Digital Transformation Leadership

The AI plateau marks a shift from hype to practical results. CIOs who focus on governance and measurable value can navigate this period successfully.

You’re investing too much to get the basics wrong. Here’s what architecture, infrastructure, and networking look like when done right for AI.

The power of AI is undeniable, but without trust, its promise will remain dangerously constrained.

Aligning cloud infrastructure, data management and organisational skill sets will be the difference between firms that talk about AI value and those that actually realise it.

Interestingly, it’s not usually the quality of the technology but misaligned goals, poor planning or unrealistic expectations that caused failure.

As AI adoption accelerates, trust and security must become part of the foundation, not an afterthought.

After a flood of AI experimentation, IT leaders are going back to the basics of governance, change management, and metrics to ensure initiatives deliver key value for the business at scale.

If you are running faster in the wrong direction you will only get to the wrong place faster.

Agentic AI is not just for tech companies and, no, it won't replace all jobs. As you move forward, ignore the rumor mill.

AI’s next leap isn’t about thinking faster—it’s about lasting longer. The future of intelligence depends on engineering trust and endurance.

Most AI projects never scale. MIT's Paul McDonagh-Smith explains why culture, governance and leadership, not technology, decide who succeeds.

Just as earlier migrations were guided by stars or landmarks, this migration requires us to create our own compasses. These are not technical specifications but ethical bearings, meant to keep institutions human, even as cognition itself diffuses into every tool and transaction.

Past digital transformation struggles can offer valuable lessons to develop more effective strategies for AI implementation.

The 2025 Artificial Intelligence and Business Strategy report, from MIT Sloan Management Review and Boston Consulting Group, looks at enterprise adoption of agentic AI.

Five years ago, cloud was the answer to everything. With AI, that's no longer so clear.

Actionable guidance from leading CIOs and experts to balance innovation and data protection in the age of AI.

IMD’s AI Maturity Index assesses how the top 300 companies in the Forbes Global 2000 are advancing their AI strategies. One key pattern is that AI maturity requires strong ethical governance.

Many enterprises will delay a quarter of their planned AI spending as they hunt for more positive impact on their bottom lines, Forrester predicts. Others contend spending will hold — but get smarter.

Why AI initiatives stall under accumulated technical debt — and how CIOs can overcome it. 

As organizations race to achieve outsized benefits from AI, CFOs must address a frequently overlooked driver of optimal AI returns: internal control structures.

Most generative AI projects fail to show measurable ROI despite billions in investment. Experts point to weak data infrastructure as the underlying cause preventing enterprise AI from reaching profitable scale.

Too many organisations are still operating as they go. Measuring the quality of agent behaviour is often ad hoc, based on gut feel rather than consistent benchmarks, which undermines trust and makes it incredibly difficult to prove value.

While a large percentage of IT and business leaders believe their AI efforts will meet or exceed expectations, only a small number have successfully deployed projects thus far.

We’re on the cusp of a new software-enabled business model that will determine winners and losers in the coming decades. We call this service-as-software. Specifically, we believe enterprises will begin to organize knowledge work in new ways that harmonize islands of automation into a build-to-order assembly line for knowledge work.

Stuck in AI pilot purgatory? The real win is focusing on business problems, not hype, and scaling smartly with the right mix of tools.