Why Are Marketers Becoming Disenchanted With the Promise of AI?
According to Forrester, 88% of marketers today use artificial intelligence (AI), but most are disappointed in the results.
Despite the boom in usage, AI is often poorly understood, and oversold. Many don’t have a clear idea about what AI and Machine Learning actually is, or can do, leading to the terms misleadingly becoming a catch-all for automation and falling short of marketers’ expectations. Below Peter Gasston & Natasha Momin at VCCP explain for CEO Today.
The Forrester report indicates that AI used by marketers does not address operational and technical complexities which drive marketing performance. But in order to get the most out of AI technologies you need to not only have the capability to execute its recommendations but also the ingredient to fuel it – data.
It’s easy for a business to see AI and machine learning as a ‘magic’ solution, figuring they already capture plenty of data, so using AI must be an easy way to get valuable new insights from it. But the mere existence of the data isn’t enough by itself.
For AI to be a viable solution, you must first clearly identify the problems which you’re trying to solve, and subsequently set the goals that you want to achieve. These goals should be quantifiable, and measurable. Then by working backwards from those goals you can identify where AI and machine learning can help.
When people come and say “I’ve got this massive amount of data—surely there’s some value I can get out of it,” I sit them down and have a strong talk with them. What you really need to be doing is working with a problem your customers have or your workers have.
After this definition stage you need to make sure the data to be fed into the learning algorithms is in the right format, and in sufficient quantity, to be useful. If the data that’s available is incomplete or inconsistent, the results won’t marry up to expectations.
Giving the machines the right data often means bringing it together from multiple siloed sources, which can involve some organisational changes and implementation of new processes – which many don’t consider when they first set out to use AI technologies.
The final step is to have the right people in place to make sense of the results and apply them successfully to the business, using their creativity and knowledge of the market.
To get the most out of machine learning at your organization, you need the right team and the right mindset. The latter requires a cultural shift that prioritizes and rewards experimentation, measurement, and testing throughout your organization.
This can be a lot of work, so it’s no surprise that many companies offer services to remove at least part of this burden. For many businesses the service that’s being offered can help; but there are also agencies selling ‘AI solutions’ to clients without the technical knowledge to do so, and companies selling automation services to clients without clearly explaining that it isn’t always the perfect solution, which often means there’s more work to be done.
[Marketers] need to study how AI makes decisions so they can learn from, adapt to, collaborate with, and generate business results from it.
The general dissatisfaction of many of the respondents in the Forrester study indicates that there needs to be better education about what machine learning can do and the resources involved in using it effectively—and less hand-wavy ‘magic’ offered as a business solution. And as AI tech becomes more proficient and businesses understand the value of usable data (thanks GDPR), the results from using AI can only improve.