

Artificial intelligence and machine learning have captured a lot of attention in recent years but theyâre part of a much larger subset of technologies, called intelligent automation.
At their core, these technologies share the aim of automating thought â by analysing large amounts of data â but they output these insights in different ways.
In a recent podcast, Vertical Leapâs Managing Director, Chris Pitt, and Head of Services, Lee Wilson, discuss how intelligent automation can help large businesses scale their marketing efforts. Here is an except below and you can listen to the full podcast here.
âThere are different subsets of intelligent automation and the first one is robotic process automation, RPA. This is about all the things that we do on a daily basis that are boring, labour-intensive or fairly technical, but consistently technical. And because of that consistency itâs stuff that we donât do very well naturally ourselves, as humans.
âSo RPA is not clever in the sense that itâs doing any thinking but what it is doing is taking away those manual repetitive processes and automating them.
âAn example in search might be link analysis or data collection; or it might be looking for technical fixes on your website â lots of things that we do on a daily basis that donât necessarily need any creativity or freeform thinking.
âYes, it totally is â not just from a marketing perspective but across the whole business. There are tasks people do every day in businesses that could be done consistently better by technology. That doesnât mean we should be replacing people. What it means is we free them up to do more of the tasks that the technology canât do, such as strategy or implementation.
âI think thatâs the key, for search generally, to shorten the distance to results, reduce the amount of âpeople timeâ required to do the mundane things and free up more time to go a bit deeper which means we can be a bit more creative â and the technology can go deeper and wider than people can so it can identify more things for us to do. It achieves much greater scale.
âWhat I like is the freeing-up of highly-skilled experts to focus on more of the creative, unique and experiential actions that are fundamentally human.
âIt means that we can deliver more exciting and impactful marketing campaigns, but it also means that people have more time to nurture customer relationships and truly understand the businesses that theyâre working with, the people that theyâre working with, and to provide increasingly tailored and valuable search marketing solutions.â
âThatâs exactly right and there are a variety of examples. Take citations [a reference to a business online that includes the business name, address, and phone number] as an example. When you think about a simple task like building citations, if youâre working with a company thatâs got hundreds of locations, even if you only build one citation per location (which wouldnât really work), youâd be building loads of citations on a monthly basis.
âSo you can see how quickly the amount of human involvement can build up and how much resource is wasted just by doing a very monotonous, repetitive task that may well end up with errors because humans arenât suited for that type of activity.
âBut when you automate this kind of activity using intelligent automation, you can achieve more, at scale, with more efficiency, and all whilst the actual people can be focussing on implementation, creativity and strategy.
âWe talked about RPA and thatâs the first stack, automating repetitive tasks.
âThen youâve got analysis and insights and when I say this Iâm talking about the things that we typically look for â the âknown-knownsâ. We know weâre looking for something, we know what weâre looking for and we probably look for it quite often â so it only makes sense to automate this process.
âAnd the last part of the stack is the part that everyoneâs been talking about, which is AI and machine learning. This helps us find the âunknown knownsâ â the stuff we know that we donât know about but we should probably find out, and this is where machine learning plays a real key part in looking at data right across the spectrum, much deeper than we could without automated intelligence.
âSo, at the bottom of the stack, youâve got robotic processing automation, which is automating everything that we do that could be automated and then youâve got data insights and data science telling us where we are now and where weâve got to go.â