What CIOs are working on, Part 1 of 4
Over the past six months, I’ve run guidance kickoffs with CIOs and CTOs across financial services, insurance, government, higher education, travel, and manufacturing in Europe and APAC.
These are working sessions where we learn the organization’s context, constraints, and challenges, then agree on a practical action plan for the next 3–6 months. That plan blends relevant research, frameworks, and tools with follow-on sessions in which analysts work alongside the client to adapt best practices, learn from peer experiences, and apply our models to their specific environment.
Four themes emerged from these 30-plus conversations: 1) modernizing fragile core systems; 2) strengthening data and AI foundations; 3) evolving operating models and talent; and 4) connecting security, cost, and value to the board. This short series takes each theme in turn. In this first post, I’ll focus on what CIOs are actually doing to modernize their core technology estates.
How CIOs Modernize Fragile Cores
Most organizations today start from a similar place: hybrid, fragmented environments that mix decades-old on-premises systems with selective cloud and SaaS adoption. This hybrid reality stems from accumulated technical debt, incremental digital build-out, and the practical constraints of running critical legacy platforms while modernizing around them.
Three initiative patterns recur when CIOs talk about modernizing these cores:
Run structured cloud migration and infrastructure modernization programs. Many leaders are shifting from opportunistic migrations to deliberate programs that assess workloads, determine the best landing zone for each (cloud, SaaS, hybrid, or remaining on-prem), and build defensible business cases before committing. This requires disciplined workload assessment, clear cloud strategy, and phased roadmaps. The emphasis is on fit-for-purpose placement rather than “cloud-first at any cost” and on sequencing cloud migrations so they reduce risk and improve resilience rather than simply moving problems from one platform to another.
Rationalize and refactor application portfolios around core platforms. Another common initiative is application portfolio rationalization: reducing the number of overlapping systems, simplifying integration patterns, and refactoring or replacing applications that constrain change. These environments typically contain hundreds or even thousands of applications, often with multiple enterprise resource planning or customer systems performing similar roles in different divisions. Organizations are pursuing structured rationalization programs to consolidate platforms, standardize integration, and clarify the systems that are true systems of record. In parallel, several clients are decomposing monolithic core applications into more modular services or moving selected capabilities to SaaS so that future business change is held hostage by a single legacy codebase. We recently introduced the REAP model to help CIOs continuously reassess, extract, advance, and prune applications as part of these modernization programs.
Protect critical cores while modernizing the surrounding architecture. In regulated sectors such as tax administration, insurance, and healthcare, many CIOs accept that core systems on mainframe or long-lived platforms will remain in place for years. Their modernization initiatives focus on strengthening the architecture around those cores rather than replacing them outright. In my conversations, this shows up in the form of investments in modern data platforms, better integration layers, and targeted use of cloud for new workloads, analytics, and customer-facing services. Tax authorities and insurers are working on plans that combine incremental refactoring, tighter integration, and stronger governance around legacy cores so that they can meet new digital, data, and resilience requirements without triggering unmanageable risk.
In the next part of this series, I’ll share what these same CIOs are doing on data and AI foundations, as almost every modernization story now comes with a parallel ambition to become more data-driven and to use AI safely and productively.
