Artificial Intelligence is no longer just a pilot project; it has become architecture. That’s the dominant interpretation from the Flexera 2026 IT Priorities Report: for the third consecutive year, AI integration leads the global tech agenda, and 1 in 3 IT leaders (33%) ranks it as their top priority for next year. The ambition is clear —94% are seeking ways to incorporate AI into their stack— but management gaps remain: only 19% state that measuring usage and effectiveness (ROI) of AI is an explicit priority for 2026. The result: increased investments, pressure on costs, and a metric gap that could tarnish the productivity promise.
A thermometer with 834 real decisions
The study includes insights from 834 IT decision-makers across various regions. Beyond AI leadership, the top priorities are reducing IT costs (24%) and mitigating security risks (22%). The message for technical teams is clear: innovate with AI, yes; but with fiscal discipline and robust controls in an increasingly distributed environment.
The new spending equation: AI, SaaS, cloud… and FinOps
The report confirms a shift in budget cycles.
- 80% of leaders increased spending on AI applications and more than a third believe they are overspending.
- 73% admit that their SaaS and cloud infrastructure costs have risen, and 67% say the cloud heavily impacts their budget.
The takeaway for tech-focused outlets is straightforward: FinOps is leaving the trenches and becoming a cross-cutting function. Capacity reservations, rightsizing, shutdown policies, savings instances, internal catalogs of approved AI, and chargeback/showback per business unit are no longer best practices but are becoming the operating system for the cloud in 2026.
Complexity and visibility: the blind spot
The acceleration of AI is happening amid an already complex landscape. 85% perceive visibility gaps as a material risk, and 58% have experienced incidents caused by unauthorized SaaS (shadow IT). This explains two technical trends:
- Tool consolidation: fewer point solutions, more platforms that unify discovery, inventory, governance, and optimization in a single view.
- SSO and data perimeter: homogeneous access controls, data tagging, and unified telemetry to know what is running, where, and at what cost.
Data: access exists; value, less so
Almost 9 out of 10 teams claim access to the data they need to make decisions, but 94% believe they must invest in tools to extract value. Translated into technical agendas: less data lakes and more data products; less custom ETL and more quality standards, lineage, and data contracts so AI doesn’t “drink from the river” but from curated sources.
By 2026, many organizations will upgrade from testing AI to operating AI: MLOps with drift metrics, model observability, bias controls, and security (prompt injection, data exfiltration, model stealing)—plus governance connecting business, finance, and risk.
Sustainability: rising priority, pending execution
IT sustainability is now a strategic issue: 94% say it’s becoming more important, but 87% admit they need to improve their approach. For the tech ecosystem, this means implementing technical indicators —PUE, WUE, heat reuse factor, carbon footprint— both in data centers they own and in cloud providers. AI is not free from an energy perspective; 2026 will be the year to measure and decide: efficiency per watt, per euro, and heat reuse in on-prem or colocation deployments.
Implications for the tech stack
1) Reference architecture for “operational” AI.
- Data layer with contracts, catalogs, and quality (SLA/SLO).
- Models layer with feature store, MLOps, model registry, automated testing, and observability (data and concept drift).
- Platform layer: gateways for AI, policy engines, guardrails, and end-to-end encryption.
- Cost layer: FinOps integrated with AI telemetry (cost per prompt, per token, per inference, per training pipeline).
2) AI-first security.
Zero Trust applied to model APIs, sensitive data tokenization, AI supply chain security (base models, weights, artifacts), prompt injection detection, and output safeguards (red teaming, content filters).
3) Governance and ROI by design.
AI KPIs that combine productivity (time saved), quality (corrections rate), risk (audit findings), cost (€ per inference, € per experiment), and adoption (internal MAU/WAU). No dashboard, no ROI.
Practical recommendations for 2026 (with a technical bias)
- Standardize AI usage through a corporate catalog (approved providers, guardrails, prompt templates) and block the rest by default.
- Implement cost tracking at origin: tag by project, team, and environment; apply shutdown policies and reservations; enable real-time spending alerts.
- Monitor AI like microservices: trace prompts, latency, success rate, cost, drift, and output quality per use case.
- Eliminate shadow IT with SSO and continuous SaaS discovery; create a fast track to “legalize” useful tools and a slow track to retire redundant ones.
- Measure sustainability per workload, not just per data center: provider’s PUE/WUE, location (energy mix), model efficiency, and heat reuse (how much heat is recovered).
Reading material for a technical committee
- AI will be the priority strategic axis —33%— but the ROI gap persists —19% prioritize it—: operating AI requires metrics and governance, not just models.
- Spending on AI, SaaS, and cloud accelerates (80%, 73%), and budget pressure rises (67%): FinOps with real telemetry is unavoidable.
- Visibility is critical —85% see it as a risk—and unauthorized SaaS has already taken a toll—58%: consolidate platforms and standardize controls.
- Sustainability is becoming a decision criterion: 94% prioritize it, and 87% say they need to improve execution. In AI, efficiency per watt and per euro dominate.
Conclusion: 2026, from “testing AI” to “operating AI”
The Flexera report projects a 2026 where technology and management must converge. Integrating AI without curated data, observability, and FinOps is an invitation to overspending and risk. The industry has already committed to investing in AI; now it’s time to build the platform — both technical and governance — that makes it sustainable, secure, and profitable.