Here's the AI dilemma puzzling executives today: while 78% of organizations have adopted generative AI, a staggering 80% see no significant impact on their earnings. This gap between deployment and results highlights a critical issue with implementation.
This is Part II of my AI workflow management series. The previous article focused on grasping AI automation workflows via task mining and process discovery. Now, we’ll delve into the crucial next step: implementing structured strategies that turn process knowledge into tangible business value.
To successfully integrate AI workflow automation, organizations should adopt a methodical approach. In this article, we'll explore how to navigate this journey—knowing when to create custom solutions versus opting for off-the-shelf products, the importance of prototyping, common hurdles, and more.
Step 1: Pinpoint Key Pain Points
Not every workflow stands to gain equally from AI. Concentrate on processes that promise the highest business impact while remaining technically feasible for automation. Many organizations are exploring internal hackathons to uncover potential AI use cases that enhance efficiency and value. This encourages teams to step back from their routine and evaluate how their work is executed and which tasks are most critical.
This should be the guiding principle for embracing any new technology: always, always start with the issue you aim to address, then incorporate the tech accordingly.
Process Selection Criteria
Focus on customer-facing workflows that directly influence revenue or customer satisfaction. For example:
- *Customer Prospecting in your sales funnel, where quality leads and personalization enhance the sales pipeline.
- Loan Application Workflows in banking, where speed can provide a competitive edge.
- Customer Onboarding Sequences, where seamless experiences foster customer retention.
Target knowledge-intensive processes that require expertise scalable through automation, and pay particular attention to cross-functional workflows that carry coordination challenges. AI workflow automation is particularly adept at managing processes that span multiple departments, ensuring context is maintained during handoffs and automatically managing exceptions.
Step 2: Selecting an Approach to AI Workflow Automations
In my earlier article, I talked about task mining and process discovery—understanding the actual workflow versus what's outlined in formal processes. It's crucial to have this foundational knowledge before embarking on AI workflow automations; after all, you can't automate something you don't fully grasp. With that foundation set, it’s time to evaluate your options.
Take Your Time: Assess First
Before jumping into the creation of intricate AI workflow automations, I recommend taking a moment to review the resources you already have at your disposal. It’s highly likely that the AI capabilities built into your current tools have features waiting to be uncovered. Analyze the existing AI workflows, how they’re functioning, and explore ways to enhance them.
However, there are situations where ready-made solutions might not suffice, especially if you aim to implement AI workflow automations for more intricate processes. These complex workflows often require AI agents, which may necessitate more customized solutions. Research from McKinsey supports this view: "Off-the-shelf agents may streamline routine workflows, but they rarely unlock strategic advantage."
Here’s a guide on when to opt for off-the-shelf versus custom solutions:
When to Opt for Off-the-Shelf AI Workflow Automations
Commercial platforms are ideal for standard business processes like document routing, basic approval workflows, and classifying customer inquiries—processes that don't typically set you apart from your competitors. Choose off-the-shelf solutions when your workflows are common across industries, lack proprietary logic, and when a quick deployment is more critical than customization.
For instance, CRM solutions like Microsoft Copilot can be effectively integrated into document-heavy approval processes, while Salesforce Einstein serves well for standard lead qualification workflows. HubSpot AI is also a great choice for automating routine marketing tasks. These tools are particularly useful for components such as automated email routing based on content analysis, basic data extraction from standard forms, and enhancing productivity within existing business functions.
When to Pursue Custom AI Workflow Automations
Custom development becomes vital for complete business processes that differentiate your organization from others in the market. It’s worth investing in bespoke AI workflow automations when you’re dealing with complex, multi-system orchestration needs or workflows that involve proprietary business rules and specialized expertise.
Tailored workflows excel in transformational scenarios—such as comprehensive customer resolution processes that span multiple departments, adaptive supply chain orchestration responding to real-time market changes, or complex decision-making processes that must adhere to unique regulatory requirements.
[Are you looking: Scalable data integration]
Step 3: Prototyping and No-Code Platforms
Before diving into a full-fledged development team and committing to significant resources, savvy organizations often take the prudent step of prototyping. The primary objective here is to see if AI-driven workflow automations can effectively address your specific challenges. For instance, you might want to test whether a workflow can efficiently categorize and route customer support tickets based on their content and urgency.
Once you've pinpointed your issue and assessed the AI capabilities of your existing tools, prototyping offers a swift and economical way to try out various solutions.
Engaging in prototyping enables you to uncover unexpected hurdles, confirm your assumptions about workflows, and present value to stakeholders ahead of scaling. This proactive approach also helps avoid the expensive error of developing intricate custom solutions when simpler alternatives could be just as effective.
No-code platforms are perfect for prototyping, empowering business users to create and test automated workflows without needing coding skills. For example, Make.com (formerly Integromat) serves as a great starting point, featuring advanced visual workflow building, over 1,000 app integrations, and powerful yet accessible data manipulation tools. Microsoft Power Automate offers a robust suite of AI workflow automation, particularly beneficial for those within the Microsoft ecosystem. Another user-friendly option is Zapier, which remains a popular choice for beginners.
Here are some effective approaches for prototyping:
Begin with the most significant pain point you identified earlier. Create a simple prototype that tackles the essential problem without getting bogged down by every possible edge case. Your main goal should be to showcase the primary advantages of AI workflow automation tailored to your specific situation.
Establish clear success metrics in advance. Decide what “success” looks like for your prototype—such as reductions in processing time, enhancements in accuracy, or cost savings that warrant further investment.
Engage end users from the outset of the prototyping phase. Those who currently manage the manual tasks often have valuable insights about exceptions, edge cases, and the complexities of real-world applications.
Prepare for iterations based on the prototype’s performance. Utilize the insights gained to refine your strategies, adjust your focus, or pivot to different solutions before pursuing full implementation.
While prototyping can be immensely beneficial, it does come with its share of challenges. Organizations often deal with the complexities of integrating older systems that can resist AI connectivity, requiring innovative solutions through APIs and middleware. Additionally, executive teams sometimes expect quick results, while prototypes demand time to illustrate their value. It can also be a challenge to allocate resources effectively, as this requires personnel who possess both business domain knowledge and AI technical expertise, highlighting the need for cross-functional collaboration.
[ Good Read: How AI Agents Are Redefining DevOps and SRE Workflows in 2025]
Step 4: Measure Your AI Workflow Automations
Companies that adopt structured AI workflow automation see tangible business outcomes. Take Leaf Home, for instance: their task mining efforts across 13 business areas, coupled with AI automation, yielded $120,000 in cost savings.
When designing AI workflow automations, it’s crucial to establish key performance indicators (KPIs) as measures of success. Typically, these KPIs can be grouped into four categories:
Time Savings Metrics: Hours saved per week, reduction in task completion time, shorter cycle times for end-to-end processes.
Cost Reduction Calculations: Savings on direct labor, decreased costs from errors, fewer manual interventions required.
Quality Improvements: Better accuracy rates, increased customer satisfaction scores, enhanced compliance adherence.
Process Efficiency Gains: Quicker cycle times, fewer bottlenecks and delays, increased throughput capacity without needing more resources.
With the process mining market projected to reach $12.1 billion by 2028, the value of AI workflow automation is becoming widely acknowledged. Organizations that fail to embrace these capabilities may find themselves at a competitive disadvantage as they become standard practice.
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