Engineering teams hate the ‘same old song’ situation: being woken up at 3am to fix a toilsome, comparatively minor task, such as remedying a disk space issue. Dissatisfaction rises when skilled professionals are asked to do basic activities that — in a more digitally mature operational environment — could be managed preventatively with automation.
A recent survey into the cost of downtime revealed that more than 70% of IT leaders report that remediation, mobilizing responders, collaboration between teams, and internal communications with stakeholders are yet to be fully automated. Other examples of relatively thankless, yet vital, activities that could be automated include managing manual releases, handling recurrent password resets, dealing with repeated identical alerts or creating users on systems. Any task that an engineer can do easily but is low value and uncreative can be considered toil.
Teams required to do this are at greater risk of burnout. That’s bad for engineer experience and, ultimately, employee churn, corporate productivity and profitability. Individual agency is important, and skilled engineering talent should be able to use all their cognitive ability for a fulfilling working experience.
A more operationally mature organization can ensure that engineering talent works with automation and improves their operational maturity: preventative and proactive, enjoyable and sustainable. This is particularly important for organizations seeking to incorporate AI into their stack. AIOps creates more need for faster responses, and to ensure data integrity, security and compliance at pace. So, without automation, it’s hard to see how teams can ensure the fast throughput and operational flow that enable a platform for stable, reliable AI outputs, let alone the flexibility to innovate as well.
Global Field CTO at PagerDuty.
Automation creates stability for AI
Leaders are increasingly keen on AI as a human-supporting business enabler, but engineers see great value from automation first as a basis for ensuring the business delivers on its operational promises.
For the engineer, automation removes toil, taking the most common, burdensome and low-value tasks out of their remit. It can reduce the need for human oversight over the routine. Automation, often involving AI, becomes an assistant, helping engineers to solve bigger, urgent or strategic problems quicker.
Automation assistance, often itself using machine learning or AI within the codebase, can be used to create rules and runbooks ensuring technology fails gracefully, minimizing the number, extent and duration of incidents. Common, repeatable incidents should be analyzed and automated away, creating a preventive mindset where the engineering team can learn from incidents while also reducing the cognitive load from attending to ‘firefighting’.
Without digital operational maturity safeguarded by automation, iterating AI adoption is unlikely to be a smooth process. Consider that AI solutions seldom meet user requirements in their first form. Being able to react quickly, upgrade, connect and scale is key. Doing this at pace and safeguarding budget means managing the software engineering elements quickly and efficiently. Inevitably, it takes automation to manage an always-on, always-connected and always-learning environment.
Automation is foundational to resilient operations that can take managed risks as they innovate services, based on a stable environment. Neither any future AI or existing staff can have the ability to do smarter things if they don’t have such a mature and reliable IT infrastructure to launch from to guarantee levels of service to internal and external customers.
Managing the most critical asset: Talent
Talent comes to software engineering to build things and create, not to grind and manage break-fix work. Using AI and automation to free people to do the things that keep them engaged, excited, and productive is great for them and the business.
Many innovative and successful organizations share their best practices for engineering success. They do this because it’s an acknowledged truism that engineering and tech talent generally operates differently from other business roles. General advice and best practices on people management only goes so far. Great companies differentiate themselves on their engineering success. If talent is stuck using their expensive, professional skills on toil, there will be a point at which dissatisfaction and burnout arise, and there may not be spare talent to step in should key personnel leave.
Day-to-day experiences for development and operations teams are critical, but managers should consider the longer-term career planning for their folks as well. From where they are now, what skills, training, challenges do they need over the next 2-3 years to solidify their expertise and make them feel valued? Burnout is a foreseeable outgrowth of poor digital maturity and toil. Planning to avoid it from the beginning is key to employee and knowledge retention and therefore on business resilience.
Misaligned business and technology stakeholders are a familiar refrain in almost any organization, particularly larger enterprises. While the analytical and problem-solving mindset of tech talent should solve some of the common causes this stems from, getting the business involved as true partners is often the critical missing piece. Exceptional service delivery has to focus on the lens of the business needs with exceptional communication to all stakeholders (including customers), not simply the technical delivery.
As teams tackle this unified view of service ownership and delivery, solutions such as sharing a common planning framework, laddered goals, a shared vocabulary and cross-functional teams using agile methodologies all offer solutions to universal problems.
When moving from the general to the particular, always keep the employee experience in mind. A major, recurring question should be `how are we empowering people to be the most effective?` How are you going to measure it, and what plans are you going to put in place to build continual improvements? The answer would be a properly planned career with space for them to focus on building, creating, and playing to their strengths. This means they get to play a different song and stay empowered, engaged and productive.
Create a new playlist to keep on dancing
Toil, organizational digital immaturity, and a lack of career direction are killers of engagement for engineering talent. What's more, these factors also fail to set up the organization to achieve their AI goals. We need a mindset shift that spending too much time in break fix and keeping the lights on isn’t acceptable. Those organizations that focus on leveraging automation to remove toil, reduce risk, and drive velocity will find that they will deliver more from their AI investments by playing to the strengths of their engineering team - accelerating delivery, assisting in creativity, and building solutions their customers need.
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