You don’t have to look very far to see a world of optimism in the fields of artificial intelligence (AI) and machine learning (ML). IBM, Amazon and Microsoft are investing millions in developing and marketing solutions aimed at streamlining business decisions (some of which are incredibly cool, like Microsoft’s AI for Good initiative or Amazon’s language transcription). And while much of the optimism is well-founded as new applications for AI begin to gain traction in the workplace, noticeably absent are AI tools for front-line and information workers. (Full disclosure: Aerie Consulting is a Microsoft Partner).
Don’t get me wrong — I love that Amazon suggests other products I might like and Netflix is always trying to predict my movie night preferences. But those examples of AI represent a closed approach to delivering AI solutions to a passive audience. Today’s information worker is seeking to use AI and machine learning to accelerate their own productivity and enhance the bottom line for the organizations they work in. As impressive as some of the AI tools and solutions are, they’re still generally locked up in the hands of IT teams and developers who are tasked with assembling solutions for the masses.
As Office 365 users (and there are 150 million of us), we are generally being fed snippets of artificial intelligence from Microsoft as part of its product offering. Microsoft can deliver real-time audio captioning and even suggest presentation content, and those applications of artificial intelligence are great. However, most users are not empowered to easily create AI models intending to gain better insights into their own business processes. The average information worker can sometimes use business intelligence tools to gain insight into past performance, but predictive models and systems that make decisions for employees are not yet a reality.
Additionally, the effort to successfully design and implement artificial intelligence to augment human guidance is often underestimated or misunderstood by organizations seeking its benefit. The discipline of creating AI models and enabling machine learning is often overmarketed and underdelivered. As a result, some of the current hype surrounding AI will result in projects that do well but also some that fall flat. Some AI projects are already starting to see a decrease in funding, and there are those who believe investor enthusiasm is starting to wane due to AI’s failure to live up to the hype.
We should expect more from software companies, technical teams and business leaders. In order to get past the hype stage of artificial intelligence, frontline workers must be able to use AI to their own advantage. Organizations like IBM, Amazon and Microsoft should seek to let these workers solve their own AI challenges rather than relying on others to do so. Empowering frontline staff to improve their own work tasks will accelerate adoption and help AI move out of the hype stage and into reality.
Imagine a world in which the big players in the tech industry aren’t simply delivering AI as part of their solutions but are focused on delivering AI modeling tools to a broader set of users at all levels of an organization. Engineers and maintenance technicians on manufacturing floors could prevent 100% of unplanned equipment downtime. Health care resources could be scheduled with no risk of missing care. The legal industry, education and retail sectors would be completely transformed if every worker could easily model daily work scenarios affecting productivity and profitability. Until workers can make themselves more productive using artificial intelligence and machine learning without engaging IT or spending hours researching technology they don’t have exposure to, we are underperforming in the world of AI.
We should expect more from application developers, software companies and data teams; they should be focusing on the problem at hand to deliver specific AI solutions.
Certainly, there are great examples and use cases outlining artificial intelligence in the workplace. And technical teams all over the world are assembling incredibly robust AI solutions. But have you used AI to make your own work smarter and less prone to human error? Is everyone in your organization doing the same thing? If you’re like me, you’re optimistic that AI will lead to a smarter and more productive workplace, but you’re wanting to unlock its potential by putting it in the hands of everyone — not just IT and technical teams.
By Dan Sonneborn
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