AI: What’s Next? On Demand AI Means OnDemand Intuitive Marketing
By: Ai Marketing Association
Tags: Marketing Automation, Machine Learning, Artificial Intelligence, AI, Marketing

It’s not hyperbole to state that all of us — our behaviours, buying decisions and ultimately our thoughts — are constantly informed by a cascade of clever algorithms that have learned our patterns.

It’s an absolute certainty that those of us leading marketing will continue to work diligently to apply machine learning algorithms to the myriad of real world items listed in Federico’s Artificial Intelligence Marketing Manifesto just one year ago. These included Predictive Customer Service, Dynamic Product Pricing, Forecasting, plus sophisticated Image, Voice and Language recognition algorithms.

Federico, who founded the Artificial Intelligence Marketing Association (AIMA) just 1 year ago (Jan 2017) has seen the San Francisco chapter grow and expand, attracting thousands of local followers and magnetizing AI marketers (270+ in the bay area) across borders to the point that we’ve attracted 3 international conferences: The AI Congress, Business of Bots and AI Expo as partners. Also, he will be speaking at the upcoming AI Congress in Las Vegas in early May presenting “The State of AI Marketing”. As well as at the Business of Bots conference in San Francisco.

In addition, AIMA SF will BE rolling out a series of Meetups in San Francisco on the intersection of advertising and artificial intelligence.

What’s Next for AI-empowered Marketers? Decentralization…And it’s Already Here

The 3 main obstacles companies have always encountered when implementing AI to uplevel marketing results are:

Data — which at best is costly — in fact so much that both Fortune and The Economist have declared “data is the new oil”. Beyond the ever-increasing high cost of data, it is also notoriously flawed.

which at best is costly — in fact so much that both Fortune and The Economist have declared “data is the new oil”. Beyond the ever-increasing high cost of data, it is also notoriously flawed. Analysis — to ensure the data is properly interpreted.

— to ensure the data is properly interpreted. Machine Learning — believed a solution but still an obstacle. It can create new, smarter algorithms based on existing datasets, in order to deliver actionable, revenue-producing insights — such as micro and macro consumer predictions to pinpoint how and what advertisers and marketers should message out next.

Taken 1 by one, first there’s Data. Every marketer knows the obstacle of Good Data.

If you have the massive reach of say, Coca-Cola, then you have the budget and data to deliver predictions and bulls-eye marketing messages to discrete target audiences.

But if you’re an advertising or marketing professional within a smaller agency, you and your teams are beset with this first massive obstacle; let’s face it, the financial distraction of data buying eats into team focus and profits.

Same with Analysis. Every advertiser knows that there’s a reason why Data Scientists and Data Analysts are the hottest jobs right now, and stumbling blocks this massive data talent shortage presents to advertisers and marketers dependent on data gathering and interpretation.

Finally there’s Machine Learning. It’s worth noting that machine learning can function as both a solution and an obstacle. The reason: Buying, testing and applying the right algorithms to accomplish advertisers’ ever-pressing need for insights is costly and can be time-consuming. But the machine is also the solution that the advertiser can rely on to ensure their messaging remains custom, compelling and intelligent when the technology is available and easy to use.

But Thanks to Blockchain Innovators, these 3 main Obstacles are Rapidly Disappearing. Here’s What’s Next…

Several AI companies are at the forefront of removing these obstacles — or to use a Silicon Valleyterm — “disrupt” these hurdles to the point that by end of this year we expect this Terrible Trio (Data, Analysis, and Machine Learning) to all but disappear.

How do they do that?

Blockchain Innovators Are Doing It Faster, Better and Cheaper — Right Now.

Companies like Synapse.AI and Repux.io have been hard at work creating new platforms and new ways of making data cheaper, interpreting datasets in the right way and making machine learning algorithms accessible to everyone, e.g. to future AI-empowered marketers.

According to Zurich-based SingularityNET.io, a decentralized marketplace that allows AI algorithms to cooperate and coordinate, “when data is more accessible marketers can create, monetize, and use AI at scale,” said Dr. Ben Goertzel, founder.

Copyright 2018 SingularityNET.io — “The Decentralized Marketplace for AI”

Dan Gailey, CEO of San Francisco-based Synapse.ai (currently headed into ~$7M ICO) is set to empower advertisers, marketers — anyone really — to do just that.

Copyright 2018 Blog.synapse.ai — “AI Economies on the Blockchain”

Gailey is creating an entire free, open AI ecosystem which essentially functions as the Wikipedia of AI algorithms. Gailey envisions a wide open repository containing fresh data, new algorithms based on innovative machine-learning tools through which marketers (and anyone, really) can readily gain the insights they need by simply creating a free account.

“The future will be built on a fair and balanced intelligence available to all equally, by utilizing newly-created AI economies that any agent can participate in and receive some reward for doing so”, said Gailey in a February announcement about the Synapse.ai public token sale.

Adding to the decentralized data disruption is RepuX.io, an ecosystem for small and medium-sized enterprises (SMEs) that will enable them to monetize on their data by selling it to 3rd party developers. Developers will then create new applications based on SME’s data and then offer their applications for sale into the Repux.io platform.

The net-net is that the data cost and availability obstacles — which are currently the biggest obstacles — will diminish or evaporate. The net-net is empowering marketers to more readily and inexpensively deliver predictive analytics services to improve SMEs’ business processes.

“The monetization opportunities for SMEs and developers are huge; at this point in point in time, data services providers like AliBaba, Salesforce, Google represent $4T worth of services”, according to David Siu, Repux.io spokesperson.

Think About It:

What will the elimination of current the 3 major AI barriers mean for marketing professionals?

What will it mean when data and machine learning tools are free and ready to access as easily as logging into Gmail each day?

What will it mean for marketers on the front lines to have readily-consumable answers at their fingertips? To be able to log into a free repository where a simple search turns up fresh data and applicable answers?

Put yourself in the shoes of a frontline marketing professional at a small or medium-sized business. Your CEO tasks you with delivering predictions for Q3 advertising messages based on data gathered in Q1 & Q2. Prior to the intersection of data sharing and blockchain, this task would typically have represented a cost — cost of gathering the data, cost of purchasing 3rd party software to interpret the data and others, in order to arrive at the critical insights required.

Now with the advent of new AI innovations like Synapse, RepuX and others, the cycles of accurate predictive planning will be unfettered.

How this will impact the future of big data providers who currently make millions in revenue by selling customer information?

Well…we at AIMA predict this will do to the big data sellers — — and perhaps to data analysts — what Google did to Encyclopedia Brittanica salesmen. And map sellers.

Concluding, soon marketing departments — and not just them — of SMEs will be able to implement big data providers information shared on the new blockchain infrastructures, as well as access AI-drag-and-drop algorithms (AI for Dummies) on top of their current marketing stack and gain fully AI-empowered marketing heroes — — and perhaps without the need of data analysts, we’ll see…. !