The race for “Everything as AI-led” has since beginning of 2025, made AI-led SDLC adoption an intense need. Leveraging Gen AI and Agentic platforms and frameworks for the majority of erstwhile human-led SDLC phases has become a key solution architecture mandate.

Technology build, or platform implementation, programs are now expected to show a 40-50% cost reduction. Previously, this cost reduction was expected out of multi-year MSP operations, IT outsourcing, and other Cost-take out initiatives. The ramifications of such a market expectation, includes but not limited to significant impacts on financial forecasts and revenues of technology providers, staffing firms, and job market angst. However, it is apparently bringing in cost savings to the enterprise CEO, who are supposedly using these funds for cyclic enterprise transformation and higher invested capital (IC). This ideally somewhat should mitigate the job market scare.

Switching to the prime topic – this AI-led SDLC adoption if driven by visceral thinking and stochastic analysis, it could erode the long-term value and diminish purposeful business outcomes. I share this mind share from my experience, and for the broader use of the community of technology leaders, CIOs, CTOs, and Heads of Engineering, to ensure that they have built the moat for AI-led SDLC adoption.

The real productivity and economic gains stems from 2 levers:

• Using Gen AI to assist artifact creation across design, architecture, lines of code, test scripts, test cases, deployment scripts, release and backout plans, operation SOPs etc.

• Implementing Agentic process that orchestrates and integrates individual SDLC phases across the life cycle:

For example a traditional requirements analysis phase would be driven by agent that analyzes user stories in JIRA, that analyzes customer feedback over email, that decides input streams, data variables for most relevant output, raises notification or escalation to human queue, that triggers Gen AI API to generate all necessary artifacts and other contents, that aligns artifacts with outcomes and definition of done, integrates with repository, triggers emails and pushes status and triggers report generation!

This repeats across individual steps of design, coding, testing, CI/CD, deployment, monitoring and operations.

The challenge is how do we measure real productivity gains and ROI?

Typically, out of $ X savings, I have seen Gen-AI contribute to 30-45% and end to end Agentic Processes to the remaining 55-70 % of the gains, if the SDLC lifecycle is unpacked, integrated and automated with high precision.

Maximizing ROI is not easy. I have seen, measuring ROI is even more difficult as it requires considering critical factors (some of them laid below) commensurate to the enterprise in context, to ensure ROI exceeds ROIC on your AI-led investment and generates positive cashflow.

Just measuring SDLC labor efficiencies is not accurate and scalable. Enterprises need to correlate below considerations to each other and the financial model to measure the productivity gains and calculate the ROI:

ROI / ROIC Considerations	Measurement strategy
Right allocation of as-is SDLC activities between Gen AI / Agentic / human	Compare “Turnaround time” and “Talent chain”. When measuring talent measure junior / senior, niche / generalist, shoring considerations shifting because of AI-led SDLC etc
Span of tools / technology / licenses / consumption cost	Choice of AI led tools and frameworks (given at the end), infra consumption cost, contractual terms, compatibilities and licensing.
	Net new cost needed to enable all surrounding systems to integrate with Gen AI / Agent enabled services and their consumption cost.
% of adoption after AI-led frameworks have been implemented.	Tracking direct usage metrics by different SDLC personas # of users aligning with AI suggestions # of features enabled and adopted Conversion of such learnings to the rest of the team etc.
SDLC productivity metrics	Cycle time reduction First time commit right Increase in the number of backlog items Increase in the number of pull request Reduction in rework/code churn etc.
Fit for purpose validation	Human time and other tools needed to validate the outcome and certify fit for purpose.
	Human time and other tools needed to debug complex defects compare to pre-AI days.
	Human time and other tools needed to ensure audit, compliance and info-sec clearance etc.
Measuring cost of quality	Complexity and type of defects getting identified later in the phases and effort taken to get to the root cause. % of AI-led self-heal. Ideally a 20% right shift diminishes AI adoption value by 30%.
AI risk measurement	Increase in the number of bugs per lines of code. Increase in code coverage. Increase in common vulnerabilities within AI-generated code. Increase in compliance and audit findings. Increase in high impact, critical defects etc.
Ummet business goals	Impacts to immediate impact to top-line or bottom-line before systems can be triaged and remediated due to AI-led design, workflows and deployment etc.
Disequilibrium in addressable market	Impacts to Customer acquisition cost, Customer lifetime Value while identifying sales and servicing portal / mobile app gaps due to AI-led design, workflows and deployment etc.
Other direct impacts	Impacts to Revenue density (Revenue per Employee), Developer and other user personal Satisfaction with AI tools, Cost per # of tokens / GPU hour, # of minor and major releases per year etc.

Also sometimes anecdotal inputs from surveys, baselining similar industry information, expert feedback are useful to muster investment approvals through compelling business case and showing top-down alignment on AI-driven initiative.

