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Asia’s 2027 security landscape is defined by a “governance over hype” reckoning. IDC projects regional security spending will hit US$39.5 billion in 2026, while noting only 7% of APeJ enterprises feel highly prepared in GRC skills.  As Gartner forecasts 40% of enterprises will demote AI agents by 2027 due to governance gaps surfacing only in production, CISOs face a dual mandate: deploy agents at machine speed while proving verifiable, auditable control to fragmented regulators.  Fastly CISO Marshall Erwin joins us on this episode of PodChats for FutureCISO to tackle the pressing issue of managing accountability in the age of AI as viewed from the perspective of the CISO. 1.       High level view of Fastly. 2.       Do enterprises in Asia have a comprehensive, real-time inventory of all AI agent identities and their permitted data access? 3.       From a CISO perspective, are current threat models updated to include agents as active actors, not just tools? 4.       How are enterprises demonstrating to regulators across multiple jurisdictions that their AI guardrails and data flows are governed with verifiable, auditable evidence, moving beyond policy to operational proof? 5.       Have enterprises mapped every sector-specific data localisation requirement (beyond the general PDP laws) that applies to business in Asia? (global context) 6.       Are current public and private cloud architectures designed for the necessary data residency and portability? 7.       In your view is zero-trust architecture sufficiently mature to act as the control plane for AI, effectively preventing a compromised agent or partner from causing a cascading regional incident by eliminating lateral movement? 8.       How are enterprises operationalising digital sovereignty, ensuring that our reliance on global cloud providers does not compromise our technical and operational control, especially in the event of a geopolitical or regulatory shift? 9.       Advise on Accountability without control – a CISO’s sovereignty dilemma

Asian enterprises are rapidly deploying agentic AI, yet resilience lags. Singapore’s new Agentic AI Framework offers crucial guidance on governance and accountability. To be clear, traditional IAM is ill-equipped for non-human identities, which according to some statistics outnumber humans 1:144. Organisations must prepare by establishing robust identity controls and governance, as regulators demand proof of responsible AI use within their business ecosystem. In this PodChats for FutureCISO, Jasie Fon, regional vice president of Asia, Ping Identity, offers her perspective on the state of resilience as non-human identities expand their presence and influence in the workflow.  CONSIDER RE-ORDERING 1.       Why are identity governance gaps widening as agentic AI adoption accelerates?  2.       What can enterprises across Asia learn from Singapore’s early exploration of identities for AI agents?  3.       What should enterprises put in place before giving AI agents access to systems, data, and applications?  4.       How should organisations across Asia verify an AI agent’s identity and authority?  5.       Who should be accountable when an AI agent makes a decision or takes an unauthorised action?  6.       How should CIOs and CISOs in Asia prepare for AI agents becoming part of the enterprise workforce?  7.       What role does resilience play in the age of autonomous AI agents?  8.       There are many solutions out there, what should CISOs and CIOs ask when identifying the right approach and solution for their organisation?

Asian enterprises remain prime cyberattack targets, with adversaries increasingly bypassing traditional defences through sophisticated delivery models. From AI-generated phishing to software supply chain poisoning, attackers exploit trusted channels and legitimate services to infiltrate networks. These novel approaches demand a fundamental security strategy shift toward containment and resilience rather than prevention alone. In this PodChats for FutureCISO, Raghu Nandakumara, Vice President of Industry strategy, Illumio, talks about strategies CISOs and security practitioners will want to consider mitigating trust should it be used as an attack vector. 1.       How should AI governance evolve to address both the risks of using AI tools and the risks of attackers exploiting AI interest? 2.       How would you assess current user training and AI-themed social engineering? 3.       What are the SOC implications of attackers increasingly targeting the software acquisition workflow? 4.       Strategies to mitigate trust as an attack vector How should enterprises rethink "trust" in software supply chains when legitimate update mechanisms are being compromised? 5.       With AI enabling personalised phishing at machine speed, what role should behavioural controls and containment play where human detection inevitably fails? 6.       How do enterprises defend against a campaign where the malware is old and the delivery method is new? 7.       For Asian organisations where unsanctioned AI adoption is widespread, how can security teams govern what they cannot see? 8.       Does this represent a broader shift in attack patterns that should influence 2027 security investment priorities? 9.       2027 is just around the corner, what is your advice for CISOs as they set in place strategies to mitigate trust as an attack vector, including investment priorities.

