You want to reinvent or automate your existing business processes using AI. That’s a smart and logical first step in AI transformation. But there’s a critical prerequisite many organizations are only just beginning to understand.

Agentic AI has moved AI capabilities beyond simple assistance. Agentic AI systems can plan, make decisions, execute multi-step actions, and constantly learn on their own. So when you integrate agentic AI into your organization, you’re doing more than adding just another technology tool. You’re adding a new type of colleague to your workforce.  

Indeed, MIT Sloan’s Nov. 2025 The Emerging Agentic Enterprise reports that 76% of the global executives across 21 industries now see agentic AI as a coworker rather than just a tool.  

It’s a change with far-reaching consequences.

In an organization where humans and AI are working together, it does not make sense to bolt AI on top of inefficient business processes designed decades ago for sequential human work.  

As it is, legacy processes have accumulated crippling bottlenecks, excessive handoffs, and layers of operational complexity over time.

Inserting AI agents into the equation doesn’t fix these problems. It only accelerates inefficiency and introduces serious new operational and governance risks.  

The question leaders need to address today is: why build transformative AI on top of legacy processes designed for yesterday’s business environment?

IBM’s 2026 CEO study found that the organizations most successful at AI deployment are those who aggressively rethink and redesign workflows.

McKinsey’s State of AI in 2025 report says workflow redesign is one of the strongest contributors to meaningful business impact. The research showed that high-performing companies that capture meaningful value with AI are almost three times more likely than their peers to have redesigned their workflows.  

In this article, you’ll discover:

• Why legacy business processes break in an AI-driven organization

• The risks of layering agentic AI onto outdated business processes

• How to identify which processes are ready for AI-first redesign

• Five business processes that are the strongest candidates for agentic AI  

Why Legacy Business Processes Fail in an AI-Driven Organization

Let’s start with an agreement of what “business processes” means. We define business processes as the operational workflows, approvals, decisions, coordination activities, and systems interactions that drive how work gets done across the enterprise.

Pre-AI processes were built on assumptions that no longer hold. People were the primary coordination layer. Systems couldn’t coordinate in real time. Decisions needed multiple escalations. And work had to move sequentially through departments, which slowed further as multiple functions were addressing the process each from their own perspective.  

Humans held everything together, connecting and synchronizing every part of the process.

As time passed, organizations added even more layers of approvals, backlogs, routing mechanisms, and repetitive validation steps. Together, they functioned as a safety net to manage risk and natural human limitations.

All those assumptions collapse when you throw agentic AI into the mix. Now the coordination layer can shift from humans to agents in some areas.

Intelligence now lives inside the workflow. AI agents can analyze context, make decisions, orchestrate actions across systems, trigger responses, adapt dynamically, and learn continuously.  

Therefore, legacy processes built for a slower, human-only world turn out to be major barriers to AI scaling, operational agility, and enterprise-wide transformation.  

EY research underscores that true AI ROI won’t come from automation alone. It requires organizations to reinvent processes, operating models, and roles.  

Leaders Beware: A New Problem “Silently” Enters the AI Landscape

Many organizations have “inadvertently” begun adopting AI and agentic functionality, not because of a deliberate strategy, but because vendors are rapidly embedding these powerful capabilities into existing software and tools.  

If you don’t put thoughtful strategies in place, new operational, compliance, and governance risks quickly emerge.

So tackling workflow redesign for agentic AI has now become a high priority forced upon organizations.  

This analogy might help. Imagine you bought a new sports car built for maximum speed. If you drive that car only on winding, deteriorating roads riddled with potholes, you aren’t going to experience its true performance capabilities.  

It doesn’t matter how powerful the engine is. The underlying infrastructure limits your car’s performance and increases risk. If you push the car too hard, you can cause serious damage.  

The same principle applies to your business. Layering advanced agentic AI onto outdated processes creates the illusion of progress while leaving dangerous structural weaknesses untouched.  

Unlike potholes and damaged roads, outdated processes don’t usually announce themselves as an imminent threat, which makes them even more dangerous.  

In a hurry to move forward with AI, leaders often don’t recognize how processes that “worked fine in the past” are a serious risk to AI transformation – that is, until hidden costs, operational failures, and governance issues start to surface.

The Risks of Layering Agentic AI on Top of Outdated Business Processes  

One main consequence of not rethinking processes is that you accelerate inefficiency.  

Agentic AI makes broken or outdated workflows run faster without fixing them. This produces faster bottlenecks, faster errors, and faster decisions based on flawed logic.  

Other common consequences are:

• Inconsistent outcomes at scale

• Compliance violations

• Duplicated cleanup work

• Eroded trust in AI from the people in your workforce

Employees develop AI-alert fatigue when they constantly have to correct, monitor, or override chaotic AI-generated outputs. And trust in AI diminishes over time, which slows adoption.

This is just another flavor of the recurring warning that if you don’t set the right foundation for AI, larger problems, failure, and risk loom ahead. Regardless, organizations often launch promising AI automation pilots that generate excitement and early wins. But scaling stalls or value reaches a plateau, and the ROI is disappointing.  

Common failure points include data silos, unclear decision ownership between humans and agents, excessive governance layers, and workflows still built around outdated human constraints.  

