AI: From Hype to Reality

AI: From Hype to Reality

Skill shortage makes companies opt for tools that don’t need AI expertise

Skill shortage continues to be the major hurdle for organizations planning to implement Artificial Intelligence (AI) initiatives, according to the latest Gartner survey. It says, “Organizations were required to have already deployed artificial intelligence (AI) or intend to deploy AI within the next three years. However, organizations that are not yet employing AI are much more likely to identify the issue of skills availability as a critical obstacle relative to organizations that are employing AI.”

Overcoming the AI skills gap will be crucial in facilitating AI adoption. The lack of available expertise is the main barrier to widespread AI adoption, according to GlobalData, a data analytics and consulting company, headquartered in London.  Overcoming the AI skills gap will be crucial in facilitating AI adoption. The lack of available expertise is the main barrier to widespread AI adoption.

Focus on tools that don’t need AI expertise

In 2021, a fragmented competitive landscape, coupled with the scarcity of AI talent and a growing number of potential acquirers, will underpin continued and healthy consolidation. Companies will focus on developing tools that do not require AI-expertise. Automated machine learning, AI libraries, and application programming interfaces (APIs) will allow small and medium-sized enterprises (SMEs) that cannot build up AI-expertise to take advantage of the operational improvements AI can offer.

Covid19 has accelerated digital transformation and helped AI hype turn into reality. It can be found everywhere, from wearable tech to automated home devices, smart cities, cars, offices, and more. The technology is embedded in a range of systems, making it challenging to identify revenue explicitly generated by AI. That said, GlobalData forecasts that the market for AI platforms will reach $52bn in 2024, up from $29bn in 2019. AI is one of the most hyped technologies, with reality often falling short of vendors’ world-altering promises.

2021 agenda: Delivering tangible AI outcomes

However, 2021 will be less about making bold statements and more about delivering tangible benefits. The industry will focus on business use cases that deliver rapid operational improvements. These will be aimed at automating business processes, providing intelligent business insights, and improving customer engagement. COVID-19 has acted as a catalyst for AI adoption and, with further disruptions expected in 2021, the importance of AI in the workplace will grow.

The focus will be on AI solutions that solve business problems (including conversational platforms, intelligent automation, AI-powered cybersecurity, and content personalization) rather than headline-grabbing applications. In 2021 AI ethics will be key to regaining consumer trust As AI becomes more pervasive and embedded in life-changing decisions, the need for transparency has intensified.

Data privacy to impact AI implementation

There have been plenty of high-profile cases in recent years where AI has contributed to bias and discrimination, including the use of facial recognition for policing and bias in CV-scanning software. While data privacy measures will affect any company involved in AI, regulators will ponder rather than act on AI-specific regulation in 2021.

The notable differences in the way countries approach the issue became even clearer in 2020, with the US advocating tech industry-led efforts, while China followed a government-first approach, and Europe encouraged a more privacy and regulation-driven approach. For their part, tech companies will ramp up internal regulation to regain public trust and foster widespread adoption of their products.

Explainable AI, synthetic data training, and federated learning will be fundamental in this process. Good intentions will not suffice, and companies determined to win their consumers’ trust and avoid PR disasters will need to take tangible steps. These should include improving diversity and finding concrete metrics against which to measure their progress.

Companies with access to large repositories of data to power their AI models, such as Alphabet, Amazon, Microsoft, Alibaba, Baidu, Apple, Tencent, Facebook, IBM will be the winners in the AI league table while losers will be incumbents in all industries that fail to prioritize it will ultimately lead to extinction.

© 2024 Praxis. All rights reserved. | Privacy Policy
   Contact Us