As we look toward 2026 and 2027, data centre leaders across Southeast Asia and Hong Kong stand at a critical juncture. Surging AI demand is colliding with power constraints and increasingly divergent regulatory landscapes, as seen in Singapore's high benchmarks and emerging hubs like Thailand and Johor. The challenge has shifted from merely collecting sustainability data to embedding green energy as a strategic differentiator. For CIOs, success now depends on forging partnerships to navigate these complexities, building AI-ready and resilient infrastructure while developing the skilled workforce essential for execution In this PodChats for FutureCIO, Govind Choudhary, GM of Southeast Asia and India at Digital Realty, shares his perspective on how CIOs are embedding sustainability into AI infrastructure. 1.            What is Digital Realty? 2.            How do you see data centres in Asia addressing the growing gap between sustainability data collection and meaningful action? 3.            How should CIOs, in partnership with data centre operators, navigate the diverging regulatory landscapes across the region? 4.            Given the accelerating use of AI, how should CIOs design and build the right infrastructure for AI workloads while maintaining sustainability? 5.            Do should CIOs, again working with DC operators, address the skilled workforce requirements to execute this strategy? 6.            How should CIOs manage sovereign data requirements while leveraging regional scale? 7.            Any recommendation on how to build resiliency against power constraints and grid instability? 8.            How should CIOs embed sustainability as a competitive differentiator, not just a compliance cost? 9.            Not all DC operators are equal, what is your advice when selecting their operator/partner to address calls for embedding sustainability not just in their AI infrastructure but across all their compute needs – local and international?

Singapore's AI maturity has rebounded, but a critical execution gap threatens to undermine record investments.  While agentic AI adoption has doubled to 51% in 2026, only 10% have redesigned end-to-end workflows. This "Pacesetter" minority achieves significantly higher ROI, highlighting the difference between buying AI and building an enterprise around it—a lesson for Southeast Asia. In this PodChats for FutureCIO, Colin Tan, country manager for ServiceNow Singapore, explains to us what the most recent ServiceNow study reveals about agentic AI adoption in Singapore. Colin, welcome to PodChats for FutureCIO. 1.       Strategy & Ambition: In the ServiceNow study, 68% of organisations cited inadequate data accuracy, access, and management as on-going challenges. How do we modernise our data architecture to provide the clean, connected, and real-time intelligence AI agents need to act reliably at scale? 2.       Infrastructure & Scale: As agentic AI generates exponentially more network traffic and computational demand, is our current infrastructure resilient enough to handle this surge, or are we risking a "digital congestion collapse" that undermines performance and agility? 3.       Workflow Orchestration: While 51% of Singapore enterprises now use agentic AI, only 10% have redesigned end-to-end workflows. How do we shift from automating isolated tasks to orchestrating autonomous, cross-functional workflows that deliver measurable business transformation? 4.       Taming "Agent Sprawl": As autonomous agents proliferate across departments, they risk creating new technical debt and fragmentation. How do we architect a unified control plane to gain visibility, manage permissions, and prevent chaotic, disconnected agent deployments? 5.       Governance & Risk: Governance is the prerequisite for autonomous AI. How do we build a living governance framework that provides continuous oversight, embeds trust and transparency, and ensures accountability before agents operate at scale? 6.       Singapore’s IMDA published the world's first governance framework for agentic AI. How are Singapore enterprises keeping pace with the standard the government has set for the country? 7.       Measuring New Value: As AI shifts from efficiency to revenue creation and new business models, how should we evolve our metrics to accurately capture the value of AI-enabled agility, innovation, and competitive differentiation—not just cost savings? 8.       Lesson from Pacesetters: The ServiceNow study shows that a "Pacesetter" minority, who redesign work around AI, can achieve significantly higher ROI than those that simply layer AI onto existing processes. What can we learn from Pacesetters?

