As AI tech becomes more available, barriers of talent and resources mount

Dive Brief:

The five top priorities in artificial implementation for CEOs are IT, information security, innovation, customer service and risk, according to an IBM Institute for Business Value survey in collaboration with Oxford Economics. With the exception of risk, each of these priorities grew in importance for business leaders between 2016 and 2018.
The inventory of talent and resources has remained the top barrier to AI technology adoption throughout this period, growing from 43% to 63%, and closely followed by regulatory constraints and legal, security and privacy concerns related to data. Availability of the technology, customer readiness and executive support have become less a barrier in the last two years.
With more businesses investing in AI, topline growth and the customer remain the biggest priorities over cost savings, capital investment or organization structure. Almost 80% of respondents ranked customer satisfaction as the primary objective of AI investment, followed by 70% for customer retention improvement.

Dive Insight:

AI isn't always a go big or go home game. When starting out with technology, many companies, including Pinterest, start small and add complexity over time.

Maturity and ubiquity in the technology now have pioneering companies moving from point to enterprise-level solutions, developing systems that extend across the business and tie in distinct platforms and groups. With more big tech companies moving into the AI development space — including most recently Alibaba — the hardware, platforms and software needed to build these solutions are more accessible than ever.

The talent constraints in AI will be an ongoing problem with no quick fix: Companies filling skills gaps for the short-term through hiring, upskilling and bringing in contractors also have to balance investing in skills and talent needed farther down the line.

In the case of legal, privacy and security questions dogging AI, more companies are rolling out tools to address data integrity, bias and outcome explainability. IBM and Google, for example, recently released bias detecting tools for developers to test AI and machine learning models.