There are seemingly thousands of artificial intelligence solutions for marketers – but only four real questions potential AI marketing professionals need to ask when deciding what will work for their business.
1. Does this problem really require AI?
What is the problem you’re trying to solve? This is always the first question to be asked – there is no point plugging in technology just so you can boast about having the latest and greatest toy. Brands should have a defined problem set or desired outcome in mind before even considering marketing AI platform solutions. The challenge or objective should guide the AI journey – and reveal whether they actually need AI or not.
For example, if creative optimisation is the goal, consider whether adding an AI vendor into the mix will change results significantly. For brands with a strict, relatively small creative set, no AI system will help them better understand if changing “click now” to “buy now” will have material impact.
On the other hand, for brands with hundreds of thousands of creatives, AI can help sort the performers from the non-performers by revealing the signals in the noise. This can be a real game changer.
You also need to be able to explain why traditional methods have failed until now, and why AI is the key to solving the issue.
2. What does the machine do, versus what does your marketing team do?
Do you want the AI to analyse data and provide you with recommendations to execute on your own? Or do you want it to take those actions on your behalf in pursuit of KPIs?
An AI that helps with decisioning vs. an AI that makes and acts on the decisions in real time operates at two very different levels. For organisations that aren’t particularly sold on one option, an alternative question to ask is, “What level of automation is sufficient to solve our workflow and scaling challenges?”
3. Will marketing AI play well with other systems and data?
AI requires massive data sets to perform at its best, so marketers will want to be able to integrate it with other systems, giving it access to many datasets. The two questions to ask here are: “Can the AI at hand be integrated with our other systems?” And, straight to the point, “Can this AI be enriched with external data sources?”
If the AI can be integrated with existing systems, how long does the vendor’s typical integration take?
Weigh up time-to-market and your ability to integrate large datasets against the end benefits among the vendors under consideration.
4. Who’s building it?
AI is very challenging to build, so it’s important to understand the DNA of the vendor’s team. How much of the team is dedicated to research and development? If, for instance, they have 100 people but only five of them are in R&D, it’s likely they have relatively simple technology.
Also consider who is on the R&D team. Do they have backgrounds from high-profile research universities and on-the-ground practical AI experience? Or, are even the most senior members of the team learning AI on the job?
AI comes with a price tag, so it’s important for marketers to know exactly what they’re paying for.