Now before you roll your eyes and attempt to escape from the prospect of yet another blog on the subject of AI, bear with us, for we’re not here to preach about ‘transformational’ tools that seem to have the polarised objective of either revolutionising performance or making jobs (whether in-house or agency side) obsolete.
In fact, we’re not here to preach about anything, but it would be foolish to not recognise the increasingly prominent role of AI in our everyday personal and professional lives, especially the latter where the topic seems to be shoehorned into every corporate statement or roadmap, regardless of development stage.
We draw an analogy from F1 – one of the most technologically advanced sports on the planet – where innovation, ingenuity and marginal gains are crucial to stimulating performance and getting a fraction ahead of the competition. The technology used has developed rapidly (and in recent years, a little more ethically) with hundreds, sometimes thousands of people, working behind the scenes to collectively drive performance.
Ultimately though, the culmination of all that coordination, effort and industry is filtered through a single point, which is of course the driver. Put an inexperienced and untrained operator in charge of an F1 car and the results are likely to be clumsy, sub-standard and most probably very expensive, when the inevitable crash happens. Even the most confident of day-to-day drivers would struggle to get an F1 car to move without intensive training, let alone deliver a competitive lap.
The same principle applies to the multitude of AI-tools available. Without a shadow of doubt there are tools out there that can help streamline processes or save time but don’t allow yourself to be blinded by the promises made – look at everything holistically in terms of input (do you have the qualifications to drive this piece of technology effectively?) and output (will the finished product be more effective or efficient than how you’ve achieved this in the past?).
This might sound very basic in terms of approach and evaluation, but too often this pre-analysis stage is missed and marketers leap into the unknown to implement an AI tool they’ve never test driven in the hope it’ll act as a magic bullet (and simultaneously impress the C-Suite) when the reality is far more sobering.
So what are we saying here? We say embrace AI, but do your research and training before committing to any tool. Don’t be the backmarker struggling to find the right gear on an F1 track full of experienced drivers, or the damage to your performance and reputation might become a much harder problem to solve.