From Intent to Action: 9 Critical Factors that Improve Automation Value

Ever been in a meeting where automation was called out as the silver bullet to all problems followed by unanimous nods across the room? Automation sounds simple but stands as an umbrella term bringing many complex factors into consideration. Not realizing these factors leads to an initiative that lacks acceptable returns or worse, creates engineering debt.

Before we look at these factors, let’s look into the background. Automation comes from the Greek word automatos meaning “acting on its own”, but when does anything ever act on its own? It begins with the intention to act in response to an event. This event forms the genesis of an action or a sequence of actions that resolve the intention. Some of these actions can run into exceptional scenarios and might need to be checked up on. Some of these exceptions might call for manual intervention. Some of these manual intervention if recurring, might need further automation. If there is doubt about the accuracy of the actions taken, a trace of the reasoning is needed to answer the how, what, when, and why’s. Analysis of which, might help decide further evolution in this automation.

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Once we consider these factors in what we attempt to bring into being, we can support the lifecycle of a healthy and maintainable automation.


Acceptance of every initiative boils down to the value derived against the cost. It’s important to identify value and weigh it against the expenditure required for automation. Now, forecasting these benefits isn’t always easy. Sometimes, impossible. In such a case, careful observation of demand patterns can help base your decisions.

Prioritizing investment in the most valuable areas creates opportunity for quick validation. A drop in value against the cost indicates a need for improvement in the automation strategy. At the same time, a surge in demand (even at a later time) can signal an opportunity to expand the scope of automation.

Capitalizing on success while minimizing risks and resource allocation is the key. A phased approach to automation, influenced by changing value-cost ratios helps justify investments. In a finance department, automation could be applied to invoice processing. The value is derived from reduced manual data entry, fewer errors, and expedited payment cycles. The cost encompasses the development of automated workflows, integration with accounting software, and ongoing monitoring. The value can be gauged by tracking metrics such as processing time per invoice, error rates, and cash flow acceleration. If the value of faster payments and increased accuracy justifies the expenses associated with automation, the initiative becomes a wise investment.


Risks exist in forms other than cost. They extend to legal, ethical, and technical and relate to organizational domains. Along with technical feasibility, the rules and policies that apply to your industry and region are just as important. The risk is damage to reputation, legal compliance, and customer trust which outweighs any productivity gains.

Once you’ve assessed both cost and risk, the path forward becomes clearer. By prioritizing the reduction of risk, you gain clarity to build against a well-defined scope. This also ensures there isn’t a compromise on legal, ethical, or other organizational considerations.

Consider a scenario where a company aims to automate its CRM system to enhance customer interactions. While automating customer data management might streamline operations, there’s a risk of violating data privacy regulations like GDPR. If the automated solution inadvertently exposes sensitive customer information, the repercussions could be severe, including fines and reputational damage. In this case, the risk of non-compliance with data privacy regulations outweighs the potential gains from automation. The organization might need to descope certain functionalities or implement stringent security measures to ensure compliance and safeguard customer data.


With the intention to act and the scope for action established, a trigger for action must also be decided. This is a balancing act and requires a deep understanding of the process, user behavior, and business goals. An inappropriately placed trigger can introduce bottlenecks or add excessive complexity. This greatly diminishes the value of the automation effort. Moreover, a trigger too aggressively placed might lead to significant development efforts. Eventually reducing the perceived value against the building cost.

It is important to consider the technical aspects. But that’s not all it takes to set up a perfect trigger. A keen awareness of user expectations and behavior is also necessary. Focus on keeping manual intervention and build effort low is key to ensuring maximized value.

In the IT domain, incident management automation intends to swiftly address and resolve system issues. The trigger for this automation should be real-time monitoring systems that detect anomalies or system failures. If the trigger is set to manual notification by end-users, critical incidents may go unaddressed until reported, resulting in downtime and customer dissatisfaction. Choosing the right trigger, such as automated monitoring tools, ensures that the system proactively identifies and addresses issues, minimizing downtime and maintaining service reliability.

Exceptions & Failures

Automation that runs complex, interdependent tasks is more likely to run into errors. Many of these errors will occur due to exceptional scenarios that need human attention. By identifying these scenarios ahead of time, you can create a comprehensive SOP. This prepares your maintenance team to address exceptions effectively. A well-prepared SOP can streamline manual interventions, ensure consistency, and reduce downtime. The SOP can also provide instructions for handling undocumented exceptions such as an escalation matrix. This maintains operational continuity even in unfamiliar situations.

It is also important to be able to restore automation after recovery. If a system failure causes an operation to halt, being able to resume the same operation after a fix is applied, minimizes business impact. Handling exceptional scenarios like these reinforces the credibility and value of automation.

In a hospital’s patient records automation, there might be instances where the system encounters incomplete or conflicting patient information. These exceptions can’t be resolved automatically and might necessitate human review. The SOP could guide the maintenance team on how to reconcile conflicting data, verify patient information, and ensure data accuracy. Additionally, intermittent errors like network interruptions might disrupt the automation temporarily. The ability to resume automation seamlessly after resolving the error prevents service disruptions and maintains workflow efficiency.


It is common to run into situations where automation requires human attention. Could be for review of actions taken, or addressing errors that halt execution. Notification mechanisms in the form of emails, text messages, phone calls, or even automated alerts to external systems (such as webhooks) can be implemented. These notifications add value by reducing response and resolution time.

Moreover, mechanisms like webhooks and APIs create a dynamic ecosystem. Allowing new automation to interact with existing ones. This approach ensures that your automation efforts remain flexible, adaptive, and capable of accommodating future enhancements.

