AI budget is broken: a 90‑day realignment that pays for itself
Reallocate your AI budget to data, integration, evaluation, and governance to cut waste and speed ROI.
Most AI budget plans look sophisticated on paper and underperform in practice. Spend piles into compute, platforms, and shiny pilots while the unglamorous work that creates value gets starved. The result is idle capacity, shelfware, and models that never touch a customer workflow. I am going to show you a simple way to make the AI budget honest, fund what actually produces ROI, and do it without slowing innovation…through integrated strategy and governance.
The pattern behind underperforming AI budgets
If you feel your AI spend is not translating into outcomes, you are not imagining things. Three forces create the gap.
First, the budget tilts toward infrastructure and tools. Training clusters, model APIs, and overlapping platforms dominate. When those lines are heavy, something else is light, and it is usually data readiness, integration, and evaluation. Those are the muscles that push AI into production and keep it useful.
Second, commitments outrun utilization. Reserved cloud capacity and auto‑renewed licenses look like momentum but behave like concrete. When average GPU utilization sits low and half of your seats are dormant, you are paying for the right to say you are serious about AI while cash seeps out the side. That is not frugality or speed. That is waste.
Third, governance shows up as a memo instead of an operating model. Without a portfolio intake, simple stage gates, and basic telemetry, projects sprawl, risk hides, and compliance shows up after the fact. Costs rise. Delivery slows. Trust erodes.
The fix is not complicated. Hold the total AI budget flat. Reallocate to the parts of the lifecycle that create value. Enforce a few rules that keep builders moving and keep money from burning.
What great looks like in the ledger
Here is the shape you want. In a $50M program, move dollars out of pure compute and overlapping tools into data, integration, evaluation, observability, security, and governance. That is it.
Use this chart in your board deck. Use the “get the data” link in the chart to download a CSV file to manipulate with your own data.
Data acquisition and prep costs rise from 10% to 15%, which funds clean, representative, well‑labeled data, and the pipelines to move it. It eliminates months of rework later.
Compute training drops from 25% to 20% now, and can drift toward 18% in quarter two. You are not going slower. You are using capacity better, scaling to need, and eliminating idle.
Integration and APIs jump from 5% to 8% now, then to 12% next quarter. This is the difference between a demo and something customers actually use.
Platform and tools fall from 20% to 15% now, then to 12%. Consolidate. Negotiate. One in, one out.
Governance and risk goes from 2% to 5% now, then to 8%. This is not bureaucracy. It is how you prevent a $5 million mistake with a $1 million discipline.
Evaluation and red team steps up from 1% to 3%. If you haven't tried to break your model before shipping it, your customers will do it for you.
Observability and monitoring doubles from 2% to 4%. You cannot manage what you cannot see.
Smaller programs follow the same pattern with different names on the lines. For a $5M budget, keep compute down to 25%, lift data to 12%, lift integration to 8%, lift governance and evaluation to a combined 5%, and trim platform sprawl.
Use this chart for mid‑market planning.
Where the waste hides and how to shut it off
Think of waste as a tax you do not have to pay. Spot it early. Assign owners. Measure it weekly.
Duplicate tools and unused licenses. Track cost per active user and the share of dormant seats. If more than 40% of paid seats are idle, you have a policy, procurement, and visibility problem, not a growth problem.
Idle compute. Measure average GPU utilization and idle hours. Sub‑30% is money on fire. Set a utilization target, schedule jobs, and rightsize instances. Use cloud for peaks, not for baseline waste.
Shadow AI. Pull card spend and API logs to see where unapproved services are in use. That pattern tells you where the process is too slow and where value is being created outside the lines. Build a sanctioned sandbox so exploration stays in daylight.
Underfunded data and weak integration. Count models that fail due to data issues and the share of built models that never ship. If your productization rate is low, you have a budget problem masquerading as a talent problem.
Missing evaluation and red team. Catalog post‑launch defects and incidents that would have been caught by basic testing. Those are preventable losses. Fund the pre‑deployment checks and watch the incident rate fall.
Compliance rework and fines. If legal, audit, or a regulator forces you to retrofit transparency, documentation, or bias controls, you did not save time by skipping them. You just moved the invoice to a later date and multiplied it.
Use this short table to assign owners in week one.
The operating model that makes the AI budget honest
Governance is a process for making decisions, allocating scarce resources, and maintaining accountability. Run it like an operator, and everyone will move faster.
Establish a minimal AI governance office with oversight from Finance. Finance keeps the spend honest. The CAIO and CISO keep risk and architecture tight. Legal reads the horizon. Product and engineering own delivery. Together, they enforce one simple rule: if a proposal lacks a funded integration plan and a real KPI, it does not get a dollar.
Use four lightweight stage gates. Keep them short. Enforce them.
Business case and KPI. What problem, what metric, what dollar value? No buzzwords.
Data readiness. Provenance, quality, representativeness, and access.
Risk review. Privacy, bias, safety, security. Document assumptions and mitigations.
Pre‑deployment validation. Performance, abuse cases, red team results, and rollback plan.
Start light. Then harden to an independent red team for high‑impact flows.
Make documentation useful. Every model receives a simple, structured record that includes its intended use, limitations, data lineage, test results, monitoring plan, and the names of individuals responsible for the outcomes. Call it a model card if you want. The key points are reuse, auditability, and faster incident response.
Monitor in production like an adult. This is not set‑and‑forget. Track performance, drift, fairness, abuse, latency, and cost. Alert when thresholds are crossed. Retrain on a schedule, not when a headline forces your hand.
