By Alexander Igelsböck, CEO and Founder, Adverity
Consistent benchmarking was never easy in the ever-shifting marketing sector, but radical evolution fuelled by artificial intelligence (AI) has now made it almost impossible. As fast as new efficiency bars are set, fresh advances are emerging to help marketers smash them with streamlined, speedy, and increasingly data-steered activity. All of which begs a vital question: with no defined standard to aim for, how can brands ensure they stay ahead of the crowd?
The obvious option is striving to adopt tools faster than competitors. But with eight in ten brands across the globe actively integrating GenAI into marketing strategies, the chances of winning this race are slim. Aiming for smarter use, however, is a more achievable ambition — and one that’s especially important at a time when sophisticated solutions are being hailed as the “saviour” of marketing teams facing declining budgets and demands for better results.
Automated creative isn’t special anymore
Emerging innovations only provide a leading edge until they turn into mainstream staples, and over the past year, AI-assisted content production has perfectly illustrated this hard truth. In early 2023, half (49%) of marketers were using GenAI to accelerate content creation and versioning. At the time, such support was a real advantage for those who felt they could not meet rising content requirements; with demand soaring at an annual rate of 54%.
The situation, however, is changing. Not only is GenAI expected to be implemented by 71% of organizations by the end of 2024, but it’s also predicted that 30% of marketing messages from larger enterprises will be AI-made by 2025. What this means doesn’t need much spelling out: we’re approaching the point where rapidly generating new content at scale will no longer set brands apart because everyone else can do the same. Moreover, marketers are also facing a growing risk of heavily automated production leading to creative homogeny and reduced impact.
On an immediate basis, this problem could be partially tackled through greater intelligent personalization — i.e., the next benchmark of AI-fueled efficiency. But for marketers striving to enhance their long-term differentiation, ensuring they outmanoeuvre rivals will involve looking deeper and bolstering their ability to harness timely, accurate, and robust data.
Empowering independent data mastery
While AI has long helped fuel scaled and efficient data use, tools powered by generative models are adding a different self-service flavour to analytics. Using solutions built to field natural language queries, any member of the marketing team can ask simple data questions and instantly receive clear, actionable answers. Similarly, smart transformation has brought on-demand convenience to data coordination: allowing users to get tailored code for configuring data however they want by entering plainly worded instructions.
Like previous advanced tech iterations, these tools will weave their way into standard industry practice, sooner or later. But wider adoption won’t necessarily dilute their value. By enabling friction-free independent data handling, GenAI-enriched analysis equips marketers to achieve lasting insight-driven success — with access to real-time data about shifts in consumer behaviour and sector trends continuously informing agile, relevant, and refined strategies.
There is, however, one main proviso: the benefits of streamlined data assessment are entirely dependent on how the data behind GenAI tech is managed. After all, the quality of what comes out of analytics will always be determined by what goes in.
The optimal foundation for success
For most marketers, the importance of responsible AI governance is apparent, as shown by findings that one in ten CMOs feel GenAI needs legislation and 77% predict applications at their company will be subject to imminent regulation. But there is still too little recognition of the need to go beyond routine compliance if they want to make sure intelligent tech is fed on the right basis of reliable, quality data to steer genuinely good decision-making.
Meeting this objective means first focusing on getting their data foundations right by making sure they cover two crucial prerequisites for effective use:
Fundamental engineering: good infrastructure matters
Data management infrastructure rarely gets the attention it should, especially considering the vital role it plays in enabling and maintaining the flow of useful insight. The construction of basic pipelines directly impacts how swiftly and easily teams can tap relevant data, as well as the chances of duplication, discrepancies, and unconscious biases compromising both the validity of GenAI outputs and the marketing decisions they guide.
This makes it paramount for businesses to fine-tune the fundamentals before layering over intelligent tech. For instance, in addition to retiring legacy processes that increase the risk of human error, such as manual data wrangling and outmoded spreadsheets, that will likely include establishing smooth orchestration setups where other forms of machine learning help to automate multi-source data collection, connection, syncing and cleansing, so that AI tools can draw on an ever-refreshing store of unified, up-to-date, and trustworthy information.
Prioritising people: enabling data-led productivity
Although user-friendly GenAI analytics tools will do much to reduce the intimidation factor of wielding data to steer streamlined activity, they can’t cultivate a data-centric culture on their own. Addressing ongoing barriers to data-enabled working, such as low data literacy, will still call for a top-down effort to communicate the broader gains sophisticated analytics will bring and how it affects employees at an individual level, within and outside the marketing team.
In particular, investing in tailored training that shows workers how to activate the insights GenAI assessment produces, and illustrates where doing so can bolster their time efficiency and productivity, is critical to improving confidence and usage.
Marketers are used to constantly moving goalposts, but the big tech age has upped the ante significantly. Thriving in the AI-fuelled normal is going to require a seemingly contradictory mix of going with — and against — the crowd. To be specific, brands will need to keep pace with the march of hyper-speed content production and personalization, while also leveraging insights afforded by smart analytics to determine what they can do differently to develop campaigns that cut through the noise. And as a key element of that, they’ll need to make sure intelligent tools are supported by smooth system synchronicity and pro-data sentiment.