Even as Singapore strengthens its national digital identity with device-bound passkeys to secure the login, the live video session remains a gaping exposure layer. Once authenticated, high-value activities—remote approvals, KYC reviews, and workforce verification—are conducted over mainstream video platforms that rely on trust and manual checks.  This is dangerously insufficient as deepfake video injection attacks become more capable and affordable. The question for CISOs is no longer just “who logs in,” but “who is actually present in the frame.” In this PodChats for FutureCISO, Dominic Forrest, chief technology officer at iProov, highlights these trends and solutions to mitigate against the inherent security risks that come with live video sessions. 1.       How would you define the current state of identity and session security in Southeast Asia and Hong Kong, given the rapid digital adoption and escalating AI-driven threats? 2.       Device-bound passkeys effectively secure the login session against phishing. However, once a user is “inside” a live video session, what new exposures emerge that passkeys simply cannot address? 3.       With remote workforce verification and high-value approvals now routine over video, what gaps remain in current manual verification processes? Why are these gaps particularly concerning for CISOs? 4.       How do injection attacks work, and why are they so difficult to detect with traditional verification methods? 5.       How are attackers evolving their techniques, and what makes these attacks especially dangerous for banks and fintechs? 6.       Traditional liveness detection—such as blink checks and smile prompts—is failing against modern AI-generated deepfakes. What newer technologies are emerging to verify genuine human presence in real time during live video interactions? 7.       How can continuous presence assurance at the video layer complement device-bound passkeys and national digital identity schemes, help CISOs build a more layered and resilient defence? 8.       Finally, how are regulators in the region responding to these threats, and what should CISOs be prioritizing on their roadmaps to stay ahead?

Across Southeast Asia and Hong Kong, finance teams are transitioning from ledger-keepers to strategic drivers of business resilience. The imperative for real-time cash visibility clashes with fragmented markets, forcing a move beyond digitisation. Technologies like automation and AI are viewed as essential, with 95% of regional tax and finance leaders prioritising data and AI tools to support innovation and predictive analytics.  Yet adoption is tempered by concerns over data integrity and the escalating threat of AI-enabled fraud. Singapore lost S$913 million to scams in 2025, recovering only S$140.5 million.  In the first half of 2026, Hong Kong recorded 20,613 overall deception and fraud cases with total financial losses reaching HK$3.5 billion with investment scams costing victims roughly HK$1.65 billion, nearly half of all monetary damage from fraud during this period. CFOs are prioritising robust verification controls, with 58% giving equal priority to payment speed and security, yet only 43% rate their ability to deliver both as strong.  In this episode of PodChats for FutureCFO, Karthik Manimozhi, Global President at Eftsure shares his views on how to drive cash resilience with visibility, velocity, verification. 1.       How can CFOs establish a centralised data architecture and governance framework that ensures data integrity, underpins successful AI deployment, and mitigates the risk of information leakage to third-party vendors?  2.       In an environment of high economic and environmental volatility, how can CFOs mature their cash flow visibility from a near-real-time snapshot to a predictive, AI-driven forecast that actively models for uncertainty?  3.       Amidst divergent monetary policies and tariff uncertainty, how can CFOs/finance team leverage AI and automation to strengthen their working capital velocity and accelerate receivables, ensuring liquidity is not trapped or eroded?  4.       To what extent does current technology infrastructure and partnership with banks enable the "always-on" treasury, essential for managing trapped cash and intra-day liquidity across different time zones?  5.       As fraud tactics grow more sophisticated, including the rise of deepfakes, how can CFOs move beyond reliance on traditional human verification to establish cryptographic, AI-powered controls that verify identities and payments before they are authorised? 6.       For finance teams operating across multiple jurisdictions in the region, how can they balance the drive for automation with the reality of fragmented local payment rails and complex, evolving regulatory landscapes?  7.       As the focus of sustainability shifts from branding to bottom-line impact, how should CFOs integrate real-time energy cost data and supply chain carbon exposures into their core treasury and cash flow models? (repeated due to signal problem) 8.       With many firms increasing AI budgets but few reaching advanced capability, what do you recommend as a strategic framework for upskilling finance talent to oversee autonomous AI systems?

