Copilot, and Bard – all of which can be accessed free of charge and do not require any technical knowledge to use – has changed everything. According to the latest SimilarWeb data, ChatGPT currently boasts more than 100 million regular users, with the website hosting 1.6 billion visits in June 2023 alone.
Such is the rise of generative AI that nearly one-quarter of C-suite executives surveyed by McKinsey say they are personally using generative AI tools for work, while 40 percent plan to increase their investment in AI overall. This is little surprise, given that generative AI could deliver total value in the range of $2.6 trillion to $4.4 trillion in economic benefits annually. To put this into context, this is equivalent to the entire GDP of the United Kingdom in 2021. And we’re just at the beginning.
AI’s role in fintech marketing
The most popular use cases for generative AI so far include product and service development, customer service operations, back-office support and our very own function, marketing, and sales. In fact, 14% of marketing and sales professionals report using generative AI regularly – more so than any other business function.
I wouldn’t be surprised if these figures are even higher in our sector. Let’s face it – the organisations many of us work for are no strangers to AI. For years we’ve outpaced other industries, adopting AI and machine learning models to drive growth, improve customer experience and unlock new revenue streams. It’s likely that many of us have used facial recognition for seamless payments in-store, or taken advantage of highly personalised offers powered by AI.
There are no signs of adoption slowing down, either. With a 2021 survey from J.P. Morgan Chase highlighting that 89% of respondents already use mobile apps for banking and 41% want more personalised banking experiences, there is definitely an appetite for integrated tech and the kind of comprehensive data gathering enabled and processed through AI and machine learning. At Cashflows, for instance, we recently rolled out a new AI-powered tool called Fast Onboarding, which significantly reduces the time it takes for a business customer to be onboarded, whether directly or via a partner, making the process as easy and streamlined as possible without cutting corners on regulatory compliance or compromising on KYC and AML. The platform automates bank validation and self-populates data from official sources (such as Companies House) wherever possible. Of course, we augment this with human intervention where needed. But already, we’re hearing that it’s been a boon when it comes to enhancing the customer experience; Fast Onboarding has enabled over 80% of applications to be approved within one business day, driving fast, consistent decisions on applications.
So how can marketers ensure that they are equally as innovative with the use of generative AI? Over the past few months, I’ve seen people put ChatGPT4 to the sword, using the platform to analyse large datasets of campaign performance and, in turn, inform marketing strategies, helping businesses to not only win new customers but boost retention and loyalty among existing ones too. In a function in which in text-based communications and personalisation at scale are the definitive driving forces, I’ve found generative AI to significantly improve efficiency.
The future of AI
While generative AI may have taken hold pretty rapidly in marketing functions, its continued rollout requires close and careful consideration. Although the dangers must be managed with due diligence, I firmly believe that the rewards can outweigh the risks. That said, with a small proportion of senior executives describing themselves as ‘very concerned’ about the risks of generative AI, this is likely a conversation which will continue for years to come.
To win these executives over, a number of barriers must be addressed. First, generative AI has the potential to become to run-away technology that exacerbates societal imbalances and spreads inequality further. Over recent years AI has made the headlines after reflecting racial prejudices in healthcare or displaying sexist traits in automated recruitment systems. If these biases were to leak into marketing materials, brand reputation, shareholder relationships and sales could be irreparably damaged. Large language models – based on the aforementioned training data – can also aid the creation of purposefully harmful content, if not stewarded closely. Ethical development is therefore essential if we are to reach the hallowed economical and societal benefits of generative AI.
Secondly, large language models including ChatGPT and Bard are trained exclusively on publicly available data. For now, there are insufficient safeguards in place to protect marketers against plagiarism or breaches of copyright. When using generative AI to produce advertisements, headlines or social media posts en-masse, human oversight is essential to not only meet each company’s brand values, but also to avoid inadvertent breaches of intellectual property rights.
Finally, despite what the headlines say about generative AI’s advanced capabilities, it is not infallible. Occasionally, ChatGPT and Bard have been known to generate falsehoods and inaccuracies but present them as factual with a strong degree of confidence. Known as ‘AI hallucinations’, these demand a human touch to ensure that any output is factually correct.
For now, the attention of many marketers will remain fixed on the potential of generative AI. As use cases continue to develop at pace, and the ROI of deploying these tools becomes greater, more organisations will ramp up investments over the coming months and years. To succeed, all marketers must stay ahead of the noise, utilising AI to provide creative solutions to genuine problems, rather than looking to innovate for innovation’s sake. Keeping deliberation, care and process at the fore is critical for marketers to achieve the best outcomes for consumers and businesses alike.
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