By Maribel Lopez, Lopez Research
A few practical thoughts on where AI spending goes wrong — and what separates the organizations getting it right. Most organizations aren’t failing at AI because the technology doesn’t work. They’re failing because of decisions made before a single model was deployed. Decisions such as how to scope and fund an initiative, what success was supposed to look like, and whether anyone was measuring whether they got there. I recently joined Tom McHale, CFO and VP of Business Operations at SunStream Business Services and Apptio, an IBM company, for a webinar conversation about where spend management goes wrong. McHale shared how he has navigated technology trade-off decisions as a CFO for years. Our observations converge on the same patterns. Here are the four pitfalls McHale and I spoke about during the session — and what organizations can do about them.
Pitfall 1: The Board Issues an AI Mandate Without Funding the Foundation
Seventy-two percent of companies Lopez Research surveyed had received a directive from their board or senior management to implement AI last year. Most of those mandates arrived without acknowledging the trade-offs required to fulfill them. The pressure is real, and organizations that don’t leverage AI within their apps and services will fall behind. The problem is fixating on the technology without resourcing the operational requirements underneath it. To move into AI effectively, you need data quality, governance, and a clear plan for budgeting for ongoing costs. Boards often ask for AI outcomes without understanding the foundational work it takes to deliver them. There is also a funding gap that sneaks up on organizations. Many companies attempted to fund AI by reallocating from existing cloud or operations budgets. That worked at the margins. It does not work at scale. Internal capital reallocation as the primary AI funding source jumped from 50% to 67% in a single year in Apptio’s 2026 Technology Investment Management Report. At some point, there is not enough money in the couch cushions to do what is being asked. McHale also shared that most management teams expect first-class technology at bargain-basement prices. He brought up the reality many organizations face when he shared an example: you can’t always make trade-offs between technologies, such as funding a batch scheduler in a mainframe environment or investing in AI. You need both. What to do: Before responding to an AI mandate, attempt to map the real cost. That means data preparation, governance infrastructure, security review, and ongoing model costs — not just tool licenses. Bring that full picture to leadership. The conversation about tradeoffs is easier to have before you start spending than after you have run out of budget.
Pitfall 2: Failing to Define Problems and Measurable Outcomes
In the early days of AI adoption, running experiments made sense. Organizations needed to learn what the technology could do. That phase is over. In 2026, no one should be running an AI proof of concept without a production path and a timeline. In research Lopez Research conducted in mid-2025, 85% of companies said they were struggling to find AI ROI. When we looked at why, three causes kept surfacing. First, there was a data quality problem. Second, the use case was too vague to measure. Third, there were no metrics, monitoring, or observability in place to gauge whether the initiative was working. The fourth issue is that fewer than half of the organizations had a governance strategy, which tends to create downstream compliance and legal exposure. All these solutions are foundational solutions that require time and money. And we didn’t even discuss the cybersecurity concerns, which is always one of the top three technology spending categories. Selecting AI technology solutions before defining what you are trying to accomplish is like buying a full set of hammers, screwdrivers, and impact drivers before knowing what you are building. The tools are not the strategy. What to do: Understand what specific organizational strategic goal or KPI you’re trying to achieve before you start. “Improve customer experience” is too vague. Whereas something specific enough to measure, like reducing billing errors by 80% to improve customer satisfaction, or improving deployable software development velocity by 15%, allows you to understand the impact and the metrics, and provides a set of requirements for AI tool selection. If you cannot define success before you deploy, you are not ready to deploy. Note: I am researching the merits and detriments of an “AI use cases” versus “creating reusable AI skills/capabilities with AI agents”. See the March Newsletter on Yumm Brands for more on this. Given that I don’t yet have solid guidance on how to build and scale reusable AI skills, I maintain that you need to understand which real business problems you need to apply AI to, which helps winnow the platform selection.
Pitfall 3: Assuming the Budget You Can See Is the Actual Spend
Shadow AI is this year’s shadow IT. Every technology wave produces a version of this problem. Employees find tools that help them work faster, stand them up without IT involvement, and pay for them however they can — personal credit cards, discretionary budget lines, expense reports. It adds up quickly and never shows up in the official budget. McHale shared a real example from a prior role. After conducting a full audit of actual spend at a Fortune 500 organization, the actual IT budget was double the official number. Shadow IT had been absorbing that difference for years. With AI tools accessible to anyone with a credit card and a browser, the same dynamic is accelerating. The financial risk is significant. An employee can spend $20 to $300 per month on AI tools, such as ChatGPT and Claude Code. Untracked AI spend scales fast across an organization. But the non-financial risk may be more serious. Unvetted tools accessing company data, unapproved models processing sensitive customer or employee information, and no audit trail if something goes wrong. The governance and security risks posed by shadow AI are not hypothetical. McHale put it well: defining clear objectives at the start, having someone accountable for documenting them, and treating governance as an ongoing discipline rather than a one-time checkbox is what separates organizations that can scale AI from those that cannot. Organizations that lack centralized visibility into AI spend will discover this the hard way. When it comes time to request a budget increase for next year, leadership will ask why more money is needed, given that things seemed to work fine with what was available. The answer — that it was all going on personal credit cards — is not a conversation anyone wants to have. What to do: Treat AI spend tracking as an urgent priority, not a future initiative. Establish a process for centralizing AI tool procurement now, or at least provide guardrails for AI spending. This is not about restricting what employees can use. It is about knowing what is being used, what it costs, and what data it can access. Shadow AI that stays invisible today becomes a budget and compliance problem tomorrow.
Pitfall 4: Confusing Operational Maturity with Technical Maturity
This is one of the more subtle pitfalls, and it trips up organizations that are genuinely sophisticated technically. A company can have strong cloud infrastructure, capable engineering teams, and real AI experience — and still be operationally immature in managing AI investment. The gap is most evident in IT financial management. IBM Apptio’s survey data shows that 59% of ITFM professionals are confident their forecasts are highly accurate. The tools and processes many teams rely on to produce those forecasts were not designed for the pace or variability of AI spend. AI costs scale with usage in ways that are difficult to predict. They appear across every function in the organization. They change as models are updated, as usage grows, and as new capabilities are deployed. Managing that with processes built for a slower-moving environment creates real risk, even when the people running those processes are skilled and confident. Yet the potential visibility gap is where budget surprises live. What to do: Audit your financial management practices against the specific demands of AI spend. Variable usage-based costs, multi-cloud workloads, hybrid AI, and distributed AI tools across business units require practices built for that environment. The goal is not to find fault with what you have been doing. The goal is to identify where the current setup leaves gaps that AI spending will widen.
The Pattern Behind the Pitfalls
These pitfalls are not independent. These pitfalls interconnect. An AI mandate without a real budget forces organizations to fund initiatives on the margins, leading to cuts in data, governance, observability, and security. Without visibility into spend, shadow AI accumulates, and real costs stay invisible. Without defined success metrics, there is no way to know whether cutting those corners mattered. The organizations that are getting AI right did not avoid these problems by being smarter. They avoided them by doing the less exciting work first: defining use cases clearly, understanding true costs before committing, building governance before it was required, and measuring outcomes from day one.While the technology changes, the adoption challenges remain remarkably consistent. Every wave has its version of the couch cushions problem — organizations moving fast on an exciting new capability without the financial and operational discipline to sustain what they are building. Focus on the foundation first. The shiny AI tools can follow. Subscribe to my LinkedIn newsletter here. Also, you can subscribe to the AI with Maribel Lopez podcast on your channel of choice here.
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