For CIOs in Malaysia and Singapore, 2026-2027 marks a defining reckoning. While 88% of organisations now use AI in some function, the staggering reality is that 84% of pilots never reach production, with 95% delivering zero measurable P&L impact.  The challenge has decisively shifted from experimentation to industrialisation. Success now hinges on treating AI as an infrastructure investment, not an innovation project. The core obstacles are structural—data quality, system integration, and governance gaps—not model capability.  Agentic AI compounds the urgency, demanding a "neutral control plane" for orchestration and auditability. The channel ecosystem is stepping in, with partners offering readiness assessments to bridge the deployment gap.  The 2026 CIO must architect for production-grade resilience, aligning technology with business process redesign and outcome-based economics. In this PodChats for FutureCIO, Lynn Toh, Senior Director, Advanced Solutions, Tech Data APAC, discusses the key trends impacting CIOs and their organisations as they look to integrate AI into day-to-day operations. 1.       How can CIOs redesign business processes before deploying AI, ensuring we automate the right workflows rather than just digitising inefficiency? 2.       What governance framework and infrastructure are needed to move AI pilots into production, addressing data quality, security, and integration gaps that cause 41% of stalls? 3.       How can CIOs architect a "neutral control plane" to manage agentic AI, ensuring least-privilege access, audit trails, and oversight for autonomous decision-making? 4.       With agentic AI raising the stakes, how should CIOs redesign identity and zero-trust architectures to secure at machine-speed, autonomous actions? 5.       How can CIOs measure success by business outcomes and ROI, moving beyond pilot counts to metrics that justify board-level investment? (may want to rephrase) 6.       How should CIOs evolve pricing and investment models—perhaps towards consumption or outcome-based models—to make scaling AI economically sustainable? 7.       What role can channel partners play in conducting AI readiness assessments and bridging the gap between pilot and production deployment? 8.       What should be the CIO strategy for workforce transformation, building cross-functional buy-in and AI literacy to ensure adoption beyond the pilot team? 9.       Synthesizing everything we’ve covered, what is your advise for CIOs for 2027?

For Southeast Asian CIOs, the window to merely experimenting with AI is fast closing. The imperative now is to industrialise intelligence, moving from proof-of-concept to production-scale agentic systems that deliver tangible business value.  Yet, as the "token economy" dictates, success hinges on a foundational element: your data platform. The key to controlling costs and ensuring trusted, real-time outcomes lies not in the fragmented data stack that exists in many organisations today, but in a unified architecture that makes retrieval accuracy a competitive advantage. This demands a new strategic focus.  In this PodChats for FutureCIO, Thorsten Walther, Managing Director, CXO Advisory Asia, MongoDB, shares the most prevalent issues facing CIOs and their enterprises in navigating their AI journey while managing the economics of AI integration and use. As we move from AI experimentation to production, how can we rationalise a fragmented data and retrieval stack to reduce latency and governance risk, particularly given the need to manage highly dynamic, unstructured data?Given that the "token economy" makes retrieval accuracy a direct cost-control lever, how can we implement a unified data platform to improve retrieval quality and reduce expensive LLM retry loops?How do we ensure our data architecture provides the schema flexibility needed for rapid AI iteration, avoiding the rigidity of relational models that slows development and creates technical debt?With the rise of agentic AI, how can we architect a system for "agent memory"—blending short and long-term context—that allows agents to act on the current state of data, not stale copies?How can we improve retrieval accuracy by natively combining semantic understanding with precise keyword search, while also using reranking to refine results, without adding external systems that create sync delays?For regulated enterprises, how can we bring these production-grade AI retrieval capabilities inside our compliance framework, avoiding the choice between innovation and data sovereignty?What is our strategy to move beyond the complexity of managing separate databases, search engines, and vector stores to a single, unified platform that reduces operational overhead?How do we build a flexible, "production-ready" data foundation that allows us to pivot quickly as model providers and agent frameworks evolve, without being locked into a rigid stack?How can we best equip our developer and agentic workflows with the necessary skills and best practices to avoid common pitfalls like over-normalisation, ensuring agents build on a robust data model?With a significant focus on the ASEAN market, how can we leverage local partnerships and expertise to accelerate our AI modernisation journey and address specific regional regulatory and data challenges?

