Framework — AI Agents · LLMs · Workflows
Know when to build an AI Agent — and when cheaper tools are smarter
Not every process needs a fully autonomous agent. Our AI Agent Suitability Model helps you decide when to use workflows, LLM assistants, or full agentic automation — based on complexity, risk, and ROI.
- Business-first, vendor-agnostic analysis
- Sweet spot between cost and capability
- Designed for CX, EX & operations
Outcome: a clear recommendation — deterministic automation, LLM copilot, or full AI Agent — with a quantified business case behind it.
Three automation paths
Choose the right level of intelligence — not just the most expensive
We score your use case across six capability dimensions and map it to one of three implementation paths: deterministic automation, LLM assistants, or full AI Agents. Each path balances cost, risk and business value.
Path 1 · Deterministic
Workflows, rules & RPA
For predictable, repeatable tasks with clear rules and stable environments.
- Structured inputs and known procedures.
- Few exceptions; low decision complexity.
- Best economics for routine processes.
Ideal for: standard notifications, simple routing, status updates, basic back-office tasks.
Path 2 · Assisted
LLM copilots & light orchestration
For work that needs understanding of language and context, but still benefits from human oversight.
- Summarise, draft, classify and recommend next actions.
- Humans approve or refine outputs before execution.
- Lower cost than full agents; faster to roll out.
Ideal for: case triage, email & chat assistance, knowledge discovery, insight generation.
Path 3 · Agentic
Full AI Agents in the sweet spot
For dynamic, multi-step workflows where agents can perceive, decide, act and learn across systems.
- Agents orchestrate tools, data and workflows end-to-end.
- Handle variability, exceptions and trade-offs in real time.
- Improve over time with feedback and performance signals.
Ideal for: collections optimisation, service resolution, logistics reactions, IT auto-remediation.
How the model works
From business requirement to clear automation decision
Every engagement starts with your business outcomes. We then analyse your workflows across capability dimensions, build a cost vs value profile, and recommend the smartest level of automation — not just the most advanced.
Understand the problem, not the tools.
We start with the journey: who is involved, what success looks like, and where friction lives. This gives us a high-resolution view of the process before we talk about AI.
Score the use case across capabilities.
Perception, decisioning, actioning, agency, adaptability, and knowledge versatility are each scored from 1–4 to understand whether the use case sits in deterministic, assisted, or agentic territory.
Map to our three-path decision model.
Based on your scores, we recommend the right path: workflows & rules, LLM-assisted automation, or full AI Agents. We always consider operational risk, governance and explainability.
Quantify the economics and ROI horizon.
We compare the cost of each option (tokens, execution cycles, API calls, oversight) against value levers like time saved, SLA uplift, deflection, collections, or revenue impact.
Design, pilot and scale with confidence.
Once the path is chosen, we design the solution architecture, run a contained pilot, and scale only when the value is proven and governance is in place.
At the end of the assessment, you receive a one-page view of each candidate use case with:
- Capability scores (1–4) across all six dimensions.
- Recommended automation path (1–3).
- Estimated ROI horizon and risk profile.
- Implementation effort and dependency notes.
Next step
Let's find your AI Agent sweet spot
Share one or two candidate workflows — customer-facing or internal. We’ll run them through the iAhub AI Agent Suitability Model and come back with a clear recommendation, including an indicative ROI view.
Prefer a workshop? We can run a half-day session with your leadership team.