Digital Transformation Leadership

Organizations that master these fundamentals turn pilots into production at twice the rate of those that don’t.

Using AI to boost productivity is unlikely to create a sustainable advantage. The real value from AI will lie in reshaping offerings, business models, and market structures before competitors do.

Organizations must transition from superficial AI experimentation towards integrated operating models that deliver measurable impact.

Don't let early success blind you; if your AI's behavior is getting harder to explain, it is time to stop patching the old setup and fix the architecture.

With the emergence of agentic AI, the picture changes significantly, requiring a rethinking of the very essence of the concept of a “business process”—from a static, predefined set of steps to a dynamic, adaptive, and intelligent AI-first system.

Why is most AI producing almost nothing of value? The answer is less about the technology and more about how enterprises are deploying it. 

Enterprises deploying agents today can already meaningfully reduce risk by centralizing credential management, isolating execution environments and designing systems that explicitly account for prompt injection and model-driven attacks.​

Twelve themes separate companies that are truly rewired for AI from their peers.

It's not the tech that's the problem, it's your business case, says Forrester

Podcast hosts talk about $300-per-day agents, and AI experts say that figure is realistic if IT teams don’t put limits on their use.

How to control AI agent costs across tokens, infrastructure and ops before unpredictable workflows push spending past the value they deliver.

It is a striking paradox: Organizations are investing in AI at unprecedented levels, yet nearly 95% of AI pilots never reach production. AI is often described as a productivity accelerator. In practice, it quickly becomes a stress test, revealing how decisions are made, how authority works and whether the operating model can really handle AI.

The question of who controls AI is the critical org-chart issue at the dawn of the AI era, and it will influence a company’s strategy, investment levels, and the distribution of power and influence among leaders. How can organizations decide? 

LLM costs can rise fast as enterprises scale AI. CIOs need tighter control over token usage, pricing volatility and prompt efficiency.

Agentic AI isn’t just changing how systems operate; it redefines how value is created and who controls it.

Scaling agentic AI requires turning unstructured data into governed, reusable assets that systems can interpret and trust. Data leaders can start by building shared foundations and enforcing standards.

While AI has generated remarkable successes in specific applications, the broader transformation many organizations expected remains elusive. Understanding why some companies thrive with AI while others struggle reveals crucial insights for leaders navigating this complex landscape.

Previous waves of digital transformation, like the arrival of computers, the internet and cloud, made it necessary to rebuild our technological foundations. And lots of organizations that didn’t realize this aren’t around today. Agentic AI has the potential to be as transformative as any of those, and just as likely to need a rethink from the ground up.

CIOs have long struggled to balance enterprise tech budgets—and big investments in AI are compounding the problem. Our research shows how they can reallocate expenditures to generate maximum growth.

​Across industries, CEOs, CIOs and other enterprise leaders are having the same conversation and asking the same question: “We want to adopt AI. Where do we begin?”​ ; It’s an important question, but it’s not the first question most of you should ask. The first question should be, “Are we ready?”​

As organizations deploy artificial intelligence more widely, routine tasks are being automated, and some work is shifting from people to AI systems. Yet, many firms are finding that the returns on their investments in such systems are elusive.

AI debt is an operational tax. What initially feels like momentum gradually reveals the cost over time with fragile releases, unpredictable performance, escalating maintenance costs and black-box systems.

The old org chart is dying. Success now means learning to lead "agentic teammates" that handle the data grunt work so humans can focus on the big decisions.

Most AI projects stumble due to faults in the organization, not the models themselves. Leaders need to know when to fix and refocus an initiative internally, or when to kill it.

Transformation is coming for every organization, far faster and far more horizontally than any wave of technology that came before it.