A 2026 Wolters Kluwer report reveals that a striking 83% of APAC CFOs see AI adoption as a key force reshaping finance, while 72% believe its impact will be significant within three years. As understanding of what AI do for finance teams, we are starting to see the adoption narrative move from a discretionary "innovation project" to a necessary part of the governance and controls framework—a language CFOs and compliance officers understand intimately.  To be clear, anxieties about moving fast (in the adoption journey) remain persistent as is maintaining trust, a theme echoed in the Deloitte survey which found CFOs reinforcing fundamentals and cost discipline even as they invest in AI. In this PodChats for FutureCFO, Nikhil Parambath, Regional Vice President for Asia at BlackLine, offers some insight into how CFOs and the finance leadership can finetune their adoption strategies as AI moves from a nice to have to a compliance imperative. Nikhil, welcome back to PodChats for FutureCFO. 1.       Across Asia, CFOs are being asked to close faster and support real-time decisions, yet many close processes remain stitched together with spreadsheets and manual workarounds. Where are finance teams in the region still most vulnerable to this "spreadsheet dependence," and what specific risks does this create for a CFO's ability to 'trust the numbers' in a volatile environment?  2.       BlackLine uses the term "self-driving close." In business terms, what does this operating model look like in practice, and what are the biggest misconceptions finance leaders in Southeast and Northeast Asia have about it?  3.       As agentic AI evolves from copilots to autonomous actors, which specific close activities—such as reconciliations, journal entries, intercompany matching, and variance analysis—are the safest and most impactful to automate first, giving finance teams the fastest return on confidence and efficiency?  4.       Agentic AI is only as good as the data it operates on. Before a CFO can confidently let parts of the close run on "autopilot," what critical data, process, and control foundations need to be in place to ensure governance, security, and an auditable chain of trust? Foundational readiness 5.       Controllable autonomy As the system starts handling the "heavy lifting" of routine tasks, the finance professional's role is shifting from processor to validator and strategist. How do you see the role and skillset of finance teams evolving over the next 2-3 years, and how should CFOs prepare their people for this transition to an advisory role?  6.       In a region marked by rapid digitalization yet persistent skills gaps, what are the key implementation challenges for CFOs in places like Singapore, Hong Kong, and Japan who are trying to scale AI-led finance?  7.       As AI agents become more autonomous, questions of accountability arise. How can CFOs adapt their internal controls and compliance frameworks to effectively "govern" AI, ensuring the autonomous close meets regulatory standards?  8.       Finally, how does achieving a "self-driving close" free the CFO's office to focus on what matters most—such as strategic analysis, partnering with the business, and steering the organization through volatility?

For Asia’s CIOs, 2026 signals the dawn of the Agentic Era—where AI shifts from chatbots to autonomous co-workers. Yet consumer ease with LLMs hides enterprise realities: compute demand straining grids, legacy networks choking progress, and “vibe hacking” agents undermining trust.  By 2027, reasoning models like Claude Mythos will force CIOs to govern agentic swarms, rewriting IAM and latency for a new digital order. In this PodChats for FutureCIO, Jeetu Patel, president and chief product officer, Cisco, answers critical technology questions CIOs and Heads of Technology must grapple as quickly as they can. Risk and security 1.       With AI agents “vibe hacking” executive styles, how do we defend enterprises when traditional security fails—and who’s accountable when autonomous agents act without human oversight?  2.       Southeast Asia prioritizes data sovereignty. How can CIOs keep AI fast and efficient without illegally moving sensitive data across borders? Infrastructure reality 3.       AI agents generate 100x more network traffic than humans. With hybrid AI adoption surging, how do we prevent a digital congestion collapse in 2026? 4.       Power grids are strained, yet data centres must act as one. Should CIOs embrace complex distributed clusters or invest in smaller, localized GPU pools? Readiness for 2027 5.       Agent-to-agent commerce is coming. As consumers and partners start using AI agents to interact with businesses, how can enterprises ensure their backends are "machine-readable" and they don't become invisible in this new agent-driven economy? 6.       With AI shifting from automation to orchestration, how should ROI be measured—cost savings, or agility and revenue creation? 7.       Scaling agentic systems makes old monitoring tools obsolete. Should CIOs rebuild telemetry and observability stacks from scratch to manage high-speed interactions? The challenge of industrializing AI is about moving from isolated pilots to "AI factories". With plans to scale agentic systems, old monitoring tools are obsolete. Should CIOs in the region be planning to rebuild their telemetry and observability stacks from scratch this year to manage these complex, high-speed interactions? 8.       Our topic is The digital traffic jam of 2026. What is your suggestion/suggestions for CIOs and even board as they look to embed AI in the way of work?

