A Step-by-Step Guide to AI in Workflow Automation

Here's the AI paradox that's confounding executives everywhere: 78% of companies have deployed generative AI, yet 80% report no material contribution to earnings. The deployment vs impact spread reveals a fundamental implementation problem.

This is Part II of my AI workflow management series. While the previous article emphasized understanding AI automation workflows through task mining and process discovery, this guide focuses on the next critical step: structured implementation that transforms process knowledge into business value.

Organizations can successfully implement AI workflow automation by following a structured approach. In this article, we’ll cover exactly that, knowing when to build custom solutions versus using off-the-shelf products, prototyping, common challenges, and more.

Step 1: Identify Key Pain Points

Not all workflows benefit equally from AI implementation. Focus your efforts on processes that deliver maximum business impact while being technically feasible to automate. Many companies are hosting internal hackathons as they work to identify use cases where AI can drive efficiency and value.  This approach enables teams to step out of their day-to-day to look more broadly at how their work gets done and the key tasks involved.  

This is how companies should approach any new technology: always, always start with the problem you want to solve and then add the tech into the mix.

Process Selection Criteria

Prioritize customer-facing workflows that directly impact revenue or customer satisfaction. Examples include:

• Customer Prospecting in your sales funnel, where the quality of leads and personalization drive sales pipeline
• Loan application workflows in banking, where speed creates a competitive advantage
• Customer onboarding sequences where smooth experiences drive retention

Target knowledge-intensive processes requiring expertise that can be scaled, and focus on cross-functional workflows with coordination overhead. AI workflow automations excel at orchestrating processes spanning multiple departments, maintaining context across handoffs, and automatically routing exceptions.

Step 2: Choose an Approach to AI Workflow Automations

In my previous article, I discussed task mining and process discovery—discovering how work is actually done versus what is documented in processes. AI workflow automations struggle without this foundational understanding—you can't automate what you don't fully understand. With that step out of the way, it’s time to take stock of what’s a. 

Take Your Time: Evaluate First

Before diving headlong into creating complex AI workflow automations, my best advice is to spend some time with what you’ve already got. It’s likely that the AI features present in the tools your organization already uses have yet-unexplored features. What are the AI workflows in your current tools, how are they functioning, and how could they be optimized? 

But sometimes off-the-shelf solutions don’t cut it, especially if you want to build AI workflow automations for more complex workflows. These types of workflows typically involve AI agents, for which you’ll likely want to build customized solutions. McKinsey research backs this up: "Off-the-shelf agents may streamline routine workflows, but they rarely unlock strategic advantage." 

Here are some key factors for deciding between off-the-shelf vs custom solutions:

When to Choose Off-the-Shelf AI Workflow Automations

Commercial platforms excel in standardized business processes, such as document routing, basic approval workflows, and customer inquiry classification, which don't represent competitive differentiators. Deploy off-the-shelf solutions when workflows are common across industries, without proprietary logic, and speed of deployment matters more than customization.

Examples include CRM solutions, such as Microsoft Copilot, integrated into document-heavy approval processes, or Salesforce Einstein, for standard lead qualification workflows. HubSpot AI can be beneficial for automating routine marketing tasks. These tools are well-suited for workflow components such as automated email routing based on content analysis, basic data extraction from standard forms, and productivity enhancements within existing business processes.

When to Build Custom AI Workflow Automations

Custom development becomes essential for end-to-end business processes that differentiate your organization from competitors. Invest in custom AI workflow automations when facing complex, multi-system orchestration requirements or workflows that contain proprietary business rules and specialized domain expertise.

Custom-built workflows excel in transformational scenarios, such as end-to-end customer resolution processes spanning multiple departments, adaptive supply chain orchestration responding to real-time market conditions, or complex decision-making processes that incorporate unique regulatory requirements. 

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Step 3: Prototyping and No-Code Platforms

Before building a full implementation team and committing to large-scale development, smart organizations start small with prototyping. The goal is to validate whether AI workflow automations can solve your specific problem before investing significant resources—for example, testing whether a workflow can automatically categorize and route customer support tickets based on content and urgency level.

After identifying your problem and evaluating existing AI features in your current tools, prototyping allows you to experiment with solutions quickly and cost-effectively.

Prototyping helps you discover unexpected challenges, validate assumptions about your workflow, and demonstrate value to stakeholders before scaling. It also prevents the costly mistake of building complex custom solutions when simpler approaches might suffice.

No-code platforms are ideal for prototyping, allowing business users to build and test automated workflows without requiring programming expertise. Make.com (formerly Integromat) is a good starting point, offering advanced visual workflow building, over 1,000 app integrations, and sophisticated data manipulation capabilities that are also accessible to non-technical users. Microsoft Power Automate provides comprehensive AI workflow automation, particularly valuable for organizations in the Microsoft ecosystem. Zapier is another example (and remains one of the most beginner-friendly options).

‍Here are some best practices when prototyping:

• Start with your highest-pain workflow identified in Step 2. Build a minimal prototype that addresses the core problem without trying to solve every edge case. Focus on demonstrating the fundamental value proposition of AI workflow automation for your specific use case.
• Set clear success criteria before building. Define what constitutes a successful prototype—specific metrics like processing time reduction, accuracy improvements, or cost savings that would justify further investment.
• Involve end users early in the prototyping process. The individuals who currently perform manual work often have valuable insights into exceptions, edge cases, and real-world complexities.
• Plan for iteration based on prototype results. Use learnings to refine your approach, adjust scope, or pivot to different solutions before scaling to full implementation.

Prototyping isn’t without its challenges, of course. Organizations frequently encounter the complexity of integrating legacy systems, where decades-old systems resist AI connectivity, necessitating creative workarounds through APIs and middleware. Executive teams often expect rapid returns, while prototypes require patience to demonstrate value. Resource allocation can be tricky as well, requiring personnel with both business domain expertise and AI technical knowledge, making cross-functional collaboration essential.

Step 4: Measure Your AI Workflow Automations

Organizations that implement structured AI workflow automation see concrete business results. Leaf Home's experience demonstrates the potential: task mining across 13 business areas combined with AI automation delivered $120,000 in cost savings.

Key performance indicators for AI workflow automations include:

As organizations design AI workflow automations, KPIs are the recommended barometer for their success. Usually, these KPIs fall into four categories:

• Time Savings Metrics: Hours saved per week, task completion time reduction, decreased cycle time for end-to-end processes
• Cost Reduction Calculations: Direct labor savings, error cost reduction, reduced manual interventions

• Quality Improvements: Accuracy rate improvements, increased customer satisfaction score, improved compliance adherence

• Process Efficiency Gains: Faster cycle times, reduced bottlenecks and handoff delays, increased throughput capacity without additional resources

The process mining market reaching $12.1 billion by 2028 indicates widespread recognition of the value of AI workflow automation. Organizations risk competitive disadvantage as these capabilities become standard practice.

The Path Forward: Start Small, Start Smart

Consider where your organization stands today. You likely have scattered AI experiments, promising pilot programs, and executives asking hard questions about returns on investment. The temptation is to either double down on experimentation or retreat to familiar manual processes. Neither approach wins.

Start small, but start smart. Choose one customer-facing process where delays or errors create visible friction. Form your three-person implementation team—business expert, process designer, technical implementer—and give them a time-bound challenge to deliver measurable improvement. (And establish governance frameworks from day one to prevent agentic sprawl.)

The organizations that will thrive in the coming decade will master the disciplined implementation of AI in workflow automation. The difference between success and failure often comes down to seemingly mundane details: choosing the right first process, building the right team, and measuring the right metrics.