Where automation drives the highest ROI

Not all processes are created equal when it comes to automation. The best candidates are those that are:

Repetitive: Tasks performed frequently with little variation, like invoice processing or employee onboarding.
Rule-based: Steps that follow predictable logic, such as data validation or form approvals.
High-volume: Processes that occur at scale and strain team capacity.
Error-prone: Areas where human mistakes are common and costly.

For example, automating IT service requests can drastically reduce resolution times, freeing up tech teams to focus on more strategic work. According to McKinsey, automation can cut operational costs by up to 30% while improving output quality [2].

Before implementing automation, use ROI calculators and benchmark similar use cases in your industry. Look beyond cost savings and consider long-term impact on team performance and customer experience.

Identifying the right processes: tools and frameworks

A systematic approach

Start by mapping out workflows across your organization. Talk to team leads and individual contributors to understand daily pain points. Then apply a decision model, like the Impact vs. Complexity Matrix, to prioritize opportunities:

Quick wins: High impact, low complexity (e.g., automating expense approvals)
Strategic bets: High impact, high complexity (e.g., automating a custom sales pipeline)
Low-priority tasks: Low impact, regardless of complexity

Another effective tool is Process Potential Analysis (PPA), which scores tasks based on frequency, time spent, and standardization [3]. The more structured and time-consuming a process is, the better suited it is for automation.

Risks of automating the wrong process

Choosing the wrong process can backfire. Risks include:

Minimal ROI: Automating low-value tasks drains resources with little payoff [4].
Operational disruption: Misaligned automation can break workflows or introduce delays.
Team pushback: Employees may resist if automation feels unnecessary or intrusive [5].

To mitigate these risks:

Vet processes carefully using business logic and stakeholder feedback.
Pilot initiatives on a small scale to validate assumptions.
Build a clear change management plan to guide teams through the transition.

Measuring automation success

Success isn’t just about time saved. Look at a broader set of metrics:

Error reduction: Fewer manual mistakes lead to higher quality outcomes.
Customer satisfaction: Faster, more consistent service improves client experience.
Scalability: Automated processes should grow with demand without increasing headcount.
Compliance: Automation ensures adherence to regulations through consistent execution [6].

Use a mix of internal analytics and user feedback to evaluate impact. Review KPIs regularly to guide improvements and demonstrate value to stakeholders.

Technical feasibility: what makes a process automatable?

From a technical standpoint, assess:

Stability: Has the process remained unchanged for a while?
Standardization: Does it follow consistent rules and formats?
Data accessibility: Is the required data structured and available?
Tool integration: Can your systems connect with automation platforms easily?

Processes with many exceptions or unclear ownership tend to require more custom work, increasing cost and complexity [7]. In those cases, it may be worth reengineering the process before automating.

Adapting to change: maintaining flexibility in automation

Business logic and regulations change over time. To stay adaptable:

Design automation in modules so updates can be made without rewriting the entire workflow.
Use tools with low-code customization to enable non-engineers to adjust rules and logic.
Conduct quarterly reviews to catch compliance updates and process changes early [9].

An agile automation strategy avoids tech debt and keeps systems relevant as the business evolves.

The role of AI in automation

AI and machine learning can boost automation by:

Identifying patterns in unstructured data to suggest automation opportunities.
Handling decision-based tasks like fraud detection or lead scoring [8].
Improving accuracy by learning from past outcomes.

For instance, integrating AI with customer support automation allows chatbots to triage tickets based on urgency and sentiment, leading to faster resolution and higher customer satisfaction [8].

Choosing the right tools: off-the-shelf or custom?

Evaluate whether your current tools meet your needs by checking:

Functionality: Do they support the workflows you want to automate?
Ease of use: Can business teams use them without engineering help?
Integration: Do they work with your tech stack (ERP, CRM, etc.)?
Scalability: Will they grow with your business?

If off-the-shelf solutions fall short, consider a custom platform tailored to your business. While more resource-intensive, it offers greater control and flexibility in the long run.

Governance: managing automation company-wide

To avoid silos and ensure alignment:

Create a Center of Excellence (CoE) to oversee automation strategy and best practices [9][10].
Involve cross-functional teams (IT, Ops, Legal) in planning and execution.
Set clear standards and documentation protocols for automation workflows.

Governance ensures that automation efforts scale effectively and stay aligned with strategic goals.

Are you automating the right way?

Identifying the right processes to automate requires both strategic foresight and technical diligence. Focus on high-impact, rule-based, and error-prone tasks that align with business goals. Use proven frameworks to prioritize opportunities, and regularly measure results against a full range of KPIs.

Automation isn’t just about saving time. It’s about enabling your teams to work smarter, scale faster, and deliver better experiences.

Get in touch now and let’s explore how to scale your automation strategy together.