Having all measurement considerations solidified does not mean we have mitigated barriers to adoption. I advise barriers to be noted and continually mitigated contextually to ensure economic gains are sustainable:

• Model hallucination or bias leading to low trust in output / quality and more human scrutiny
• Lack of trust in ensuring that all company policies, infosec guidelines, other regulatory, legal and IP guardrails, cybersecurity practices have been factored in
• Lack of Right TPT– right Talent in right Place at the Right time
• Too many new aged tools with high stake claims and low proven maturity at industrial scale
• Vendor lock-in, tools and product internal dependency unknowns, licensing and contractual volatilities
• Lack of buy in from different units of the enterprise plagued by insecurity, cultural resistance, lack of user enablement and right change management
• Lack of enterprise awareness of approved AI tools, use cases & policies
• Organizational impediments in transparently measuring and considering all levers of ROI or strategic alignment
• Lack of integrated organizational roadmap for enterprise level AI initiatives trickling down to portfolio and project level AI adoption – like a SAFE-led-AI-model
• Fear of cost of infra consumption and licensing cost – overuse of GPU / tokens
• Lack of existing enterprise architecture isolating multiple legacy toolchain & systems from being able to adapt to AI-led new aged system
• Lack of governance & responsible AI-controls

I also caution that to scale AI-led SDLC gains we do not need to impact our workforce hastily. Enterprises are advised to take context aware decisions based on the enterprise maturity continuum, growth projections etc:

• Pilot some departments or low-risk application stacks with no staffing or monetization alterations
• Once stable efficiency and ROI are getting realized at steady state, take variant of calls, based on where your enterprise lay:
  • Workforce optimization, if no or shrinking business growth
  • Cut back on future hiring or current staffing while keeping delivery targets unchanged, if some growth expected in similar domain
  • Risk mitigated and talent aware smaller hiring plans and / or redeploying with up-skilling / cross-skilling freed capacity to higher‑value work, if some growth expected but in new domain

CIO/CFOs are advised to build AI competencies with a mix of OPEX or CAPEX model and do not be gung-ho on new hiring, old retiring, complete outsourcing etc and resort to a strategy that best matches the enterprise long term strategy and stakeholder value. My average take is:

• 25-40 % new hiring around AI platform / IDP / GenAI (model / LLM / fine-tuning) expertise – again based on the enterprise growth curve.
• 60-75 % up skilling and cross-skilling Engaging across all SDLC personals
• Adjusting senior-to-junior developer ratio, adjusting niche / generalist technology ration, adjusting business vs technical proximal resourcing
• Considering AI platform or framework carefully to see how much can be leverage out of the box vs how much falls on internal IT responsibility for integration, workflow mining etc.
• Careful selection and reliance on AI-led outsourcing partner

Before we finish this topic, I have shared some example AI platform or framework options that CIOs / Head of Engineering can choose to drive AI-led SDLC operations based on their current state:

Platforms	Recommended for
Traditional platforms extended with AI agents
GitHub Copilot Agents Microsoft Fabric Agents GitLab Duo Agent Platform Google Vertex AI Agent Builder Amazon Q Developer Pro AWS Bedrock Agents ServiceNow “Now Assist for DevOps”	Already compatible existing ecosystem Familiarity with existing talent and process Low incremental platform changes Low incremental cost
SDLC integrated AI agents
Cognition AI Cursor IDE Dev Agent Atlassian Rovo Dev Agents Replit Dev Agent PagerDuty Incident-Response AI Agent Copilot’s DevOps mode Zencoder Zen Agents	Green field implementation or refactoring Diverse requirement source Complex code structure, refactoring, or context-heavy tasks, High incremental cost
Minimalist AI-native IDE
Lovable Windsurf Replit ghostwriter OpenAI Codex	Green field implementation Faster stand-up of simple apps Low integration or design complexity Low incremental cost
Open-source agents
OpenDevin LangChain Agents Auto-GPT Proprietary built on Meta’s Llama, Mistral	Limited support to SDLC team Possess open source infosec policy related challenges No cost

In summary, to put all above in practice – first identify your enterprise current state and near-term, medium-term, long-term technology and business objectives. Next go to identifying your AI-led tool strategy, architecture and design to integrated it in a all or some approach to the enterprise. Next identify all correlated business, engineering and talent metrics will be impacted after this deployment. Extract all the metrics and translate them into $ terms to aggregate the impact to Invested Capital. Calculate total Cost to roll AI-led operations and finally measure to ensure that your ROI exceeds ROIC sustainably.

Note: All the above are industry experience based, or in playing a Fractional CTO and Expert advisor role to enterprises and research teams, and bears no investment or technology selection bias. Happy to chat for your specific needs !!