We have all heard about the promise of Agentic AI revolutionizing enterprise process automation. But Agentic AI isn’t here to take over your workflows – it’s here to make them move faster. By accelerating decisions, surfacing the proper context at the right time, offloading small but frequent tasks, connecting the dots across enterprise silos, and intelligently avoiding rework, Agentic AI helps restore flow. It removes the friction that traditional workflow tools, rules engines, and RPA often leave behind. The result: better experience, high productivity, focus, and lower cost.
The business case is becoming more concrete. Kimberly-Clark, the maker of Kleenex and Huggies, unlocked over $140 million in business value through intelligent automation across departments like sales, finance, HR, and customer service. By automating over 260 processes and saving 1.6 million hours of manual effort, they didn’t just cut inefficiencies; they improved customer satisfaction, boosted employee focus, and enhanced forecasting and personalization at scale.1
As enterprises adopt Agentic AI, the question has shifted: it’s not “what can we automate?” but “where does intelligence remove friction that other tools couldn’t?” It’s about augmentation in the right places, not replacement.
A Guide to Finding the Friction
Friction refers to inefficiencies, bottlenecks, and repetitive tasks that slow workflow and, therefore, limit user delight and productivity. Traditional automation often struggles with five core types of friction, each of which can be resolved by applying the right agentic approach.
- Decision Delays: Decision latency slows workflows when rules change often, engines either lack flexibility, or judgment calls are too complex to code. Agents act as accelerators; extracting context, comparing against dynamic rules, and packaging recommendations that help people move faster without giving up oversight.
Example: Consider the mortgage industry, decisions hinge on a complex mix of state and federal regulations, investor guidelines, borrower variability, and risk thresholds. Profiles vary by income, geography, credit history, and more. Underwriters are essential for managing complexity and attempts to replicate their art of judgement through rules often lack trust. Agents can help by adapting in real time and applying reasoning to decisions, pulling required documentation, skipping unnecessary steps, and assembling context-aware recommendations. This reduces exception handling, speeds up approvals, and helps underwriters focus on edge cases where human oversight adds the most value.
- Context Surface Constraints: Work slows when data is scattered across systems, dashboards, and emails, or when too little information can be accessed. Agents solve this by assembling and summarizing relevant real-time insights from multiple platforms and can deal with vast amounts of information and sources dynamically, so people don’t have to dig.
Example: At a global media entertainment company, support teams handle complex inquiries, like royalties, tax deductions, or payment schedules—where data is often fragmented across contract systems, billing platforms, and communication threads. Agents can unify these sources quickly, structuring information into clear, modular context blocks. This eliminates delays from manual lookups and helps support staff deliver faster, more accurate responses with less effort.
- Micro-Judgment and Task Delegation: Small, repetitive decisions and documentation tasks add up quickly. Agents can triage requests, complete forms, capture notes, and recommend actions, keeping workflows moving without human delay.
Example: In a corporate IT helpdesk, incoming requests range from simple password resets to more involved technical issues. Previously, all tickets were queued for human review, slowing resolution and overwhelming staff. With agents in place, routine tickets are triaged automatically, forms are filled, steps are logged, and common issues are resolved instantly. For edge cases, agents escalate with complete context, enabling IT teams to focus on higher-impact work and reduce backlog.
- Cross-Silo Fragmentation: Workflows that span departments, teams, and systems often get bogged down in handoffs and the complexity of systems integration. Agents operate across those silos, interpreting context and triggering actions without waiting on humans.
Example: Agriculture provides a strong use case: agents deployed across grower, distributor, and retailer systems align production and delivery in real time by bridging fragmented systems and surfacing dynamic signals. For example, agents can ingest weather forecasts, demand signals, and logistics updates to adjust harvest timing, labor planning, and transportation coordination. This enables producers to avoid spoilage, reduce labor inefficiencies, and match supply with market needs, all without manual re-planning or delays across siloed teams.
- Rework and Discovery Loops: When information doesn’t surface at the right time and proactively, tasks must be repeated or escalated for human intervention. Agents close the loop between insights and actions, predicting what is needed to ensure that workflows get completed the first time.
Example: Field service teams offer another compelling example; delays often stem from missing parts, inaccurate diagnostics, or surprises on-site. Agents help prevent repeat visits by analyzing sensor data, service history, and technician notes to flag potential issues and recommend actions in advance. This minimizes rework by ensuring technicians arrive with the right parts, context, and instructions—enabling first-time resolution and reducing service backlog.
Summary of Friction Types and Agentic AI Applications
| Friction Lens | Signs You See This Friction | Agentic AI Role | Example Process |
| Decision Acceleration | Manual exceptions, policy uncertainty, decision lag times | Packages recommendations for review | Complex approvals |
| Context Surface Expansion | Information scattered across tools/systems, or not comprehensive | Aggregates and presents key context | Inquiry resolution |
| Micro-Judgment and Task Delegation | Repetitive approvals, forms, triage, administrative tasks | Automates small decisions and tasks | Internal request handling |
| Cross-Silo Coordination | Delays at handoffs across teams, fragmented systems that are hard to integrate | Orchestrates workflows across silos | Multi-team process flow |
| Rework and Discovery Loops | Repeat visits, escalations, callbacks | Predicts what is needed for resolution before follow-ups | Recurring issue resolution |
From Framework to Action: Putting Agentic Friction Resolution to Work
Step 1: Spot the Friction
Start by identifying where work slows down. Map your important processes and score them against a few questions: Are complex decisions stuck in manual review? Are support agents piecing together data from multiple systems? Do simple approvals still require human effort? Are workflows stalling across departments? Are you seeing rework loops frequently? These patterns are symptoms of deeper friction; decision latency, fragmented context, micro-tasks, cross-silo gaps, and inability to predict resolution.
Step 2: Match the Right Lens
Once friction is identified, the lens determines how an agent can resolve it. Each lens is a strategy embedded in how the agent acts:
- Decision acceleration helps users make fast, policy-aligned calls by interpreting data and suggesting next steps. Ideal for underwriting or approvals. → Intelligent Decision Agents
- Context surface expansion assembles the right data from fragmented systems and presents it in real time. Critical in support, claims, or reviews. → Information Aggregator Agents
- Micro-judgment delegation allows agents to triage, complete forms, or document actions. Best for IT operations and routine workflows. → Assistant Agents
- Cross-silo coordination keeps multi-team workflows on track by synchronizing inputs across systems. Common in procurement or onboarding. → Coordination Agents
- Insight-to-action enablement turns signals into action, like triggering next steps in field service or compliance when an anomaly appears. → Resolution Prediction Agents
Step 3: Make It Operational
Start small and stay focused. Assign dedicated teams to known friction areas and act as center of excellence squads, and build a backlog of high-drag processes, queues, escalations, and stalled handoffs – pilot agents where the cost of friction is highest. Treat agentic AI not as an overlay, but as an embedded layer that restores flow. Over time, this becomes an enterprise-wide capability; targeted, measurable, and owned.
Conclusion: From Friction to Flow
Legacy tools weren’t built to address all friction points. The ones they leave behind can damage user experience and drive-up costs. That’s where agentic AI comes in. Not to replace them, but to extend their value where it matters most. Agents unlock enterprise flow, whether it’s accelerating decisions, surfacing context, automating non-value-added tasks, simplifying handoffs, or reducing rework.
Each friction point is a chance to improve speed, clarity, and consistency, one agent at a time. And as these agents take on more of the burden, your teams regain time, focus, and capacity to move the business forward. It’s not about replacing; it’s about intelligently augmenting workflows where it matters most.
