Artificial intelligence (AI) seems to be on everyone’s lips. Because of the term’s ubiquity, it’s easy to downplay it as a buzzword. In reality, the concept has been around at least since the 1950s, so it’s hardly a novel trend.
A confluence of technology advances in the fields of artificial neural networks, statistical algorithms, computing power, and data analysis has impelled the growth of AI on an unprecedented scale. AI is speeding up exponentially, and commercial companies can no longer ignore its underlying potential (and, possibly, certain threats involved).
The technology can deliver a substantial qualitative change to business organisations, and create new opportunities for company growth. Major corporations have already started investing in its adoption, but many start-ups and SMEs are slow to act.
For executives looking to get there, I’ve outlined a five-step modernisation plan:
1. Identify business needs and specific requirements of the marketing organisation before considering the role of a technology.
Before you adopt artificial intelligence, ask yourself why. Define the use cases and then figure out how AI will help you achieve them, rather than the other way around. There’s a temptation with any new and exciting technology to put the cart before the horse, to look for a technology solution before you realise your problems and goals.
2. Create a strategy for integrating AI initiatives within the current marketing operation.
That means starting by identifying the changes to data management and governance that will be necessary for AI and machine learning use cases. Companies often struggle with aggregating data, which remains segmented in silos. This results in AI and machine learning analyses that aren’t fully informed. Is your data clean and up-to-date? Aggregated data can make you money; redundant and conflicting records will just make a mess.
3. Develop criteria for evaluating AI and machine learning applications, services and vendors.
I recommend looking for vendors with AI and machine learning expertise, of course, as well as open-source application programming interfaces (APIs). These act as ready-made links between the new analytics capabilities, enabling smooth transitions between different communications channels. If you don’t have API capabilities internally, fear not. Many vendors are cloud-based, eliminating the need for dedicated internal IT resources.
4. Manage organisational change.
Historically, marketers launched campaigns by deciding what to distribute. It’s easy to default to what’s worked in the past. However, the strongest campaigns break free of convention. AI enables more of a conversation, which empowers consumers to decide how to engage with brands. Learn from those conversations and use that data to fuel future marketing. AI gives you the tools to deploy the personalised communications – dynamic content, for instance – consumers ultimately crave.
5. Cultivate closer collaboration among marketing, data science, and line-of-business organisations.
Like data, people shouldn’t be siloed. Marketers should communicate with the technology department, which will probably have to develop a new IT infrastructure. The finance department would help monitor the spend there. A cross-functional team with a wide array of expertise performs better and alignment means they’re all marching towards the same objective.