The CFO’s lens on value, time, and risk
If you want a budget that pays for itself, measure the three numbers that matter and tie them to bonuses.
Time to value. Set a goal that new initiatives deliver measurable value within 12 months. That forces ruthless prioritization,and it rewards integration.
Dollars freed and redeployed. Cut shelfware and idle capacity, and move that cash to data, integration, and evaluation. Treat it like a flywheel: savings fund the controls that create more savings.
Incident and rework trend. Drive post‑launch defects toward zero by paying for evaluation up front. Track rework hours and compliance findings as a cost line. If that line falls, your governance is working.
The curves tell a simple story. When utilization rises from painfully low to respectable, you free millions without sacrificing throughput. When you raise data quality investment from token to disciplined, project success rates jump and rework collapses. These are not academic shifts. They are cash.
The GPU curve is a conservative savings model grounded in what real-world environments report about utilization and what FinOps teams routinely achieve when they schedule, right-size, and consolidate. Weights & Biases’ telemetry shows that nearly one-third of users average under 15% GPU utilization, which means you are often paying premium rates for idle silicon. A 2024 Microsoft Research study, conducted across 400 real-world enterprise jobs, found average utilization at or below 50% and cataloged hundreds of concrete causes, ranging from input bottlenecks to mis-sized jobs. Put those facts next to FinOps guidance and field cases and the math gets straightforward: if your baseline sits in the teens or low forties, moving toward 50% through workload scheduling, fractional allocation, and instance right‑sizing frees meaningful dollars without sacrificing throughput. One training program that cut its A100 count in half after discovering an average utilization of ~40% saw training times hold steady, while costs dropped by around 50%. Our $50M curve reflects that playbook: multi‑million savings appear as you climb from 15% to 50% average utilization, with diminishing returns beyond that as you start to trade cost for throughput buffer. Weights & Biases
The data‑quality curve models something boards feel in the P&L but rarely budget for explicitly: projects slip, rework piles up, and adoption lags when the data is not clean, complete, and governed. Qlik’s 2025 research reports 81% of companies still struggle with AI data quality, and 96% of U.S. data professionals warn that weak data practices could trigger crises, while Monte Carlo’s work on “data downtime” ties poor quality to measurable revenue impact and longer time to resolution. Independent analysis from RAND identifies data readiness and problem scoping as top failure drivers, and McKinsey’s State of AI shows that “high performers” invest in these foundations to scale value. Taken together, the evidence supports a simple operating thesis: shifting from token 5% data spend to a disciplined 15–20% on data readiness and observability sharply improves success rates and shrinks time to value, with diminishing returns if you push far beyond that band. Our curve encodes that relationship so Finance can see the payoff from moving dollars into data early rather than paying for rework late. Qlik
The 90‑day plan that pays for itself
You do not need a committee year. You need three months of adult supervision. This is the sprint I run with executives who want outcomes and receipts.
Days 1 to 30. Baseline and brakes.
Issue the mandate from the CEO and CFO.
Inventory projects, licenses, contracts, GPU usage.
Cut the obvious waste now. Rightsize idle instances. Reclaim dormant seats.
Publish a short AI policy and launch a sanctioned sandbox to pull shadow AI into daylight.
Tag all AI-related cloud and license expenses so Finance can view them by project.
Select one flagship use case and run it through the light-stage gate this month. Prove the motion before you scale it.
Days 31 to 60. Build the loop.
Hold a portfolio review with Finance. Merge duplicates. Pause low‑value ideas.
Reallocate the first six percent from compute and tools into data, integration, evaluation, governance, and observability.
Finalize stage gates and documentation templates.
Publish model cards for customer‑facing AI.
Train approvers, builders, and operators on the new way of working.
Days 61 to 90. Enforce, measure, communicate.
Enforce gates on every active project.
Stand up an independent red team for high‑impact launches.
Turn on production monitoring and incident logging.
Publish a 90‑day metrics report: utilization trend, dollars freed, models to production, time to value, incidents avoided, and vendor contract changes.
Lock platform consolidation and renegotiated terms.
What changes on the ground
When someone says “we need more GPUs,” you now respond with a utilization report and a scheduling plan. When a vendor pitches another platform, you show a one-in, one-out policy and ask for proof of unique value within six months. When a product owner pushes to ship, you point to four pages of stage gates that can be completed in days, not months, and you run them. When a model makes a decision that appears incorrect, you review the model card and the monitoring dashboard, and you correct the data or adjust the guardrails before it becomes a headline.
Builders still move. Risk becomes visible. Finance sees dollars freed and redeployed. Customers see fewer mistakes and more valuable features. That is the point.
What to do next
If you own the AI budget, bring the charts to your next steering meeting and make three commitments.
Hold the AI budget flat for two quarters and reallocate fifteen percent toward data, integration, evaluation, governance, and observability.
Tie executive bonuses to time to value under twelve months for new AI.
Publish model cards for every customer-facing AI flow and keep them up to date.
Then run the 90‑day plan and don’t blink. Your team will adapt faster than you expect when the rules are clear and the scoreboard is visible.
Key Takeaway: Your AI budget does not need to grow. It needs to be honest. Reallocate to data, integration, evaluation, and governance, raise utilization, and measure time to value. The rest is noise.
Call to Action
If you want real‑world examples and playbooks, explore prior blogs on RockCyber Musings and the services menu at RockCyber. If you want to align risk to business value the way boards expect, read my book The CISO Evolution: Business Knowledge for Cybersecurity Executives.
👉 Subscribe for more AI security and governance insights with the occasional rant.