A look into Tech’s next revolution and what it might mean for engineering managers
Artificial Intelligence (AI) is becoming increasingly prevalent in the workplace and has the potential to disrupt many roles, including engineering management. Over the past decade, we had seen AI replacing repetitive tasks in all industries. For examples, self-serve checkouts replace human cashiers in the retail industry. In the tech industry, AI is increasingly being used to automate data entry tasks, such as scanning documents, extracting data, and entering it into a database. For many industries, chatbots and virtual assistants are now used to provide customer service support, including answering customer inquiries, processing orders, and resolving common issues.
Therefore, the question that many people are asking now in 2023 with tech layoffs and big tech companies like Facebook and Salesforce announcing the reduction of their front-line engineering managers is that would AI eventually replace engineering managers entirely. In this article, I will explore the potential for AI to replace engineering managers.
The measures of success for engineering managers
“What Do Engineering Managers Do?”, is a very popular question for software engineers and even for engineering managers themselves. You will see thousands of articles, videos and blog posts on this topic, mine included. This goes to show that engineering manager role is complex, diverse and involves a range of responsibilities.
To know what engineering managers are expected to do, it’s important to understand how the success of an engineering manager is measured.
The success of an engineering manager is measured across three dimensions:
- Value shipped to customers. Usually in the form of features that customers can use to help them achieve their goals such as an ability to search for a particular product at an e-commerce store or an ability to track your personal expenses.
- Reliability of the underlying services. Platform reliability is often neglected because the work done on reliability is not easily visible like features. However, it’s extremely important because whatever you are shipping to customers will not reach its full potential if it’s slow, insecure, full of errors, or inaccessible. Reliability includes not just uptime, but also security and performance.
- Team engagement. When a team is high performing, the outcomes that the team produced multiplied. A few indicators of a high-performing team are they work towards a shared goal but are not afraid to challenge each other in order to achieve the shared goal; they enjoy working with each other; they have fun; they collaborate well, and last but not least, they take calrisks and they are always improving themselves.
3 key skills required from engineering managers
Therefore, engineering managers not only need to have foundational software development knowledge, they also need to have project management, people management and team management skills. Most engineering managers in tech companies come from software development background and while they are technical, they tend to lack business acumen and leadership and management skills.
So let’s take a look at how AI can be leveraged to enable and assist engineering managers to perform better in their roles.
Project management
When managing a software project as an engineering manager, my advice has always been to follow a structured approach that involves analyzing requirements and scope of work, collaborating with cross-functional squads, creating a project plan with timeline and staffing, and leading the development team consisting of software engineers at various levels and expertise.
Once the project is in flight, engineering managers should closely monitor progress of the work, ensuring that team members are following the project plan and risks and blockers are addressed in a timely manner. As projects are executed by people, this inevitably requires engineering managers to facilitate regular communication among people; with team members to provide feedback and guidance to keep the project on track and with stakeholders to update them on the project’s progress and to get feedback on the work. It doesn’t matter whether the project is being executed in a waterfall approach or an agile approach, the activities that I mentioned should still happen.
Leveraging AI in project management
By integrating AI-based tools into project management, engineering managers will be able to conduct more comprehensive and sophisticated data analysis around team capacity, staff allocation, work breakdown, work status and help them make more informed decisions about the project. This would have them to optimize resource allocation, identify areas for process improvement, and better predict future trends.In addition, by utilizing AI-based testing tools, they could improve the quality assurance process, identifying and resolving any issues before they became critical. AI-powered testing tools could also help to minimize the risk of human error and improve overall efficiency.
Other administrative related tasks such as scheduling meetings, taking notes, creating a summary of key updates regularly can also be done by AI. AI can facilitate collaboration among team members by providing intelligent chatbots or virtual assistants that can handle basic queries, manage schedules, or help with routine tasks. This can free up more time for team members to focus on high-level delivery tasks and collaborate with each other.
While human expertise and leadership are crucial to the success of software projects, integrating AI-based tools into project management can provide several advantages, including increased efficiency, improved decision-making, and enhanced quality assurance.
People management
Engineering Managers are the coaches and mentors for software engineers. As people managers, they provide clarity, guidance, encouragement, feedback and steer their software engineers in the right direction in their professional lives. This is usually done through having frequent one on one’s, knowing the person’s motivations, understanding what matters to them, creating a personal development plan with S.M.A.R.T (Specific, Measurable, Achievable, Realistic and Time-bound) goals and discussing about them regularly.
Leveraging AI in people management
AI has the potential to revolutionize the career development of software engineers by providing personalized learning experiences that cater to their unique needs and interests. AI-based tools can analyze the skills and knowledge gaps of individual software engineers against the expectation of their roles as well as their professional goals, and then provide targeted learning resources and training programs that are in alignment. This approach ensures that software engineers can acquire the skills and knowledge they need to advance their careers in a more efficient and effective manner.
