Highlights
Agentic AI is fundamentally changing how people interact with enterprise software. This blog explores the shift from human-led workflows to AI-driven execution, wherein humans communicate with intent and intelligent agents deliver outcomes. Through real-world examples including product management, the blog demonstrates how AI agents can analyze information, generate recommendations, and prioritize actions. It also highlights the evolving role of product managers in designing AI-powered interaction ecosystems rather than traditional software workflows.
The End of Software as Product Managers Know It
For the past few months, every conference, webinar, and product roadmap discussion has revolved around Agentic AI.
Most conversations focus on LLMs.
Some focus on copilots.
Others debate autonomous agents.
But I think we’re missing a far more fundamental shift.
Agentic AI, rather than changing software, is changing how humans interact with software.
As Product Managers, we spend years pretty much obsessed over workflows. The questions buzzing around in our brains include the ones I’ve jotted below:
Fig: Questions for Product Managers
Those questions made sense because enterprise software required humans to operate every workflow.
I don’t think that assumption will hold true for much longer.
And that’s why I’ve authored this blog for you.
Enterprise Software: Built Around Tasks
Nitor Infotech has honed certain core capabilities for SaaS products, platforms, and marketplaces. We’ve paired them with select success stories in this awesome document.
Every enterprise application follows a similar pattern:
- A user logs in.
- Navigates menus.
- Searches for information.
- Applies filters.
- Downloads reports.
- Moves to another application.
- Repeats the process.
Whether you’re using Salesforce, SAP, Jira, ServiceNow or Workday, the interaction pattern is remarkably similar.
- The software exposes functionality.
- The human orchestrates the workflow.
For decades, we optimized these workflows.
But we never questioned whether humans should be orchestrating them in the first place.
Agentic AI finally does this questioning!
The Future with Agentic AI: It Isn’t Fewer Clicks
Many people believe AI simply reduces clicks.
I have a different opinion.
Reducing clicks is an optimization.
Agentic AI changes who performs the work.
Instead of:
Human → Software → Result;
we are moving toward:
Human → Intent → AI Agents → Business Outcome
Rather than an incremental improvement,
it’s a completely different interaction model.
Interaction Will Move from Commands to Outcomes
Think about how we currently use enterprise software.
A Product Manager doesn’t actually want to:
- Open Jira
- Export tickets
- Compare velocity
- Review customer feedback
- Open analytics
- Read support cases
What they really want is:
“Tell me which feature deserves investment next quarter.”
Today’s software gives them data.
Tomorrow’s product should give them a recommendation.
The interaction is shifting from requesting information to requesting outcomes.
Real-World Example 1: Product Management
Imagine I’m responsible for a SaaS platform with thousands of customers.
Today my morning starts with my coffee brew of choice and:
- Jira
- Azure DevOps
- Mixpanel
- Salesforce
- Zendesk
- Confluence
- Excel
I have to spend nearly two hours understanding what’s happening before I can decide what to do. That is way more time than what I can ideally spare.
In an Agentic ecosystem, my morning could begin with a single request:
“What deserves my attention today?”
Behind the scenes, multiple specialized agents collaborate like this:
Fig: Collaboration amongst Specialized Agents
The product says a “no” to presenting dashboards.
It presents priorities. And as a Product Manager, that is far more valuable than another analytics screen.
Real-World Example 2: Credit Bureau Platforms
I’ve spent considerable time working with Credit Bureau products, and this is where I believe Agentic AI will fundamentally reshape interaction.
Today, an underwriter manually reviews:
- Credit score
- Trade lines
- Delinquencies
- Income
- Fraud indicators
- Banking history
- Internal policies
Each data point comes from a different system.
Tomorrow, the interaction becomes:
“Evaluate this applicant for a ₹30 lakh home loan and explain the decision.”
Specialized agents retrieve bureau data, verify identity, analyze cash flow, compare lending policies, evaluate fraud risks, and generate a recommendation.
The underwriter no longer navigates five systems.
Instead, they review, challenge, or approve the AI’s recommendation.
Their value shifts from gathering information to exercising judgment.
Real-World Example 3: Supply Chain
Imagine: A warehouse manager notices delayed deliveries.
Today’s interaction looks like this:
- Open ERP.
- Check inventory.
- Call suppliers.
- Review transportation data.
- Open warehouse dashboard.
- Export reports.
Tomorrow’s interaction becomes:
“Restore on-time delivery above 95% by next week.”
The AI investigates supplier delays, inventory shortages, transportation disruptions, warehouse capacity, and alternate sourcing options.
Instead of showing five dashboards, the system returns:
“Supplier B is causing 78% of delays due to raw material shortages. Moving orders to Supplier C will restore service levels within four days while increasing procurement costs by only 1.8%.”
Do you notice the difference?
The software doesn’t provide plain information.
It proposes actual action.
Real-World Example 4: Customer Success
Customer Success teams spend much of their day monitoring dashboards:
health scores, support tickets, renewal dates, product adoption, and usage trends.
An Agentic platform changes the interaction entirely.
Instead of monitoring 200 customers, the Customer Success Manager asks a simple but pivotal question:
“Which customers need my attention this week?”
The system identifies accounts showing early signs of churn, explains why, estimates business impact, drafts outreach emails, schedules executive reviews, and suggests retention strategies.
It is then that the manager can focus on deepening relationships rather than monitoring dashboards.
AI in customer success: AI can analyze customer emotions. Can it design it? – Nitor Infotech Blog
Real-World Example 5: Healthcare
Doctors don’t wake up wanting to navigate Electronic Health Records.
They want to treat patients.
Imagine you are a doctor. You walk into a consultation room and say:
“Prepare me for this patient.”
An AI ecosystem deftly gathers up previous consultations, laboratory trends, imaging reports, prescriptions, allergies, medication interactions, and preventive care recommendations.
Voila! The physician starts the consultation informed rather than overloaded.
Again, note how the interaction changes.
The software prepares the doctor.
The Product Manager’s New Responsibility
As Product Managers, we have historically designed workflows.
I believe our role is evolving toward designing interaction ecosystems.
Instead of asking questions like:
- What screen comes next?
- Which button should users click?
- How can we simplify navigation?
We’ll increasingly ask questions like:
Fig: Questions product managers will ask
These are fundamentally product strategy questions (not UX questions).
Key Takeaways: The Products That Will Win
The next generation of enterprise products won’t compete because they have the most features.
They will compete because they eliminate operational complexity.
- The winning products will have one characteristic in common:
- They will make software invisible.
Users won’t measure success by how quickly they navigate an application.
They’ll measure success by how quickly they achieve a business objective.
- That is a profound shift.
The interaction model evolves from operating software to orchestrating intelligent agents. - And I believe that that’s the most important product transformation we’ll witness over the next decade.
Allow me to wrap up my ideas for this time around.
As product leaders, we’ve spent years asking:
“How do we build better software?”
Agentic AI forces us to ask a more important question:
“How do we build software that no longer needs to be operated?”
That, in my view, is the real disruption. This is about replacing manual orchestration with intelligent execution, all while keeping humans responsible for judgment, governance, and strategic decisions.
Contact us at Nitor Infotech to understand what we do in the field of Agentic AI.
Frequently Asked Questions
1. What is the use of agentic AI in product management?
Agentic AI helps product managers move beyond navigating tools and analyzing dashboards to focusing on business outcomes. Instead of manually reviewing data across product analytics, customer feedback, development, support, and revenue systems….Read more
