Mini-series: Artificial intelligence and the power and potential of machine learning – Part 3

If you missed the earlier posts, this is the third instalment in Mediative’s educational mini-series on ‘Artificial Intelligence and the Power and Potential of Machine Learning’. Previous posts looked at:

    1. What are the differences between artificial intelligence, machine learning, and deep learning?
    2. How can artificial intelligence, machine learning, and deep learning be leveraged to benefit businesses?

Here we look at what marketers should be doing today to harness the power of AI, ML and DL

Part 3: What should marketers be doing today to harness the power of AI, ML and DL?

1. Ask yourself, what are your goals?

The reality is, not every business will benefit from AI and ML, and not every lean start-up can afford them. So the first step for businesses is to look at what their business goals are, before determining if and how AI can be integrated into the organization, and what level of investment is required. Your question to yourself should be “how do I generate more leads” not “how can I use a chat bot in my company”. Knowing what you want to achieve comes first, before deciding which technology can be used to achieve that.

2. Have a solid understanding of the technology available

Be mindful that the technology and strategies can get quite complex, and businesses need to ensure that if they are going to build their own (rather than going the cheaper, and less complex route of buying from Google, Amazon or other vendors), that they have the technical resources to make AI a reality in the organization. eMarketer, in their report, Artificial Intelligence for Marketers 2018, quotes US digital agency Isobar’s vice president Dave Meeker as saying (regarding AI) “Marketers should learn how to use it, but don’t try to make it” because “you’re not ever going to be able to keep pace with 500 computer scientists and data scientists at Google working on that stuff.”

3. Do your due diligence when choosing partners or vendors for AI

Approach any partner or vendor with your goals at the forefront, and always make sure you retain control over your data, and insist on total transparency from the vendor. Make sure the partner, vendor, agency etc. can sufficiently meet your needs, and if not, look elsewhere, and perhaps be prepared to increase your investment if the return is there. Don’t jump into anything too quickly, and consider a partial roll-out when you are ready to launch to get a full understanding of how it will work in a live environment before full-deployment.

AI has the potential to be very complex and costly – but it doesn’t necessarily have to be. Amazon, Baidu, Google, IBM, Microsoft and others do offer machine learning platforms for businesses, aimed at beginners. Research each platform and decide if one of them is right for your business. Below is a chart comparing the basics of each of these platforms:

(Source: http://searchbusinessanalytics.techtarget.com/feature/Machine-learning-platforms-comparison-Amazon-Azure-Google-IBM)

Josh Ong, director of global brand strategy and communications at Cheetah Mobile believes that these platforms are helping
make AI more accessible. “Investing in AI from an infrastructure, technology and talent perspective, it’s all quite expensive,” he said. “You need to be at a certain scale to have the kind of data that really makes AI useful.” (1)

Agencies and AI

Agencies are also stepping up their AI game, increasing investments in technical resources and building technology partnerships in an effort to help their clients navigate the array of AI solutions. More and more agencies and advertisers are now leveraging AI solutions for programmatic advertising and marketing automation. 37% of agency professionals worldwide believe AI, chat bots and voice interfaces will have a significant effect on their clients’ marketing approaches over the next 12-18 months (1).

AI won’t replace marketing professionals, but will definitely transform the way we do marketing and navigate through high volumes of information and decision-making process with precision in a matter of seconds.

Barriers to AI implementation

There’s a multitude of studies that cite barriers to widespread implementation and adoption of AI, and these include technology, expertise, cost, lack of transparency, loss of control, privacy and security among the biggest. A lack of ‘ownership’ of AI within organizations is also seen as a problem that needs to be overcome.

One of the biggest barriers to widespread AI adoption is the disparity between what AI is capable of, and what marketers are capable of. That is, while the possibilities for AI are literally endless, marketers’ understanding of it is much more limited. A March 2017 survey by WBR Digital and Persado reported that 76% of US and UK retail marketers said there was confusion or lack of clarity about what AI marketing could be used for. Lack of appropriate in-house skills was a barrier for 46% of respondents, and in a further study, 70% of business decision-makers believed their marketing team lacked technical skills to leverage AI. Maybe their first investment should be in AI-powered talent development and corporate education!

In the Emarsys commissioned Forrester survey it was revealed that many decision makers believe that either their internal processes can’t be adapted to handle AI or that the present staff lacks the technological skill set to adapt. However, there is also disparity between what decision makers believe and what the internal staff thinks to be true because Forrester found that only 29% of users feel they lack the skills to implement the technology.

An Infosys-commissioned study of 1,600 business and IT leaders cited 54% of respondent saying ‘employee fear of change’ was the top barrier to AI adoption.

“People think [AI is] about taking over somebody’s job. But I think it’s to help you do better by letting the machine do things it can do very well, but then allowing the human to bridge that gap.” (2)

AI is also out of reach for smaller businesses who simply do not have the resources to implement the technology. A December 2016 Salesforce survey revealed that despite the hype around AI, 61% of small business owners said their businesses are not ready, citing it is too complex for what they need, and only 6% said there are actively using AI tools to automate business processes.

Next week, for the fourth and final instalment in this educational mini-series on ‘Artificial Intelligence and the Power and Potential of Machine Learning’, we look at great examples of companies doing AI and ML right!

Download the eBook: “AI and the Power and Potential of Machine Learning

Source:

      1. Artificial Intelligence for Marketers 2018: Finding the Value Beyond the Hype, eMarketer, October 2017
      2. Artificial Intelligence for Marketers: The Future is Already Here, October 2016
Rebecca Maynes
Rebecca Maynes is Mediative’s Manager, Content Marketing and Research. Her expertise lies in the creation of engaging thought leadership for Mediative. From compiling eBooks and case studies, to conducting research, analyzing data and writing white papers and reports, Rebecca is an integral part of Mediative’s Marketing and Research team. Rebecca began her career with Yell.com in England, and, after emigrating to Canada in 2005, she has gone full circle, joining Mediative, a Yellow Pages Group Company, in 2009. Prior positions include Marketing for a B2B Software company. Rebecca graduated from Cardiff University in Wales, UK, with a First Class Honours BSc in Business Administration.