Measuring the Impact of AI on Operations
In speaking with technology leaders and colleagues, including Krissy Basu, I find a common question: "How does AI fit into my business?". A reasonable question, but perhaps better, is, "How can I use AI to accelerate my business outcomes?" and "What do I need to do to prepare for an AI-enabled infrastructure?".
I am sharing a scenario where the impact of AI on operations can be measured, answering the first of the two questions above. In a previous article, I introduced the concept of Focus Factor. In software development, teams often find that a significant portion of their time is not spent directly on new feature development but rather on activities necessary to support and maintain the overall health and efficiency of the product and infrastructure. This concept can be categorized to include:
Defect Remediation (~25% of team capacity) - This involves identifying, diagnosing, and fixing bugs or issues within the software. While not contributing directly to new feature development, defect remediation is crucial for maintaining the quality and reliability of software, indirectly supporting user satisfaction and trust.
Operations and Infrastructure Work (~25% of team capacity) - This category encompasses many tasks, including deployment, monitoring, scaling, and optimizing infrastructure. It also involves ensuring the operational efficiency and reliability of the software. These tasks are essential for the smooth running of the software but do not add new capabilities.
Valuable Backlog (~50% of team capacity) - This work focuses on implementing new features or enhancements explicitly contributing to the product's value proposition. Most stakeholders focus on it, as it directly aligns with new capabilities or improvements visible to the end-users and customers.
With insights into the distribution of team time across these categories, a leader can more accurately predict delivery timelines and better manage stakeholder expectations. Moreover, it highlights the importance of paying attention to the time and resources needed for maintenance and support tasks, which are vital for the long-term success and stability of the software. It also serves as a reminder that increasing the throughput of new features may require strategies to reduce time spent on defects and operations or find more efficient ways to handle those necessary tasks. Here enters the power of AI.
The impact of AI on an organization that recognizes the importance of Focus Factor can be transformative, enhancing efficiency, decision-making, and innovation across various aspects of operations. Here are some ways AI can influence and optimize an organization's focus and resource allocation:
Enhanced Efficiency in Defect Remediation - AI can significantly reduce the time and resources allocated to defect remediation through predictive analytics, automated testing, and issue identification. Machine learning models can predict potential defects before they occur or identify patterns that human analysts might miss.
Optimization of Operations and Infrastructure Work - AI can automate routine operations and infrastructure tasks. By automating these tasks, an organization can reduce the time spent on operations and infrastructure work.
Forecasting and Planning - Organizations that recognize Focus Factor can use AI to improve their forecasting accuracy and planning efficiency. AI models can analyze historical data to predict future performance trends, helping leaders make informed decisions about resource allocation, hiring needs, and deadline setting by understanding the team's capacity.
When a team leverages AI to address the low-hanging fruit in an engineering context—tasks that are relatively easier to automate or optimize—it can significantly free up resources, time, and mental bandwidth. This freed capacity and focus can be redirected to collaboration, innovation, and value delivery. Some examples might include:
Innovation and New Features Development - With routine tasks automated, engineering teams can dedicate more time to designing and developing innovative features that meet emerging market needs or create new markets altogether.
Technical Debt Reduction - Technical debt accumulates when shortcuts are taken to meet short-term objectives, often at the expense of long-term quality and maintainability.
Quality Enhancement - Teams can focus on enhancing product quality through defect remediation and implementing more thorough testing methodologies, such as integration testing, performance testing, and security assessments.
Professional Development - By automating routine tasks, organizations can afford to invest in their team's professional development, offering opportunities for learning new technologies, practices, and skills, leading to enhanced employee satisfaction and enabling a more skilled and versatile workforce.
New Technologies and Practice Exploration - Teams will have the capacity to research and experiment with emerging technologies or innovative development practices. This exploration can uncover new ways to solve existing problems or new product or service development avenues.
Implementing AI has the power to transform Focus Factor within an organization significantly. By adopting and refining the measurement of Focus Factor, leaders can observe AI's influence on team performance, as evidenced by an increased ratio of value-added work, innovation, and quality. Through the lens of the Focus Factor, AI emerges not merely as a tool for task automation but as a strategic ally, reallocating team focuses from routine tasks to areas ripe for innovation and value creation.
