{"id":114,"date":"2026-02-18T12:33:30","date_gmt":"2026-02-18T12:33:30","guid":{"rendered":"https:\/\/maddaisy.com\/index.php\/2026\/02\/18\/the-non-ai-agenda-what-cios-are-actually-prioritising-beyond-artificial-intelligence-in-2026\/"},"modified":"2026-02-18T12:33:30","modified_gmt":"2026-02-18T12:33:30","slug":"the-non-ai-agenda-what-cios-are-actually-prioritising-beyond-artificial-intelligence-in-2026","status":"publish","type":"post","link":"https:\/\/maddaisy.com\/index.php\/2026\/02\/18\/the-non-ai-agenda-what-cios-are-actually-prioritising-beyond-artificial-intelligence-in-2026\/","title":{"rendered":"The Non-AI Agenda: What CIOs Are Actually Prioritising Beyond Artificial Intelligence in 2026"},"content":{"rendered":"<p>Global IT spending will hit $6.15 trillion in 2026, a 10.8 per cent rise on the previous year. But dig beneath that headline and the distribution is strikingly lopsided. <a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2026-02-03-gartner-forecasts-worldwide-it-spending-to-grow-10-point-8-percent-in-2026-totaling-6-point-15-trillion-dollars\">Gartner&#8217;s latest forecast<\/a> projects AI spending to surge 80.8 per cent and data centre outlays to climb 31.7 per cent, while communications services and device budgets limp along at low single digits. The message is clear: AI is swallowing the budget. The question is what happens to everything else.<\/p>\n<p>That question matters because, as maddaisy has documented over recent weeks, the AI investment thesis is under strain. <a href=\"https:\/\/maddaisy.com\">PwC&#8217;s 2026 CEO Survey<\/a> found that 56 per cent of executives cannot point to measurable revenue gains from their AI programmes, and governance frameworks are lagging well behind deployment ambitions. If the technology absorbing most of the budget has yet to prove its return, the priorities being squeezed to fund it deserve closer scrutiny.<\/p>\n<h2>The squeeze is real \u2014 and deliberate<\/h2>\n<p>John-David Lovelock, a vice president analyst at Gartner, describes a dynamic in which runaway AI spending is forcing CIOs to find savings elsewhere \u2014 and IT services providers are bearing the brunt. The logic is straightforward: buyers expect their vendors to be using AI internally, and they want those efficiency gains reflected in lower fees.<\/p>\n<p>&#8220;CIOs need to find somewhere that they have control of their budget, and they can pick on the services companies because they&#8217;re using AI,&#8221; <a href=\"https:\/\/www.cio.com\/article\/4128960\/ai-gold-rush-to-drive-2026-it-spending-as-it-services-get-the-squeeze.html\">Lovelock told CIO.com<\/a>.<\/p>\n<p>But not every non-AI line item can be trimmed without consequences. Several priorities are growing more urgent precisely because of AI adoption, not despite it.<\/p>\n<h2>Cybersecurity: AI&#8217;s expanding attack surface<\/h2>\n<p>The most immediate non-AI priority is, paradoxically, driven by AI itself. Dmitry Nazarevich, CTO at software firm Innowise, notes that his company&#8217;s security spending increase &#8220;is directly related to the increase in exposure and risk to data associated with the increased attack surface resulting from the introduction of generative AI.&#8221;<\/p>\n<p>This is not a theoretical concern. Every new AI model integrated into enterprise workflows introduces new vectors \u2014 data exfiltration through prompt injection, model poisoning through compromised training data, and the simple reality that agentic systems with write access to business processes can do real damage if they malfunction or are manipulated. As enterprises move from experimental AI pilots to production deployments, security spending is not optional \u2014 it is the prerequisite.<\/p>\n<h2>Data foundations: the unsexy precondition<\/h2>\n<p>Salesforce CIO Dan Shmitt offered a telling anecdote about an AI agent on the company&#8217;s help site that surfaced two conflicting answers to the same question. &#8220;Our first reaction was to assume the model was wrong,&#8221; he said. &#8220;The truth was that our data and content needed more consistency.&#8221;<\/p>\n<p>This pattern \u2014 blaming the model when the real problem is the data \u2014 is remarkably common. Capgemini&#8217;s <a href=\"https:\/\/www.capgemini.com\/insights\/research-library\/technovision-2026-guide-for-ctos-and-cios\/\">TechnoVision 2026 framework<\/a> places &#8220;thriving on data&#8221; as one of its nine foundational technology domains, emphasising data sharing, AI-driven insights, and sustainable data practices. The message is consistent across vendors and analysts: AI systems are only as reliable as the data infrastructure beneath them.<\/p>\n<p>For CIOs who have spent two years funding AI pilots, investing in data quality, master data management, and integration architecture may feel like a step backwards. It is not. It is the work that determines whether those pilots ever graduate to production.<\/p>\n<h2>Technical debt and modernisation<\/h2>\n<p>Legacy systems are not merely an annoyance in an AI-first world \u2014 they are a bottleneck. Nazarevich highlights that increased modernisation spending at Innowise is &#8220;partly a result of the fact that legacy or outdated systems limit the effectiveness of AI technology and delay deployment schedules.