In a world where buzzwords come and go, Artificial Intelligence has been remarkably imperishable. The technology which first emerged as a concept in the 1950s, there has been a relatively constant flow of technologies, products, services, and companies that claim to be AI. It is quite likely that a solution you are investing in today is being referred to as AI-enabled or machine-learning-driven.
The reality today for most organizations is that AI and machine learning form a rather small piece of the overall analytics pie. Indeed, research conducted by London-based investment firm MMC Ventures revealed that 40 percent of Europe’s artificial intelligence startups did not use any AI at all. Furthermore, the offerings of many startups and analytics providers, even if quite advanced, fall short of even basic AI.
As an emerging technology, AI faces and will continue to face its fair share of challenges. On the one hand, consumers remain wary about adopting new tech. Envisioning a world where humans are displaced by AI-empowered machines gone amuck may be haunting a few late adopters. On the other hand, companies express frustration that AI has yet to prove itself to be the magic pill that will streamline every business process and pave a path to bountiful profits.
So how do you tap into the real potential of AI?
Building a diverse team to solve the ‘impact’ conundrum
According to research by MIT, it was found that it is imperative to have a diverse AI team. In fact, An effective diverse team needs experts from each of the non-STEM disciplines to contribute to the understanding of the text and how we as humans use words to communicate. (For the sake of convenience, we’ll use “English majors” as shorthand for all of the non-STEM disciplines.) These domain experts will notice the subtle distinctions between U.S. English and British English and how grammatical rules change depending on which stylebook is used; they’ll discern subtleties of rhythm and word choice along with echoes of a poet’s phrasing or a novelist’s style. In short, these English majors contribute a valuable perspective that engineers may not naturally share.
Integrating the human element
There needs to be a focus on being aware and purposeful of the intentions and emotions we hope to evoke through any given artificial intelligent experience. The goal is to identify and articulate the core pain-points to solve and the positive value that mitigation of those pain points would drive.
An organization struggling to build meaningful consumer engagements, for instance, could identify the core issues that are driving the state of stasis and then decide how AI could be used as a means to unlocking it.
Eliminate the bias in your system
Getting “clean” data – that hasn’t been trained and tuned by prejudices – still remains a major challenge when it comes to constructing AI platforms that provide accurate results. At least at this stage, human activity continues to play a major role in the AI production process.
The entire AI process is why employing mindful AI techniques is crucial for achieving ethical and responsible AI models that augment human potential.
In a nutshell, applying a human-centric, mindful approach to AI comes down to this: Identify and focus on the needs of people first, so that AI applications serve human needs – and are aware of the human responses necessary to drive human progress.
To know more about you you can reshape your business and people practices by AI, don’t forget to attend the exclusive session by John Sumser, Principal Analyst and Founder, HRExaminer on A Brief Look at AI in HRTech Today, on 14th August at People Matters TechHR 2020. Click here to register for the event. You can access the agenda for People Matters TechHR 2020.