Organisations working with artificial intelligence (AI) or machine learning (ML) have four projects related to AI/ML in place, and are expected to add six projects in the next 12 months and 15 more within the next three years, according to a report by consulting services company Gartner.
The study, titled ‘AI and ML Development Strategies’, found that organisations foresee substantial acceleration in their adoption of AI-driven applications. By 2022, organisations expect to have 35 AI or ML projects in place, on average, Gartner said in a statement.
The study was conducted in December 2018 through an online survey with a panel that included IT and business professionals (106 Gartner research circle members) based in Asia-Pacific, Europe, the US and South America. The participants were well-versed with the business and technology aspects of ML or AI, either currently deployed or still being planned at their respective organisations.
As many as 40% organisations said customer experience was the top incentive to harness AI technology while 20% said automation of tasks was the top motivator for adopting AI-driven technology. Processes such as invoicing, validation of contracts and robotic interviews are some areas where automation can be implemented, the Gartner statement said.
Nearly 56% organisations, the study found, use AI internally to support decision-making and give recommendations to employees.
The challenges to the adoption of AI technologies were varied and span a number of problem-areas, the study found. As per the Gartner survey, 56% said lack of skills was the top challenge to the adoption of AI. While 42% of those surveyed said that understanding AI use-cases was the top challenge, 34% said concerns regarding data scope or quality prevented AI adoption.
The survey said many organisations utilise efficiency as a target to measure the success when they seek to determine the merits of a project.
“We see a substantial acceleration in AI adoption this year. 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,” said Jim Hare, research vice president at Gartner.
Skill gaps can be addressed using service providers, partnering with universities, and establishing training programs for existing employees, Hare said. “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.”
Recent reports on and instances of adoption of AI:
- An IDC report found that only 25% organisations it surveyed have adopted an enterprise-wide AI strategy
- One97 Communications, which runs payments platform Paytm, deployed AI to achieve a higher payments success rate
- Startups from Intel India’s incubator arm showcase AI and IoT products