Oracle Java SE: the per-employee pricing trap enterprises are still walking into.
3–10×
Typical cost increase after the Jan 2023 model switch
100%
Of headcount counted — full-time, part-time, and contractors
In January 2023, Oracle replaced per-processor and Named User Plus metrics with the Java SE Universal Subscription — priced per employee, not per Java user.
Pricing runs from $15/employee/month for organisations under 1,000 staff, scaling down to ~$5.25 for headcounts above 40,000. Every employee — including contractors — counts.
Oracle’s audit cadence has risen sharply since 2024. Download history and lapsed renewals are both known triggers.
Your move: Run a full Java discovery sweep before your next renewal. A credible OpenJDK migration timeline is your strongest negotiation lever.
Copilot at $30/user/month: why 40–70% of enterprise seats sit idle in year one.
35.8%
Active usage rate across provisioned Copilot seats (2025–26 data)
$360
Annual cost per user for the Copilot M365 add-on
Microsoft 365 Copilot is a $30/user/month add-on requiring a qualifying base license — E3, E5, Business Standard, or Business Premium. It adds 40–60% to your per-user M365 cost depending on your tier.
Enterprises that commit to full-tenant deployment at EA renewal — without a validated pilot — routinely find 40–70% of seats unused in the first 12 months.
Pilot Copilot in 2–3 specific roles (analysts, finance, legal), measure active usage over 90 days, then right-size the commitment.
Your move: Pull prompt counts from the M365 Copilot admin centre before the renewal table. Low utilisation is your strongest lever on add-on price and commitment term.
FinOps X 2026: why AI token spend has become the next software cost crisis.
24×
Growth in projected monthly AI token consumption by 2030
120
(quadrillion) Monthly AI tokens expected to be consumed globally by 2030
FinOps X 2026 marked a major shift in how organisations view technology spend. What was once a cloud cost management discipline is rapidly evolving into a framework for managing AI economics, with token consumption emerging as a key driver of enterprise technology budgets.
AI costs are no longer driven primarily by infrastructure. Organisations must now monitor model usage, agent activity, token consumption, and AI-generated business value.
Traditional FinOps and ITAM practices were not built to govern AI token consumption. Increasingly sophisticated AI agents, larger context windows, and automated model interactions can dramatically increase usage without corresponding visibility.
Your move: Establish AI spend governance before large-scale adoption. Track token consumption, align AI costs to business outcomes, and ensure cloud, FinOps, procurement, and ITAM teams have a shared view of AI usage before costs become embedded in operational budgets.
Microsoft Enterprise Agreements: the cloud pricing discount many organisations are about to lose.
Up to 12%
Potential increase in Microsoft cloud
licensing costs
0%
Tier-based EA discount remaining for Online Services after the change
Microsoft is removing tiered pricing for Online Services under Enterprise Agreements from 1 November 2025. Organisations renewing or purchasing new cloud subscriptions after this date will move to standard Level A pricing, regardless of their size or user count.
Large enterprises have historically benefited from scale-based EA discounts of 6%, 9%, or 12%. Under the new model, those discounts will no longer apply to cloud services.
Microsoft positions the change as a pricing simplification initiative, but many industry analysts view it as part of a broader strategy to encourage migration from Enterprise Agreements toward CSP and Microsoft Customer Agreement.
Your move: Assess your next Microsoft renewal now. Review EA, CSP and MCA-E pricing scenarios, identify any price-protected subscriptions, and quantify the financial impact of losing EA discount tiers before entering negotiations. Organisations with upcoming renewals may still have opportunities to secure more favourable commercial terms.
Enterprise AI: why organisations are questioning the "buy, don't build" software model.
$400M
Annual software spend Starbucks is seeking to optimise
$2B
Broader cost-reduction programme driving technology reviews
Starbucks has signalled a significant shift in enterprise technology strategy by developing AI-assisted replacements for software currently provided by major vendors such as Microsoft and IBM. The initiative forms part of a wider effort to reduce technology spending and re-evaluate whether expensive commercial applications still deliver sufficient value in an era of AI-assisted software development.
AI is fundamentally changing the economics of enterprise software. Historically, organisations accepted high licensing costs because building equivalent solutions internally was slow, complex, and expensive. AI-assisted development is changing that equation, enabling businesses to create highly tailored applications faster and at lower cost, particularly where commercial platforms require extensive customisation.
The story is not simply about replacing software vendors. Starbucks continues to rely on Microsoft’s Azure and AI infrastructure while targeting specific application-layer solutions for replacement. The broader message for enterprise IT leaders is that software products delivering poor business fit, high licensing costs, or significant customisation overhead are increasingly vulnerable to internal AI-led alternatives.
Your move: Identify high-cost applications that require significant customisation or generate user dissatisfaction. Before the next renewal cycle, assess whether targeted AI-assisted development, process redesign, or platform consolidation could deliver comparable functionality at a lower long-term cost while reducing vendor dependency.
ServiceNow SAM Australia Release: why AI is finally reducing the manual burden of Software Asset Management.
5
Major SAM enhancements introduced in the Australia release
1
(Platform) Unified view across software, cloud and contract data
ServiceNow’s Australia release introduces a significant shift towards AI-assisted Software Asset Management, focusing on automating some of the most time-consuming activities faced by SAM teams. Key enhancements include AI-driven contract entitlement extraction, automated SaaS connector remediation, Microsoft 365 licence assignment automation, advanced contract lifecycle management, and guided lifecycle reporting.
The biggest opportunity is the reduction of manual effort. Software entitlement management has traditionally required months of contract review, data entry, validation, and ongoing maintenance. The new contract entitlement extraction capability enables AI to analyse complex legal agreements and automatically populate entitlement records, helping organisations accelerate licence position reporting while reducing data quality risks.
The release also addresses a growing challenge for SAM teams: maintaining reliable SaaS consumption data. Failed APIs, authentication issues, and connector outages can create reconciliation gaps and potential compliance exposure. ServiceNow’s new AI agents proactively identify integration failures, explain root causes, and support automated remediation to help maintain trustworthy software usage data.
Your move: Assess how much SAM effort is currently spent on contract interpretation, entitlement maintenance, SaaS reconciliation, and licence fulfilment. Organisations using ServiceNow SAM should prioritise these new AI-driven capabilities to reduce operational overhead, improve compliance visibility, and free SAM resources for optimisation and strategic licensing activities.
ITAM in China: the software governance market that's quietly taking shape.
6%
China’s share of global enterprise software spend
2027
Target year for foreign software replacement initiatives
China is the world’s second-largest enterprise software market, yet IT Asset Management remains relatively immature compared to Western markets. Awareness of formal ITAM practices, standards, and tooling remains limited, with many organisations relying on spreadsheets, operational teams, and fragmented processes rather than dedicated governance frameworks.
Government localisation programmes and increasing software visibility requirements are creating new demand for software governance. Organisations are being required to better understand software ownership, deployment, and usage as technology sovereignty initiatives continue to gain momentum.
Trade pressures, cybersecurity priorities, and software legitimacy requirements are becoming important drivers for ITAM adoption. As organisations seek greater control over technology estates and AI deployments, visibility and governance are becoming strategic business capabilities.
Your move: Establish a software visibility strategy now. Accurate inventory, usage intelligence, and governance processes will become increasingly important as software compliance, localisation requirements, and AI adoption continue to evolve.
