Artificial Intelligence

Machine intelligence: Build your own vs. as-a-service

Machine intelligence is about to become ubiquitous, which means enterprises face an important decision: when to develop machine intelligence specialties in-house versus when to leverage third-party resources.

AI in healthcare: The unevenly distributed future is here

There is a rapidly growing body of use cases for AI technologies in healthcare. Mainstream adoption may be a bit further along. rn

APIs, ecosystems, and the democratization of machine intelligence

Business leaders should stop thinking of machine intelligence in theoretical terms and start thinking of it as a force ready to be embedded in countless aspects of daily life.

3 steps to create a digital banking relationship center

The digital banking relationship center is at the heart of todayu2019s digital bank, and CIOs can help improve the digital banking customer experience by taking three steps

The hidden risk of blind trust in AI’s ‘black box’

Generative AI, large language models (LLMs), and AI service management (AISM) have the potential to redefine the entire ESM sector completely.

The hidden risk of blind trust in AI’s ‘black box’

Companies intent on weaving AI more tightly into the fabric of their businesses are seeking to better explain how algorithms make their decisions, especially where risk and regulations are involved.

Can ‘things’ be social?

What will be the impact of IoT and AI to the customer experience, today and tomorrow?

Automation technologies, robotics and artificial intelligence in the workforce

Automation has already started to reshape every company's workforce, including yours.

The ‘A’ in AI needs to be for accountability

AI as a way to overcome cognitive bias in physicians

Clinical judgement by physicians is often clouded by certain cognitive biases. AI holds the promise of being able to overcome these biases and deliver medical recommendations that are consistent and more accurate than is seen currently.

How computational linguistics went from Cold War communications to transforming enterprise work

How has computational linguistics evolved and transformed for enterprises today?