Why AI democratization will bring more power to the enterprise

Along with all the analytical and operational gains artificial intelligence (AI) brings to the enterprise, there is another, more fundamental change taking place. As the technology becomes more adept at understanding human speech and intentions, we stand at the cusp of a dramatic transformation in the relationship between humans and the digital universe.

Using techniques like natural language processing (NLP) and neural networking, AI will very likely bring an end to the graphical user and even command line interfaces, which require a fair amount of mastery to operate, in favor of a more conversational approach in which operators merely state what they want and the system understands and responds. That’s right, no more clicking or tapping through endless menus, no more finding the right app – just ask, and it is yours.

More AI power to the people

For the enterprise, this presents a significant opportunity to democratize AI across the workforce, just like the PC put computing power that was once reserved for elite professionals into the hands of everyday employees. But while this marks the potential for a dramatic boost in worker productivity, it also poses some risk; namely, that this much power in the hands of so many will lead to unfortunate outcomes, either accidentally or by design.

The pressure is already on for organizations to implement AI at scale. John Roese, global chief technology officer at Dell Technologies, pointed out to ZDNet recently that AI terms like machine learning and process autonomy have already become familiar lexicon in the business suite. In some cases, AI is emerging as the third member in the business relationship, providing key support for deal-making and cooperative engagements. As this democratization continues, Roese says we can expect to see smaller data science and analytics teams because so much of the development and number-crunching they do now will be shared among more digitally empowered knowledge workers.

Ideally, democratized AI will one day be considered the ordinary way to interact with the data universe. Daitaku, for one, envisions a world in which digital assets will be available on demand with little to no involvement from IT. This will allow all aspects of the enterprise – including IT, which will remain a critical asset to the business model – to focus on more strategic goals rather than become bogged down with short-term, ad-hoc requests. This, in turn, should drive up the value of both human and digital resources by shifting the focus to more data-driven decision-making and innovation. 

To help bring this about, the enterprise should concentrate on two key initiatives, says Joe Hellerstein, cofounder and CSO of Trifacta:

• Develop a more effective human-to-AI interface. This doesn’t just necessarily mean a more conversational one, but a system that can function as an augmentation of human work using multiple communications channels.
• Integrate diverse skillsets. One of the key benefits of democratization is to get more people to work together. AI has the ability to enhance communication across different skillsets and disciplines, but this must be a cultural shift, not just a technological one.

This will require the development of both data and AI engineering capabilities, as well as the adoption of low-code/no-code tools and apps that can help users customize their own interactions with the data environment.

Data confusion

Like any other transition, however, democratization is not without its challenges. Helen Yu, founder and CEO of Tigon Advisory Corp., says one of the key obstacles is the disarrayed state of data in most organization. With so much siloed, multi-formatted architecture still in use, gaining a full view of data is difficult to achieve, and this hampers the ability for AI to obtain a complete version of truth. Data fabrics and updated governance are the keys to solving this problem.

Broad democratization will also require an open platform capable of supporting many different skillsets and processes, and even then, users must learn to utilize this valuable tool in the most effective manner – which starts with properly defining the problems they wish to solve.

Since the dawn of the computing age, even the most knowledgeable users have struggled with the complexity the technology presents. The goal has traditionally been to train humans to understand and engage the digital world on its terms. With democratization, we may finally get some reciprocity from our machines as they come to understand how to engage with us.