Some takeaways from a new report focused on AI from Dublin-based Research an Markets:

• The total market for AI in IT and networking will reach $10.4 billion globally by 2028
• The enterprise segment will lead the AI-enabled networking market at $3.9 billion by 2028
• Combination of AI and distributed cognitive computing will lead to computation-oriented communications
• AI-enabled networking will support many solutions, such as incremental growth of $724M in the asset tracking market by 2028
• AI-enabled networking will facilitate significant cost savings for CSPs and substantial new services realization across industry verticals

This report assesses the impact of various networking products and solutions including embedded equipment, components, and software platforms (network automation, optimization, and transformation). The report also evaluates the role of SDN, edge computing, NFV, and augmented intelligence in the formation and support of AI-driven networking ecosystems.

In addition, the impact of 5G networks, IoT technology and systems, and network analytics functions are also analyzed. The report assesses technologies, products, and solutions from key solution providers, identifying key companies in each segment of the competitive landscape. This includes companies focused on AI specifically and related areas such as intent-based networks and experiential networking.

With 85% of all chipsets globally shipping AI-equipped, over 63% of all electronics will have some form of embedded intelligence by 2026. The AI in next-generation networking market as a whole will ultimately be much larger infrastructure vendors embed AI within virtually all software and platforms.

Infrastructure is anticipated to be one of the primary focus areas for AI as network operators seek to reduce costs and improve efficiencies while simultaneously reducing the incidence of errors and adverse network events. AI technology will play a key role in the transformation of network intelligence to become increasingly self-driven.

Technologies like cognitive computing, machine learning, deep learning, and predictive application will be fundamental to the transformation of network configuration automation and operational autonomy.