With the adoption of Artificial Intelligence (AI) expected to significantly increase, businesses are advised to reorganise themselves to ensure sufficient resources are available to support projects.

According to Gartner's 'AI and ML Dev elopement Strategies' study, organisations working with AI or machine learning (ML) have, on average, four AI/ML projects in place.

"We see a substantial acceleration in AI adoption this year," said Jim Hare, research vice president at Gartner. "The rising number of AI projects means that organisations may need to reorganise internally to make sure that AI projects are properly staffed and funded. It is a best practice to establish an AI Centre of Excellence to distribute skills, obtain funding, set priorities and share best practices in the best possible way."

Respondents expect to add six more projects in the next 12 months, and another 15 within the next three years, which means that in 2022, those organisations expect to have an average of 35 AI or ML projects in place.

CX, task automation motivators

Gartner adds that 40% of organisations named CX as their top motivator to use AI technology.

While technologies such as chat bots or virtual personal assistants can be used to serve external clients, most organisations (56%) today use AI internally to support decision making and give recommendations to employees.

"It is less about replacing human workers and more about augmenting and enabling them to make better decisions faster," Hare said.

Automating tasks is the second most important project type - named by 20% of respondents as their top motivator. Examples of automation include tasks such as invoicing and contract validation in finance or automated screening and robotic interviews in HR, the research company affirmed.

The top challenges to adopting AI for respondents were a lack of skills (56%), understanding AI use cases (42%), and concerns with data scope or quality (34%).

"Finding the right staff skills is a major concern whenever advanced technologies are involved," said Hare. "Skill gaps can be addressed using service providers, partnering with universities, and establishing training programs for existing employees. However, establishing a solid data management foundation is not something that you can improvise. Reliable data quality is critical for delivering accurate insights, building trust and reducing bias. Data readiness must be a top concern for all AI projects."

Risk with the rage

Yusof Seedat, head of global geographies for Accenture Research, said, "The fast adoption of new technologies may be all the rage, but it comes with significant risks if companies do not update the way they plan and execute cybersecurity. For example, 92% of companies in South Africa base their cybersecurity investments solely on today's known risks and cybersecurity needs, and do not consider future business needs in the investment plan."

Notably, more than 85% of South African executives point to cybersecurity concerns regarding artificial intelligence (AI), making it the riskiest new technology in their view.

"The same AI technology that enables banks, for instance, to create sophisticated profiles of individual consumers to customise loan offers can also be used by hackers to track consumers' online activity to steal account passwords," said Seedat.

In a recent thought leadership article, author Henrique Vale, Software, GBC MEA, Nokia, stated that AI, machine learning, robot process automation (RPA) and enhanced connectivity are evolving and adapting to fit different use cases and industrial requirements.

"Their abilities and potential are constantly changing to meet market needs and demands," he said.