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In brief
- Enterprise AI coding spend crossed $2,068 per employee this year, and three major vendors changed their pricing in one quarter.
- AI costs are easier to measure, but business value is difficult to prove.
- A real cost-per-outcome framework needs a named owner, a defined business outcome, and a reporting cadence that reaches finance.
Meet the Interviewee
Arkadiusz Drohomirecki
Lead, Agentic AI R&D cluster
Coordinates multiple teams working on internal intellectual property development and customer-facing projects. Overseeing initiatives spanning activation pods, GenAI accelerators, academic research, and agentic AI solutions across different domains. Responsible for aligning R&D efforts with both internal strategic goals and customer engagement needs, ensuring the delivery of high-quality agentic AI capabilities.
Grzegorz Biziel
Senior R&D Engineer
Specializes in AI-driven software systems, modern SDLC practices, and applied machine learning. With a background spanning enterprise software, IoT platforms, robotics, computer vision, and most recently agentic development. He has extensive experience with deep learning, multimodal AI, and production-grade ML systems using technologies such as PyTorch, TensorFlow, OpenCV, Vision Transformers, SAM-based architectures, and Generative AI pipelines.
Inna Abolikhina
Agentic AI Expert
Leads agentic engineering engagements and enablement programs, helping organizations move from AI experimentation to adoption across engineering and product teams. She works with technology leaders to assess readiness, define adoption roadmaps, and align AI initiatives with business priorities. She also leads SoftServe’s agentic engineering stream for private equity, helping firms build reusable capabilities and scale adoption across portfolio companies.
Measuring agentic AI ROI means tracking spend and business outcome together, not separately, using a named owner, a defined outcome, and a reporting cadence that reaches finance.
Anthropic just cut its newest model's cached-token pricing by 75%, days after reports that one major enterprise customer used up its annual AI budget early and had to start monitoring usage closely. GitHub Copilot, Cursor, and Anthropic restructured pricing inside the same quarter, and enterprise budgets still reference a seat-license number that does not reflect real spend. A lower bill is different from a good outcome.
Is this disruptive, or overhyped?
Real, and smaller than advertised. Vendor claims point to order-of-magnitude gains. DORA's own research puts the true figure at 2% to 18%, a meaningful lift.
What do companies overlook?
Enterprise AI spend crossed into serious money this year, rising 50% to roughly $2,000 per employee. Model costs fell 280-fold, and people assume that means the cost problem is solved. Agentic workflows read files, plan changes, and iterate through rounds of edits, a pattern that draws on tokens unlike the simple exchange behind that figure.
What the falling price tag hides shows up in the code: GitClear's research spans more than 600 million commits. Churn has nearly doubled since AI adoption began. Copy-pasted duplicate code has climbed 81%, the highest level on record.
A cost-per-outcome metric does not exist inside organizations we work with. Counting pull requests merged against dollars spent resembles judging a factory by parts produced, without checking whether a single part works once it leaves the line. This gap echoes the cloud cost problem of 2014.
How should companies adapt?
A peer-reviewed Microsoft Research study found adopters merged roughly 24% more pull requests. What separates the leaders is five disciplined habits:
- Match model tier to task value
- Treat context as a cost lever
- Document structure beneath a single config file
- Scope shared skills to the problem they solve
- Separate specification from execution
What's the highest-value opportunity, and what's the obstacle?
The opportunity is a real cost-per-outcome framework built from three parts:
- Full loaded spend
- The relevant business result
- A quality check against each team's own baseline
The obstacle: Cost-per-outcome framework requires a named owner, a defined business outcome, and a finance reporting cadence.
Feature shipped, a defect caught early, and a cycle that got shorter don't show up on an invoice. Someone has to link that work to what it costs.
How engineering and finance leaders should design a measurement layer:
Q&A
What is FinOps for AI coding?
FinOps applies the same discipline used to control cloud spend, shared visibility across engineering and finance, a named owner, a regular reporting cadence, to AI coding costs specifically, replacing guesswork with a real, measured budget line.
How does agentic AI change enterprise software costs?
Agentic AI tools read files, plan changes, and iterate through several rounds of edits, consuming tokens in a pattern that doesn't match older, simpler AI interactions. That shift is why total spend keeps rising even as per-token prices fall.
Why did GitHub Copilot, Cursor, and Anthropic change their pricing in 2026?
All three moved away from flat, predictable seat pricing toward usage-based billing tied to actual token consumption, a shift that better reflects how much AI agents really cost to run, but makes monthly spend far harder to predict in advance.
What is a cost-per-outcome framework?
It ties AI spend to a real business result, not a proxy. A feature shipped or a defect caught early counts. Tasks completed or hours saved don't. SoftServe builds this framework directly into how it partners with engineering teams.
Why do AI coding costs keep rising even as token prices fall?
AI coding agents consume tokens differently than a single chat exchange. An agent reads a file, plans a change, edits code, runs tests, and repeats, each step adds cost. That's why total spend keeps climbing even as the price per token keeps falling: agents use far more tokens per task than a single question and answer ever did.
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