Introduction
AI Is Now an Enterprise Resource. So Why Are We Still Managing It Like Software? Across industries, a quiet operational mismatch has taken root in corporate finance and IT departments. Most companies track artificial intelligence as a standard software line item, even though AI is increasingly doing the actual work rather than merely supporting human workflows. This disconnect creates severe blind spots in corporate budgeting, cost accounting, and strategic oversight.
According to research by Irina Shymko of Langate EMEA, published on October 4th, 2026, and referencing data from Gartner and McKinsey, treating intelligent systems like traditional applications no longer works. Traditional software governance was built for stable objects. In contrast, AI behavior shifts constantly with underlying models, prompt updates, training data, runtime contexts, connected tools, and surrounding workflows. As enterprises scale their deployments, this outdated management approach threatens to derail budgets and obscure true return on investment.
What Changed: The Shift from Software to Enterprise Resource
For decades, enterprise software followed a predictable governance model. Organizations purchased licenses or subscriptions, deployed stable code bases to servers or user devices, and anticipated minor variations in resource utilization. Software costs scaled predictably based on seat counts or core server capacity.
Artificial intelligence has completely disrupted this dynamic. AI systems do not just sit passively on a desktop or server waiting for human input; they actively execute business logic, synthesize data, and make autonomous decisions. AI adds an entirely new layer to operational cost: decision-making capacity. Yet, corporate accounting departments routinely categorize these dynamic deployments under legacy software budgets.
This operational shift coincides with massive financial scaling. Gartner expects global AI spending to reach $2.59 trillion in 2026, representing a 47% increase year over year. Despite this staggering capital allocation, the mechanisms used to monitor, evaluate, and control these expenditures remain rooted in a bygone era of static IT management.
Business Implications: High Spend, Low Attribution
The financial disconnect between enterprise AI spending and measurable business return is starkly illustrated by recent data. McKinsey’s State of AI 2025 report found that 88% of organizations use AI in at least one business function. However, only 39% of those organizations can attribute any EBIT (Earnings Before Interest and Taxes) impact to their deployments. Furthermore, for the majority of companies that do see a positive impact, that financial contribution amounts to under 5% of EBIT.
This data reveals a critical flaw in how enterprises evaluate AI success. Billions of dollars flow into technology initiatives without clear linkages to bottom-line profitability. Because organizations treat AI like software—tracking licenses rather than output-driven value—they struggle to isolate where value is generated and where capital is wasted.
Additional budgetary pressures loom on the horizon. Gartner predicts that through 2028, at least half of generative AI projects will overrun their budgets due to poor architectural choices and a distinct lack of operational know-how.
Technical and Economic Realities of Inference and Agents
Understanding why traditional software management fails requires examining the underlying unit economics of AI. Unlike traditional software, where running an additional calculation has near-zero marginal cost, AI relies heavily on inference operations that consume varying amounts of computational power and tokens.
There is a persistent belief that technological deflation will automatically solve enterprise AI cost overruns. Gartner forecasts that by 2030, running inference on a trillion-parameter model will cost providers over 90% less than it did in 2025. However, cheaper token unit prices will not automatically save companies money if agentic workflow token consumption increases at a faster pace.
The shift toward autonomous agents introduces severe cost multipliers. Gartner expects the inference cost per agentic workflow to more than quintuple by 2028. This surge happens because agents use roughly 5 to 30 times more tokens per task than a standard conversational chatbot. As agents iterate, execute sub-tasks, and query external tools, their token consumption skyrockets.
This dynamic creates unexpected economic tipping points. Gartner predicts that by 2030, the generative AI cost per resolution in customer service will exceed $3. That figure is higher than the operating cost of many offshore human agents, undermining the foundational cost-reduction thesis that many enterprises use to justify customer support automation.
Limitations and Uncertainties
While the broader macro trends are clear, significant uncertainties remain for individual enterprises. It remains unclear whether specific organizations can accurately list every active AI workflow or know their precise cost per successful outcome without implementing entirely new tracking mechanisms.
Furthermore, attribution models are notoriously difficult to standardize when AI assists human workers rather than replacing them entirely. Without granular visibility into token usage, task complexity, and workflow dependencies, corporate leadership groups are often left operating in the dark.
A Proposed Five-Part Governance Model
To bridge the gap between legacy software management and modern AI reality, a fundamental restructuring of enterprise oversight is required. Irina Shymko proposes a comprehensive five-part governance model tailored specifically to artificial intelligence as an enterprise resource:
- Visibility: Establishing end-to-end tracking of all AI models, prompts, data pipelines, and connected tools across every department.
- Attribution: Linking AI expenditures directly to operational outcomes and EBIT impact rather than treating costs as generic IT overhead.
- Optimization: Continuously balancing model performance against inference costs, particularly as agentic workflows increase token consumption.
- Accountability: Assigning clear operational ownership for AI behaviors, model drift, and budget adherence to business unit leaders rather than just IT departments.
- Continuously Adapting: Maintaining resilient operational frameworks that can accommodate rapid shifts in underlying model capabilities and pricing structures.
What to Watch Next
As global AI spending marches toward the projected $2.59 trillion milestone in 2026, corporate boards and finance chiefs will face mounting pressure to demonstrate concrete returns. Key milestones to monitor include how enterprises adjust their budgeting software to track token consumption, whether agentic workflow architectures are optimized to counteract rising token multipliers, and if regulatory pressures force greater transparency into AI operational costs. Organizations that transition away from software-style management toward resource-centric governance will be best positioned to capture sustainable value.