IT leaders say AI is adding new costs faster than organizations can measure the benefits. This forces CIOs to weigh rising spending against uncertain productivity gains.
Here's what seven IT leaders had to say about AI's affect on IT costs:
AI has not reduced IT costs in most organizations and is often adding new ones.
New spending on AI tools, licensing, integration and governance is offsetting savings.
Productivity gains are easier to see than measurable cost reductions.
AI often shifts costs across the business rather than eliminating them.
Organizations are seeing the strongest returns when they apply AI to specific operational problems.
AI has not reduced IT costs in most organizations -- in many cases, it's adding new ones.
IT departments are paying new costs for AI model usage, software licensing, system integration and governance, but are not seeing straightforward savings. AI is reducing some costs in specific areas, such as manual work and certain hiring needs. However, these savings rarely offset the cost of AI, especially since it is not a one-and-done expense. It requires ongoing investments in model updates and retraining, infrastructure upgrades, and integration maintenance that leaders often underestimate.
To find out how AI is affecting IT costs in practice, TechTarget asked seven IT leaders the same question: "Is AI reducing your IT costs, or is it just adding to them?"
What they describe is not a single outcome, but a combination of rising costs and limited efficiency gains that do not yet reduce overall IT spending.
Is AI reducing your IT costs, or is it just adding to them?
"The honest answer is no. AI has increased the IT costs instead of bringing them down. We made a deliberate decision not to limit AI tools to the developer team only, but gave them to everyone -- operations, compliance, middle office and finance. That's over 700 people, most of whom would be alarmed to be called engineers.
That costs more than equipping a handful of developers, and we watch the spend carefully. But costs are only half the ledger -- the half that is easy to read. They have a number, a currency and a date. Productivity gains don't have these metrics. So, when a developer tests code faster, or someone in operations builds their own dashboard in an afternoon instead of joining a three-year backlog, the savings never show up as a credit on an invoice.
Anyone claiming they can already prove the gains beat the costs is making it up.
Richard ForssCTO, Exante
AI costs money -- as everything useful does -- and that is not a big deal. Costs are easy to count and benefits are not, so organizations that only trust what they can count will decide AI has failed. Six months in, we see code tested faster and non-technical staff building tools that genuinely make their jobs easier -- not yet as a clean percentage that would survive a board paper, but often enough to see that it isn't a fluke.
Anyone claiming they can already prove the gains beat the costs is making it up. But most useful technology works this way -- you pay the bill before you can show the benefit. Ask me again in a year. I don't expect a different answer."
-- Richard Forss, CTO of Exante, a global prime brokerage firm
"AI is adding costs while creating the illusion of savings. Enterprises measure AI ROI by output volume, including things like tickets processed, reports generated or lines of code. They do not measure the downstream cost of acting on that output. The hidden cost stack nobody budgets for includes token consumption at scale, engineer hours fixing hallucinated code and wrong architecture decisions, and rework cycles when AI-generated analysis leads strategy in the wrong direction. It also includes compliance exposure from unreviewed AI output that gets signed off and acted upon, and customer experience recovery after AI interactions drive net promoter score down.
When leadership signs off on AI-generated analysis without reading it, the cost is not in the AI. It is in the undetected error multiplied at scale. AI is not reducing IT costs yet for most organizations. It is redistributing them upward and making them invisible until they surface as project failures, compliance gaps or strategic misdirection. CIOs who are ahead of this have stopped measuring AI by what it produces and started measuring it by what it causes."
-- Frank Meltke, CEO of Contraco, a digital transformation consulting firm
"AI is not reducing costs at all for many organizations. In some cases, it's proving more expensive than the workforce it was supposed to replace. Companies spent the last two years building business cases around productivity gains, headcount reductions and operational efficiency, but many are now discovering that the long-term cost of AI extends far beyond the initial software purchase.
Organizations moved faster on deployment than they did on financial analysis.
Linda ZecherCo-founder, Cyber Knowledge Partners
Part of the problem is that organizations moved faster on deployment than they did on financial analysis. AI initiatives were often championed by innovation teams, business units or technology leaders eager to demonstrate progress, while finance teams entered the conversation later. By that point, companies were already absorbing growing cloud consumption costs, model usage fees, integration expenses, governance requirements, cybersecurity controls, compliance reviews and vendor management overhead that were never fully reflected in the original projections.
Organizations are discovering that AI has not eliminated the need for people. It simply changed the nature of the work. Employees are still needed to validate outputs, monitor performance, review decisions, manage risk and intervene when systems produce inaccurate results. In some cases, companies reduced headcount expecting AI to fill the gap, only to find themselves operating with fewer experienced employees and a costly technology platform that is not being utilized to its full potential."
-- Linda Zecher, co-founder of Cyber Knowledge Partners, a cybersecurity and AI advisory firm
"It depends on where and how you apply it. The companies that treat AI as a blanket cost-cutting measure are going to be disappointed, while those that treat it as targeted leverage can see real returns. Companies that are deploying AI strategically to solve specific problems and are committed to finding savings are seeing results.
