Minto Group

Anca Preda

Enterprise Resilience Authority

The core principles are ownership of execution and a collaborative approach. Technology is always the easy part, but developing the business rationale, managing organizational change, and establishing measurable metrics are, in my experience, the keys to a successful digital transformation.

Every initiative should begin with a measurable business result, not a technology milestone. Teams know exactly what success looks like and how it will be measured. Transformation often stalls when decisions sit in silos, which is why integrated steering groups with shared KPIs accelerate delivery and reduce rework. Prioritization is equally essential. Leaders must be comfortable saying no. The discipline to focus on the few initiatives that truly move the needle is often the difference between progress and noise. Ultimately, transformation must be seen as a leadership behaviour and a technology purchase.

From AI Ambition to Enterprise Resilience

As AI becomes embedded in everyday business operations, organizations must strike the right balance between innovation and trust. It is not a balance we achieve overnight, but a journey of continuous improvement and refinement. AI can eliminate repetitive work, accelerate decision cycles, and overall productivity improvements, but these are just incremental tweaks. The true value and differentiation come from unlocking new business models and fundamentally redesigning business processes.

That potential, however, depends on a strong foundation. I recommend starting with data governance by establishing the core AI principles you want to adhere to, setting guardrails around data lineage, quality and access and embedding ethical standards into every deployment. We started our journey by developing realistic, actionable AI policies and ensuring that all AI deployments comply with ethical principles and include operational resiliency. Ambition drives innovation; caution preserves trust and they are both required for sustainable AI adoption.

  The real value of AI is not automating today's work. It is creating entirely new ways for organizations to operate, compete, and grow.  

That same principle of balancing innovation with resilience extends beyond AI and into cybersecurity, which has become a boardroom responsibility rather than solely an IT function.  Cyber resilience must evolve from a technical posture to an enterprise‑wide risk discipline.

Boards should oversee cyber risk with the same rigor applied to financial or operational risk, supported by clear metrics, scenario planning, and long-term investment roadmaps. At the same time, organizations need to foster a zero-trust culture built on least-privilege access, continuous verification, and secure-by-design thinking. Because breaches are inevitable, success is measured not by prevention alone but by how quickly an organization can detect, contain, and recover from an incident. Cyber incidents require coordinated action across legal, communications, operations, and technology. Preparedness exercises should be routine. Equally important is vigilance across the supply chain, where third-party relationships have become one of the largest sources of cyber risk. Leaders must demand transparency and enforce shared security standards. Today, cyber resilience is no longer a technical accessory. It is a strategic business capability.

Why Data Governance Determines AI Success

Data is abundant, but trusted insights are far more difficult to achieve. In my experience, the divide comes down to disciplined process, ownership, and secure access. Organizations need defined accountability for data quality and stewardship because, in the absence of strong ownership, both data quality and the value of insights deteriorate. Just as important is a unified data platform.

When data lives in fragmented systems, trust erodes. We invest in integrated architectures that make data consistent and accessible. Building a data-driven culture also requires strong data literacy through training, coaching, and shared standards for interpretation. Most importantly, insights must be embedded into everyday workflows. Data creates value only when it informs decisions and drives action. Collecting data is easy. Converting it into trusted, repeatable decisions is a leadership discipline.

The ability to turn data into action will matter even more as new enterprise technology reshapes operations. I believe that a combination of AI, process automation and real‑time data will be a consequential technology shift. Together, these technologies have the potential to fundamentally change the ways we strategize. Leaders will increasingly manage systems that recommend, simulate, and optimize decisions at scale. Operating models will keep shifting from static annual planning to continuously adaptive operation as organizations respond faster to changing business conditions. Leadership itself will also change, shifting from directing individual tasks to coordinating outcomes across human and digital teams.

As these capabilities grow Leaders will need updated policies and new governing models for accountability, transparency, and risk in autonomous systems. I strongly believe that the best way to ready ourselves and our organizations is to invest in digital literacy, modernize data foundations, and operating models that embrace continuous change.