The AI Gold Rush: How to Make Money off AI and Machine Learning!
The AI Gold Rush Will Drive the Creation of New Artificial Intelligence and Machine Learning Jobs
We’re on the verge of the AI Gold Rush. And like the infamous historical gold rush, only a precious few with plans, and backup plans, will become the success stories that panned for gold and struck it rich. Real economic growth will be achieved by those companies which are selling the equivalent of picks, food, supplies, shovels, and jeans for Artificial Intelligence & Machine Learning.
Think of all the tools required; Training Data, Governance Tools, Consulting and Integration Services, and most critical – the creation of New Sustainable Revenue Models.
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Startups, incumbent tech companies, and corporate innovation centers already have started using Artificial Intelligence and Machine Learning to solve real business problems across manufacturing, healthcare, transportation, energy, and nearly every other industry beyond high tech. The first successes are no-nonsense; e.g. take a process that you know well, and move the heavy lifting to AI, enabling humans to do the more creative thinking. This approach is characterized by short-term wins, intended to be cross-company scalable, with a focus on immediate value creation.
“There are many parallels between internet retailing in the nineties and artificial intelligence today: those that embraced electronic intermediaries were able to redefine and grow within a click and mortar world. Whereas others have seen their brick and products fade and seem digital substituted. In both situations, commoditization, or fading away, awaits those that wait too long to ride the wave of social disrupting technology”.
The 2020 Mark: Augment Existing Jobs and Transition to Jobs That Are yet to Be Created
Over the coming two to five years, we can expect a profound transformation for knowledge workers and professionals as their daily tasks are infused by AI and ML. However, it will likely be much different than what Sci-Fi movies have made us think. AI and ML will not do the job autonomously; rather, AI will relieve the human from repetitive work, and force (and assist) humans to make choices and decisions faster and easier.
It is just the opposite than we anticipated – humans have to decide when to tell machine learning to do the work. This is a prime example of the human component of machine learning and the importance of creativity when machine learning is in play
Leveraging technology, like using artificial intelligence in processes, augmenting tasks, will actually strengthen the economy. Let us take JFK’s premise as a truth: As we have the talent to invent new machines that reduce jobs, we also have the talent to invent new jobs. The AI gold rush will, in fact, drive the creation of new AI jobs.
Gartner predicts that by 2020, Artificial Intelligence will create more jobs than it eliminates. Think of job roles like ‘Citizen Data Scientist’, ‘Best Practice Training Data Creator’ or ‘AI trainer’ for a variety of industries and domains (think regulatory, law or finance). It will also create executive jobs such as the Chief Data Officer, AI Ethics & Governance officer, or AI training-property protection (the secret sauce of how companies do things). There will also be data monetization related jobs, where companies will see both monetization of their AI-enriched data as well as AI-trained data services to their industry or value chain.
AI will become a positive job motivator, as per the Gartner’s prediction, the number of jobs affected by AI will vary by industry; through 2019, health care, the public sector, and education will see continuously growing job demand while manufacturing will demonstrate the greatest growth. The predictions are that starting in 2020, AI-related job creation will cross into positive territory, using AI where it matters, reaching two million net-new jobs by 2025.
The year 2020 is an exciting turning point for AI. The pivotal moment of mainstream AI usage. Various analysts have already projected that by 2020 around 70% of the data that a company uses will come from external data streams and IoT devices. By 2020, experts predict that 50 billion things will become connected to the Internet.
To put this in perspective, that means nearly seven connected things for every person on the planet. Those numbers need Artificial Intelligence technology, powered by machine learning, to do the filtering, inference, and prediction to make this all work, as billions of things, data, business processes, and people become connected; The current predictions are that $19 trillion of value will be created. Many of us would justify the term “AI gold rush” with that number.
Your Choice to Make: Going for Gold vs Making Gold off Inventing Jeans or Supplying Goods
The core question that companies need to ask is related to their core competence–
“Is Data the core asset that I monetize — or — Is Data the glue that connects my processes which have made my products or services successful?”