Remember that AI simply amplifies whatever processes already exist, whether good or bad. Without rethinking your processes, you multiply yesterday’s inefficiencies instead of unlocking agentic AI’s true potential.  

Market Evidence: Why AI Business Process Redesign Matters

What does the research say? Is the problem really that bad? Yes, it is. The last few years have exposed a consistent pattern.  

IBM reported in May 2025 that only 25% of AI projects deliver the expected ROI, often because of missing processes and organizational changes.  

Deloitte notes that only 30% of enterprises are redesigning core business processes deeply around AI, but those that do will capture far greater value.  

McKinsey’s State of AI in 2025 survey shows that 94% of organizations don’t get significant value out of AI. The 6% who do, called “high performers,” are the ones that work to transform their business, redesign workflows, and set best practices for transformation.  

The emerging message from the market is that successful AI transformation requires organizations to redesign workflows, rethink how work gets done, modernize operating models, and redefine human roles suitable for an AI-driven enterprise.  

A key point to understand is that organizations shouldn’t simply redesign workflows. They must redefine how work itself gets coordinated, executed, and managed in an AI-driven enterprise.

How to Identify Business Processes Ready for AI Redesign

Organizations often assume they need to start with the most advanced or customer-facing AI initiatives first. In reality, the best starting points are usually processes already creating operational friction across the business.

Pay close attention to processes spanning multiple departments. They are often especially valuable candidates for redesign because they contain the greatest coordination complexity, delays, and communication overhead.

Strong candidates for redesign often share several characteristics:

• Too many approvals or handoffs  

• Repetitive manual coordination  

• Slow cycle times  

• Heavy reliance on email or spreadsheets  

• Fragmented systems and disconnected data  

• High administrative overhead  

• Frequent bottlenecks or rework  

• Decision-making delays  

• Inconsistent outcomes across teams  

• Employee frustration with routine operational tasks  

These processes tend to consume significant time and resources while limiting agility and scale.  

They also create the ideal conditions for agentic AI systems to streamline coordination, accelerate decisions, and improve operational responsiveness.

5 High-Impact Business Processes to Redesign for Agentic AI

Now the obvious question is where should organizations begin?

Not every workflow requires immediate reinvention. But some processes are especially vulnerable to the limitations of legacy operating models, such as those involving excessive coordination, manual decision-making, repetitive administrative work, fragmented systems, or large amounts of unstructured information.

The strongest candidates for AI-first redesign are typically high-volume, high-friction workflows where delays, bottlenecks, escalations, and operational complexity have accumulated over time.  

These processes often consume significant employee effort while slowing responsiveness, increasing costs, and limiting scalability.

When agentic AI enters these environments, organizations should take the opportunity to holistically rethink how work gets done instead of simply automating existing steps.

The following business processes consistently stand out as strong candidates for high-impact, AI-first redesign:

1. Contract review and document-intensive processes: Traditional, non-AI approaches to lengthy documents take a significant amount of time. AI dramatically compresses review cycles while improving consistency and accuracy.  

2. HR and employee operations processes: AI can transform onboarding, policy guidance, employee support, workforce planning, and internal requests.

3. Finance and procurement processes: Outdated sequential approvals and manual reconciliation notoriously lead to delays and errors. Redesign workflows to support real-time analysis, predictive forecasting, autonomous coordination, and continuous compliance monitoring.

4. Supply chain and operations processes: Legacy approaches are often reactive and rely on planning and executing work in batches rather than instantly. Agentic AI systems enable dynamic planning, adaptive logistics optimization, real-time inventory optimization, and predictive disruption management.

5. Customer service and support processes: Traditional processes rely on ticket routing, queues, and departmental escalations. An AI-first version enables end-to-end issue resolution for routine cases, all done autonomously. Outside of those, humans are notified and get involved in complex, sensitive, or high-stakes interactions where empathy, tact, and an understanding of the human side of customer interactions are critical.

The Big Picture: Building an Agentic Enterprise Through Process Redesign

The evidence shows that AI business process redesign is imperative. Its ultimate goal isn’t just efficiency. The goal is to create an AI-first organization where AI acts as a capable teammate, coordinator, and execution layer.  

The good news is that undergoing this process enables organizations to reinvent or eliminate outdated workflows designed around the limitations of a human-only workforce.  

By redesigning your processes in this way, you’ll shift your organization from outdated workflows to adaptive, intelligent systems of work.

Companies that successfully redesign business processes for AI have already separated themselves from competitors through faster innovation, greater operational agility, innovation, stronger customer experiences, and more scalable AI transformation.

Understanding which business processes need reinvention is only the first step. The harder challenge is redesigning workflows, governance models, and human roles for an AI-first enterprise.  

In our next article, we’ll explore how organizations can practically redesign workflows for AI transformation — and why operational adaptability may become the defining competitive advantage of the agentic era.

At Marlabs, we partner with leaders to navigate this exact shift. We can help you identify high-impact opportunities that feel ripe for reinvention with agentic AI. No matter how complex your organization’s challenge, you can turn AI transformation into measurable business results.

Ready to identify which business processes are limiting AI success in your organization? Marlabs can help you evaluate and redesign your workflows, modernize operations, and uncover high-impact opportunities for agentic AI-driven transformation.