AI is poised to expand throughout all aspects of business operations and daily life, yet it must first counter a limited workforce capable of developing the technology.

The worker shortage, coupled with booming AI salaries, confers a steep advantage to large companies with discretionary R&D budgets. These companies are making tremendous advancements in AI, but progress is often limited in terms of developers and users.

More infrastructure, lowered barriers to entry and a larger population of AI specialists have opened the way for democratization of AI — and these tools are changing the power dynamic.

Developers can now use infrastructure and algorithms made available by big AI players. Like building a new car, newcomers can build an AI solution using pre-assembled parts in a design that suits their needs, according to Victor Thu, global head of marketing and product marketing at Digitate. And with more experts working on AI, cross training possibilities to share skills and knowledge open up.

This year saw the introduction of many democratized AI tools, such as Oracle's cloud-based drag-and-drop algorithms and the expansion of open-source AI code from the establishment's Google and Microsoft and newcomers like the startup Algorithmia.

The democratization of AI is a multifaceted process, including education and access to large swathes of data at the individual level and availability both across internal company operations and externally across the enterprise, government and society.

Startups should probably focus on trying to use what has been done before for their application ... just taking a model from another field and being able to put it up for your own field, that has a tremendous impact.

—Kian Katanforoosh, adjunct lecturer at Stanford University

Uber's Michelangelo platform offers an exemplary case study of democratization across a company's operations. The ML platform, built with open-source and in-house code, applies end-to-end ML as a Service workflows and models across Uber's operations, from coordinating rides to food deliveries.

By creating a uniform, underlying ML system, Uber standardized workflows and tools across users and teams and opened up an infrastructure capable of storing, deploying and managing large models. Uber deployed Michelangelo about a year ago, so it is still in its infancy, but the platform is a custom tailored solution to the company's technology needs that will grow with Uber's expanding technology portfolio.

Yet not every company has the resources or talent wealth of Uber, or Google or Microsoft for that matter. Quasi-AI as a Service models will help democratize AI across the enterprise, especially among companies without the means or desire to develop a custom system from the ground up. Drag-and-drop frameworks and cloud-based AI and ML services are the early frontrunners for democratized AI in action.

A lot of the progress is dependent on universities and big tech companies offering their AI knowledge and tools to the public. "I think it's a requirement for AI to democratize itself to have big companies and leaders in AI to open their code," said Kian Katanforoosh, adjunct lecturer at Stanford University and head teaching assistant for Andrew Ng's deep learning Coursera courses.

The composition of today's tech industry offers a ripe environment for democratization. Big players such as Google, Microsoft and AWS became AI companies because the market demanded it, according to Katanforoosh. Their access to powerful hardware and compute power allow them build models smaller organizations or individuals cannot do alone.

There are a lot of companies that are building AI for the sake of building AI, and they are looking for a problem with their solutions instead of identifying what the problems are before they build a solution to attack it.

—Victor Thu, global head of marketing and product marketing at Digitate

"In the short term, they do have a vested interest to make the AI algorithms in the form that they are trying to provide to their customers as strong as possible, and to that extent even supporting multiple open-source communities to celebrate that goal," said Thu. By offering their platforms up, big companies can breed dependencies on their technology as the foundation of new AI products.

But tech startups formed in the last couple of years are AI companies from scratch, like the hundreds in Silicon Valley alone working on AI solutions, said Katanforoosh. These new players can expand AI use cases with the technology already in place from established powerhouses.

"Startups should probably focus on trying to use what has been done before for their application," said Katanforoosh. "Because there's so many things where deep learning is not used, where artificial intelligence is not used, just taking a model from another field and being able to put it up for your own field, that has a tremendous impact."

Like any new tech, companies need to make sure what they want is what they are getting, lest they squander a heavy investment.

"Fundamentally, they need to figure out what problems they are trying to solve, because there are a lot of companies that are building AI for the sake of building AI, and they are looking for a problem with their solutions instead of identifying what the problems are before they build a solution to attack it," said Thu.

Democratized AI tools, with pre-trained technology that cuts the learning time for an AI system down from months to years, will bring more companies to AI, according to Thu. By not having to start from scratch, it will be less scary and risky of an investment.

Quantum computing is looming on the digital horizon, and when it finally arrives smaller players will have access to more compute power. But until then, the coming years should see many more tools hitting the market, especially through cloud service offerings.