Serge Lucio is the VP and GM of Agile Operations Division, Broadcom Inc.
Generative AI (GenAI) is transforming enterprise operations, driving significant productivity gains and reshaping IT roles. Its impact on value stream management (VSM) will unfold across near-, mid- and long-term cycles, primarily through three key themes derived from GenAI’s evolving capabilities: IT optimization in the near term, the rise of knowledge base innovation in the mid-term and the pursuit of scaling hypothesis validation in the long term.
Although VSM traditionally emphasizes “change” (innovation), AI’s initial impact, particularly GenAI’s capability for IT optimization, will likely focus on the “run” side (sustaining activities). Automating repetitive tasks, streamlining workflows and improving operational efficiency can free up human capital for more strategic initiatives. This “run” side optimization lays the groundwork for a more efficient and agile organization, better equipped to embrace future innovation initiatives, including knowledge base innovation and scaling hypothesis validation.
Realizing AI’s potential requires a strategic approach, balancing short-term gains while preparing for ubiquitous AI. This involves understanding the current AI adoption landscape, adapting capacity planning, leveraging GenAI and building robust knowledge bases.
Current AI Adoption Landscape
The current state of AI in VSM is diverse and dynamic. A 2025 Broadcom survey reveals a complex adoption landscape. Some organizations are fixated on the long-term potential of GenAI, while others are focused on the short-term exploration of how AI can augment existing VSM practices. This variance highlights the need for a balanced approach, acknowledging the hype while focusing on practical applications. Assessing current capabilities, resources and strategic goals is the first step toward a tailored AI strategy for VSM. This assessment should consider existing VSM maturity, data infrastructure and addressable business challenges.
Near-Term Focus: Optimizing The “Run” With VSM Fundamentals
Over the next couple of years, I expect that large enterprises will prioritize IT optimization as a key GenAI application, seeking to augment staff with GenAI to improve productivity. IT staff will be a primary target for these gains as large language models (LLMs) can be easily trained on large volumes of content, from source code and standard operating procedures to project management best practices. Broadening AI adoption and showing its value is key. This means anchoring AI in core VSM principles:
• Strategic Portfolio Management: AI-driven productivity allows shifting investment from sustaining to innovation. Value stream analysis finds high-return AI areas aligned with business goals.
• Investment Management: AI-powered tools provide data-driven insights into project performance, ROI and portfolio health for better investment and resource use.
• Productivity Analysis: Measure AI’s impact with leading indicators (e.g., engagement, feature usage and cycle time), not just lagging ones (revenue), for real-time insights and faster adjustments.
Adapting To The AI Era: Capacity Planning And Skills Transformation
AI inevitably impacts capacity planning and skills. As automation and GenAI advance, agentic AI will likely automate some IT roles. This is a challenge to manage. Demand for some roles may fall as new AI roles (e.g., development, management and oversight) emerge. Organizations must adapt workforce planning and training, investing in upskilling and reskilling to prepare employees for the AI era’s evolving demands. Predictive analytics can anticipate shifts, helping ensure the right resources and skills are available.
Generative AI: Reshaping Software Development And Beyond
GenAI is already disrupting software development and other creative processes via:
• Accelerated R&D: GenAI speeds R&D by answering complex questions, synthesizing information and providing valuable insights, enabling broader exploration, faster innovation and quicker time to market.
• Content Creation: Automating content creation (e.g., documentation, marketing materials and code) frees professionals for more strategic, high-value creative work.
• Enhanced Interactions: GenAI powers intelligent chatbots and assistants, potentially improving customer service, internal communication and user experience with personalized 24/7 support.
Abundant open-source data for training LLMs fuels this disruption. One study found that early adopters of GenAI tools are seeing a 66% increase in employee productivity, and these gains will likely grow as LLMs evolve.
The Long-Term Vision: Continuous Experimentation And The Knowledge Base
VSM is about taking an idea from inception to value delivery and measurement. AI, especially GenAI, accelerates the “build and deploy” phase. As AI writes more software, differentiation shifts from speed to the quality of innovation. Rapid ideation, testing and scaling validated hypotheses become key competitive factors. This emphasizes the importance of the “ideation and prioritization” and “value measurement” stages of the process.
As organizations shift from a project-to-product model and adopt continuous experimentation, investment shifts from traditional software building to functional experts creating training knowledge bases and testing models. New structures, processes and tech emerge, with evolved roles for:
• Product Management: Focus on the “ideation and prioritization,” identifying opportunities, designing experiments, measuring and iterating quickly.
• Functional Expertise: With AI automating tasks, the demand for domain experts grows. They’re vital for creating GenAI training knowledge bases, developing model tests and ensuring accuracy, reliability, ethical AI, safety and compliance for human-centric tasks.
• Data Analysis: This is essential for “value measurement,” designing and interpreting AI experiments, and using data visualization, statistical analysis and machine learning for insights and decisions.
Building, maintaining, securing and ensuring compliance of knowledge bases is key to VSM and needs substantial investment. This, combined with robust measurement and feedback via leading indicators, is crucial for continuous improvement and quality innovation. A comprehensive, secure, accessible, updated knowledge base is vital for long-term AI success.
Conclusion: Embracing The AI-Powered Future Of Value Delivery
VSM and AI present both challenges and transformative opportunities. Although the “build and deploy” phase becomes faster, the need to invest in knowledge bases, training AI agents and measurement remains. By combining these investments with a focus on VSM fundamentals—optimizing IT, fostering knowledge base innovation and scaling hypothesis validation—and a data-driven approach, organizations can unlock AI’s full potential. The future of value delivery is here, powered by AI.
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