What happens when organizations do not have a single-pane-of-glass visibility of cost, usage and outcomes across AI, cloud and cybersecurity.

As firms reallocate funds to invest in AI, it’s not about whether companies should invest in AI, but what they are being forced to sacrifice to do so. 

In cost-sensitive Asia Pacific, is your organization willing to sacrifice some cybersecurity budgets to fund AI innovation? If not, what goes?

IBM Apptio’s 2026 Technology Investment Management Report offers a compelling look at the practical reality of innovation budgeting.  We discussed the key findings with Matt Pinter, APAC Field CTO, Apptio.

Apptio’s 2026 Technology Investment Management Report highlights that the central tension for leaders right now isn’t just about AI, but about justifying budget decisions to boards. Where are most of the budget cuts coming from?

Pinter: Across Asia, budgets themselves are not contracting. In markets such as Singapore, Australia, and Japan, technology investment continues to increase. What has changed is the level of scrutiny applied to how that spend translates into outcomes.

As technology budgets grow, boards are asking more precise questions around value, especially whether investment is driving measurable results and whether it can be defended with confidence. That shift is driving a closer look at existing spend rather than a pullback from innovation.

When organizations gain clearer visibility into their technology estate, areas that sit outside the spotlight naturally come under review. Ongoing run‑the‑business costs, such as cloud services, infrastructure, and application portfolios, often surface first, particularly where utilization or outcome alignment has not been well understood.

This dynamic is especially visible in how AI is being funded. Across the region, AI investment is largely being supported through internal capital reallocation rather than net‑new budgets. As a result, leaders are optimizing what they already have and redirecting spend toward initiatives where value is easier to articulate, rather than reducing overall investment.

What are the hidden risks organizations face by deprioritizing cybersecurity budgets in the AI innovation race?

Pinter: In Asia, cybersecurity is not slipping down the priority list. Across Singapore, Australia, and Japan, cybersecurity and AI continue to rank side by side as top technology priorities.

Where tension emerges is not in intent, but in evaluation. Cybersecurity, cloud, and AI are also among the most cost‑sensitive areas of technology spend. At the same time, many organizations report ongoing challenges with ROI confidence and data trust, particularly when financial and operational insights are fragmented.

Without a connected view, different parts of the technology portfolio can be assessed through different lenses. AI initiatives may be discussed more visibly in terms of growth and opportunity, while cybersecurity is often framed in cost or risk terms. When that happens, trade‑offs become harder to assess holistically, even though both areas are recognized as essential.

The underlying issue is not reprioritization, but incomplete visibility. Organizations that connect cost, usage, and outcomes across AI, cloud, and security are better positioned to scale innovation while maintaining resilience, confidence, and trust at the board level.

What advice would you offer to CIOs and business leaders facing this budget dilemma?

Pinter: What stands out from the report is that this is less a question of funding availability and more one of confidence. Investment levels are rising, but assurance that spend is delivering outcomes has not always kept pace.Pinter: What stands out from the report is that this is less a question of funding availability and more one of confidence. Investment levels are rising, but assurance that spend is delivering outcomes has not always kept pace.

Across Asia, leaders are committing more resources to AI, cybersecurity, and digital modernization, yet many still operate with fragmented financial and operational views. In that environment, decisions slow down and scrutiny increases, particularly when internal trade offs are required to fund innovation.

Enterprise financial intelligence addresses that gap. By unifying insights across finance, IT, and the business, organizations can move beyond static reporting and toward clearer accountability. This becomes especially important as AI funding increasingly relies on reallocating existing budgets rather than accessing new ones.

Governance also plays a central role. AI and cybersecurity are no longer experimental investments, and expectations around discipline have risen accordingly. Clear milestones, shared visibility, and consistent decision frameworks allow innovation to scale without eroding confidence.

Ultimately, the organizations that perform best are not those that spend more, but those that can clearly connect technology investment to outcomes. When insight replaces assumption, leaders gain the ability to reallocate capital with confidence and turn increased scrutiny into a competitive advantage.