The biggest misunderstanding in the market is that the savings show up as straight reductions in cost or headcount. They do not. Across the industry, licensing is shifting toward consumption, usage is climbing and AI spend is rising as licensing models and model costs go up. Our own costs reflect that. We have spent the last two-plus years making strategic investments to place AI into our software development lifecycle (SDLC) where we can automate tasks, build at higher velocity and improve quality.
For us, the value showed up in how we build and test software. AI now writes about 95% of our test cases, which lets us reduce that team by roughly 20% and redeploy those people to more strategic work. On the build side, we carried years of tech debt and a roadmap that always outran our capacity. AI let us accelerate that roadmap and ship faster than we could have staffed for, which has translated into better customer retention and sales. We have automated a good portion of our SDLC, with 53% of all our code now AI-generated and code volume per developer up almost 200%. That is the capacity we did not have to hire to deliver the same roadmap.
On the same note, the industry can oversell how evenly these cost opportunities show up and to what degree. We have looked at AI for many specific tasks and found that, in some cases, it adds cost, even when the output quality is higher.
It is also worth watching how this plays out on the vendor side. Nearly every software vendor is looking at how to monetize AI by adding new capabilities to their products, and they will charge for it -- even though most companies are likely using less than 25% of the functionality they already pay for today."
-- Greg Ingino, CTO of Litera, a legal technology software company
AI is not reducing IT costs yet in a broad, clean, CFO-friendly way. In most enterprises, it is still adding a new cost layer on top of cloud, security, data engineering, integration, governance and training. AI savings sound great in board decks, but the bill shows up first in infrastructure, licenses, pilots and people trying to make the technology usable.
That said, AI is starting to create real savings in narrow, repeatable workflows. Help desk triage, code documentation, knowledge search, test automation and basic reporting are where the early ROI is showing up. But those savings only happen when the company has clean data, clear process ownership and the discipline to shut down old tools after the AI workflow works.
The bigger issue -- and this is a huge deal -- is that many companies are treating AI like a magic budget cutter instead of an operating model change. AI does not lower costs by itself. It lowers costs when CIOs redesign workflows, remove manual steps, consolidate platforms and hold teams accountable for measurable productivity gains.
-- Mark Vena, CEO and principal analyst at SmartTech Research, a technology research firm
"We are starting to see AI reduce costs in very practical ways, but I think the bigger impact is productivity rather than outright headcount reduction. Last year we rolled out AI to our development team and the feedback has been positive, particularly around code generation, automating repetitive tasks and reducing time spent context switching between tools and documentation.
One developer used it to create tools that automated painful migration and environment setup processes that would previously have taken days or weeks manually. In one case, a utility to parse and correct migration errors reduced a task that would have taken weeks down to under eight hours, while also creating a reusable tool for future projects. That is where we are seeing the value right now -- not AI replacing experienced developers but helping skilled people remove repetitive bottlenecks and move faster.
That said, AI is still adding costs in other areas. There are licensing costs, governance considerations, security reviews and the need for oversight to make sure outputs are accurate. I think many organizations are still in the phase of balancing those additional costs against longer-term efficiency gains. The companies that will see the best return are probably the ones using AI to solve very specific operational problems rather than implementing it simply because they feel they should be."
-- Rhys Collins, managing director at Total Systems, a UK-based insurance software provider
"AI has not reduced our IT costs -- it has restructured them. Whether that nets out as a saving depends entirely on what you treat as the baseline. Our actual infrastructure spend is roughly flat year over year, but the work that spend is supporting has expanded materially, including content production, internal automation, intake and routing, and content systems that would have required additional headcount to operate at this volume.
The trap many CIOs fall into is expecting AI to reduce the line item it is adding spend to.
Elijah FernandezCTO, Cerevity
So, if you measure cost per unit of output, AI has reduced our costs significantly. If you measure cost in absolute dollars on the IT line, you find that the savings showed up somewhere else on the P&L, not as a smaller cloud bill.
The trap many CIOs fall into is expecting AI to reduce the line item it is adding spend to, when in reality AI tends to shift cost across categories. We are paying more for model APIs and orchestration than we were a year ago, but we are paying less for things we used to need that are no longer required, such as contract writers, certain operational coordinators and third-party tools whose function got absorbed into our own AI workflows.
The CIOs who tell you AI is just adding cost are usually looking at one column of the spreadsheet and ignoring the columns that got smaller. The ones telling you it has dramatically reduced costs are often understating the new line items they had to add to make that possible.
Where I do see clear net reduction is in the categories where AI replaces work that was either expensive or never going to scale -- the kind of operational labor that did not produce visible output but quietly consumed headcount and attention. The discipline that determines whether you end up in net savings or net cost is whether you are willing to actually retire the workflows AI is replacing, rather than running both in parallel out of caution.
The companies that are net positive on AI investment are the ones that made the harder organizational decisions, not just the ones that adopted the tools the fastest. The companies still arguing about whether it is paying off are usually still hedging on whether they trust it enough to fully commit."
-- Elijah Fernandez, CTO of Cerevity, a telehealth therapy platform for high-achieving professionals
Tim Murphy is a site editor and writer for the IT Strategy team at TechTarget.
