This post is the culmination of months of curiosity and exploration into the world of agentic systems. Like many, I first encountered these concepts through reading articles and watching countless YouTube videos. But it didn’t stop there. Over the past year, I’ve taken my curiosity to new heights by immersing myself in industries far removed from my usual expertise. I worked undercover at a meal kit fulfillment center and a warehouse distribution center, observing firsthand how inefficiencies cost millions in lost revenue. I also explored the cybersecurity mergers and acquisitions (M&A) landscape, building a tool to analyze SEC filings and understand acquisition trends.
Additionally, I’ve developed a side project in data security and privacy compliance, helping organizations identify data vulnerabilities and automate compliance. My journey into agentic systems has also involved reading academic papers, engaging with expert discourse on Twitter, and observing the rapidly evolving conversations around AI. These experiences have shaped my understanding of agentic AI and how it can revolutionize workflows across industries.
The Problem: Manual, Disconnected, and Outdated Workflows
Despite decades of advancements in enterprise software, many industries remain burdened by inefficiencies. From HR and IT to manufacturing and logistics, workflows are often plagued by repetitive tasks, data siloes, and outdated systems. Even the most modern SaaS tools often act as passive repositories of data, waiting for humans to intervene.
Consider this question:
What repetitive tasks in your organization could benefit from automation? Are your current tools truly helping you work smarter, or are they just digitizing old inefficiencies?
Before SaaS: The Age of Spreadsheets and Siloes
Let’s rewind to a time before SaaS. For decades, businesses relied on manual processes, spreadsheets, and on-premises software. This era was defined by:
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Data siloes: Information scattered across physical files, spreadsheets, or isolated systems.
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Process siloes: Teams operated in isolation, with little collaboration across departments.
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Knowledge siloes: Institutional knowledge was rarely documented, leaving gaps when employees left.
These fragmented systems led to inefficiency, errors, and delays. Businesses faced challenges in scaling their operations or ensuring consistency in their workflows.
This timeline illustrates the evolution of enterprise workflows, from manual processes to agentic AI.
The SaaS Revolution: A Stepping Stone
The SaaS revolution of the 2000s brought significant changes. Companies like Salesforce, SAP, and Workday transformed enterprise workflows by making tools:
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Accessible: Cloud-based software became available anytime, anywhere.
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Collaborative: Teams could share data in real time, breaking down siloes.
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Efficient: Automation reduced manual tasks, saving time and cutting errors.
However, SaaS tools remain largely passive. They wait for human input to execute tasks. For example, consider HR workflows:
- HR submits a request to IT to set up new user accounts.
- IT manually configures permissions, tools, and systems.
- HR receives confirmation and updates internal records.
Even with advanced tools like Okta, these processes rely heavily on human involvement.
Agentic AI: The New Frontier
Agentic AI systems are the next leap forward. These systems don’t just digitize workflows—they autonomously act, learn, and improve. They analyze context, make decisions, and take proactive actions without requiring constant human intervention.
Imagine a system that can:
- Understand Context: Recognize when a request for access or resource allocation is needed.
- Take Action: Automatically provision accounts, configure permissions, and update logs.
- Continuously Learn: Adapt to new tools, compliance requirements, and user behaviors.
This is the promise of agentic AI.
Universal Example: Calendar Coordination in Enterprises
Scheduling meetings is a universal pain point, even in tech-savvy workplaces. Here’s what often happens:
- Cross-department coordination involves conflicting schedules and time zones.
- External collaborations require manual back-and-forth via tools like Calendly.
- Rescheduling disrupts workflows and creates frustration.
How Agentic AI Transforms This:
1. Autonomous Calendar Management: Automatically find the optimal meeting time based on priorities and availability.
2. Conflict Resolution: Adjust schedules dynamically without manual intervention.
3. Context Awareness: Factor in organizational policies, deadlines, and time zones.
4. Seamless Integration: Generate agendas, share documents, and summarize meetings autonomously.
Case Studies Born from Experience
1. Meal Kit Fulfillment Center Operations
While working undercover at a fulfillment center, I observed costly errors in meal bagging and shipping. For example:
- Incorrect items placed in boxes led to customer dissatisfaction and financial losses.
- Miscommunication across shifts caused delays and inefficiencies.
Agentic AI Solutions:
- Real-Time Ingredient Verification: Use computer vision to verify accuracy in packing.
- Dynamic Shipping Line Monitoring: AI sensors track inventory and flag inconsistencies.
- Automated Quality Assurance: AI identifies and corrects recurring errors proactively.
2. Cybersecurity M&A Insights
I built a tool to analyze SEC filings and uncover acquisition trends in the cybersecurity space. The goal was to understand what makes certain companies attractive targets.
Agentic AI Extensions:
- Automated Market Analysis: Gather and analyze data to predict acquisition trends.
- Strategic Insights: Identify valuation metrics and customer growth patterns.
- Actionable Recommendations: Guide startups on how to position themselves for acquisition.
3. Data Security and Privacy Compliance
My side project focuses on helping organizations:
- Discover and classify sensitive data.
- Identify vulnerabilities and compliance risks.
- Automate data governance policies.
Agentic AI Extensions:
- Continuously monitor and secure cloud and SaaS environments.
- Prevent data leakage with real-time policy enforcement.
- Offer dynamic dashboards for compliance audits.
This comparison highlights the time saved by using agentic AI for HR onboarding.
Expanded Practical Applications Across Industries
Agentic AI isn’t limited to HR or cybersecurity. It has the potential to transform:
- Finance: Automate compliance audits and fraud detection.
- Manufacturing: Predict equipment failures using IoT data.
- Insurance: Streamline claims processing and approvals.
What if your organization could cut HR onboarding time by 75% or reduce compliance audit times by half?
This pie chart shows the disproportionate amount of time spent on repetitive tasks versus strategic work.
Agentic AI is not just a future possibility; it is already transforming industries. By embracing agentic workflows, organizations can streamline operations, reduce errors, and free up time for strategic work. The future of enterprise software is here, and it’s agentic.