APAC enterprises face data sovereignty fragmentation, IT talent shortages, and sustainability mandates. Enterprise storage is now pivotal to AI success—reshaped by engines like IBM's 5th Generation FlashCore Module (FCM5) that autonomously handle deduplication, encryption, and compression. Yet CIOs must secure data end‑to‑end, from ransomware recovery to quantum‑safe archival, procuring storage that adapts, protects, and optimizes relentlessly for the AI era. In this PodChats for FutureCIO, Barry Whyte, Principal Storage Specialist and Master Inventor at IBM, to talk about the forgotten technology that is core to the continuing development and use of AI in the enterprise. 1.       What are the three most critical pain points facing APAC enterprises as AI transforms storage from passive repository to active computational engine?  2.       How do you see data sovereignty regulations, skills shortages, and sustainability mandates compound these challenges across the data lifecycle? 3.       How is AI fundamentally reshaping storage technology development—from computational offload (deduplication, compression, encryption) to autonomous performance tuning? 4.       Where do autonomous AI agents deliver maximum value in the storage lifecycle—real-time ransomware recovery, predictive capacity planning, or dynamic workload optimization?  5.       What does this mean for CIOs architecting infrastructure that must adapt to unpredictable generative and agentic AI workloads? 6.       How can APAC CIOs prioritize use cases that address region-specific constraints like space-limited datacentres and carbon neutrality deadlines? 7.       As post-quantum cryptography transitions from "decades away" to "plan now," how should APAC’s financial and government sectors rethink storage security architecture to protect AI training data and models across their entire lifecycle—from ingestion to archival—against "harvest now, decrypt later" threats? 8.       Given that AI is compressing hardware refresh cycles while demanding greater capital efficiency, how should APAC CIOs and storage architects balance cloud-adjacent consumption models with on-prem computational storage investments to optimize TCO across the AI data lifecycle? 9.       What is your recommendation for CIOs architecting storage to power AI agility?

Before Stuxnet, there was fast16: a state-grade sabotage framework that didn’t steal data or crash systems. It silently altered engineering simulations and calculations—poisoning digital twins while everything appeared normal. Now imagine AI giving attackers the power to find such weaknesses in hours. As Singapore expands protections beyond core CII, fast16 is a warning: tomorrow’s breach won’t hold your data hostage. It will corrupt the simulations and AI models you trust to run your plant, bridge, or refinery. And you won’t know until something breaks. In this PodChats for FutureCISO, Vitaly Kumluk, cybersecurity researcher at SentinelOne Lab, SentinelOne, sheds light on what cybersecurity teams need to know and be prepared against this new breed of cyberthreats. 1.       What is the role of a researcher in the cybersecurity space? 2.       In a nutshell, what is fast16 and what makes it different from other categories of cyber threats? 3.       How do we identify which of our engineering simulations, digital twins, and AI training pipelines are most vulnerable to silent output manipulation—and do we have any validation layer that checks results against physical or independent models? 4.       How do we detect an attack that changes calculations but leaves systems running normally? 5.       What stops an AI-powered attacker from finding a hidden weakness in our simulation software? 6.       How do we assess and continuously monitor the integrity of simulation outputs from external partners, cloud-hosted digital twins, or legacy OT environments we cannot directly instrument? 7.       Our incident plan covers ransomware. Do they cover a scenario where a state-grade actor has been quietly corrupting our engineering decisions for six months? How do we roll back trust in our own data? 8.       If an attacker manipulates a supporting system’s simulation to cause a real-world failure, will current cyber insurance or legal framework treat that as a “breach” or as a “design error”? 9.       How do we differentiate between adversarial attacks on the AI’s availability (denial) vs. subtle corruption of its reasoning or output distribution—and which defensive architectures apply? 10.   Given what we now understand about fast16, what is your recommendation for moving forward?

