Premkumar Balasubramanian, CTO, Hitachi Digital Services, breaks down the metrics and trade-offs boards care about in 2026.
CIO&Leader: In 2026, what are the top business outcomes businesses expect from technology, and how have those expectations shifted over the past three years?
Premkumar Balasubramanian: In 2026, businesses expect technology to deliver the following:
- Revenue growth through AI-driven personalization and automation
- Improve Operational resilience via predictive analytics and autonomous systems
- Rapid innovation with generative AI and low-code platforms
- Sustainability and compliance through green tech and transparent data
Over the last 3 years focus has shifted from
- Cost reduction and cloud migration to AI-powered growth and resilience:
Organizations moved from simply cutting IT costs and migrating workloads to cloud toward leveraging AI for new revenue streams, predictive operations, and business continuity. - Basic digital transformation to intelligent automation and ecosystem integration:
The focus evolved from digitizing processes and moving to digital channels to deploying AI-driven automation and integrating ecosystems for end-to-end efficiency and innovation. - Reactive security to proactive, AI-driven risk management:
Security shifted from responding to breaches after they occur to anticipating threats using AI, implementing zero-trust architectures, and embedding resilience across the enterprise.
CIO&Leader: How are CIOs demonstrating measurable value from digital, AI, and transformation investments to the board, and which metrics truly matter today?
Premkumar Balasubramanian: CIOs are tying technology investments directly to business outcomes, demonstrating how digital and AI initiatives drive revenue growth, improve margins, and enhance customer experience. They emphasize tangible ROI through automation savings, productivity gains, and accelerated innovation cycles, presenting these results in terms the board understands—financial impact and competitive advantage.
The key metrics that matter today are:
- Revenue impact: growth attributable to tech initiatives
- EBITDA uplift: margin improvement from efficiency
- Automation rate: cost-to-serve reduction
- Innovation velocity: time-to-market for new products and features
- Risk posture: cybersecurity and compliance scores
CIO&Leader: Where do expectations align with reality, and where do execution complexity and trade-offs create the biggest challenges on the ground?
Premkumar Balasubramanian: Expectations largely align with reality for delivering automation efficiency, data-driven insights, and improved customer experiences—these outcomes are visible and achievable across industries. On the ground, however, execution is far more complex: teams must integrate modern platforms with legacy systems, close critical talent gaps in AI and cybersecurity, and balance innovation with compliance under tight data-governance standards. Trust in AI remains a hurdle as hallucinations and model inaccuracies can undermine decision quality, requiring robust guardrails, human-in-the-loop workflows, and domain-specific evaluation to validate outputs. Organizations also face trade-offs between scaling AI quickly and controlling costs, while governing technology at scale through model risk management, observability, lineage/provenance tracking, and continuous monitoring—becomes essential for mission-critical applications where reliability, accountability, and rapid recovery are non-negotiable; cultural resistance further slows adoption even when the tech capability exists.
“Teams must integrate modern platforms with legacy systems, close critical talent gaps in AI and cybersecurity, and balance innovation with compliance ” ~ Premkumar Balasubramanian
CIO&Leader: How has the cybersecurity conversation evolved from protection to enterprise-wide resilience, recovery, and accountability?
Premkumar Balasubramanian: Over the past few years, cybersecurity has moved from a perimeter-centric, “keep the bad guys out” mindset to an enterprise mandate for resilience, rapid recovery, and accountable risk management. We now operate on an assume-breach posture, architecting for continuity with zero-trust principles, real-time threat intelligence, and tested incident playbooks that compress recovery windows and protect revenue. Governance has matured as well: boards expect quantifiable risk reduction, clear ownership across business and technology, and demonstrable compliance, with executives accountable for outcomes—not just controls. In short, security is no longer a defensive cost center; it’s a core capability that safeguards operations, brand, and customer trust at scale.
CIO&Leader: Which technology risks are now viewed as enterprise risks, and where are accountability boundaries still unclear between the CIO, CISO, and business leaders?
Premkumar Balasubramanian: Technology risks have firmly crossed into the enterprise risk domain, with boards now viewing cyber threats, data privacy breaches, AI misuse, and cloud concentration risks as strategic issues that can impact revenue, reputation, and regulatory standing. The challenge is that accountability boundaries remain blurred: who owns AI governance when models drive business decisions—the CIO for platforms, the CISO for controls, or business leaders for outcomes? Similarly, data integrity versus data security creates tension between CIO/CDO and CISO roles, while incident response often lacks clarity on final authority during crises. Even cloud risk management and third-party dependencies expose gaps, as procurement, security, and IT share overlapping responsibilities without a unified framework. The reality is clear: without explicit RACI models and board-level risk ownership, these gray zones will slow decision-making and amplify exposure as technology becomes inseparable from core business strategy.
CIO&Leader: How are boards reassessing cloud costs, ROI, and modernization priorities, and what tensions does this create for long-term digital strategy?
Premkumar Balasubramanian: Boards are scrutinizing run-rate cloud spend vs. value, pushing FinOps discipline, rightsizing/repurchasing commitments, and prioritizing modernization that ties to revenue, resilience, and AI readiness (data platforms, app refactoring, security).
Tensions for long-term strategy:
- Cost control vs. agility: optimize spend without slowing innovation
- Lift-and-shift vs. refactor: quick savings vs. durable ROI and performance
- Single-cloud simplicity vs. multi-cloud resilience: vendor lock-in vs. portability
- Centralized FinOps vs. decentral product autonomy: governance vs. speed
- Near-term savings vs. AI/data investments: opex reductions vs. future growth
CIO&Leader: What do CIOs need more from boards to deliver outcomes successfully, clearer direction, investment discipline, patience, or shared risk ownership?
Premkumar Balasubramanian: CIOs need clear strategic direction, consistent investment discipline, and above all, shared risk ownership to align technology bets with business priorities. Patience is critical for long-horizon initiatives like AI and modernization, but boards must also actively champion change and accept trade-offs.
Top asks:
- Clarity: prioritize outcomes over projects
- Discipline: fund transformation, not just cost-cutting
- Patience: allow time for ROI on complex programs
- Shared accountability: cyber risk, data ethics, and innovation bets
CIO&Leader: What is the most underestimated technology risk or opportunity for 2026–27, and how do expectations differ across sectors?
Premkumar Balasubramanian: One of the most underestimated risks for 2026–27 is the integrity of AI models and the provenance of data. As organizations increasingly rely on generative and predictive AI, the rise of deepfakes, synthetic data, and unverified sources threatens trust and could trigger significant regulatory backlash. At the same time, the biggest opportunity lies in the convergence of AI and edge computing, enabling real-time automation in sectors like manufacturing, healthcare, and mobility—unlocking new revenue streams and operational resilience. Expectations vary widely across industries: financial services often underestimate compliance risks tied to AI while overestimating the near-term impact of quantum computing; healthcare sees transformative potential in AI diagnostics but faces challenges in patient data governance; manufacturing is leaning into autonomous operations but must contend with OT cybersecurity vulnerabilities; retail is betting on hyper-personalization while grappling with ethical AI and bias concerns; and the public sector views citizen services as a major opportunity but struggles with the pace of legacy modernization.