Agentic AI: Welcome to a new era in business process services

Business operations are changing — fast. We’ve moved from manual workflows to basic automation, and now we’re entering a completely new territory with agentic AI, which is ushering in a fundamental shift in business process services (BPS).

Instead of rigid scripts and rule-based bots, we’re talking about AI agents that can make decisions, adapt on the fly, work together and deliver outcomes without a human constantly holding their hand. It’s how we make the leap from automated tasks to intelligent workflows that can adjust to changing conditions.

Let’s explore what this means and how organizations are already making it work.

What’s the next frontier for business process services?

We’ve seen wave after wave of digital transformation. First came digitalization, then robotic process automation (RPA), followed by AI-enhanced analytics. The next wave is even bigger: services as software (SaS).

With SaS, it’s systems, not people, that do the work. Just like software, they’re modular, scalable, subscription-based and delivered through application programming interfaces (APIs). Instead of selling hours of labor, organizations can now deliver packaged, outcome-focused capabilities that are ready to plug in and use.

The secret ingredient is agentic business process as a service (BPaaS), which involves AI agents autonomously running entire business processes from start to finish. There are no manual handoffs, and no delays as you wait for someone to act on information or click the “Next” button.

It’s like going from a taxi service that always needs a driver to a self-driving car that knows where to take you, avoids traffic jams and adjusts routes in real time.

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How are organizations rethinking service delivery?

For decades, services scaled in a straight line: more volume meant more people. The more customers you had, the more people you had to employ to handle the workload and maintain service standards.

Now, it’s more like a curve: the volume grows, but the head count doesn’t need to.

Let’s take a shared services center as an example. Traditionally, processing more invoices meant hiring more processors. But with agentic AI, an intelligent agent can scan invoices, match them to purchase orders, flag discrepancies and even trigger payments under certain conditions, all without human intervention.

Because these services are modular and API-enabled, they can be consumed on demand. Need invoice reconciliation? Call the API. Need claims assessment? Call a different API. Each is a ready-made building block you can combine to suit your needs.

The result is business agility, faster time to value, and services that feel less like long-term outsourcing projects and more like plug-and-play digital products.

How do agentic BPaaS and SaS transform delivery models?

Think of agentic BPaaS as the engine and SaS as the dashboard:

• The engine (BPaaS) is where you’ll find the AI agents. They execute the work from start to finish, guided by goals instead of inflexible rules.
• The dashboard (SaS) is how organizations access and use these capabilities — like choosing your preferred features in a streaming service.

Now, instead of signing a multiyear managed services contract to handle customer onboarding, you could subscribe to an onboarding module that uses an agent to verify documents, set up accounts and send out welcome communications.

This approach allows you to focus on the “what” — your business priorities — while the agents take care of the “how,” executing tasks quickly, accurately and securely to get results.

How is agentic AI disrupting and creating value in BPS?

In traditional BPS, tasks often relied on scripts, templates and automation that were pretty much set in stone, especially in contact centers and back offices. A human agent in a contact center followed a standard script, or a bot processed transactions in a specific way. If something unexpected happened, the process stalled until a human fixed it.

Agentic AI changes this completely. For example:

• In contact centers: An AI agent can summarize calls, resolve issues and escalate only when it truly needs human judgment.
• In insurance: It can run real-time eligibility checks, assess claims and initiate payouts without manual touchpoints.
• In finance: It can reconcile accounts, flag anomalies and update ledgers while keeping a perfect audit trail.
• In human resources (HR): It can automate employee onboarding, wellbeing monitoring and résumé screening, among other functions, to turn an HR department into an instantly responsive, 24x7 service center.

One NTT DATA client, an insurance provider, saw case-handling time drop by 40% while resolution accuracy went up by 30%. That’s not a small optimization — that’s transformation.

What are the top 3 steps to take before adopting agentic AI in BPS?

Before jumping into agentic AI for BPS, there are three non-negotiables:

1. Align on outcomes: Are you trying to improve speed, cut costs or improve the customer experience? You’ll design differently depending on your goal. Getting this clarity up-front prevents wasted effort and focuses your AI agents on the right priorities.
2. Get your data ready: Smart agents need full, real-time context to make good decisions. Without access to accurate, up-to-date information, even the most advanced AI will make poor or incomplete choices.
3. Think collaboration, not replacement: Humans still have a role. The goal is for AI to handle the repetitive stuff so humans can manage exceptions and strategy. This balance keeps operations adaptable while using human insight where it matters most.

Remember, the real power is in adaptive collaboration, not just automation.

What are 5 factors to consider when creating your agentic AI roadmap?

If you’re planning to integrate agentic AI into your business processes, here’s a simple framework:

1. Prioritize high-impact, repeatable use cases: Start where automation will save the most time or unlock the most value. This leads to early wins that build momentum and secure buy-in for broader adoption in your organization.
2. Map agent roles and escalation paths: Define what agents do, and when they hand off to humans. Clear boundaries prevent confusion and simplify collaboration between AI and people.
3. Design a governance layer for safety and trust: Set guardrails for compliance, privacy and ethical AI use. Strong governance reassures your stakeholders that innovation won’t come at the cost of security or integrity.
4. Build modules for future scale: Create services as components that can evolve independently. This allows you to add capabilities or swap out technologies without disrupting the entire system.
5. Measure as you go to prove value: Regularly tracking progress against ROI and KPIs helps you adjust strategies before small issues become roadblocks.

In short: Think big, start smart and scale with confidence.

A quick scenario: How it works in real life

A global insurance provider takes an average of 10 days to process claims. The process involves many handoffs, manual document checks and endless back-and-forth with customers.

They introduce an agentic AI claims agent that allows customers to upload their claim documents online.

Within minutes, the AI agent verifies the completeness of the documents, runs fraud detection and cross-checks policy details. In just a few hours, the claim is either approved for payout or escalated to a human for exceptions.

The result? Average processing time drops from 10 days to one day. Customers are happier, staff spend less time on paperwork, and the business can process more claims without hiring more people.

Let’s redefine what’s possible

Agentic AI is not just about faster and cheaper. It’s also about making your business smarter and more adaptable. It’s how we’re modernizing and accelerating business processes.

The shift from rigid workflows to intelligent orchestration is leading us to digital services that can adapt in order to deliver outcomes with minimal friction.

This transformation is already happening. How quickly will you embrace it?

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