AI Strategy6 min read

AI Terms for Leaders: Hyperscalers, Agents and Observability

Part 1 of a plain-English series on AI jargon: hyperscalers, observability, AI agents vs agentic AI, and personal vs company productivity, with a question to ask for each.

Haojun See

Founder & Director, On The Ground

Updated 22 October 2026

What this AI terms series covers

This is part 1 of a five-part, plain-English guide to the AI terms business leaders keep hearing. Each post takes a handful of terms, explains them without jargon, and ends each one with a question you can put to a vendor or to your own team. The terms follow the vocabulary of the Singtel AI.dea – AI bridge programme, run with SIM Academy (Programme Architect: Michelle Yeo). The explanations and examples here are our own. Part 1 covers four terms that come up in almost every AI conversation with a leadership team: hyperscalers, observability, AI agents versus agentic AI, and personal versus company productivity.

Hyperscalers: renting AI computing instead of owning it

Hyperscalers are the few cloud providers big enough to rent computing power to almost anyone. The main three are Amazon Web Services, Microsoft Azure and Google Cloud. When a vendor sells you an AI tool, the heavy lifting usually happens in one of their data centres, whether or not their name appears on your contract. For an SME, renting changes the maths of AI. There is no server room to build and no hardware to replace every few years. You pay for what you use, adding capacity for the year-end rush and releasing it in January. This is called elasticity. The flip side is that a usage-based bill has no natural ceiling. A tool that costs little in a pilot can cost far more once fifty staff use it every day. Running large AI models also uses a lot of energy, which matters if customers or lenders ask about sustainability. Ask: which cloud does this run on, which region holds our data, and what will the monthly bill look like at full use rather than in the pilot?

Observability: seeing why an AI tool behaves the way it does

Observability means being able to look inside a running AI system and see what it did, in what order, using which data, and at what cost. It goes a step beyond monitoring, which mainly tells you whether a system is up. The difference matters because AI tools seldom fail loudly. An assistant that answers customer questions doesn't crash when its answers get worse; it keeps answering, a little less accurately each week. That gradual slide is called drift, and you only catch it if someone checks the quality of the output, not just the uptime. In practice, good observability means logs and step-by-step traces you can open when a customer complains, plus regular checks on accuracy and running cost. For AI agents that take actions, the trace is also your record of what was done and why. Ask: if this tool gives a customer a wrong answer next month, how will we find out, and can we see exactly how it got there?

AI agent vs agentic AI: how much the software does on its own

An AI agent is software that is given a goal and works out the steps itself. Agentic AI usually means several agents working together on a bigger job. It helps to place both on a range of automation that is easy to confuse: • Rule-based bots follow a fixed script and stop when something unexpected happens. • Robotic process automation (RPA) copies what a person does on screen, such as moving figures from emails into a spreadsheet, exactly the same way each time. • AI assistants or copilots, such as ChatGPT or Microsoft Copilot, help when asked; nothing happens until you type, and you decide what to use. • AI agents take a goal, such as chasing every invoice more than 30 days overdue, decide what to do, use tools like email and your accounting system, and keep going until the job is done. • Agentic systems split a large task across several specialised agents and combine their work. Each step along the range saves more effort and adds more risk, because a wrong assumption can become a wrong action before anyone looks. Be wary of products sold as agentic that are really scripted bots; analysts call this agent washing. Ask: what can this agent do without a person approving it, and if it makes a mistake, how far can that mistake travel?

Personal vs company productivity: why individual gains don't add up

Personal productivity is one person doing their own tasks faster with AI. Company productivity is a whole process, such as quoting, onboarding or claims, becoming faster or cheaper. The first is easy to buy. The second is what shows up in the accounts. Many firms roll out ChatGPT or Copilot to everyone, then find the monthly numbers look much the same. Usually each person saves a few minutes here and there, but the process around them stays the same: the same approvals, the same hand-offs, the same waiting. Minutes saved across a team rarely add up to a lower cost per order. Company-level gains need someone to redesign the workflow, connect AI to the systems where the work happens, and measure business numbers such as turnaround time or cost per transaction. Both levels are worth doing. Just don't expect the first to deliver the second. Ask: which business process will be measurably faster or cheaper in six months, and who owns that number?

Next in the AI terms series

Part 2 explains the main types of AI, from predictive models to generative AI. The series continues with AI risk terms, AI strategy terms and AI architecture and governance terms. If you'd like to work through these terms with your own team, the AI.dea programme page has a form to register your interest.

Frequently asked questions

What is a hyperscaler in AI?

A hyperscaler is a very large cloud provider, such as Amazon Web Services, Microsoft Azure or Google Cloud, that rents out computing power, storage and AI models by usage. Most AI tools that businesses buy run on one of them. Renting keeps upfront costs low, but bills grow with use and your data sits in their data centres.

What is the difference between AI monitoring and observability?

Monitoring tells you whether a system is running and alerts you to known problems. Observability lets you look inside the system to see what it did, step by step, and why. AI tools often get worse gradually rather than failing outright, so observability is what lets you spot and explain that drift.

Why hasn't giving staff AI tools improved company productivity?

Personal AI tools help individuals finish tasks faster, but they don't change the process around those tasks. Approvals, hand-offs and waiting times stay the same, so the time saved is scattered rather than turned into lower costs. Company-level gains come from redesigning a whole workflow and measuring business numbers such as turnaround time.

AI Strategy

Types of AI Explained: Predictive, Generative, Vision and More

AI Strategy

AI Strategy Terms: Ambition, Readiness, Quick Wins and RICE

AI Strategy

AI Risk Terms: Hallucination, Workslop, Shadow AI and More

PreviousTypes of AI Explained: Predictive, Generative, Vision and More Next AI Strategy Terms: Ambition, Readiness, Quick Wins and RICE