Vivatech the largest European tech conference, hosted a series of talks about the long term implications of AI on our lives, and I was privileged to join the discussion in an interview led by @Techcrunch chief editor Mike Butcher.
Here are the key highlights.
The rules have changed, driven by digital-first companies
From the days of Mad Men to 2019, the role of marketers has changed. As well as their tool kit.
From notebooks and whiteboards to predictive analytics and machine-learning recommender systems, CMOs are much closer to engineers than they ever were.
What has happened over the past few years is the emergence of a “Marketing-engineering” role or “ Marketing scientists”. A marketer who understands numbers, who understands data pipelines and who understands engineering systems. Someone who works with these data systems and tries to figure out ways their team can do less repetitive data analysis, and free up time so they can think creatively.
CMOs have to reinvent themselves, and the way they hire.
With the removal of manual processes which are slow-paced and prone to human error, companies are rapidly changing the way they operate. Decisions are frequent, data-based, made lower in the organization, and increasingly automated.
There are 3 major consequences on today’s companies:
- People’s roles and functions within the organizations, including the marketing organization, are going to start shifting around. Operators will become analysts, and will be asked to make more, frequent decisions on the business.
- We’re starting to see new demand for a different category of tools as these new roles emerge and these functions start to change. Spoiler alert, those are AI based.
- This will eventually change market dynamics: which companies become successful in certain spaces, who wins, and with what tools in their stack?
From Business Intelligence to a new category: Operational Intelligence.
In this new world, it’s not enough to have an automated data-capture system.
Decisions need to be made in real time, multiple times a day. And those decisions are made not by CMOs but by data analysts, who sit lower in the organization. They need new tools. Tools that increase their operational efficiency by contributing to the analysis, recommendation and implementation of the suggested changes.
In a world where companies are now equipped with tools to surface the data, the next exciting wave of innovation comes from tools that surface answers to data related questions.
Creating a hybrid marketing team: AI and Humans.
It’s not so much of a question of AI vs CMO or AI vs business analysts. That is a false debate. The question is: How fast can you create a hybrid team? A team with algorithms supporting your team’s human decision making. AI based solutions complement human weaknesses. They enhance a team who relies on the standard “automated data collection” and “Human data analysis” combo, by contributing to the analysis/ recommendations piece.
Ultimately, the decision is human — but instead of having to decide based on the options given only by business analysts, you get to see an additional recommendation, generated by a machine learning-based system.
What advice do you have as a conclusion?
Instead of focusing on AI vs CMO debates, get ahead of the curve by educating yourself on the options available for your business for a hybrid approach.
Start by BI and move to OI: Tools that are designed to increase the operational efficiency of your marketing teams.
You’ll want to look for tools which have those 3 things:
- The recommendations/ analysis have to be immediate. It can’t be eventual like it is with BI tools. : “I need an answer to this question” and MAYBE get it answers 2 weeks later. It has to be answered in real time.
- Continuous learning. There needs to be a feedback loop between the user and the machine. The AI sends a recommendation, the operator approves or rejects it. If approved, the machine implements the change (like our product Aiden, does for marketers), tracks the results and learns. If rejected, the machine learns from the user, and feeds this back into the next recommendations.
- Self-service as opposed to full service. You can’t have someone who goes and does all of the work for you (service), nor can you have a black box machine learning tool that decides without context. The tool has to give you insights that are catered to you. You have to be able to ask the questions to the tool (perhaps using Natural Language Processing in some cases). The tools have to enable you to do things by yourself and advance your understanding of the business, not take it away from you.
Aiden.ai is an AI-as-a-service that saves marketers time and money by preparing daily actionable recommendations that marketers can approve in a click. To request a demo or meet us in San Francisco, drop us a note!