Agentic AI – Are you ready?

A story of vanishing exceptions queues

At 22:30 on a Thursday, Lara, CFO of a global manufacturer, refreshed the weekly dashboard she had learned to dread.

For years, her SAP “exceptions queue” had been a black hole for cash. Thousands of invoices that did not quite match a purchase order, a delivery note, or a tax rule sat waiting for a human to fix them. Each one meant delayed payment, tense supplier calls, and more working capital locked up than she was comfortable admitting to the board.

Worse, she knew that hundreds of people spent their days re‑keying data, checking prices, emailing buyers, and chasing warehouses for confirmations – highly capable staff reduced to human glue between systems. In many large enterprises, 20–25% of ERP users spend most of their time managing such exceptions, and roughly 14% of invoices fall out of the “happy path” into manual handling.

Six months earlier, Lara’s team had launched what they thought was “just another AI project”: an assistant that could read invoices and answer simple finance queries. Useful, but hardly transformative.

Then they gave that assistant three extra capabilities:

• Access to Standard Operating Procedures describing how exceptions should be resolved in different scenarios.
• Secure tools to search the ERP, raise or update transactions, and send templated emails to suppliers.
• A feedback loop so that, when a human corrected it, the system could learn and adjust.

Instead of simply describing the issue, the AI agent now worked the case: it looked up related POs and goods receipts, compared quantities and prices, proposed the correct resolution, and, once a human approved a few hundred examples, started closing straightforward cases autonomously.

Within a quarter, the backlog had almost disappeared, average days‑sales‑outstanding had dropped, and Lara had reassigned dozens of people from “exceptions clerking” to higher‑value analysis and supplier negotiation. The process was still governed and auditable – but it no longer depended on sheer human stamina.

That is an agentic AI use case: not an AI that merely answers questions, but one that perceives, reasons, acts across systems, and improves over time.

What makes this ‘agentic’?

Traditional GenAI is largely passive: you ask; it responds. Agentic AI is active. It:

• Perceives: gathers context from documents, systems, and events.
• Reasons and plans: breaks a business goal into steps, chooses tools, and sequences actions.
• Acts: calls APIs, updates systems, triggers workflows – not just drafts emails.
• Learns: uses feedback, logs, and outcomes to improve policies and prompts over time.
  

MIT Sloan describes this shift as the move to semi‑ or fully autonomous systems that can execute multi‑step plans within business workflows, rather than sitting at the edge as glorified chatbots. Gartner expects that around 15% of day‑to‑day work decisions in enterprises will be made autonomously by agentic AI by 2028, especially in process‑heavy domains like ERP, finance, and supply chain.

The story above is one such domain, but the same pattern shows up in customer service, trade finance, and document‑heavy onboarding – exactly the areas where Noventiq is already deploying agentic solutions on AWS, from AI‑enabled document management (uDMS) to multi‑step agentic workflows for buyers and service desks.

Three levels of Agentic AI readiness

You can think of organisational readiness for agentic AI on a simple 3‑level scale, scored roughly from 1 to 9 – with 10 reserved for bleeding‑edge research systems.

1. Level 1 – Basic agentic (scores 1–3)

You are here if:

• You run single‑agent GenAI or Retrieval-Augmented Generation (RAG) assistants tied to one main knowledge base (e.g. “chat with our policies”), sometimes with light tool use like creating a ticket or drafting an email.
• Most interactions are read‑only: the AI explains, summarises, or recommends, but humans still execute the actual steps.
• Technically, you have basic APIs in place, some data preprocessing, and simple error handling, but limited memory or context management beyond the immediate prompt.

This is a good start: you reduce search time and improve self‑service. But your AI is still essentially an informed co‑pilot.

Typical signs

• Your flagship use case is a chatbot or co‑pilot embedded in one workflow.
• Governance is light; business teams experiment in silos.
• Success is measured in “time saved per query” rather than end‑to‑end process impact.
  

2. Level 2 – Intermediate agentic (scores 4–6)

• At this stage, you move from “one smart assistant” to agents that can synthesise and act across multiple data sources and systems.

