Digital Transformation Leadership

Many IT leaders are being asked to defend AI results they can’t explain, leading to some disappointment over vendor and platform choices.

More than half of enterprises say their AI projects have fallen short. Here’s what tech leaders must do differently.

The ultimate winners are not those who move the fastest but those who act with the most purpose and sense of responsibility.

Agentic transformation is different. It changes who (or what) does the work. It is the shift from humans operating systems to humans orchestrating intelligent agents that sense, decide and act.

The road to meaningful AI adoption should be paved with employee incentives, writes Wharton’s Scott Snyder.

Business leaders are losing patience when it comes to AI investment, with many now expecting to see measurable returns within months, not years.

The emergence of AI across the business world has only heightened the need for solid and lasting leadership at the highest levels.

Successful AI initiatives aren’t defined by sophisticated algorithms or cutting edge models. They’re anchored to clear business outcomes.

IDC predicts that up to 20% of large enterprises will see lawsuits, fines, and CIO firings due to inadequate agent controls by 2030 . Today’s state of AI governance makes that a likely reality, experts say.

Combining leadership skills and technical excellence is necessary for long-term success with technologies like AI

Enterprise AI is more challenging because it relies on clean data and on delivering a high degree of accuracy with higher stakes at play. AI must be integrated into workflows and governance embedded from the start.

Artificial intelligence is often hailed as a game changer, but too many firms discover that their bold AI pilots collapse when their operating models can’t support them.

Digital trust cannot be delegated to technologists alone. It has to be championed at the board and executive level.

Tech leaders face a choice in modernizing enterprise IT architecture: incremental change or full-scale transformation. Here are the benefits and pitfalls of each path.

To act autonomously and effectively, AI agents need optimized, AI-ready processes and the process data and operational context that only comes from process intelligence.

Workforce transformation is an imperative – but it must be systemically implemented.

The CIO and CTO roles are being rewritten in real time—transforming from operational guardians into agentic architects of the enterprise’s AI and data platforms.

Many companies are pouring money into AI, analytics, and CRM platforms, yet struggle to translate those investments into measurable revenue growth. The problem isn’t technology adoption but the failure to redesign how commercial organizations generate insight, make decisions, and coordinate action. Companies that succeed treat digital as a commercial operating model transformation, making four shifts that turn digital from a perceived tax into a durable growth dividend.

Organizations are grappling with AI strategy implementation, torn between extensive experimentation and focused deployment.

Value creation in an AI-first enterprise becomes continuous rather than episodic. AI-first leaders measure dynamic outcomes such as adoption, trust, growth and learning, enabled by adaptive, self-improving systems.

Why are so many companies failing to get the return on investment they expected from AI solutions?

AI, gen AI, and agentic AI have raised the stakes for digital transformation. Insights on how athletes train can help companies address the challenges.

AI won’t reduce complexity if it’s bolted on. 

As businesses adopt AI, they need to rethink how they make money. Understanding these four new business models is a place to start.

You cannot govern what you cannot see, and right now, most organizations are flying blind.