TECHNOLOGY SPEND VS BUSINESS VALUE: ARE WE MEASURITHE RIGHT THINGS?
Technology budgets are under pressure, but cutting spend does not automatically create value.
In many finance-led organisations, the natural response is to tighten controls, freeze projects, and demand immediate ROI. That is understandable. But here is the uncomfortable truth: most organisations do not have a technology spending problem. They have a value measurement problem.
Why? Because technology spend is increasing for reasons that do not always show up neatly in traditional “project delivery” metrics.
Globally, IT spending continues to grow at pace. Gartner forecasts worldwide IT spending to reach R105.75 trillion in 2026, up 13.5%, with the fastest growth driven by AI infrastructure, data centre systems, software, and cloud services. On the cloud side, IDC forecasts public cloud services spend to surpass R16.55 trillion in 2026, driven by SaaS and fast-growing PaaS as organisations scale AI and modernise applications.
So, the real question is not: “Why are we spending more?”
The better question is: Are we measuring the value of what we are buying and building, especially as technology spend shifts from once-off projects to ongoing platforms, subscriptions, and usage-based consumption?
What Is Driving Technology Budgets Right Now?
Finance, treasury, and accounting teams are feeling the impact of several technology spend trends:
Cloud consumption replacing CAPEX with OPEX
Costs now fluctuate based on usage, environments, data volumes, and consumption patterns, often without strong accountability.
SaaS and licensing sprawl
Organisations are accumulating overlapping tools for automation, reporting, workflow, analytics, and collaboration. The FinOps Foundation notes that FinOps practices are increasingly expanding beyond cloud to manage SaaS spend and licensing as well.
AI add-ons and infrastructure
AI features are now embedded across software stacks, but value capture often lags adoption. McKinsey reports that 88% of organisations use AI in at least one function, yet nearly two-thirds have not started scaling AI enterprise-wide.
Security and resilience investment
More controls, monitoring, identity management, recovery capabilities, and resilience measures are now necessary, but they are not always tied to measurable business outcomes.
This is why leadership teams can look at the IT line item and think:
“We are spending more, but why does it still feel hard to get things done?”
The Trap: Outputs Are Easy to Count, but Outcomes Are What Matter
Traditional technology scorecards are still dominated by delivery metrics:
- On time / on budget
- Milestones achieved
- Tickets closed
- Number of automations deployed
- Reports or dashboards produced
These metrics are not useless. They are simply incomplete.
You can go live with a new AP automation platform, add AI copilots, migrate a data warehouse, and still fail to move the needle on working capital, cash visibility, close timelines, or control effectiveness.
The bold truth:
If you measure success by projects delivered, you will build a company that delivers projects – not one that improves cash flow performance, accelerates decisions, or reduces operational risk.
What “Value” Really Looks Like
In finance functions, value shows up in practical, measurable places:
Cash visibility
Faster and more accurate views of cash positions.
Decision speed
Quicker approvals, fewer handoffs, and shorter cycle times.
Close efficiency
Fewer reconciliations, fewer late adjustments, and fewer surprises.
Working capital impact
Better forecasting, stronger collections, and fewer disputes.
Control strength
Fewer policy exceptions, fewer audit findings, and lower fraud exposure.
Value becomes visible when technology delivery is connected to the business outcomes leaders actually care about: speed, adoption, and risk reduction.
This trio is board-friendly because it translates technology investment into business reality – especially important as spend shifts toward cloud, AI, and subscription-based models.
A Better Scoreboard: Speed, Adoption, and Risk Reduction
1. Speed: How Fast Can We Decide and Act?
In finance and treasury, delays are expensive.
Measure:
- Time-to-decision: time to approve payments, settle exceptions, release credit notes, or approve journals
- Time-to-change: time from identified issue to deployed fix, such as a workflow rule, integration, control, or report
Why it matters:
Faster cycles reduce operational drag without requiring more tools. Speed also shows whether spend is going into the right areas – such as integration, workflow, and data quality – rather than just shiny front-end solutions.
2. Adoption: Are People Actually Using the Capability?
A tool that is not used delivers zero value.
Measure:
- Active usage: real weekly or monthly utilisation and key feature usage – not just “users created”.
- Manual work removed: validated reductions in spreadsheets, email approvals, rework, and handoffs.
Why it matters:
Many organisations are buying AI features and automation capabilities, but McKinsey’s research shows that most are still early in scaling and capturing real value. Adoption is the bridge between technology spend and measurable impact.
3. Risk Reduction: Did Controls and Resilience Improve?
For financial operations, risk is not theoretical.
Measure:
- Exceptions and rework: fewer reconciliation breaks and fewer overrides.
- Operational resilience: fewer incidents affecting payments, reporting, or close processes – and faster recovery when issues occur.
- Control effectiveness: fewer audit findings, fewer policy deviations, and stronger traceability.
Why it matters:
If spending increases because you have added platforms, AI, and more integrations, but controls do not improve, leadership will rightly ask what they are paying for.
Common False Positives: Where Spend Looks Good, but Value Is Missing
Be cautious of these patterns:
- Automation deployed, but exceptions still require manual escalation.
- AI features licensed, but workflows were not redesigned, so people continue working the old way.
- New dashboards delivered, but forecasting accuracy does not improve and decisions are not faster.
- Cloud migration completed, but unit costs do not drop and service reliability does not improve.
- More tools purchased, but integration and data quality remain the bottleneck.
These are clear signs that the organisation is measuring delivery rather than business change – while technology spend continues shifting into recurring subscriptions and variable consumption.
The Bottom Line
In a cost-pressure environment, the organisations that win will not be the ones that spend the least. They will be the ones that prove value the clearest.
With IT and cloud spending continuing to grow – especially across AI, software, and data platforms – measurement discipline is now a leadership requirement, not a reporting nice-to-have.
Start measuring what leaders care about: Speed. Adoption. Risk reduction.
When the scoreboard changes, decisions improve. Technology spend becomes easier to defend – and easier to cut wisely where it does not deliver.
If this article resonates with you, connect with Aphile Shanbangu on 021 819 7802 or at nshabangu@wauko.com.
References:
- Sources: gartner.com, biztechreports.com
- Source: data.finops.org
- Source: mckinsey.com
