The history of artificial intelligence: Complete AI timeline 10 top AI jobs in 2024

Democratization of AI creates benefits and challenges

What happens when you expand the use of AI beyond a circle of experts? To prevent business challenges, leaders must make smart investments in AI tools and training for workers.

AI democratization puts AI into the hands of users without specialized AI or even technical knowledge, thereby empowering these individuals with the benefits and opportunities of the technology.

Increasingly, IT leaders seek ways to extend the benefits of AI capabilities across the enterprise. The influx of new AI-based tools helps do exactly that. In some ways, this democratization is about simply extending low- and no-code tools, which enable nondevelopers to build and deploy software, into AI. But it's also about sharing vetted data and building data literacy across the enterprise. This does not mean every professional writes machine learning scripts. It means that business professionals understand the power of AI, develop the right use cases and apply the findings to achieve business outcomes and insights.

Enabling AI democratization in the enterprise is doable, thanks to decentralized governance models and AI-focused services coming into the market. But, like every endeavor involving new technology, democratization comes with both benefits and challenges.

Ways to achieve AI democratization

AI is no longer confined to small circles of developers and enthusiasts. Data analysis and machine learning services, like Google Colab and Microsoft's Azure OpenAI Service's various models, make it easier than ever to include a larger circle of employees in AI development by enabling anyone to write and share code for projects. Enterprises must appropriately train business users on what AI is and how AI can apply to everyday tasks to utilize the technology effectively.

Arpit Mehra, practice director at analyst firm Everest Group, recommends enterprises use decentralized governance models to enable data and technology learning strategies. Examples include the following:

  • Data democratization. This enables data accessibility for business users throughout the organization. This helps them get familiar with data structures and how to interpret and analyze data.
  • Data and AI literacy initiatives. These help business users build a general understanding of AI and its potential, as well as the implications of AI systems and how to engage with them.
  • Self-service low-/no-code and automated machine learning tools. These provide pre-trained algorithms and offer step-by-step guidance that helps business users build, train and publish AI models and systems.

Arun Chandrasekaran, distinguished vice president and analyst at Gartner, also recommended that companies prioritize investments in specialized and domain-specific intelligent applications that focus on training in areas like customer engagement, customer service and talent acquisition.

Benefits and potential challenges of AI democratization

At a high level, democratization of AI places AI capabilities in the hands of more employees, reduces the barriers to using AI, cuts expenditures and supports the development of highly accurate AI models.

"Making AI technologies more accessible expands the possibilities of what businesses can accomplish," said Michael Shehab, labs technology and innovation leader at professional services giant PwC U.S.

Business leaders must fully understand who will use AI modeling and development tools to establish safe and responsible AI standards.

For example, since AI democratization enables businesses to upskill their employees with valuable digital skills, the approach can boost worker productivity. This can help companies mitigate IT talent shortages, while saving on costs. AI democratization helps professionals add intelligence to their applications, which, in turn, makes it easier to automatically identify trends and patterns hidden in large data sets.

AI democratization obstacles and challenges can wipe out these benefits. Deploying these new systems and capabilities without proper guidance makes them susceptible to bias. Poor training and implementation can lead to executives basing decisions on inaccurate data or biases.

Business leaders must fully understand who will use AI modeling and development tools to establish safe and responsible AI standards. There's a risk of making undetected mistakes that look plausible on the surface but don't hold up under scrutiny, said Ed Murphy, senior vice president and head of data science at 1010data, a provider of analytical intelligence to the financial, retail and consumer markets. Teams need to thoroughly test the applications they develop to avoid automating errors.

To minimize risks, upskill and reskill workers. Implement a defined training plan so nontechnical business teams can participate in the business's steps to adopt, build and deploy AI solutions.

"The lack of the right expertise can prevent organizations from building and deploying AI models, while inadequate training and understanding can reduce adoption rates," said Everest Group's Mehra. Also, consider an infrastructure to simplify AI development, training and deployment. He recommended teams explore how MLOps technology could help achieve faster and more effective outcomes.

The benefits of democratizing AI will come to businesses once they realize that they should no longer confine AI access to a small group of experts. When exploring AI training and implementation methods, businesses must also be wary of these caveats to reap the benefits of these efforts.

Next Steps

Main types of artificial intelligence: Explained

What is trustworthy AI and why is it important?

The future of AI: What to expect in the next 5 years

AI regulation: What businesses need to know

Steps to achieve AI implementation in your business

Dig Deeper on AI business strategies

Business Analytics
Data Management