Staying in control: ensuring AI doesn’t drive your strategy


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Marketing technology combines a complexity of data, customers, processes, and systems, all of which require specialist know-how and capabilities to successfully optimise. And of course, there is no uniform approach – every business is different.

People, time and money are all valuable assets and cannot be wasted, so technology should be an enabler that supplements existing resources in order to solve business challenges. With the march of AI and Machine Learning (ML), many businesses are feeling the pressure to adopt this new technology, while it is causing fear and anxiety in others.

While there’s no doubt that, in some cases, AI has found a its place within marketing technology – providing marketers with better personalisation, and predictions of customer behaviour and much more, for example – concerns remain. What does it mean for an organisation’s technology stack? Will it impact employees and their roles? Just how will AI/ML change marketing technology? To try and make some sense of this, let’s take a look at the interplay between technology and strategy, i.e. how it is deployed in a business.

Strategy vs. technology

While marketing strategy and technology don’t need to be opposed, unfortunately they often are. Conversations about the new processes and procedures that have been adopted because of the implementation of new technology that doesn’t quite mesh with the organisation are not uncommon occurrences. But technology should be an enabler, not an obstruction.

And while many – indeed most – martech organisations are not AI companies, AI and ML increasingly have a role to play. For example, Machine Learning Models are embedded in many software solutions. ML models can be used alongside existing tech such as data bricks, Python and R. AI bots can be used internally to scan documents to help find specific details and information, and in many cases, bots are now being used instead of a search function on websites.

But there are a number of fears associated with adoption AI, including: loss of jobs; loss of control; biased models e.g. social inequality or bias caused by bad data; lack of transparency; privacy violations and compliance; and – taken to its extreme – full world domination and takeover!

Source: Pexels

Putting strategy first

In order to ensure that technology choices are driven by strategy, and not the other way round, it’s important not to fall for a tech solution just because it is new, or fashionable, or even just because it has been sold well! Trying to make a strategy fit around that doesn’t work. Equally, don’t be cheap. Cost is often a key driver in a technology choice, quite rightly, but you have to be honest about your tech choices. Will it do the job? Would a bit more budget get a better, more correct and more relevant solution?

It’s also important not to be a slave to tech conventions. A lot of businesses set very stringent tech rules, and won’t deviate. Organisations that spend a lot of money on a tech project only to find that the choice of tech doesn’t fit their strategy are going to suffer. You need a solution that will actually do the job, so if you have to think outside the box (or your usual choice of technology provider) then so be it.

The impact of AI/Machine Learning

In principle, the advent of AI and Machine Learning shouldn’t impact technology choices too much. However, because AI/ML solutions can help in ways we often can’t imagine, it does. For example, you want to use personalisation to drive engagement, but bandwidth restricts how many segments and variants of email and web ads you can serve. But AI means no limits or bounds, so that changes your approach, and both frees up resource and allows different tech to be used. So it is now almost necessary to create a ‘dream scenario’ strategy and then look to see what tech can help solve it.

With these considerations in mind, marketing departments can both incorporate AI/ML into their technology choices and ensure that this fits with their strategy moving forwards. The key is to put strategy first – thinking about the four pillars of AI strategy: vision, value, risks and adoption – and then seek out a technology solution that will be able to support its delivery.

Scott Logie
Scott Logie is Chief Commercial Officer at leading data solutions provider Sagacity Solutions, and Chair of the Customer Engagement Committee of the DMA (Data & Marketing. Scott has worked in the Direct Marketing industry for over 20 years, both on the agency and client side but always with the same outlook: to put customer data first in any marketing decision. He is an engaging, innovative and creative thinker. A highly experienced data-based marketer, Scott has worked with insurers, charities, automotive, FMCG, government and retail brands including some of the biggest in the country.


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