Artificial Intelligence (AI) could help organizations to improve customer experience, automation customer interactions, real time assistance, data analyzing etc. Organizations adopting AI technology to make the customer interaction journey successful and for the task automation.
According to a recent Gartner survey – Organizations that are working with AI or ML (machine learning) have on average four AI/ML projects in place.
Currently, Organizations are working with 4 AI projects but in near future they show intent to add more projects, Respondents expecting to add 6 more projects within a year and they also have plan to have 15 AI projects in next 3 years. Accordingly we can expect organizations working with AI /ML projects will have 35 average AI / ML projects.
Average Number of AI or ML Projects Deployed
Jim Hare research VP at Gartner “We see a substantial acceleration in AI adoption this year, The rising number of AI projects means that organizations may need to reorganize internally to make sure that AI projects are properly staffed and funded. It is a best practice to establish an AI Center of Excellence to distribute skills, obtain funding, set priorities and share best practices in the best possible way.”
Customer Experience (CX) and Task Automation Are Key Motivators
Organizations adopting AI to enhance customer experience and automation. From the survey – 40% organizations adopted AI technology to advancement the customer experience. Technologies like chat bots or virtual personal assistants can be used to serve external clients, most organizations (56%) today use AI internally to support decision making and give recommendations to employees.
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.
Major challenges adopting AI for respondents organizations 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 Mr. 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.”
The Gartner survey showed that many organizations use efficiency as a target success measurement when they seek to measure a project’s merit. “Using efficiency targets as a way of showing value is more prevalent in organizations who say they are conservative or mainstream in their adoption profiles. Companies who say they’re aggressive in adoption strategies were much more likely instead to say they were seeking improvements in customer engagement,” said Whit Andrews, distinguished vice president, analyst at Gartner.