In many organizations, the Software Development Life Cycle (SDLC) is just one of many layered, handoff‑driven processes. Finance, HR, procurement, customer service and other business functions all operate as “stacks” of sequential steps, often owned by different teams. Each layer adds coordination overhead, delays, and opportunities for misalignment.
The same forces that can collapse the SDLC stack (agentic AI orchestrating multiple phases in a single, continuous flow) can also collapse business process stacks. Imagine AI not only generating code and tests, but also triggering procurement approvals, updating HR systems or reconciling financial entries as part of the same workflow. The principle is the same in both SDLC and business process stack collapses: fewer silos, faster cycles and humans focused on higher‑value work. These are the calculations to help map those efficiency gains within your organization.
From Traditional to AI-Assisted Workflows
In a traditional SDLC, total labor can be expressed as:
Ltrad = Treq + Tdev + Ttest + Tops + Tmaint + Tcoord
Where each ‘T’ represents the time spent on requirements, development, testing, operations, maintenance, and coordination.
In an AI‑assisted, collapsed stack, the equation changes:
LAI = TAI + Toversight + Tcoord,AI + Tmaint,AI + Terror + Tintegr
Here we add real‑world factors:
- TAI = AI execution time across multiple phases
- Toversight = human supervision and decision‑making
- Tcoord,AI = residual coordination
- Tmaint,AI = maintaining and retraining AI systems
- Terror = correcting AI mistakes
- Tintegr = integrating AI into existing systems
The labor savings post AI assistance is:
ΔL = Ltrad − LAI
And the percentage reduction is:
%ΔL = (Ltrad − LAI) / Ltrad * 100%
Avoiding Over-Optimism
Automation gains aren’t limitless. As AI takes on more tasks, the remaining work often becomes harder and more context‑dependent. To reflect this, we apply a diminishing returns factor: d (0 < d ≤ 1):
TeffectiveAI = d * TAI
This keeps labor projections realistic, especially at high automation levels.
Finding the Tipping Points
Tipping points, in regards to Agentic AI collapsing business process stacks, is when AI reliably handles enough workload to justify structural changes (such as consolidating roles or teams) without harming quality or resilience. In layman terms, below are the calculations to help answer the question, “When will AI take my job?”
If a team has N members and AI covers a fraction (f) of the workload:
Nominal coverage = f ⋅ N
A naïve threshold for reducing one role is f ≥ (1 / N) , but in practice, leaders need to add buffers for uneven distribution, stability, and skills coverage:
fsafe ≥ (1/N) * βdist * βstab * βcov
Only when f exceeds the fsafe threshold (and quality metrics hold steady) organizational changes should be considered.
Why This Matters
When agentic AI implementations collapse the SDLC and subsequent business process stacks, isn’t just a technical shift; it’s an enterprise operating model shift. The same orchestration logic that streamlines the SDLC can streamline finance approvals, HR onboarding, supply chain coordination, and customer service resolution.
Done right, it can:
- Increase throughput, without sacrificing quality
- Flatten silos, and speed decision‑making
- Elevate human work, toward strategy, creativity, and ethics
- Enable resilience, by keeping humans in the loop for high‑risk decisions
The formulas above aren’t crystal balls, they’re planning tools. They help leaders quantify potential gains, factor in hidden costs, and decide when (or if) to realign any work unit from a team to the entire organization.
Bottom Line
The future of work isn’t AI replacing humans. It’s AI and humans working together in streamlined, collapsed stacks that span both technical and business processes. The organizations that measure carefully, move deliberately, and invest in people will be the ones to turn this vision into a sustainable advantage. Governments can apply these labor‑impact concepts and formulas at an industry level to quantify AI‑driven workload shifts, identify sectors approaching automation tipping points, and proactively design retraining, redeployment, and policy interventions to prevent large‑scale layoffs.
Feel free to reach out if you have any questions.
Matthew Kilbane is a seasoned leader with expertise in AI Governance, technology business management, and IT program leadership. With a decorated 20-year career in the U.S. Army, extensive service with the Department of Homeland Security, and experience in Fortune Global 250 companies, he excels at building high-performing teams and driving innovation. Holding an M.B.A., advanced certifications, and a background in cutting-edge AI technologies, Matthew brings a passion for problem-solving and advancing technology for positive public and private sector impact.
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