In essence, notifications connect automation’s technical efficiency with human decision-making. By implementing effective notification strategies, you enhance the impact of your automation. This aspect can be better understood through a concrete example.

For an automated financial transaction system, imagine an instance where a high-value transaction triggers multiple security alerts. The automation could generate an urgent phone call to the designated security analyst, highlighting the potential risk and prompting an immediate review. Simultaneously, if the system detects a failed transaction due to technical glitches, it might send an email notification to the IT support team, ensuring a swift resolution.

Skill to Operate

Some automation implementations need an operator. It could be to trigger the automation or assist the automation during operation. Users needing effort to develop proficiency to operate can discourage adoption. Scaling a team of operators is also difficult if the necessary skill levels are too high.

Striving to keep required skills as low as possible enhances the value generated by automation by improving impact. You can achieve this by offering user-friendly interfaces, comprehensive training, and clear documentation. This can ensure that automation doesn’t become a barrier for users with varying levels of expertise.

Imagine an analytics team using an automated script to generate monthly reports from a complex dataset. In situations where manual intervention is needed, such as fine-tuning parameters or choosing specific subsets of data, the level of technical expertise required can impact the automation’s value. To mitigate this, the team provides comprehensive training sessions and detailed documentation on script usage. Additionally, they create an intuitive web interface that simplifies parameter selection, transforming a potentially daunting task into a user-friendly experience. By ensuring that user intervention demands minimal technical skill, the automation’s value is magnified as more team members can confidently contribute to insights generation.


Automation consumes resources. A reality that becomes particularly obvious when it comes to scalability. An increase in demand can push automation to its limits. When automation can’t scale gracefully, it risks degrading performance, causing frustration, and reducing the value it’s intended to provide.

To ensure that value remains high, it’s essential to align resource allocation with demand as closely as possible. This involves leveraging serverless platforms that automatically allocate resources based on real-time needs. Alternatively, using queue-based systems can prevent resource overload by controlling the rate at which automation processes incoming requests.

Imagine a customer service automation utilizing chatbots to address customer queries. During peak hours, if numerous customers initiate chat conversations simultaneously, the system might struggle to provide prompt responses. A scalable approach could involve routing chat requests through a queue system. This not only prevents resource overload but also ensures that each customer receives timely assistance, maintaining customer satisfaction even during high-demand periods.

Logs & Metrics

Analyzing the performance of the automation, whether functionally or technically, is also important. Logs that capture the context of inputs, decisions, and system states provide a clear narrative of the automation’s actions. This helps establish trust in the outcome.

Moreover, the value of automation remains theoretical unless backed by concrete usage metrics. Capturing data such as demand patterns, turnaround times, and operational costs helps validate the automation’s effectiveness. They also unearth insights for continuous improvement. These metrics can reveal areas where the automation excels and pinpoint potential bottlenecks or areas for enhancement, guiding strategic decisions on future investments.

In the financial sector, an automated investment recommendation system might suggest portfolio adjustments to clients. Without a transparent record of the data sources, algorithms, and market trends considered in generating these recommendations, clients might be skeptical of following the advice. By maintaining comprehensive logs that trace the journey from data inputs to output recommendations, the automation bolsters its credibility. Additionally, tracking metrics such as the success rate of recommendations, time taken for portfolio adjustments, and cost savings due to reduced manual analysis reaffirms the value of automation.


Every automation will show room for improvement. Either by optimizing the approach or extending the scope. Keeping the implementation flexible for changes/updates supports such improvements. A rigid implementation that lacks room for extension or adaptation limits its ability to evolve. On the other hand, a flexible architecture enables low-cost experimentation and continuous improvement.

By allowing the introduction of new systems, data sources, or behavioral changes you support innovation. The result is an automation strategy that retains its value against evolving needs.

Imagine a marketing team utilizing an automation platform to send out email campaigns. Initially, the automation ensures emails are sent at optimal times based on general engagement data. However, upon analyzing metrics, the team realizes that certain segments of customers exhibit higher engagement rates during specific days of the week.

To capitalize on this insight, they extend the automation by incorporating customer segment-specific scheduling. Now, emails are tailored to each segment’s engagement patterns, resulting in increased open rates and click-through rates. By allowing for this extension based on metrics analysis, the automation adapts to customer behaviors, significantly boosting campaign effectiveness and overall value.


To summarize,

  • Balancing value against costs ensures efficient allocation of resources, allowing for phased automation investments.
  • Mitigating risks related to legal, ethical, and technical aspects safeguards automation value.
  • Well-placed triggers align with user behavior, optimizing automation’s value while minimizing development effort.
  • Managing exceptions and intermittent errors through comprehensive SOPs reinforces automation’s reliability and value.
  • Effective notifications bridge technical efficiency with human decision-making, enhancing automation’s impact.
  • Minimizing required operator skills through user-friendly interfaces eases adoption.
  • Scaling gracefully with demand ensures automation continues to deliver value without degrading performance.
  • Comprehensive logs and metrics build trust and provide data for continuous improvement of automation.
  • Flexibility for changes and extensions supports ongoing improvements and retains automation’s value.

Automation is more than just a mechanism to transfer data or calculate solutions. With the right implementation a dull, lethargic process can turn into a transformative force. By listing these factors, I hope to help you as an engineer or a decision maker to boost your processes with automation. If you have feedback please leave a comment or drop a message to om [at] 0x8 . in.

Further reading