Every CIO knows the budgeting cycle can feel like trying to fit the future into a spreadsheet. This year, that challenge has a new twist: artificial intelligence.
By the time 2026 rolls around, many organizations will be moving past pilots. In fact, 71 percent of business leaders now say their companies are regularly using generative AI in at least one function, according to McKinsey. This progression suggests we can no longer treat most AI efforts as experiments.
From my own experience leading IT budgeting discussions, I’ve seen how quickly AI moves from experimentation to expectation. The questions I get are no longer “Should we explore AI?” but “How much should we invest, and where will it pay off?”
That momentum means 2026 must be about shifting the discussion away from what AI costs to what it creates, not just productivity but measurable enterprise value.
That means treating AI the way we treat every other business investment. It may be the most talked-about technology in decades, but the financial principles do not change. CIOs should apply the same discipline, structure and accountability to AI as they do to ERP, cloud or cybersecurity.
The key is to evaluate AI by the same value categories that govern all technology spending and to be explicit about which ones each initiative is meant to impact.
AI value is business value
There is a growing temptation to create special frameworks or funding models for AI, as if it exists in its own category of value. In reality, the fundamentals of measuring technology value have not changed. I’ve learned that the surest way to cut through AI hype is to apply the same investment logic we use for every other initiative.
AI can help companies grow revenue, reduce costs, improve efficiency and manage risk. Those are the same enterprise value levers that have guided technology investment for decades. What is new is the scale, speed and potential reach of those outcomes.
When you classify AI initiatives alongside other IT projects, the best way to cut through hype is to apply the same business value categories you likely already use for investment planning.
This approach echoes EY.ai value accelerator’s findings that while cost improvement remains a primary driver, AI also enables organizations to improve decision-making processes, unlock new revenue streams and raise their employer brand value.
Every initiative CIO’s fund should clearly link to one or more of the following value areas:
- Revenue growth: Driving sales or margin improvement through smarter pricing, personalization or customer engagement.
- Efficiency and cost reduction: Automating manual work, streamlining operations or improving decision accuracy to save time and money.
- Asset utilization: Using predictive analytics to improve equipment uptime, optimize supply chains or reduce working capital.
- Risk mitigation and compliance: Strengthening controls, monitoring anomalies and anticipating regulatory or security threats.
These categories are not unique to AI. They are universal business drivers. The difference is that AI can now influence multiple categories at once, sometimes in unexpected ways. For example, an AI-driven forecasting tool can improve revenue accuracy, reduce inventory and lower logistics costs.
By framing AI value through the same lens as every other investment, CIOs avoid isolating it as an experimental spend and keep it grounded in financial and operational outcomes.
Budget for scaling, not experimenting
Most organizations have proven that AI can generate insights, automate workflows and improve customer or employee experiences. The question now is not whether AI works, but how to scale it responsibly and efficiently.
That is where budgeting discipline matters most. Scaling AI requires investment across multiple layers such as data readiness, model governance, change management and cybersecurity. These investments are not unique to AI; they are foundational for any digital capability.
When preparing 2026 budgets, I find it helpful to classify AI investments in three ways:
- Embedded AI: Capabilities built into enterprise platforms such as ERP, CRM and collaboration tools. This is where most value will come from over the next 12 to 18 months. CIOs should focus on adoption and productivity, not custom development.
- Differentiating AI: Projects where AI is the primary driver of competitive advantage, such as predictive maintenance or intelligent pricing. These require clear business cases, defined success metrics and disciplined governance.
- Foundational investments: The shared enablers that support all digital initiatives, including data quality, governance and infrastructure. Without these, even the best AI ideas cannot scale.
This structure is not unique to AI budgeting. It is the same run, grow, transform model CIOs have used for years. The principle is to make AI a visible part of that model, not a special category floating outside of it.
Gartner now forecasts that global AI spending will exceed $2 trillion by 2026, reflecting the extent to which AI will be embedded in every system. At that scale, AI investments can no longer float outside normal financial discipline. CIOs must evaluate them through the same investment lens as every other major initiative.
Measure value with existing levers
The hardest part of budgeting for AI is not predicting cost but defining value. I often remind my team that we don’t need a new ROI model just because the tool is smarter. Traditional metrics: productivity, quality, revenue influence, risk reduction and adoption still apply. The key is applying them with consistency and discipline.
Here are five value lenses that can help frame AI’s contribution in familiar business terms:
- Productivity: Quantify time saved or throughput gained and convert that into cost avoidance or redeployed capacity.
- Quality: Measure accuracy improvements, defect reductions or decision consistency and translate those into efficiency or margin gains.
- Revenue influence: Track how AI-enabled capabilities, such as personalization or recommendations, contribute to sales or pricing improvement.
- Risk reduction: Estimate avoided losses, penalties or downtime through earlier detection or prediction.
- Adoption and engagement: Measure utilization of AI-enabled tools and correlate that with performance outcomes.
None of these metrics are new. They are the same levers CIOs already use to justify investment in technology. The difference is that AI often touches several at once.
For example, an AI assistant that saves employees time also improves accuracy and customer response speed. By mapping benefits to existing categories, CIOs can evaluate impact with credibility and consistency rather than reinventing the measurement process.
A more balanced conversation with the CFO
When CIOs treat AI like every other investment, the budget conversation with finance becomes simpler and more productive. It shifts from defending novelty to discussing value.
CFOs already understand frameworks for ROI, payback period and NPV. The role of the CIO is to translate AI initiatives into those same financial terms. That means presenting clear business cases, defining measurable outcomes and establishing stage gates to continue, pause or stop funding based on results.
This approach also aligns AI with broader enterprise priorities. If a company is focused on margin improvement, then AI investments should focus on automation or yield. If the goal is growth, they should focus on customer engagement or predictive selling.
Treating AI with this level of fiscal discipline sends an important message. It says that technology is not chasing trends; it is advancing strategy.
Final thoughts: AI is not a special budget category
After several years of AI exploration, my biggest takeaway is that the fundamentals haven’t changed. AI doesn’t replace business discipline; it rewards it.
The best CIOs will resist the urge to treat AI as something separate from their core investment framework. Instead, they’ll apply the same standards, the same categories of value and the same rigor that have guided technology decisions for years.
AI is not a line item to indulge; it is a capability to manage. It should earn its place in the budget the same way every other investment does, by proving it creates measurable business value.
That’s how I believe we move from pilot to profitability, and from curiosity to accountability.
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