AI Democratization Part Five: Ongoing AI Development Challenges & Evolving Solutions
Why citizen developers and AI literacy are key to transforming AI hype into realityAdd bookmark
Has AI innovation plateaued? Some experts say we may be entering an “AI Autumn”
Relying on a handful of experts to develop responsible, explainable, and operational AI is proving to be problematic. Why AI democratization is key to successfully transitioning promising AI models into powerful AI systems.
READ PART ONE: Are Citizen Developers the Key to Applied AI Success?
READ PART TWO: Data Governance as the Engine Powering AI Success
READ PART THREE: Enabling the Citizen AI Developer with Low-Code and Auto-ML
READ PART FOUR: Building the AI-Ready Workforce of the Future
Today, successful applications of artificial intelligence (AI) and machine learning (ML) can be found in the digital products we use everyday. From Netflix’s personalized suggestions on what to watch next to email reply recommendations to unlocking our phones with Face ID, AI is everywhere.
However, though AI has advanced significantly over the past decade, it’s also encountered a number of eye-opening setbacks that suggest we may not be as far along as we thought when it comes to applied AI innovation.
For example, Walmart terminated its contract with Bossa Nova Robotics this past summer after it’s fleet of 500 inventory management robots failed to achieve expected ROI, opting to redeploy human workers instead. Youtube’s AI moderators erroneously took down hundreds of thousands of videos last spring inspiring the social platform to bring back more human moderators. And that’s not even the worst of it.
READ NEXT: The Ultimate Guide to Data Governance
*Image sourced from https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/
KPMG recently surveyed 1,000+ business leaders who’s organizations accelerated their AI implementations in order to better navigate the COVID-19 crisis. Despite the fact that many of these implementations met or exceeded business expectations, 51% of industry respondents with high AI knowledge feel AI adoption is moving faster than it should compared to 44% of total industry respondents -the main concerns being cybersecurity, privacy and a lack of regulatory standards for AI. In addition, though nine in ten respondents agreed their firm should have an AI ethics policy, many organizations (roughly 30%) don’t have one yet. Last but not least, lower level executives such as managers and directors were less likely to say that AI initiatives deliver value compared to VPs and C-Suites.
What is very clear from this and similar research is that there is an obvious disconnect between those responsible at the top of the AI ladder and those who are actually responsible for operationalizing it. Compounding this issue is, despite the fact that most of the individual’s surveyed reported high levels of confidence in AI’s ability to “solve industry challenges” and “deliver business results,” there is still a tremendous amount of anxiety about the safety and ethical implications of AI.
*Image source from "Thriving in an AI World," https://info.kpmg.us/content/dam/info/en/news-perspectives/pdf/2021/2021%20KPMG%20-%20Thriving%20in%20an%20AI%20world%20-%20Final%20Findings%20w.notes.pdf
The Future of Citizen Developers
As companies evolve their approach to AI, they’re realizing that successfully implementing safe, sustainable and defensible enterprise AI will require more than just a handful of experts. Instead it will require wide-spread, cross-functional AI literacy. In other words, cultivate a shared understanding of what AI is, what it can (and cannot do), what it takes to make AI systems work, and how to effectively as well as ethically engage with AI solutions.
To start building the groundwork for these efforts, Gartner expects organizations to focus on these 4 key areas of democratization through 2023:
- democratization of data and analytics (tools targeting data scientists expanding to target the professional developer community),
- the democratization of development (AI tools to leverage in custom-developed applications),
- the democratization of design (expanding on the low-code, no-code phenomena with the automation of additional application development functions to empower the citizen-developer)
- democratization of knowledge (non-IT professionals gaining access to tools and expert systems that empower them to exploit and apply specialized skills beyond their expertise and training).”
The goal isn’t to create an army of AI experts, but rather empower business professionals to play an active role in ensuring that AI tools not only deliver business value, but are safe, ethical and secure.
Historical data is dead. As global corporate enterprises restructure their balance sheets to accommodate for the value of data, organizations need real-time data which must be democratized throughout the enterprise. Data acquisition, governance, visualization, and virtualization along with advanced analytics and AI- putting 'math on top of data' - all make that goal possible. Join us for lessons learned from those who are accomplishing the goal.
AI & Data Democratization Live
Historical data is dead. As global corporate enterprises restructure their balance sheets to accommodate for the value of data, organizations need real-time data which must be democratized throughout the enterprise. Data acquisition, governance, visualization, and virtualization along with advanced analytics and AI- putting 'math on top of data' - all make that goal possible.
Join us for lessons learned from those who are accomplishing the goal.Register Now