&#8221;<\/p>\n<p>This creates a compounding problem. Organisations that deferred modernisation to fund AI experiments now find that their AI initiatives are underperforming because the underlying platforms cannot support them. The <a href=\"https:\/\/www.cio.com\/article\/4081926\/strategy-beyond-ai-five-imperatives-for-2026.html\">CIO.com analysis of strategic imperatives for 2026<\/a> makes the point explicitly: if eight out of 10 strategic priorities relate to AI, &#8220;you&#8217;re likely missing some critical emerging technologies and trends.&#8221;<\/p>\n<p>Edge computing, digital twins, and platform modernisation may not generate the boardroom excitement of a generative AI demo, but they are the infrastructure on which AI capabilities depend.<\/p>\n<h2>FinOps: managing the unpredictable bill<\/h2>\n<p>AI workloads have introduced a new kind of cost unpredictability into IT budgets. Unlike traditional cloud computing, where usage patterns are relatively stable and forecastable, AI inference costs can spike with demand in ways that are difficult to model in advance. Innowise reports increased FinOps spending specifically because of &#8220;the unpredictability of computing bills created by AI workloads.&#8221;<\/p>\n<p>FinOps \u2014 the practice of bringing financial accountability to cloud spending \u2014 is no longer a niche discipline for cloud-native firms. For any organisation running AI at scale, it has become an essential management capability. Without it, the 80 per cent surge in AI spending that Gartner forecasts could easily overshoot, consuming budget earmarked for the very modernisation and security initiatives that AI requires to succeed.<\/p>\n<h2>Workforce fluency: the persistent gap<\/h2>\n<p>Technology investment alone solves nothing if the people using it are not equipped to do so effectively. Rebecca Gasser, global CIO at FGS Global, frames this as a literacy challenge: building digital and AI fluency across the organisation so that workers &#8220;can be more agile and adaptable to the ongoing changes.&#8221;<\/p>\n<p>Pat Lawicki, CIO of TruStage, puts it more directly: &#8220;We&#8217;re committed to balancing innovation with humanity: leveraging digital tools where they add real value while preserving the human connection that defines trust and empathy.&#8221;<\/p>\n<p>This is consistent with the pattern maddaisy has tracked in recent coverage of AI-driven burnout and the organisational failures behind poor AI rollouts. The technology works best when employees understand it, trust it, and can exercise judgement about when to rely on it and when to intervene. That requires sustained investment in training, change management, and communication \u2014 none of which appear in an AI spending forecast but all of which determine whether the forecast delivers value.<\/p>\n<h2>The convergence argument<\/h2>\n<p>The most sophisticated framing of the CIO&#8217;s 2026 agenda comes not from treating AI and non-AI priorities as competitors for budget, but from recognising their interdependence. Capgemini&#8217;s TechnoVision 2026 describes a shift toward &#8220;synchronicity at scale&#8221; \u2014 the idea that boundaries between digital, physical, and biological innovation are dissolving, and that the CIO&#8217;s job is to orchestrate across all of them simultaneously.<\/p>\n<p>In practice, this means cybersecurity investment protects AI deployments. Data foundation work makes AI outputs reliable. Modernisation enables AI to reach production. FinOps keeps AI costs sustainable. Workforce fluency ensures AI adoption sticks.<\/p>\n<p>The CIOs who treat 2026 as an AI-only year will likely find themselves explaining, 12 months from now, why their AI investments still are not delivering returns. The ones who invest in the full stack \u2014 the security, the data, the infrastructure, the people \u2014 are building the conditions under which AI can actually work.<\/p>\n<p>That is not a story about choosing between AI and everything else. It is a story about understanding that AI does not operate in isolation, and neither should the budget that funds it.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Global IT spending will hit $6.15 trillion in 2026, a 10.8 per cent rise on the previous year. But dig beneath that headline and the distribution is strikingly lopsided. Gartner&#8217;s latest forecast projects AI spending to surge 80.8 per cent and data centre outlays to climb 31.7 per cent, while communications services and device budgets [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"","fifu_image_alt":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[5],"tags":[39,41,25,11,42,40],"class_list":["post-114","post","type-post","status-publish","format-standard","hentry","category-technology","tag-cio-strategy","tag-cybersecurity","tag-digital-transformation","tag-enterprise-ai","tag-gartner","tag-it-spending"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The Non-AI Agenda: What CIOs Are Actually Prioritising Beyond Artificial Intelligence in 2026 - maddaisy - cultivating innovation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/maddaisy.com\/index.php\/2026\/02\/18\/the-non-ai-agenda-what-cios-are-actually-prioritising-beyond-artificial-intelligence-in-2026\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Non-AI Agenda: What CIOs Are Actually Prioritising Beyond Artificial Intelligence in 2026 - maddaisy - cultivating innovation\" \/>\n<meta property=\"og:description\" content=\"Global IT spending will hit $6.15 trillion in 2026, a 10.8 per cent rise on the previous year. 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