Digital Transformation Leadership

The more AI plays a central role in our lives, the more we need to understand how we interact with and process the world.

Leaders are treating generative AI as less of a curiosity and more an integral part of business. McKinsey research sheds light on who is using AI, how they’re using it, and the challenges that remain.

The goal is to create an environment where your DevSecOps teams can thrive with AI superpowers built into their tools, backed by the appropriate leadership.

If GenAI is a hammer, everything seems like a nail – but it isn’t appropriate for every task. Here’s a guide to help you decide when generative AI is most useful.

Yes, gen AI can be dazzling. But to deliver value, leaders will have to look beyond center stage.

Gen AI is forcing data and AI leaders to revisit their data platforms. By acting now, they can position themselves well for the data-driven future.

Enterprises remain uncertain about where to invest in AI and, crucially, where AI truly delivers competitive advantage. This playbook is aimed both at the enterprise and the providers looking to serve it.

These applications demand infrastructure that runs faster and is bigger and safer, with highly efficient power footprints at any scale.

Companies are extremely interested in generative AI as vendors push potential benefits, but turning that desire from a proof of concept into a working product is proving much more challenging.

It's never been easier to implement a technology so badly while pretending that you're doing it right.

The complex landscape of AI deployment must embrace a holistic approach that integrates ethical considerations with regulatory compliance.

The transformational technology will require every member of an organization to be a risk professional.

Successful AI requires not just lots of data but lots of high-quality data built on a well-executed data strategy.

New research shows only 30% of C-suite leaders feel confident in their change capabilities. Even fewer believe their teams are ready to embrace change.

As many employees adopt generative AI at work, companies struggle to follow suit. To capture value from current momentum, businesses must transform their processes, structures, and approach to talent.

Powerful AI technology does not guarantee adoption and use. Instead, we need to prioritize building AI innovations with clear applications for society and the market.

Wharton’s Ethan Mollick on how organizations can find artificial intelligence use cases, account for errors, and identify the most pressing concerns.

If 2023 was about experimentation, 2024 is all about results.

Are companies systematically integrating AI into their products and processes or simply running after the latest tool?

Artificial intelligence has reached an inflection point, with the recent excitement around Generative AI causing virtually every organisation to assess

Cloud-based generative AI systems that use too many resources turn out to be too complex and expensive. Here’s how you can avoid this.

AI is transforming workflows and reshaping how organizations think about data, but many companies are still in the testing and experimentation phase.

Change management and skills development are arguably the most crucial components of a fruitful generative AI strategy

A new report on generative AI reveals that many companies in much of the world are not making a profit on their AI products.

Only 1 in 4 companies have successfully launched AI initiatives in the past 12 months according to second annual generative AI survey commissioned by Lucidworks