What’s Holding AI to Be a US$14 Trillion Market in the Global Economy?

The adoption of Artificial Intelligence is rapidly garnering traction across enterprises globally as businesses begin to deploy state-of-the-art technologies. With this development, consulting giant Accenture foresees that AI will add $14 trillion to the global economy by 2035. From telecom, high-tech, and financial services firms to healthcare and manufacturing, companies are leading the way in the overall adoption of this tech.

Looking across sectors and their functions, it seems that companies are typically following the financial gains while implementing AI, appearing to gain the most traction in the areas of the business that produce the most value within industries.

However, before Artificial Intelligence takes a victory lap, there are some caveat as well as ideas being put in place to overcome them.

Talent Shortages

Lack of talent is one of the real and pressing issues in today's enterprises and tech firms seeking to adopt AI and want to shift other data-driven models of digital transformation. With the shortage of data and tech professionals with experience while capitalizing on the huge potential for growth offered by AI, training is needed to deploy the required infrastructure and organizational change. Several companies now understand and are heavily pouring capital in AI and offering the prime opportunities to people who do have the skillset.

Lack of IT Infrastructure

When coming to the adoption of AI, lack of IT infrastructure as a barrier is also worrying to many enterprise IT leaders. Building and training models require a massive volume of data, along with very fast systems. Even, high-performance computing systems are very expensive that propels the costs of implementing AI into businesses. To overcome this barrier, one solution is to leverage a cloud-based AI. There are a large number of leading cloud vendors offering Machine Learning, Analytics, and Cognitive Computing services to support AI deployment.

Lack of Budget and Data Quality

Poor data can also harm and affect the company wanting to deploy AI into their business. Besides large and increasing Big Data stores, many tech leaders consider they don't have enough of the kind of data to support their AI efforts. On the other hand, funding also helps companies to deploy AI effectively. When stakeholders are less than enthusiastic in their support of a new initiative, they are unlikely to allocate adequate funding. However, this hindrance may become less challenging in the near future as many enterprises are getting huge funding from VCs.

Lack of Strategic Approach and Regulations

AI initiatives that are not planned strategically often fail to address strategic business objectives and don't fit within an enterprise's overall plans for growth and business development. They can even fail to approach it from a strategic standpoint, with knowing the significance of adopting AI technology, and the advantages it offers. So, understanding the aims and objectives of all aspects of AI operations could assist the enterprise in adopting and deploying AI. Organizations looking to implement AI also need to be compliant with their compliance requirements. Regulations like the EU's GDPR place concerns on how personal data can be stored and utilized by companies.