In the world of enterprise modernization, a frustrating paradox has emerged: companies pour millions into new cloud platforms and SaaS applications, only to find their core operational struggles persist. To help us understand why this happens and explore a more effective path forward, we’re joined by Zainab Hussain. An e-commerce strategist with deep expertise in customer engagement and operations management, Zainab has seen firsthand how focusing on technology alone misses the mark. Today, she’ll share her insights on a ‘workflow-first’ approach that promises to untangle complexity and deliver real business results.
We’ll be exploring the hidden costs of ‘workflow debt’ and why simply replacing applications often fails to fix underlying issues. Zainab will explain how a unifying workflow layer can orchestrate work across existing systems, making processes more visible and efficient. We will also delve into how this strategy not only improves the employee and customer experience but also creates a safer, more practical foundation for integrating AI, ultimately redefining how we measure the success of modernization projects.
The text describes a paradox where companies have technically modern systems but still suffer from fragmented workflows. Can you share an example of this and explain how focusing only on replacing applications, rather than processes, often leads to these persistent operational challenges?
It’s a classic story I see time and again. A company spends a fortune migrating its old, on-premise CRM to a shiny new SaaS platform. Management celebrates the successful tech migration, but on the ground, the customer onboarding team is still struggling. They have to pull data from the new CRM, then manually input it into the ERP to set up billing, then jump to a separate compliance system for checks, and finally track approvals through email chains. The applications are modern, but the work itself is still a mess of swivel-chair integration. This happens because the project was defined as “replace the CRM.” The real problem—the clunky, cross-system process of onboarding a customer—was never addressed. The focus on applications leaves the coordination layer, that web of emails and spreadsheets holding everything together, completely invisible and untouched, leading to the same old delays and frustrations.
You mention “workflow debt”—the invisible drag caused by disconnected processes. How can an organization begin to identify and measure this debt, and what are the first practical steps a team can take to make that invisible coordination layer both visible and manageable?
“Workflow debt” is a powerful concept because it gives a name to that feeling of operational friction everyone experiences but can’t quite pinpoint. The first step to identifying it is to stop looking at org charts and system diagrams and start following the work. Pick a process that is notoriously painful—maybe it’s a customer service request or an internal procurement approval. Get the people who actually do the work in a room and have them map out every single step, every handoff, every system they touch. You’ll quickly uncover the reliance on tribal knowledge and manual workarounds. The “invisible” layer becomes visible on that whiteboard. To measure it, you can start tracking simple metrics like the total cycle time from request to resolution, or the number of manual touchpoints required. Making it manageable starts right there: by visualizing the flow, you can immediately spot the bottlenecks and redundancies that can be automated or streamlined, turning an unmanaged process into an orchestrated one.
The article proposes a “unifying workflow layer” to orchestrate work. Could you describe what this layer looks like in practice? Please detail what technologies are involved and how it coordinates tasks across different legacy systems like a CRM and an ERP without destabilizing them.
Think of the unifying workflow layer as a conductor for an orchestra. Your CRM, ERP, and other systems are the musicians—each is a specialist at what it does. The workflow layer doesn’t try to play the violin or the trumpet; it simply tells each musician when to play their part to create a cohesive piece of music. In practice, this is often a modern business process management (BPM) or workflow automation platform. It uses APIs to connect to your existing systems. For a customer onboarding process, the workflow layer would automatically trigger a task in the ERP after an opportunity is marked “closed-won” in the CRM. It would then route an approval request to a manager, and once approved, it might trigger a document generation task in another system. The beauty of this is that it’s a light-touch integration. You’re not rewriting the core code of your stable, reliable ERP. You’re simply orchestrating the flow of work between your systems, which is far less risky and disruptive.
The content suggests starting by mapping “high-friction processes” like approvals or service requests. Can you walk us through the step-by-step process of mapping one such workflow and then explain how decoupling its logic from the core applications provides immediate flexibility for the business?
Let’s take a simple capital expenditure approval. The first step is to trace its journey. An employee fills out a form, which might live in a shared drive. That form is emailed to their manager. If the amount is over a certain threshold, the manager has to forward it to their director, and so on. We map every single one of these manual handoffs and decision points. What we’re doing is externalizing the business logic—the rules like “if over $10,000, send to the director”—that currently only exists in people’s heads or email habits. Once this logic is mapped in a workflow tool, we have decoupled it from the applications. Now, if the approval threshold changes to $15,000, you don’t need a developer to change code in a core system. A business analyst can simply update the rule in the workflow platform in a matter of minutes. This gives the business incredible agility to adapt its processes on the fly without being held hostage by rigid, hard-coded systems.
You argue that this approach makes it safer to apply AI to enhance, not replace, human work. Could you give a specific, real-world scenario of how orchestrating a process first allows AI to effectively assist with tasks like predicting delays or routing exceptions?
Absolutely. Once you have a clear, orchestrated workflow for, say, insurance claims processing, you have a rich dataset of every step, its duration, and its outcome. This is the perfect foundation for AI. For example, an AI model can analyze historical data from thousands of claims and learn to predict the likelihood of a delay for a new claim based on its characteristics. When a high-risk claim comes in, the workflow can automatically flag it for an experienced adjuster, rather than letting it get stuck in the standard queue. Or, if a claim is missing a standard piece of documentation, an AI can identify the exception and the workflow can automatically route a task to the customer service team to request the missing information. AI isn’t replacing the adjuster’s critical judgment; it’s acting as an intelligent assistant, helping them prioritize their work and preventing problems before they happen. This is a much safer and more valuable application of AI than attempting a high-risk, full replacement of human decision-making.
Success is defined by outcomes like reduced cycle times, not by the number of apps replaced. What advice would you give a leader for redesigning their project dashboards to reflect these workflow-centric metrics, and which specific KPIs have you seen that truly prove the value of this approach?
My advice to leaders is to throw away the old IT-centric dashboards. Nobody in the C-suite, except maybe the CIO, truly cares how many applications you’ve migrated to the cloud. What they care about is business velocity and customer satisfaction. Your dashboard should reflect that. Instead of “Applications Decommissioned,” track “Average Customer Onboarding Time” and watch it drop from weeks to days. Instead of “Server Uptime,” measure “Manual Touchpoints per Service Request” and aim to reduce it by 80%. Two KPIs I’ve seen that are incredibly powerful are “End-to-End Process Cycle Time” and “First-Time Right Percentage.” These metrics directly measure the speed and quality of execution. When you can show that you’ve cut the time to resolve a customer issue in half while also reducing errors, you’re speaking the language of business value, not just technology.
Do you have any advice for our readers who are about to start a modernization project, to help them shift from a traditional “rip and replace” mindset to a more effective workflow-first approach?
My biggest piece of advice is to start by focusing on the seams between your systems, because that’s where the most value is hidden. Don’t start with the question, “What system should we replace?” Instead, ask your teams, “What is the most painful, slowest, most frustrating process we have?” Find that process, map it out, and use workflow automation to smooth it out. This delivers a quick, tangible win that builds momentum and demonstrates the value of this approach. It shifts the entire conversation from a massive, risky, multi-year technology overhaul to a series of focused, high-impact improvements. Modernization shouldn’t feel like a gamble; it should be a steady, evolutionary journey of making everything work better together.