A recent Gartner survey found that only 28% of AI use cases fully meet ROI expectations, with a further 20% failing outright. For Asia-Pacific CIOs, the bottleneck is clear: it is not the model, but the governance and orchestration layer beneath it.  In 2026, the challenge has shifted from experimentation to industrialisation, demanding a focus on taming "agent sprawl" and mitigating "shadow AI" risks.  As Celine Siow, VP of Sales, GM for APAC at Workato argues, the control plane must be platform-agnostic to avoid recreating fragmentation. The path to 'production-ready AI' lies in a robust, vendor-neutral governance layer that ensures trust, interoperability, and measurable business value. She joins us on this edition of PodChats for FutureCIO to elaborate further on her arguments for a platform-agnostic control plane as CIOs orchestrate the industrialisation of AI. 1.       With only 28% of AI projects meeting ROI targets, how can we build a formal "value playbook" to ensure our AI investments deliver tangible, measurable business outcomes? 2.       As "agent sprawl" becomes the new technical debt, how can we architect a neutral control plane to govern, orchestrate, and observe our growing fleet of autonomous agents? 3.       Given that 80% of workers now use unapproved AI tools, how can we move from simply blocking "shadow AI" to enabling governed, safe adoption across the entire workforce? 4.       What does an effective governance framework look like when AI moves beyond content generation to action management, requiring real-time control over permissions, escalation, and audit trails? 5.       With AI infrastructure costs set to run up to 30% higher than planned, how should our FinOps practices evolve to manage this new cost volatility, including token-based consumption?

CIOs across Southeast and Northeast Asia face a brutal budgetary paradox today: board-mandated surges in AI and cybersecurity investments clashing directly with flat overall IT expenditure.  According to IDC and Forrester, while regional tech spending is rising, geopolitical risks and inflation are eroding real purchasing power. Consequently, technology leaders are forced into aggressive reallocation.  They must defer legacy ERP upgrades, consolidate SaaS contracts, and renegotiate vendor support to fund digital priorities without expanding the top-line budget, turning run-rate IT optimisation into a critical strategic lever for margin protection. This shift is redefining the CIO role. In this PodChats for FutureCIO, we explore why IT budget strategies need to shift from cutting waste to driving innovation. For more on this, we are joined by Seth Ravin, CEO of Rimini Street. Setting the Macro Context 1.       With overall IT budgets remaining largely flat across Asia despite surging AI demands, how are CIOs fundamentally restructuring their 2026 budget cycles to accommodate these new priorities without asking for more money? The Core Trade-off: AI vs. Legacy  2.       In your conversations with customers, when boards ring-fence AI and cybersecurity spend, which specific legacy IT projects or 'run rate' operations bear the brunt of the cuts? 3.       You have highlighted the deferral of ERP upgrades; what are the operational and technical risks CIOs are willing to accept by pushing these critical modernisations down the line? Executing the Cuts: ERP, SaaS, and Cloud  4.       How are enterprises leveraging third-party enterprise software support models to immediately free up capital. Is this shifting from a tactical cost-saving measure to a strategic boardroom lever?  5.       Beyond traditional on-premise legacy systems, are we now seeing CIOs aggressively consolidating SaaS contracts and even renegotiating hyperscaler cloud commitments to fund AI initiatives? Regional and Sector Nuances  6.       CIOs at different sectors: manufacturing, healthcare, logistics, and financial services, approach how they manage their IT budgets. Are there distinct budget-cutting patterns or unique pressures specific to these sectors in the Asian market? The CIO and CFO Dynamic  7.       How has the conversation between the CIO and CFO evolved in the region? Are finance leaders now driving IT vendor renegotiations, or is the CIO leading this charge to protect innovation budgets? Strategic and Financial Implications  8.       Looking beyond 2026, if AI and cyber costs continue to scale without a corresponding increase in overall IT budgets, is the current model of funding them purely through legacy cuts sustainable? 9.       Let’s round out what we’ve covered so far. Our topic is IT budget strategies for focusing on innovation and not cost-cutting. What are your top 3 recommendations for IT budget strategies for focusing on innovation and not cost-cutting?