A survey of 500 information technology professionals sponsored by LogicMonitor shows at least half of those respondents believe their infrastructure is not equipped to handle increased artificial intelligence.
And information technology analysts believe the amount of new investment to support AI could be substantial.
A study by McKinsey Global Institute estimated that businesses would need to invest $3.5 trillion in AI by 2030 to realize AI benefits.
A study by the Boston Consulting Group suggested that businesses would need to invest $1.7 trillion in AI by 2025 to realize AI benefits.
A study by PwC suggested that businesses would need to invest $2.2 trillion in AI by 2030 to retrain and reskill workers who are displaced by AI.
Of course, IT professionals often say the existing infra is not prepared for the platforms and functions, when a new technology emerges. In many cases, preparedness is an issue precisely because the capabilities and skills needed to introduce a new platform do not yet exist. Many past examples include:
Electronic data interchange (EDI)
Enterprise resource planning (ERP)
Customer relationship management (CRM)
Supply chain management (SCM)
Content management systems (CMS)
Virtual private networks (VPNs)
Mobile computing
Cloud gaming
Telehealth
Robotic process automation (RPA).
And there will always seem to be some new thing that IT professionals report they are not prepared to handle with the existing infra. CIO magazine’s annual surveys have tended to show new concerns every year, for example.
Among the oft-cited concerns:
Lack of skilled talent. AI is a complex technology that requires specialized skills and knowledge. Many organizations do not have the in-house expertise to develop and deploy AI solutions.
Data quality and availability. AI algorithms need to be trained on large amounts of high-quality data. However, many organizations lack the necessary data or the resources to collect and clean it.
Cost. AI can be a costly investment, especially for small and medium-sized businesses. The cost of developing, deploying, and maintaining AI solutions can be prohibitive for some organizations.
Regulatory compliance. AI raises a number of regulatory concerns, such as data privacy and bias. Organizations need to ensure that their AI solutions comply with all applicable regulations.
Security risks. AI systems can be vulnerable to cyberattacks. Organizations need to take steps to protect their AI systems from unauthorized access and tampering.
Ethical concerns. AI raises a number of ethical concerns, such as bias and discrimination. Organizations need to develop ethical guidelines for the use of AI.
IT professionals believe that they need to make the following investments to adapt their infrastructures for AI, aside from acquiring new AI skills and continuing to streamline code development processes::
Data infrastructure. AI algorithms need to be trained on large amounts of data. Organizations need to invest in data infrastructure that can store and process this data efficiently.
Compute infrastructure. AI algorithms can be computationally demanding. Organizations need to invest in compute infrastructure that can run these algorithms quickly and efficiently.
Networking infrastructure. AI applications often need to access and process data from multiple sources. Organizations need to invest in networking infrastructure that can support this connectivity.
Security infrastructure. AI systems can be vulnerable to cyberattacks. Organizations need to invest in security infrastructure that can protect their AI systems from unauthorized access and tampering.
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