Let’s start with companies that are purely data-centric, those that monetize their data for product selection and insight: for these companies, data is everything. Data is their asset. Data-centric organizations will have to create their own business model to find their gold.
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Case-In-Point: Data-Centric Companies Which Create AI
A few years ago, many people thought that the creative part of streaming video services, such as Netflix or Amazon Prime, would be marginal. The recent Emmys and international film festival awards have proven otherwise. Today, as we choose our entertainment, models, data science and machine-learning assist our choice, predict our interest and drive investment for new original content!
Preparing the data used to train your own baseline AI models is incredibly time and labor-intensive. For data-centric companies, the creation of data that trains their own artificial intelligence, however, is the gold that they need to find in order to sustain their company’s value. These companies create their own capability, their unique AI code, and their own platforms. We have witnessed these singular companies which have created an entirely new world of platform economics such as the Airbnb, Facebook, and Alibaba.
Data IS their asset.
Until recently, the modern miners of AI-driven insights had been left to create their own tools and workflows. At some point, one will wonder about the growing community of data-economy prospectors – wouldn’t they be better off to focus on accelerating innovation for the primary capabilities of their specific AI-infused processes, instead of building their own underlying infrastructure. Technology companies have the opportunity to create AI-tools, leverage technology platforms and business solutions. Even most data-centric companies could leverage modern AI tools rather than build them.
Typical of new innovation, there are always trail-blazers who create their own unique solutions. However, most companies are not wholly ‘data-centric’, and they are about doing what they do best and delivering their brand promise. Most companies will not create AI from scratch.
Instead, most companies will tailor AI-off-the-shelf. This means using data as the glue, the trigger, and connector for intelligent processes.
You can think of this as similar to how companies today who customize modern Cloud ERP and Business Applications. In terms of AI, this means using AI-infused business solutions to create intelligent processes or data being used to drive the “Intelligent Enterprise”.
What does this mean to make money by creating AI versus make money by using AI?
This is the parallel of “Prospecting for gold versus inventing jeans or creating a supply chain” aka living off the mining versus living off the miners. Let’s illustrate this with examples of firms which use AI data to connect processes. Car companies can use data to predict when a car will need maintenance – ultimately using IoT connected cars to ‘inform maintenance’ of a needed repair. This can apply to an order or payment data – using data from unstructured invoices, forms or emails to execute a service.
Another example is the ability to act on stock-in-transit delays. There are a plethora of repetitive mind-numbing tasks that could be off-loaded from valuable knowledge workers, experts, and professionals in an organization.
Case-In-Point: Companies That Use Ai to Connect Processes
Full-service hotel staff answers many routine questions every day such as:
Where is the hotel gym?
When is it open?
What’s the Wi-Fi password?
What time is breakfast?
Modern hotels today are testing and using ‘conversational bots’ to augment first-line service with typical answers. Rather than use staff to answer, hotels use a ‘bot’ which appears in an ‘ask and answer’ form -such as a text service. The dialogue then connects back to staff with outcomes and more complex questions. The staff pick up then (connecting the service) and can perform more creative service to offer excellent guest experience!
In all these cases, Data is being used to create new processes that digitally enable and even transform an organization, while AI helps to answer data questions.
Of course, these companies can still become “create AI” and prospectors as well. Though let’s face it. Most companies still spend too much time and too many of their best resources building up bespoke tools, machine learning code, and custom data frameworks. This slows time to market and consumes resources that should be focusing on driving innovation and sustainable differentiation.
There will, of course, be those rare success stories. We do see Uber, Amazon, and Netflix today. Just like earlier gold rushes that have already occurred, including the original prospectors, a minority of players will make money from finding the gold (creating the AI & ML code, capability and platform); Yet many more will become wealthy by living off the miners’ gold with food, supplies, services – or inventing new products and create a market of it, like blue jeans in its day.
The real question for you is as you read this is:
How will you capitalize AI?
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