When is good enough enough?

Our goal should be to improve the quality of knowledge assets and their accuracy and relevance in use. Much of this will come from human expertise and effort, increasingly combined with the power of AI.

What are your chatbot’s pronouns?

We don't have pronouns by which we can address inanimate objects because we haven't had any occasions to have actual conversations with them.

AI technologies upending traditional KM

If we are not careful and proactive about it, the concept and importance of knowledge itself may soon become blurred or lost.

Return on … Infrastructure???

As our physical and IT infrastructure continues to grow in size, complexity, and vulnerability, people and the knowledge they possess will play an ever-increasing role.

To hyperautomate or not to hyperautomate?

The logic behind hyperautomation is clear: Automate everything that can be automated. The practicalities of that are far less clear.

Finding the weakest link

Though traditional and often reluctant to change, the supply chain sector is now reassessing its lack of embrace of technology and, significantly, rethinking long-established processes.

From robots to digital workers

As more firms use the term "digital workers" in place of bots, a spotlight is being shone on the role, importance, and increasing controversy surrounding enterprise automation.

Thinking beyond the status quo

The technologies exist today to achieve almost any corporate or departmental goal. What is lacking is the nerve to think big and think beyond the status quo—to break barriers, to collaborate, and to share.

Cognitive computing and AI begin to grow together

How do we manage the hype and promise for new inventions while making sure that they represent a realistic opportunity? Can we invent self-driving cars or a Boeing 737 MAX without exposure to the risks these innovations can pose to our lives?

Talk a little, type a lot - Will conversational interfaces survive Siri and Alexa?

For the next generation of conversational computing, it is hard to avoid the conclusion that the only companies that have enough researchers, enough processing resources, enough motivation, and, above all, enough data to deliver the much- needed improvements are the consumer giants.

Ethical issues in AI and cognitive computing

Many innovations from the past needed the insight of entrepreneurs as well as technologists to change the world. That's also the case with machine learning and AI.

The convergence of convergence

The more systems and subsystems we attempt to stitch together, the greater the unpredictability.

Cognitive Computing: Balancing the risks with the rewards from AI

The fact is that the effects of AI and cognitive computing will be even broader than current traditional computing systems. As we incorporate more and more data sources for better results, we also increase the likelihood of affecting more lives and more organizations.