• Your agents pull from several repositories (ERP, CRM, ticketing, document stores, external APIs) and handle conflicting or incomplete information.

• They can take constrained actions: opening, updating, and closing records; orchestrating hand‑offs between teams; enforcing basic business rules.
• Under the hood, you have stronger MLOps: data versioning, model evaluation, perhaps some fine‑tuning, and more robust observability.

In business terms, you start seeing cross‑system workflows automated: for example, an IT assistant that diagnoses incidents, updates monitoring tools, and orchestrates escalations, or a trade‑intelligence agent that compiles buyer risk profiles from multiple feeds.

Typical signs

• You have cross‑functional steering for GenAI and early guardrails for security and compliance.
• KPIs shift from “time saved” to measurable process outcomes: faster cycle times, fewer errors, better SLA adherence.

3. Level 3 – Advanced agentic (scores 7–9)

Here, you design multi agent systems where several specialised agents collaborate to deliver an outcome – often spanning an entire value stream such as order to cash or claims processing.

• Agents have distinct roles (planner, investigator, executor, explainer) and coordinate through a shared orchestration layer.
• They operate with goal‑level instructions (“keep DSO under 35 days”, “clear all safe exceptions before cut‑off”) rather than step‑by‑step scripts.
• You invest in distributed architectures, event‑driven workflows, observability, and policy‑driven governance to manage autonomy safely.

Only a small fraction of organisations are here today. AWS and BCG’s 2025 research found that while around 40% of qualified respondents claimed to use agentic AI, only about 10% could describe genuinely agentic, multi‑step use cases on review. Those that are furthest ahead tend to be in IT operations, customer service, and finance, often working with specialised partners.

Typical signs

• You treat agents as first‑class digital workers: onboarded, monitored, governed, and iterated like products.
• You see compound value: not only lower costs, but also better resilience, faster experimentation, and new revenue opportunities.

Where most businesses are today

Across industries, we see a consistent pattern:

• Many enterprises have Level 1 assistants live and a few Level 2 pilots in progress.
• Very few have made the leap to Level 3, mainly because of skills gaps, unclear ownership, and difficulty proving ROI beyond the first 3–6 months.
• Agentic projects rely on partners more heavily than non‑agentic GenAI: AWS survey data shows customers are 35% more reliant on partners for agentic AI and over 80% expect to maintain or increase that reliance.

In other words: interest is high, but execution is uneven, and most organisations underestimate the architectural, governance, and change‑management work required to go beyond “smart demos”.

Explore your next step with Noventiq’s AI Assessment

As an AWS partner with deep focus on GenAI and agentic patterns – from serverless AI document management (uDMS) to agentic service and buyer‑intelligence solutions – Noventiq is already helping customers move along this maturity curve on AWS platforms such as Amazon Bedrock, Amazon Q, and the broader agentic AI toolset.

Our AI Assessment is designed for exactly the questions many leadership teams are asking now:

• Where do our current pilots sit on the three‑level agentic scale?
• Which 2–3 processes (ERP exceptions, customer support, trade risk, onboarding, etc.) are the best candidates for safe, high‑impact agentic automation?
• What technical and organisational gaps must we close to move from Level 1 to Level 2, or from Level 2 to Level 3, on AWS?

In a focused engagement, we work with your business, data, and IT stakeholders to baseline your readiness, map priority use cases to AWS’s agentic AI reference architectures, and outline a pragmatic roadmap – including governance and guardrails – that de‑risks your first production‑grade agents rather than chasing experiments.

If Lara’s “vanishing exceptions queue” story feels uncomfortably close to home – or if you are unsure whether your current GenAI investments are building towards agentic capabilities – reach out to your Noventiq AWS representative to discuss an AI Assessment. Together we can identify where you are on the agentic journey today, and what it would take to let AI not just answer questions in your business, but reliably act on them.

For further details, visit the Noventiq GenAI blog and explore customer success stories and industrial use cases from the AWS Partner Network and Noventiq.

👉 Book your meeting to discuss your potential next step.