AI-based tools can also help software engineers stay up-to-date with the latest industry trends and technologies. By analyzing market trends and job postings, AI-based tools can provide recommendations on the most in-demand skills and certifications that software engineers should acquire to enhance their professional skillsets.
Additionally, AI-based tools can assist software engineers in identifying mentors within the company that can help them expand their professional networks and learn from. AI-powered chatbots and virtual assistants can also provide personalized career guidance, answering common questions and providing advice on career progression.
Team management
Team management for engineering managers include providing context, setting goals, managing work and creating the right environment so that team members are able to deliver on business outcomes effectively and efficiently while having fulfilling work relationships.
Leveraging AI in team management
AI has the potential to significantly improve the management of software development teams by helping estimate work limits, automatically creating real-time dashboards to increase visibility, providing data-informed answers to questions from stakeholders and higher ups and making smart suggestions to improve team velocity and engagement.
Besides managing work for the team, through the analysis of team members’ skills and performance data, AI-powered tools can also recommend the most suitable tasks for each team member, ensuring that their skills and expertise are best utilized. This approach can lead to higher productivity and increased job satisfaction for team members.
Moreover, AI-based tools can generate accurate metrics that can be used to track the team’s progress and predict completion dates more effectively. By analyzing historical data and current progress, AI tools provide real-time recommendations to the engineering manager, suggesting ways to adjust the sprint plan to optimize team velocity.
Additionally, AI-based tools can help engineering managers of agile teams identify and address issues in real-time, such as when the team is falling behind on a particular sprint deliverable. By analyzing team members’ workloads and performance data, AI can recommend strategies to optimize work allocation and improve team velocity.
Where AI falls short and how engineering managers add tremendous value
It’s clear that the integration of AI into engineering manager’s responsibilities has the potential to enhance productivity, improve engagement, and drive overall success of the team. With the help of AI-based tools, engineering managers can make data-driven decisions on their key responsibilities such as project management, people management, and team management.
However, it’s important to note that AI is no replacement for humans. At the end of the day, humans are executing the work and these humans need other humans whom they can talk to, especially when they need guidance, inspiration and clarity. The engineering manager role is likely to undergo significant changes in the next five years due to the rapid advancement of technology. Motivating software engineers is an important task for engineering managers to ensure that they remain engaged and productive in their roles.
Remember that software engineers that choose to work for tech companies are highly skilled professionals who value recognition, opportunities for growth, and meaningful work. They are not just there to be a cog in the machine. Otherwise, they could just be freelance developers or contractors. Great engineering managers possess emotional intelligence, which allows them to understand, manage, and express their emotions and empathize with their software engineers. By connecting software engineers on an emotional level, engineering managers are able to motivate, inspire and empower software engineers to achieve their full potential.
As highly skilled professionals, software engineers also value creativity and innovation. While AI can generate ideas and designs, it still lacks the capacity for originality, intuition, and creativity. Human intelligence is essential for developing new and innovative ideas that have not been seen before.
Lastly, there is one critical area that AI is yet to prove itself — ethics and morality. AI can operate based on algorithms and data, but it lacks the ability to make ethical judgments and decisions. This is especially important in the tech industry where gender inequality has been an issue. It is no secret that the tech industry is male-dominated and challenges for women in tech has been persistent throughout their careers. Without humans aka engineering managers advocating for equality and attracting diverse group of employees, tech companies and their offerings will not be able to survive in the market that expects equality and inclusivity. They will also miss out on achieving more significant outcomes and create a better world for all.
Will AI lead the charge and leave engineering managers behind?
While AI has the potential to automate many tasks and processes to help engineering managers in their roles, it is unlikely that it will completely replace engineering managers for at least the next decade or two. So the answer to the question, Will AI lead the charge and leave engineering managers behind?, is No. Engineering managers play a critical role in managing and motivating software engineers. Their strategic and critical thinking are also even more essential than before as the technology landscape is changing at a rapid rate. Such high level of emotional intelligence and human judgment cannot be replicated by AI in foreseeable future. Instead, AI will be used to augment and enhance engineering managers’ abilities, allowing them to make better decisions and manage their teams more effectively.
So instead of worrying, my advice to engineering managers is to understand where they can add the most value to the companies that they work for, the people that they lead and to elevate the success of their teams and companies. The rapid changes in the technology landscape are having a significant impact on the engineering management role and instead of being fearful of AI, they should welcome AI to help them enhance their impact and assist them in their routine tasks. I do not know what will happen in the future, but one thing is for sure, whatever happens: you will never be redundant if you continue to add value to others — your team, your company, your industry and your community.
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