Not all AI is equal — in fact, there is a such thing as artificial artificial intelligence… and we’re blowing the whistle on it to help you better understand what to look for (and how to detect red flags) in AI marketing solutions.
Today, every company in the tech space is touting their AI-infused software. As a piece in The Atlantic put it, “deflationary examples of AI are everywhere.” If I had a dollar for every claim I’ve heard that someone “uses AI” in their platform, I’d have enough to build my own automation software… and that costs a lot.
“On one hand, you have AI people complaining that the term has lost its meaning. On the other hand, you have reporters, startups, S&P 500 boards and every VC firm on the planet all claiming that anything slightly complex or slightly automated is AI.” – Ronald Ashri, Hackernoon
Most AI claims are slick marketing ploys, playing on that sweet spot in the minds of marketers looking to jump on the hottest trend in the space. But “real AI” is comparatively more rare. Real AI is how Amazon, Google, Facebook and Emarsys are taking CX and predictive marketing to new heights. But what constitutes actual vs. fake artificial intelligence?
“Real #AI is how @amazon, @Google, @Facebook (& @Emarsys) are taking #CX to the next level,” says @RBalasundaram CLICK TO TWEET
Understanding Real Artificial Intelligence
Most recommendation engines, triggered communications, and prediction platforms use a very simple rule-based system. “If X,” they say, “then Y.” And that’s that. They do not run simulations, take external variables into account, respond to unique behavior, and do not self-learn.
True artificial intelligence is autonomous — it does not require human maintenance and works for you silently in the background. AI is as much a part of the fabric of a system as the other tools and technologies (features, dashboards, campaign blueprints, etc.) inside of the platform. These systems will learn on their own through data experiencing as opposed to human programming.
“True #artificialintelligence is autonomous – it learns on its own & works independent of human maintenence,” says @RBalasundaram CLICK TO TWEET
Other rhetoric and traits which imply “real AI” include:
- Proactive characteristics. Reactive tools require environmental stimuli. Proactive, deliberate tools take action based on reasoning.
- Self-learning algorithms. Can a system differentiate between success and failure, and make adjustments based on results?
- Real-time decisioning. Real AI makes decisions across use cases and evaluates the best, most efficient methods to meet an end objective.
- A host of other terms including neural networks, deep learning, multiple linear regression, RFM modeling, cognitive computing, computational creativity, and predictive intelligence/analytics.
“AI platforms should do more than answer simple questions. They should be able to learn at scale, reason with purpose, and naturally interact with humans. They should gain knowledge over time as they continue to learn from their interactions, creating new opportunities for business…” — spokesperson, IBM Watson
There are a few tell-tale signs that whatever you’re looking at or whoever you’re talking to is NOT using real (autonomous) AI, including:
- If it requires a human to monitor or watch it on a daily basis, the “real AI detector test” determined it is NOT real AI!
- If it does not take informed action on predictions, simulations, or insights gathered, the “real AI detector test” determined it is NOT real AI!
- If it takes months to implement and begin driving results, the “real AI detector test” determined it is NOT real AI!
Let’s explore five commonalities among companies that are employing true AI.
5 Commonalities of Brands Employing Real AI
Amazon, Facebook, and Google set the stage for true application of artificial intelligence. The CX they create is enhanced and the insights they can gather are superior to most other brands. Their advancements provided the foundational groundwork for brands like ours here at Emarsys to innovate with AI. Here’s five things all of these companies have in common.
1. They have access to large quantities of high quality data.
With all the data these brands can collect, the algorithms they employ have more substantive information from which to glean insights and identify patterns.
2. Their algorithms are superb and community-enhancing.
In the same way marketers repurpose and reuse existing content, algorithms can do what’s called “transfer learning.” This is a machine learning technique where a model trained on one task is repurposed on a second related task.
“It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks given the vast compute and time resources required to develop neural network models on these problems and from the huge jumps in skill that they provide on related problems.” — Jason Brownlee, Machine Learning Mastery
When an already-sophisticated algorithm can be repurposed, it can benefit the entire community with enriched data sets.
Another method for improving the performance of algorithms is something called generative adversarial networks in which multiple neural networks sort of compete with one another, enhancing the performance of the overarching algorithm.
3. They have automated everything they can.
These companies all have a fully automated infrastructure which means they’re saving lots of time, resources, and manpower which they can reallocate toward continuous, strategic improvements and creative projects.
It’s no stretch to say that, today, automation, AI, and marketing success go hand-in-hand.
4. They found the right mix of technology and business knowledge.
Moving into 2020, one of the main differentiators of companies who succeed with AI and those who see less-than-desirable outcomes will be the end-to-end business knowledge incorporated within a platform/AI system being used.
With the right mixture of embedded knowledge and the right tech in place, there is no stopping industry giants like Facebook and Google, and it’s no wonder they seem to “know” so much about users/customers.
5. They understand how to innovate in today’s digital age.
At the end of the day, these companies just did it! Any stagnancy or reluctance was relinquished. They foresaw the change on the horizon, and not only embraced AI but began innovating with it. They knew AI would be much more than a trend; AI would (and now is) fundamentally augment customer experience in the digital age.
Top-tier AI achievers all have one thing in common: they’re using/doing real AI. This means they’re:
- Using lots of clean data
- Leveraging transfer learning
- Embracing automation
- Mixing tech and intelligence
- Not afraid to innovate
There are AI imposters and then there are the “real ones.” While there’s nothing “wrong” with “fake” AI, there won’t be as much depth, knowledge, or insights within those solutions as the full-fledged, self-learning, autonomous AI options — and those are comparatively more rare but much more gratifying.
Handpicked Related Content: